Ab Initio-Based Kinetic Modeling for the Design of Molecular Catalysts: The Case of H 2 Production Electrocatalysts
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A New Shutdown Dose Rate Benchmark Problem for Representative Fusion Applications
Here, this work introduces a new benchmark problem for calculating shutdown dose rates (SDDRs) aimed at fusion reactor applications. The model is designed to represent a simplified version of a typical ITER port plug. The responses of interest include neutron flux, gamma flux, and gamma SDDR at 12 different locations scattered throughout the port. This article outlines the geometry specifications of the problem, provides material definitions for the components, specifies the required responses to be calculated, and presents the source definition information. The need for this benchmark arises from the limited availability of publicly accessible references, with only one benchmark representing the typical dimensions and materials found in fusion systems. This existing benchmark has been cited extensively, reflecting the demand within the scientific community to test both established and novel workflows for SDDR calculations. However, since its presentation at a conference in 2011, the results have become increasingly well known. Moreover, the absence of formal publication and peer review has led to the details of this benchmark being extracted from secondary sources, such as subsequent studies that reference it. As a result, analysts are left with significant flexibility in interpreting the key parameters, which can be adjusted to account for unknown systematic errors, ultimately reproducing the already well-known responses. This new benchmark serves as an updated version of that earlier work, with the aim of providing a more reliable description of the materials and their impurities, which is crucial for assessing activation and subsequent gamma emission. Additionally, it seeks to provide a geometry that more closely represents an ITER port plug. The improvements in the problem definition will lead to a more reproducible benchmark problem, while also presenting the radiation transport community with a completely new challenge. The results will be published in a future article to allow analysts adequate time to analyze this problem independently.
Benchmark↗
Atm heterozygous mice are more sensitive to radiation-induced cataracts than are their wild-type counterparts
It is important to know whether the human population includes genetically predisposed radiosensitive subsets. In vitro studies have shown that cells from individuals homozygous for ataxia telangiectasia (A-T) are much more radiosensitive than cells from unaffected individuals. Although cells heterozygous for the ATM gene (ATM(+/-)) may be slightly more radiosensitive in vitro, it remained to be determined whether the greater susceptibility of ATM(+/-) cells translates into an increased sensitivity for late effects in vivo, though there is a suggestion that radiotherapy patients that are heterozygous for the ATM gene may be more at risk of developing late normal tissue damage. We chose cataractogenesis in the lens as a means to assay for the effects of ATM deficiency in a late-responding tissue. One eye of wild-type, Atm heterozygous and homozygous knockout mice was exposed to 0.5-, 1.0-, 2.0-, or 4.0-Gy x rays. The animals were followed weekly for cataract development by conventional slit-lamp biomicroscopy. Cataract development in the animals of all three groups was strongly dependent on dose. The lenses of homozygous mice were the first to opacify at any given dose. Most important in the present context is that cataracts appeared earlier in the heterozygous versus wild-type animals. The data suggest that ATM heterozygotes in the human population may also be radiosensitive. This may influence the choice of individuals destined to be exposed to higher than normal doses of radiation, such as astronauts, and may also suggest that radiotherapy patients who are ATM heterozygotes could be predisposed to increased late normal tissue damage.
Non-NASA Center↗
NbTa_BCC_SolidSolution_128atoms_VASP6
We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-tantalum (Nb-Ta). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb. For Ta, p semi-core states as valence were chosen. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for ternary alloys is AXBYCZ, where A, B, and C refer to the constituents, and X, Y, and Z are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)
36 MATERIALS SCIENCE↗
TaV_BCC_SolidSolution_128atoms_VASP6
We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-vanadium (Tanu-V). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the element V. For Ta, p semi-core states as valence were chosen. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmuller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmuller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)
36 MATERIALS SCIENCE↗
NbV_BCC_SolidSolution_128atoms_VASP6
We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-vanadium (Nb-V). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and V. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)
36 MATERIALS SCIENCE↗
NbZr_BCC_SolidSolution_128atoms_VASP6
We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-zirconium (Nb-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)
36 MATERIALS SCIENCE↗
TaZr_BCC_SolidSolution_128atoms_VASP6
We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)
36 MATERIALS SCIENCE↗
Comprehensive Thermodynamics of Nickel Hydride Bis(Diphosphine) Complexes: A Predictive Model through Computations
Explore the source record for details and available documents.
