Joint State and Parameter Estimation for Batteries Considering Input Noise Compensation
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Trevizan, Rodrigo Daniel [Sandia National Laborato↗
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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↗
Out-of-Distribution Generalization for Learning Quantum Channels with Low-Energy Coherent States
When experimentally learning the action of a continuous-variable quantum process by probing it with inputs, there will often be some restriction on the input states used. One experimentally simple way to probe a quantum channel is to use low-energy coherent states. Learning a quantum channel in this way presents difficulties, due to the fact that two channels may act similarly on low-energy inputs but very differently for high-energy inputs. They may also act similarly on coherent-state inputs but differently on nonclassical inputs. Extrapolating the behavior of a channel for more general input states from its action on the far more limited set of low-energy coherent states is a case of out-of-distribution generalization. To be sure that such generalization gives meaningful results, one needs to relate error bounds for the training set to bounds that are valid for all inputs. We show that for any pair of channels that act sufficiently similarly on low-energy coherent-state inputs, one can bound how different the input-output relations are for any (high-energy or highly nonclassical) input. This proves that out-of-distribution generalization is always possible for learning quantum channels using low-energy coherent states, as long as enough samples are used.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Anomaly detection with spiking neural networks
Detecting anomalies with a spiking neural network is provided. An input layer receives a number of inputs and converts them into phase-coded spikes, wherein each input is contained within a number of progressively larger neighborhoods of surrounding inputs. From the phase-coded spikes, a median value of each input is computed for each size neighborhood. An absolute difference of each input from its median value is computed for each size neighborhood. A median absolute difference (MAD) of each input is computed for each size neighborhood. For each input, an adaptive median filter (AMF) determines if a MAD for any size neighborhood exceeds a respective threshold. If one or more neighborhoods exceeds its threshold, the AMF outputs the median value of the input for the smallest neighborhood. If none of the neighborhoods exceeds the threshold, the AMF outputs the original value of the input.
Verzi, Stephen Joseph↗
Unlocking plant-microbial interactions in deep Mollisols in the Midwestern US: Linking depth gradients in roots, microbial activity, and soil carbon in agroecosystems
Deep-rooted plants may build soil carbon (C) stocks, but most research has focused on shallow soils, leaving gaps in our understanding of how shifts in the balance between decomposition and C inputs drive soil C accumulation with depth. Thus, our objectives were to: (1) link depth gradients in root biomass with microbial activity and soil C stocks down to 1 m, and (2) examine the potential of simple C inputs to prime soil C across depths. To this end, we dug 5 quantitative soil pits in Argiudolls under mature perennial miscanthus plots in the SoyFACE Farm (Champaign-Urbana, IL). We added 13 C labeled glucose to our soils to determine the fate of simple C inputs with depth. We found that fine root biomass, total soil C, mineral-associated organic C (MAOC), particulate organic C (POC), and microbial activity (as measured by potential enzyme activity) declined with depth. POC declined more rapidly than MAOC, resulting in an increase in the ratio of MAOC-to-POC. Root biomass, enzyme activity (either acid phosphatase or n-acetyl-glucosaminadase) activity, and microbial respiration explained 74% and 38% of the variability in soil total C and MAOC, respectively, while POC was dependent on root biomass and microbial respiration (47%). Although the incorporation of simple 13 C inputs into MAOC was similar across depths, these inputs led to greater net MAOC losses in shallow soils than in deeper soils between 50 and 100 cm. The divergent impact of simple C inputs across depths may suggest that MAOC in shallow soils is more susceptible to priming losses, while C inputs into deep soils may instead be more persistent. Collectively, our results suggest that depth gradients in soil C stocks represents a balance between inputs, decomposition, and microbial necromass production and that increases in root C inputs by deep-rooted plants may have the potential to build stable MAOC.
60 APPLIED LIFE SCIENCES↗
Quantification and prediction of error in MIMO tests
Dynamic testing of systems is most realistic of real-world conditions when multiple input and multiple output (MIMO) techniques are used. To replicate measured environmental conditions, a series of desired outputs (responses) on the system must be realized by inverting the frequency response functions (FRF) matrix for input estimation. System identification is dependent on the number of inputs and outputs, which affect the type of solutions. In input estimation, the auto spectra of the determined inputs are affected by both the auto spectra and cross spectra of the desired outputs. This research evaluates errors and investigates two sources in an experimental setting. The first significant error source is test-to-test variability in FRFs from system identification tests, quantified by magnitude variability from long-duration tests. The second significant error source is the realization of time histories from frequency domain inputs. The spectral content of the realized random process deviates from the desired content and is also quantified. The structure is a linear three-story frame fixed at the base; two inputs are provided uniaxially with the use of suspended electrodynamic shakers. The proposed model for error sources was found to be effective in predicting errors in a SISO (single-input single-output) and a square (2-input 2-output) MIMO test.