bio-inspired↗
A Preliminary Analysis of Dose Rates Associated with ITER CVCS Equipment/Area Location
A preliminary analysis of the ITER Chemical and Volume Control System (CVCS) Area was performed to assess dose rates outside the walls and ceiling of the facility after 1.5 years of operation at shutdown, 2 days, and 10 days after shutdown. For this purpose a simplified Monte Carlo computer model was developed using the MCNP (MCNP5 Ver. 1.51) code. Two components are included: the smaller filter tank and the larger ion exchanger. These pieces of equipment are associated with the Integrated Blanket ELM Divertor Primary Heat Transfer System, which will have the largest dose rates associated with activated corrosion products during operation in comparison with other systems. The ion exchanger contained two source regions, a 1.2-m-thick resin bed above a 0.55 m-thick skirt, and a 0.8-m-thick water region. The filter constituted an additional source. Thus the model consisted of three sources (filter, resin, water), homogeneously distributed within the appropriate source regions. However, much of the results (that address individual isotopes) are presented with the two sources in the ion exchanger combined. In these cases the sources are referred to as the 'ion exchanger source' and the 'filter source.' Dimensions for the facility and components, as well as source isotopes and strengths, and material densities, were supplied by US ITER. Because of its simplification, the model does not contain pipes. Consequently, radiation streaming through pipe penetrations, radiation emanating from the pipes, and shielding from the pipes were not considered in this analysis. Dose rates on the outside of two walls and the ceiling were calculated. The two walls are labeled as the 'long' wall (aligned with the X-axis) and the 'short' wall (aligned with the Y-axis). These walls and ceiling were nominally set to 30-cm-thick concrete. In the original analysis, standard concrete (2.3 g/cc density) was used. In addition to the shielding walls/ceiling, a floor and an additional wall opposite the long wall were added for photon scattering contributions. These were both 10-cm-thick, standard concrete structures. Other components (tanks, pipes, etc.), that were not included in the model, would potentially add additional scattering and shielding. Possibly these additional effects will be addressed in a later, more detailed analysis. The room was 29.6 m in length (X-axis), but was limited to 15 m in the model. The inside width (Y axis) and height (Z axis) were 4 m and 3.4 m, respectively. The origin for the model was located inside the room at the corner opposite the long wall and adjacent to the short wall at the floor level. The room was filled with air at standard temperature and pressure. The stainless steel (SS304) wall thicknesses for the ion exchanger and filter were 2.2 cm and 0.8 cm, respectively. The axial center of the filter was located 140 cm from the short wall and 100 cm from the long wall (outer surface). The axial center of the ion exchanger was located 440 cm from the short wall and 250 cm from the long wall (inner surface). The resin was assumed to be a homogeneous mixture of equal atom density fractions of hydrogen and carbon* at a specified density of 1.136 g/cc. The filter material was assumed to be homogeneous carbon at a specified density of 1.8 g/cc. If the filter media were stainless steel and the accumulated activity were the same, the dose rate outside the filter would be lower, provided the density of the stainless steel sintered filter material is significantly higher than 1.8g/cc. The densities of the water and air were assumed to be 1.0 g/cc and 1.096E-3 g/cc, respectively. The model included 10 small volumes placed outside the model adjacent to the wall at locations where the dose rates were expected to be highest and which showed to a degree the fall-off of the dose rate with distance along a particular wall or ceiling. These 'tally cells' are shown in Fig. 2 and are described in Table 1. Each cell had dimensions of 50 cm x 50 cm x 1 cm and was oriented so that the 1-cm-thick dimension was perpendicular to the wall against which the cell was located. Also, smaller tally volumes were placed at 5 cm (updated model), 30 cm, 50 cm, and 100 cm from each source, both radially from the outside of each component at the height of each source mid-plane, and vertically, along the axial centerline, above the ion-exchanger and below the filter. (For the ion exchanger, the vertical distance was limited to 75 cm because of interference from the ceiling.) These tally regions can be seen in Fig. 3. Each cell had dimensions of 5 cm x 5 cm x 1 cm, oriented so that the 1-cm-thick dimension was perpendicular to the X, Y or Z axis.
08 HYDROGEN↗
Effect of Curing Temperature and Composition on the Performance Properties of Saltstone - 12473
When freshly mixed Saltstone is placed into a vault at the Savannah River Site, the heat of hydration produced during curing causes the temperature of the grout to increase. It is not uncommon for the temperature of the grout to increase by 30 to 50 deg. C above the starting temperature depending on the vault size and location, environmental conditions and pour schedule. For example, temperatures measured by thermocouples in one cell have exceeded 70 deg. C. The elevated temperatures, once reached, can extend for weeks or even months. Recent studies at Savannah River National Laboratory (SRNL) have shown that curing simulated Saltstone mixes at elevated temperatures can lead to reduced compressive strength and dynamic Young's modulus as well as increased total porosity when compared to the same grout mixes cured at 20 deg. C. Samples cured at 60 deg. C can have hydraulic conductivities approximately 600 times greater than samples cured at 20 deg. C. Previous studies have shown that the 60 deg. C cured mix had a dynamic Young's modulus that was half that of the mix cured at 20 deg. C. However, high temperature curing may not always have negative effects on the performance properties of the grout. These results indicate that higher curing temperatures can significantly alter the performance properties that are inputs for the Saltstone Performance Assessment. The Saltstone formulation (mix design) must produce a grout waste form that meets both placement and performance properties. A statistically designed set of mixes was developed to determine key process and compositional factors that affect the performance properties of Saltstone grout. A total of 27 mixes were batched and tested. There are eight baseline mixes that contain high and low values of aluminate concentration, varying w/p ratio, and varying fly ash content in the premix. Each of the eight mixes was cured at 22, 40 and 60 deg. C. The three additional reference mixes batched at the beginning, middle and end of testing showed good reproducibility throughout the study. The results of varying the operational and compositional factors on the performance properties of Saltstone grout are: - The Young's modulus is inversely affected by the curing temperature of the grout. - The aluminate content of the salt solution increases the Young's modulus of the Saltstone for samples cured at 20 and 40 deg. C. - All grouts cured at 60 deg. C have low Young's moduli and the composition of the grout mix did not have any positive effects on the performance properties after curing at this temperature. - The fly ash content in the premix had not discernible effect on the Young's modulus. - The water to premix ratio does not affect the Young's modulus. - Grouts made with the high aluminate salt solution have lower hydraulic conductivities than those made with the low aluminate simulant. - Temperature has an inverse effect on the hydraulic conductivity. - In general, increasing the water to premix ratio and the fly ash content will increase the hydraulic conductivity. - The porosity and cured density of the Saltstone grout are primarily a factor of the water to premix ratio of the grout formulation. The correlation between hydraulic conductivity and Young's modulus investigated in previous studies was evaluated further in this report. The current data as well as data from previous studies was fitted by a second degree polynomial function with a 77.1 % R-squared value. This model can be used to estimate the hydraulic conductivity of a sample based its Young's modulus for a limited range (E values up to 7 GPa). More data points should be included to further define the relationship between these performance properties are high Young's moduli and low hydraulic conductivities. The Young's modulus should still be measured on grouts formulated in future studies to be used as screening method for samples to be sent for hydraulic conductivity measurements. Saltstone grout has a complicated microstructure that is affected by the composition of the salt solution, the dry feed mixture composition, and the conditions under which it cures. The performance properties investigated are interrelated, which can make it difficult to determine individual relationships between various factors. Porosity is a large factor in determining both the Young's modulus and the hydraulic conductivity of the samples. Although the cured density is a straightforward measurement, it is not a significant performance property that needs further investigation. The porosity measurement provides similar and more meaningful data in relation to other performance properties. Based on the results of this study, the aluminate content of the salt solution does have an effect on the Young's modulus and the hydraulic conductivity and its effects should continue to be investigated. However, a new projection of aluminate contents should be used since projections change over time. The variability of fly ash in the premix did not prove to be a contributing factor that effects the performance properties of Saltstone and does not need to be included in future studies unless significant deviations from the baseline mix are proposed. The water to premix ratio has a significant effect on the performance properties of interest and the effects of varying this factor need further investigation. It is recommended that smaller ranges of w/p are investigated in order to provide data that is more relevant to the Saltstone being placed in the vaults. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of a Reference Dose for Perchlorate: Current Issues and Status