Yousef, Odey↗
Biological mechanisms may contribute to soil carbon saturation patterns
Increasing soil organic carbon (SOC) storage is a key strategy to mitigate rising atmospheric CO 2 , yet SOC pools often appear to saturate, or increase at a declining rate, as carbon (C) inputs increase. Soil C saturation is commonly hypothesized to result from the finite amount of reactive mineral surface area available for retaining SOC, and is accordingly represented in SOC models as a physicochemically determined SOC upper limit. However, mineral-associated SOC is largely microbially generated. In this perspective, we present the hypothesis that apparent SOC saturation patterns could emerge as a result of ecological constraints on microbial biomass—for example, via competition or predation—leading to reduced C flow through microbes and a reduced rate of mineral-associated SOC formation as soil C inputs increase. Microbially explicit SOC models offer an opportunity to explore this hypothesis, yet most of these models predict linear microbial biomass increases with C inputs and insensitivity of SOC to input rates. Synthesis of 54 C addition studies revealed constraints on microbial biomass as C inputs increase. Different hypotheses limiting microbial density were embedded in a three-pool SOC model without explicit limits on mineral surface area. As inputs increased, the model demonstrated either no change, linear, or apparently saturating increases in mineral-associated and particulate SOC pools. Taken together, our results suggest that microbial constraints are common and could lead to reduced mineral-associated SOC formation as input rates increase. We conclude that SOC responses to altered C inputs—or any environmental change—are influenced by the ecological factors that limit microbial populations, allowing for a wider range of potential SOC responses to stimuli. Understanding how biotic versus abiotic factors contribute to these patterns will better enable us to predict and manage soil C dynamics.
54 ENVIRONMENTAL SCIENCES↗
WORM (Write One, Read Many)
WORM (Write One, Run Many) is an easy to use, cross platform, embedded and extensible, functional programming language designed to facilitate the creation of input-decks for computer codes that use standard ASCII text files for input. WORM makes it easy to create generic (yet, complex and powerful) reusable models. Additionally its nature allows for complex calculations and routines to be coded once and easily reused, further simplifying the creation of input decks. WORM (Write One, Run Many) is a powerful and versatile tool designed to improve the efficiency of today’s criticality safety analyst by allowing: + input decks for parametric studies to be created quickly and easily, + calculations and variables to be imbedded into any input deck, thus allowing for meaningful parameter specifications, + problems to be specified using any combination of units, and + complex mathematically defined models to be created. A very simple syntax is employed, and therefore the WORM is easy to learn. A WORM model is essentially a standard input deck with some of its numerical values replaced by WORM code. WORM code may include and evaluate the following mathematical operators and functions: addition, subtraction, multiplication, division, exponentiation, modulus, sine, cosine, tangent, arcsine, arccosine, arctangent, the natural logarithm, logarithm base 10, integer truncation, absolute value, and random number. Several common constants, e.g., pi, e, and Avogadros’s Number (both as 6.022e23 and 0.6022), are predefined in WORM. Additionally, many unit conversion factors are also predefined: millimeters, meters, inches, feet, yards, and mils to centimeters; kilograms, pounds, and ounces to grams; liters, milliliters, gallons, and fluid ounces to cubic centimeters; and angular degrees to radians. For parametric studies, WORM supports various shorthand list specifications: the explicit step size, linear interpolation, and logarithmic interpolation. The list notation sequentially assigns multiple values to a name. WORM creates an input deck for each value of the name. If multiple lists are used, WORM steps through each list individually, i.e., WORM creates input decks corresponding to each and every permutation of the list values. Additionally, a library of standard material definitions and Perl subroutines are included. Any one of these files can be incorporated into the subject model with a simple WORM read command. WORM is completely written in Perl, the Practical Extraction and Reporting Language. Perl is one of the most portable programming languages available today. As such, the WORM works on practically any computer platform.