The perchlorate anion (ClO4) is typically manufactured as the ammonium salt. The most common use of ammonium perchlorate is in the aerospace program as a component of solid rocket fuel. The perchlorate anion is exceedingly stable under environmental conditions and has been found in ground and surface waters in CA, NV, UT, AZ, TX, AK, NY, MD, WV and FL. The National Center for Environmental Assessment (NCEA) of the U.S. Environmental Protection Agency (US EPA) is in the process of developing an oral reference dose (RfD) for perchlorate. An oral RfD is a body-weight-adjusted dose that can be consumed daily over an entire lifetime with the expectation of no adverse health effects. Once developed, the new RfD will be used by US EPA as the basis of a safe-drinking-water level (SDWL) guideline. US EPA and regional regulatory agencies will then jointly or separately propose clean-up action levels for ground and surface waters at contaminated sites. The toxicological database on CIO4- as of March 1997 was determined by an expert peer-review panel to be inadequate for the purpose of deriving an oral RfD. For example, little or no experimental data existed on the subchronic, reproductive, or developmental toxicity of perchlorate. To fill gaps in the toxicological database, eight animal studies were designed by a government-industry consortium that included US EPA and AFRL. These studies were performed in 1997-1998. It has been known for many years that in the thyroid, high doses of perchlorate block the function of iodide by competing for iodide binding sites. Perchlorate was used in the 1950s-60s as a treatment for Graves' disease (a hyperthyroid condition). Because of what was already known about the pharmacological mode of action of perchlorate, specific concerns addressed in the design of the recent animal studies included the potential for developmental toxicity, notably neurological development. Upon review of complete study reports from four of the studies and incomplete data from the other two, US EPA/NCEA issued a document in December 1998 stating that the critical effects were hormone and histology data from the neurodevelopmental/behavioral toxicity study that had been performed in rats; an RfD was proposed based upon the observation of thyroid-follicular-cell hypertrophy in the 5-day-old pups (PND) of dams given perchlorate in drinking water at 0.1 mg/kg-day (the lowest dose tested) in this study. US EPA/NCEA also focused attention on a nonsignificant increase in motor activity observed in 14-day-old male pups in the same study at the same dose. In February 1999, a public workshop was convened at which an external peer-review panel determined that there was still insufficient data to support the development of a RfD. Additional studies were recommended. Since that time a clinical study revealed significant depression of thyroidal uptake of iodide in volunteers receiving potassium perchlorate at 10 mg/day for 14 days. Follow-up studies currently underway include a pathology working-group review of histology slides from the all of the previous studies, a motor activity study similar to the first behavioral study, an additional immunotoxicity study, male and female rat kinetic studies, a hormone interlaboratory study, and an effects study for thyroid and brain in developing rats. These studies are expected to be complete by mid-2000. A clinical study is planned to determine inhibition of iodide uptake and kinetic parameters in humans following 14-day perchlorate ingestion. The kinetics studies will provide data for developing physiologically based pharmacokinetic (PBPK) models for rats and humans needed to establish the RfD.
Pleus, R. C.↗
High Temperature Steam Electrolysis Process Performance and Cost Estimates
Technology readiness levels (TRLs) of electrolysis systems have dramatically increased in recent years as the interest in clean hydrogen production and decarbonization of transportation, industrial and other sectors increases across the globe. This is especially true of high temperature steam electrolysis (HTSE) / solid oxide electrolysis cell (SOEC) systems which show promise of much higher system efficiencies than other more developed electrolysis technologies. This possibility of higher efficiencies of HTSE / SOEC systems has been previously assumed to be theoretically possible but in recent years it has become less theoretical and more realistic as an increasing amount of suppliers complete lab and pilot tests showing very promising results. Research in the areas of manufacturing techniques, material selection, electrode and electrolyte compositions, and balance of plant size and integration continues at a fast pace as an increasing number of suppliers both internationally and domestically become involved. The advantages of HTSE become more pronounced when HTSE is coupled with nuclear power plants (NPPs). This is because thermal energy produced by the nuclear reactor can be used in a series of heat transfer loops and heat exchangers to vaporize HTSE feedwater, which drastically improves the economics of the process. Idaho National Laboratory (INL) has been very involved in the research and modeling of HTSE systems for a number of years, in collaboration with other national laboratories, academia, and industry stakeholders both on the hydrogen production as well as the hydrogen demand side. The modeling completed over the years on a large variety of projects has led to a wealth of knowledge at INL including in the area of the technoeconomic assessment (TEA) of HTSE systems. TEAs include process modeling of the HTSE systems to calculate system energy requirements and equipment sizing, followed by estimation of capital and operating costs to enable calculation of the levelized cost of hydrogen (LCOH). The TEA work performed has produced incremental improvements and tuning of the methods, assumptions, models, and results of the analyses as well as providing some opportunities for validating these results. The purpose of this document is to record the current baseline HTSE analyses led by INL to show the current status of assumptions and costs of these systems. Given the rapid development of this technology, the variety of suppliers entering the space, and the increasing attention government and industry are giving to such systems, this document may be updated on a periodic basis with updated analysis and assumptions. This document compiles various analyses results and approaches completed over a period of years into a single document to be used as a baseline going forward. It represents what the INL HTSE analysis group assumes to be the internal best estimate of the current operation, costs, and landscape of the HTSE industry state of the art capability for current SOEC technology in an Nth-of-a-Kind (NOAK) plant, which in this study is defined as existence of the manufacturing capacity to support previous deployment of N = 100 count of 25 MWe modular HTSE blocks (with modular equipment component cost reductions specified as following a 95% learning curve). That said, This is a public document and as such so no proprietary data was used or included in this report. There may be HTSE suppliers that have performance specifications, and cost estimates, and test data that differ from the analysis presented in this document. This document is meant to be a best conservative estimate of the technology and not an absolute reference.
08 HYDROGEN↗
High Temperature Steam Electrolysis Process Performance and Cost Estimates - DOE Hydrogen Program AMR Presentation
Technology readiness levels (TRLs) of electrolysis systems have dramatically increased in recent years as the interest in clean hydrogen production and decarbonization of transportation, industrial and other sectors increases across the globe. This is especially true of high temperature steam electrolysis (HTSE) / solid oxide electrolysis cell (SOEC) systems which show promise of much higher system efficiencies than other more developed electrolysis technologies. This possibility of higher efficiencies of HTSE / SOEC systems has been previously assumed to be theoretically possible but in recent years it has become less theoretical and more realistic as an increasing amount of suppliers complete lab and pilot tests showing very promising results. Research in the areas of manufacturing techniques, material selection, electrode and electrolyte compositions, and balance of plant size and integration continues at a fast pace as an increasing number of suppliers both internationally and domestically become involved. The advantages of HTSE become more pronounced when HTSE is coupled with nuclear power plants (NPPs). This is because thermal energy produced by the nuclear reactor can be used in a series of heat transfer loops and heat exchangers to vaporize HTSE feedwater, which drastically improves the economics of the process. Idaho National Laboratory (INL) has been very involved in the research and modeling of HTSE systems for a number of years, in collaboration with other national laboratories, academia, and industry stakeholders both on the hydrogen production as well as the hydrogen demand side. The modeling completed over the years on a large variety of projects has led to a wealth of knowledge at INL including in the area of the technoeconomic assessment (TEA) of HTSE systems. TEAs include process modeling of the HTSE systems to calculate system energy requirements and equipment sizing, followed by estimation of capital and operating costs to enable calculation of the levelized cost of hydrogen (LCOH). The TEA work performed has produced incremental improvements and tuning of the methods, assumptions, models, and results of the analyses as well as providing some opportunities for validating these results. The purpose of this document is to record the current baseline HTSE analyses led by INL to show the current status of assumptions and costs of these systems. Given the rapid development of this technology, the variety of suppliers entering the space, and the increasing attention government and industry are giving to such systems, this document may be updated on a periodic basis with updated analysis and assumptions. This document compiles various analyses results and approaches completed over a period of years into a single document to be used as a baseline going forward. It represents what the INL HTSE analysis group assumes to be the internal best estimate of the current operation, costs, and landscape of the HTSE industry state of the art capability for current SOEC technology in an Nth-of-a-Kind (NOAK) plant, which in this study is defined as existence of the manufacturing capacity to support previous deployment of N = 100 count of 25 MWe modular HTSE blocks (with modular equipment component cost reductions specified as following a 95% learning curve). That said, this is a public document and as such so no proprietary data was used or included in this report. There may be HTSE suppliers that have performance specifications, and cost estimates, and test data that differ from the analysis presented in this document. This document is meant to be a best conservative estimate of the technology and not an absolute reference.
08 HYDROGEN↗
A Dual Stage Sodium Thermo-Electro-Chemical Converter
The need for reliable and distributable energy sources is evident given the current forecasting of the world’s energy demand [1]. One prominent obstacle is the lack of an efficient direct heat-to-electricity conversion technology. The sodium thermo-electro-chemical converter (Na-TECC) is a highly efficient thermally regenerative electrochemical system (TRES) that can fill this void. The advantage of the Na-TECC over other direct conversion technologies is that it is a closed device (unlike fuel cells) requiring nothing more than a heat input (similar to thermoelectrics). It also has a high power density (> 1 W/cm2) and specific power up to 0.5 kW/kg , with the potential to achieve > 45% heat to electricity conversion efficiencies [2]. Furthermore, the converter rejects heat at a temperature hot enough to make it amenable for cogeneration, potentially under air-cooled operation. This electrochemical technology uses a special ion-selective solid-electrolyte called beta-alumina (Na2O•11Al2O3), that is highly conductive to sodium cations. A more conducting variation of this compound is beta”-alumina (Na2O•MgO•5Al2O3), where the magnesium acts as a stabilizer [3]. In the device, liquid sodium enters an evaporator where it is heated isobarically and vaporizes. The vapor is then oxidized at the interface between the anode and the solid-electrolyte. The Na-TECC generates electric power by allowing the high pressure oxidized sodium vapor ions to expand isothermally through the solid-electrolyte from the anode to the cathode. The liberated electrons are simultaneously