Sartor, Raymond↗
Final Report On Non-Intrusive Load Monitoring Of Welding Processes
The conventional method of weld process monitoring is to monitor the process output electrical power close to the point of welding. Non-Intrusive Load Monitoring (NILM) describes the method of monitoring primary or utility electrical power into a welding process well away from the point of welding. The objective of this project was to determine if there is sufficient information within the welding process input power to understand process output power characteristics, and, if so, determine if there is sufficient resolution within the input electrical characteristics to infer some measures of weld quality. To understand the relationship between process input and output power, both input and output power were monitored for two welding processes (GMAW and GTAW) and two different power source types (inverter and SCR). Welds were made with and without intentional disturbances. The results showed that there is a strong correlation between input and output power and that the influence of process disturbances are evident within the input power. A simple method using input power only was devised and successfully demonstrated to discriminate between a weld made with no intentional disturbance (a nominal weld) from a weld made with an intentional disturbance (off-nominal weld). The primary conclusions of this work is that NILM of process input power is sensitive to process disturbances that could influence weld quality and that the approach warrants additional study.
36 MATERIALS SCIENCE↗
Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models
Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.
Snyder, Abigail C.↗
Power converter circuitry for photovoltaic devices
Power converter circuitry includes a direct current (DC) input comprising a first DC input node and a second DC input node, an alternating current (AC) output comprising a first AC output node coupled to the first DC input node and a second AC output node, a first boost switch coupled between the second DC input node and a boost intermediate node, a second boost switch coupled between the boost intermediate node and a common node, a boost inductor coupled between the boost intermediate node and the first DC input node, a link capacitor coupled between the second DC input node and the common node, a first half-bridge switch coupled between the second DC input node and a half-bridge intermediate node, a second half-bridge switch coupled between the half-bridge intermediate node and the common node, and a half-bridge inductor coupled between the half-bridge intermediate node and the second AC output node.
Ayyanar, Rajapandian↗
Oxy-combustion process with modular boiler design
A modular boiler system for implementing fuel combustion is provided. The system includes a first boiler and a second boiler of a plurality of boilers, an oxygen input unit, a fuel input unit, a recycled flue gas input unit, and a flue gas separator. The first boiler receives oxygen from the oxygen input unit, fuel from the fuel input unit, and recycled flue gas from the recycled flue gas input unit. The first boiler outputs intra-system flue gas. The flue gas separator separates the intra-system flue gas into a first and second flue gas stream, transfers the first flue gas stream to the second boiler, and transfers the second flue gas stream to a gas cleaning system. The second boiler receives oxygen from the oxygen input unit, fuel from the fuel input unit, and the first flue gas stream from the flue gas separator.
Axelbaum, Richard L.↗
System and method for micromechanical logical and gate
The present disclosure relates to a mechanical AND-gate logic system that may make use of a first and second lever arms, a first pivot linkage coupling the lever arms, an output member, and a second pivot linkage coupling the second lever arm to the output member. The first lever arm has first and second spaced apart ends for independently receiving separate logic level 1 or logic level 0 input signals thereon, and an output end spaced apart from the first and second input ends. The output end is disposed generally equidistant from the first and second spaced apart ends. The second lever arm has an input end and an output end and can move in both pivoting and translating movements. The first pivot linkage couples the output end of the first lever arm to the input end of the second lever arm such that both the input end and the output end of the second lever arm are able to either pivot and or to translate. The second lever arm is only able to translate and apply a logic 1 level input signal to the output member, to thus generate a logic 1 level output signal, when a logic level 1 input signal is applied simultaneously to both of the first and second input ends of the first lever arm.
Panas, Robert Matthew↗
Using a Genetic Algorithm to Optimize Configurations in a Data-Driven Application
Users of highly-configurable software systems often want to optimize a particular objective such as improving a functional outcome or increasing system performance. One approach is to use an evolutionary algorithm. However, many applications today are data-driven, meaning they depend on inputs or data which can be complex and varied. Hence, a search needs to be run (and re-run) for all inputs, making optimization a heavy-weight and potentially impractical process. In this paper, we explore this issue on a data-driven highly-configurable scientific application. We build an exhaustive database containing 3,000 configurations and 10,000 inputs, leading to almost 100 million records as our oracle, and then run a genetic algorithm individually on each of the 10,000 inputs. We ask if (1) a genetic algorithm can find configurations to improve functional objectives; (2) whether patterns of best configurations over all input data emerge; and (3) if we can we use sampling to approximate the results. We find that the original (default) configuration is best only 34% of the time, while clear patterns emerge of other best configurations. Out of 3,000 possible configurations, only 112 distinct configurations achieve the optimal result at least once across all 10,000 inputs, suggesting the potential for lighter weight optimization approaches. We show that sampling of the input data finds similar patterns at a lower cost.