conducted through an external load and recombine with the cations at the cathode. The low pressure sodium vapor is subsequently condensed and a wick pumps the liquid into the high pressure plenum to complete the cycle. Previous Na-TECCs have achieved efficiencies of 18% due to practical losses, whereas the potential efficiencies are above 45% [4]. Thermodynamic limitations and design constraints have limited previous devices from attaining these record efficiencies. Specifically, the maximum potential voltage in these previous devices was substantially reduced by the kinetic limitations of the charge transfer in the cathode (referred to as the overpotential) and by Joule heating losses due to the solid-electrolyte resistance, electrode resistance, and current collector resistance. Moreover, previous devices experienced a steady loss in performance throughout their lifetime. The drop in performance has been attributed to the electrolyte undergoing thermal and chemical breakdown due to crack propagation, loss of sodium oxide, and the formation of molten dendrites that create electrical shorts [5]. Finally, previous designs have also had a substantial problem with proper thermal management, with some showing thermal losses as great as 50% of the heat input. To improve the efficiency, a dual stage conversion process is being explored. Rather than using one isothermal expansion step, the dual cycle will employ two separate electrolytes to allow two separate expansion steps: one at the high temperature, and another at an intermediate temperature. This new cycle is thermodynamically favorable as it allows for regeneration from the isobaric cooling of the first expansion step, and reheat to complete the expansion in the second step. This allows the engine to reach a lower condenser temperature than its single stage counterpart, thus increasing the overall efficiency. The temperature drop across each electrolyte in the dual stage device (e.g., ~ 400 K) is smaller than the temperature drop across a comparable single stage device (e.g., ~ 800 K). This is amenable for better thermal management and allows for less heat loss. This will also mitigate the thermal shock across the very thin (< 1 mm) solid electrolytes, reduce crack propagation and limit the percolation of the molten dendrites, and ultimately increase the lifetime of the device. As an additional benefit the dual stage can be designed using planer geometries, rather than tubular geometries used in previous single stage models, which can be manufactured less expensively. This modified geometry can be tailored to improve the thermal management by reducing the radiation losses, which has been the biggest source of thermal losses [2]. A dual stage concept for the Na-TECC has never been demonstrated in literature. In this presentation, the operation of a single stage device will be revisited and, by adopting concepts used for similar converters, the thermodynamic operation of the dual stage will be developed [6]. Using the dual stage approach, the efficiency and power equations have been redefined to include regeneration and reheat. Questions such as what current densities optimize the power output or which intermediate temperature maximizes the efficiency will also be answered. Initial calculations of the dual stage show that the thermal management is the most sensitive parameter. Although the ideal voltage produced by the dual stage can be higher, there are potential drawbacks. For example, by using a lower condenser temperature the dual stage device will have a higher open circuit voltage, but it will also have a larger overpotential due to the limited cathodic kinetics that occur at a lower temperature. In short, there are several trade-offs in this device that need to be balanced. It will be demonstrated that the dual stage device must have lower thermal losses than a single stage device if it is to have a higher efficiency.
14 SOLAR ENERGY↗
Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)
During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.
SMAP L4_SM↗
Analysis of Launch Vehicle Liftoff Debris: Historical Perspective from Space Shuttle and Application to Artemis I
Human exploration-class launch vehicles are inherently prone to debris due to the extreme environments generated during pre-launch operations, liftoff, and flight. The use of cryogenic propellants often requires thermal protection system (TPS) coatings, typically foam, to maintain the propellant conditions in the tank and prevent an accumulation ice on the external surface of the vehicle. Some ice growth is to be expected at umbilical interfaces, vents, flanges, or brackets where it is difficult to apply TPS. This ice may come loose at any time due to wind on the launch pad, structural vibration and acoustics after rocket ignition, or aerodynamic forces during flight. This phenomena is especially apparent on vehicles with no TPS, such as the Saturn V rockets used in the Apollo Program, see Figure 1. During propellant tanking, the thermal contraction of the underlying substrate may generate cracks in the TPS (Figure 1). Chunks of TPS can release due to the expansion of ingested gas from cryopumping or from aerodynamic forces if the crack creates an offset surface. Most foams will also have a certain amount of “popcorning” where small pieces of foam will pop off during flight because of the differential between the static surface pressure and the pressure of the gas trapped in the foam cell structure. There are a number of other coating or closeout materials that may be shed from the vehicle and become debris. During pre-launch operations and liftoff, the vehicle may also be exposed to debris originating from the launch pad or ground support equipment. This debris is separate from foreign object debris, or FOD, which is not intended to be present and is strictly controlled through operations and maintenance procedures. In this case, debris is generated from hardware and materials that are necessary for launch and are subject to the intense vibration, acoustics, and direct plume impingement of the launch environment. Examples include ice from umbilicals, tape and tie wraps that protect cables, and rust or corrosion from the launch platform. While NASA has historically been aware of debris as a potential issue that could cause a failure resulting in loss of mission, loss of vehicle, or loss of crew, the likelihood and severity of that risk was not always well understood or given sufficient weight in program and flight decisions. After the Space Shuttle Columbia accident (STS-107), the investigation found that foam TPS debris shed from the external tank was the proximate cause of the damage to the orbiter wing. Six previous observations of debris released from the foam ramp that covered the bipod connecting the forward end of the orbiter to the external tank resulted in minor changes or were determined to be accepted flight risks. Two occurrences of bipod ramp foam loss were not identified until the STS-107 investigation. Despite the damage inflicted by these debris strikes, the Shuttle Program Requirements Control Board deemed the vehicle safe to fly. During the Return to Flight effort following the Columbia disaster, NASA Engineering developed a process for the assessment of debris transport, impact, and damage tolerance to support independent assessments of risk by NASA Safety and Mission Assurance (S&MA). Under this system, each element (vehicle or ground system) defines a catalog of all expected debris based on launch history, component testing, or analysis. Debris transport analysis (DTA) is conducted using the debris catalog characteristics and potential flow transport mechanisms (e.g., vehicle aerodynamics, gravity, wind, plume-driven). The predicted debris impact locations and velocities are provided to the hardware owners, who use available test data and analysis to determine whether each component can withstand the impacts. In cases where the element hardware may be severely damaged or fail, the options are to mitigate the debris source through some change in design or operation, or to work with S&MA to try to characterize the probability of the impact and damage for program risk acceptance. Because of the differences in debris characteristics and transport, the DTA has been divided between the Liftoff and Ascent regimes. The development and application of Liftoff DTA methodology from the Shuttle Program to the current Artemis Program is the subject of this paper. Liftoff DTA covers the time from the start of pre-launch operations at the launch pad, up until the vehicle clears the launch tower and there is no longer any interaction with ground systems. Debris transport during this period is broadly classified as either gravity, wind, and plume-entrained (GWPE) or plume driven (PD). GWPE debris is generally lower speed, travelling in a forward-to-aft direction. PD transport includes flow features from the rocket ignition transient, as well as plume impingement and recirculation that occur as the vehicle lifts off the launch platform. In these cases, the debris typically moves in an aft-to-forward direction at higher speeds. The applicable transport mechanisms must be considered for each piece of debris depending on the material, and release location and time. For example, rust or metallic debris from the tower could fall (GWPE) and impact the vehicle before landing on the launch platform deck where it could be also be transported by plume impingement (PD). However, falling ice (GWPE) from an umbilical is unlikely to survive impact with the vehicle or launch platform and be available for PD transport. Modeling of debris transport is accomplished using a set of DTA tools which simulate debris trajectories subject to a reference frame acceleration (i.e., gravity) and aerodynamic drag. Where the trajectory encounters a solid surface, the debris is allowed to rebound with a specified coefficient of restitution. The drag is calculated by interpolating the fluid state at each point in the debris trajectory from high-fidelity computational fluid dynamics (CFD) simulations of the launch vehicle and pad. The CFD data may either be static (steady state or time averaged), typically for GWPE transport, or dynamic (time-accurate) for PD flow features like the ignition transient. Examples of the CFD flow field solutions for the Space Launch System (SLS) rocket and launch pad are shown in Figure 2. Typical SLS debris trajectory predictions from DTA are illustrated in Figure 3. The final version of this paper will include a more detailed examination of the Liftoff DTA process developed during the Shuttle Program, and how it has been augmented and applied to the SLS rocket under the Artemis Program. Comparisons with debris observations from the Artemis I launch will demonstrate validation of the tools and methodology.