Sinha, Urjoshi↗
Chemical and isotopic evolution of flowback fluids from the Utica Gas Shale Play, Eastern Ohio USA
For this work, hydraulic fracturing flowback fluids were collected from two Utica/Point Pleasant well pads in eastern Ohio. One site was in the wet gas zone (UPPW), while the site ~50 km south consisted of four wells on the same pad in the dry gas zone (UPPS). Samples of input fluids also were collected before and during hydraulic fracturing. Flowback fluids are Na-Ca-Cl brines with total dissolved salt (TDS) concentrations that increase over several months from ~100 to 200 g/L. The slightly higher TDS of the dry gas fluids are in part due to recycled flowback used as input fluids, and in part due to lower volume of water used in hydraulic fracturing. Concentrations of most major ions (Ca 2+ , Mg 2+ , Na + , Sr2 + , Fe, Mn, Cl – , Br – ) are similar for the five wells sampled, although small but systematic changes occur in the major element ratios over time. Most notably, an increase in the Sr/Cl ratio corresponds to an increase in the 87 Sr/ 86 Sr ratio in the fluids, suggesting interactions with a more radiogenic Sr source in the subsurface. Dissolved Ba concentrations and Ra activities were different between the two sites, reflecting a high SO 4 2– fluid used at the UPPW site, and water-rock reactions occurring during hydraulic fracturing at the UPPS site. Water oxygen (δ 18 O) and hydrogen (δD) isotopes for input fluids used in the UPPW4 well and fresh water used for the UPPS wells fall on the Global Meteoric waterline (GMWL). Flowback fluids from both sites are relatively enriched in δ 18 O and δD compared to the input, but do not appear to follow a simple mixing trend, suggesting reaction and isotopic exchange with carbonates and fractionation due to imbibition in the rock. Cl isotopes, δ 37 Cl in the FP fluids varied from ~ –0.43 to +0.13‰, the largest variation was observed in the earlier stages of flowback, while in the later stages δ 37 Cl exhibited a small but systematic increase over time, suggesting diffusion control of isotopic composition. Some of the trace species measured (dissolved Fe, Mn, thiosulfate and organic acids) do not follow the same trends as the major ions, suggesting contributions from input fluids, or microbially mediated reactions are exerting control on their concentrations. Input water chemistry exerts an important control on the Sr and Ba concentrations in flowback water. High SO 4 2– in the input fluids used for hydraulically fracturing the wet gas well leads to precipitation of barite-celestite in flowback fluids.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A resolution independent neural operator
The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input functions to be discretized at the same locations; this limits its practical applications. Here, in this work, we introduce a general framework for operator learning from input–output data with arbitrary number and locations of sensors. This begins by introducing a resolution-independent DeepONet (RI-DeepONet), enabling it to handle input functions that are arbitrarily, but sufficiently finely, discretized. To this end, we propose two dictionary learning algorithms to adaptively learn a set of appropriate continuous basis functions, parameterized as implicit neural representations (INRs), from correlated signals defined on arbitrary point cloud data. These basis functions are then used to project arbitrary input function data as a point cloud onto an embedding space (i.e., a vector space of finite dimensions) with dimensionality equal to the dictionary size, which can be directly used by DeepONet without any architectural changes. In particular, we utilize sinusoidal representation networks (SIRENs) as trainable INR basis functions. The introduced dictionary learning algorithms are then used in a similar way to learn an appropriate dictionary of basis functions for the output function data, which defines a new neural operator architecture referred to as the R esolution I ndependent N eural O perator (RINO). In the RINO, the operator learning task simplifies to learning a mapping from the coefficients of input basis functions to the coefficients of output basis functions. We demonstrate the robustness and applicability of RINO in handling arbitrarily (but sufficiently richly) sampled input and output functions during both training and inference through several numerical examples.
Deep operator network (DeepONet)↗
Kernel-based global sensitivity analysis obtained from a single data set
Results from global sensitivity analysis (GSA) often guide the understanding of complicated input–output systems. Kernel-based GSA methods have recently been proposed for their capability of treating a broad scope of complex systems. In this paper, we develop a new set of kernel GSA tools when only a single set of input–output data is available. Three key advances are made: (1) A new numerical estimator is proposed that demonstrates an empirical improvement over previous procedures. (2) A computational method for generating inner statistical functions from a single data set is presented. (3) A theoretical extension is made to define conditional sensitivity indices, which reveal the degree that the inputs carry shared information about the output when inherent input–input correlations are present. Utilizing these conditional sensitivity indices, a decomposition is derived for the output uncertainty based on what is called the optimal learning sequence of the input variables, which remains consistent when correlations exist between the input variables. Further, while these advances cover a range of GSA subjects, a common single data set numerical solution is provided by a technique known as the conditional mean embedding of distributions. The new methodology is implemented on benchmark systems to demonstrate the provided insights.
42 ENGINEERING↗