Debris↗
NASA's Habitation Development Status: Current Concepts and ISRU Opportunities
Introduction: The National Aeronautics and Space Administration (NASA) is embarking on a bold journey to return humankind to the Moon and onward to Mars with innovative commercial, international, and academic partnerships. Under the Artemis series of missions, NASA seeks to establish sustained human exploration of deep space through an objectives-based approach. This approach drives the identification of needed system functionality and the current and future capabilities which will eventually allow humanity to sustainably live beyond Earth. Providing evolvable and scalable habitation is a cornerstone function that calls for the collection and integration of current, developmental, and future technologies that can meet near-term exploration needs while growing into long-term sustained presence. NASA is advancing in-space habitation through its Next Space Technologies for Exploration Partnerships (NextSTEP) model while designing lunar, Mars transit, and Mars surface habitat government reference concepts for Artemis missions. These efforts have unveiled possible near-term opportunities for the in-space resource utilization community if human habitation is considered a future customer of space resources. NextSTEP Habitation Development: NASA is closely working with commercial partners under its NextSTEP Appendix A model to advance habitation systems in the arena of inflatable and composite habitation structures. Such efforts promise efficiencies in volumetric packaging and overall spacecraft mass respectively. Recent testing by commercial partners have helped to quantify possible failure mechanisms for inflatable structures while advancing their technology towards eventual flight certification. The advancement of such Class II habitation structures, in which the habitat is only fully deployed once in-space or on a planetary surface, is critical to providing increased habitable volume for long-duration missions with no additional mass penalties. The progression of such technology is infused into NASA’s government reference concepts for notional deep space habitation concepts. Current Government Reference Concepts: To best inform the formulation of future collaborative solicitations, NASA employs the practice of internally developing reference concepts for future exploration elements. These concepts aid in identifying the functions and capabilities needed to complete NASA missions as well as feasible solutions within the timeframe needed. Government reference concepts for a lunar Surface Habitat, Mars Transit Habitat, and Mars Sur-face Habitat are continuously being developed and updated to better guide the Agency’s overall exploration architecture. Lunar Surface Habitat. As NASA returns to the Moon, it is evaluating possible lunar surface habitation concepts. The Surface Habitat (SH) reference concept entails a hybrid metallic-inflatable structure capable of initially housing two crew for surface stays of up to 30 days in duration. While initial missions may span ~7 days in duration, consideration is being given to expanding SH’s capability to support a crew of four for up to 60 days over its 15-year design life. Functionally, the SH serves as a ‘hub’ for all Artemis crewed surface operations, providing internal volume for maintenance, medical, logistics, science utilization, and extravehicular activity (EVA) support in addition to core habitation functionality such as environmental control and life support (ECLS) and power generation and distribution among many others. Additionally, NASA has entered a study agreement with the Italian Space Agency (Agenzia Spaziale Italiana – ASI) to investigation a possible Multi-Purpose Habitat (MPH) as an additional or augmenting habit-able element for the lunar surface. Mars Transit Habitat. NASA’s current architectural concept for initial human missions to Mars entails the utilization of a transit habitat (TH) to transport a four-person crew to and from Mars orbit, departing from and returning to a lunar near-rectilinear halo orbit (NRHO), over the course of a ~1,200-day mission. While holding a similar 15-year design lifetime, TH will also support a series of analog mission activities in NRHO to gradually test the systems and interaction with lunar surface elements, some of which may be adapted for Mars surface exploration. Holding similar functional capabilities as SH, TH is sized to support much longer durations in space and greater logistical independence. Mars Surface Habitat. NASA is still exploring the concept of operations for initial crewed missions to Mars. As such, the Mars Surface Habitat (MSH) concept is still in its infancy as options for mobile, pressurized habitation and stationary habitats are being explored. It is expected MSH will leverage heavily from the lunar SH and possible lunar pressurized rover, however the very different Martian environment will likely necessitate modifications. ISRU Opportunities: Despite the advancements under NASA’s NextSTEP habitation work and continually optimized reference concepts, NASA is facing near-term mass and power challenges that may create opportunities to the ISRU community by providing yet another possible customer for space resources. While NASA desires to use regenerative ECLS systems (ECLSS) for all habitation concepts, their operation comes with initial mass penalties and maintenance overheads when compared to simpler open-loop architectures. Because of this, NASA is currently pro-posing an open-loop, consumables-based architecture for its surface habitats to achieve initial launch and delivery lander mass targets while scarring for the in-corporation of regenerative ECLSS to meet longer mission durations and sustained presence. With such an architecture, oxygen and potable water are needed consumables, which initial ISRU systems may be able to provide in a pilot capacity. Although the TH is expecting to utilize regenerative ECLSS, advancements in lunar surface-based ISRU and possible re-supply of spacecraft in lunar or Mars orbit could significantly reduce the logistical need for ECLSS related spares on TH and possibly allow a similar open-loop and consumables-based architecture. In addition to ECLSS mass concerns, the SH is facing challenges with energy storage to support operations over periods of darkness exceeding 100 hours. Both battery and fuel cell-based power architectures are being traded, opening options for external power generation and energy storage. One possibility is ISRU-produced H2 and O2 feeding external primary fuel cell systems which could supplement habitation power generation while reducing initial mass and volume until much more powerful fission power systems might be deployed. Conclusion: Significant advancements are being made in evolvable habitation concepts that span from near-term technologies, such as inflatable structures under NextSTEP, to potentially revolutionary capabilities like the lunar surface construction as funded through the Moon-to-Mars Planetary Autonomous Construction Technologies (MMPACT) project. As NASA investigates both heritage capabilities and rap-idly advancing, disruptive technologies, there are likely many near-term opportunities for initial ISRU capabilities to significantly aid human habitation and increase self-sufficiency beyond Earth.
habitation↗