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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Materials That Enhance Efficiency and Radiation Resistance of Solar Cells

A thin layer (approximately 10 microns) of a novel "transparent" fluorescent material is applied to existing solar cells or modules to effectively block and convert UV light, or other lower solar response waveband of solar radiation, to visible or IR light that can be more efficiently used by solar cells for additional photocurrent. Meanwhile, the layer of fluorescent coating material remains fully "transparent" to the visible and IR waveband of solar radiation, resulting in a net gain of solar cell efficiency. This innovation alters the effective solar spectral power distribution to which an existing cell gets exposed, and matches the maximum photovoltaic (PV) response of existing cells. By shifting a low PV response waveband (e.g., UV) of solar radiation to a high PV response waveband (e.g. Vis-Near IR) with novel fluorescent materials that are transparent to other solar-cell sensitive wavebands, electrical output from solar cells will be enhanced. This approach enhances the efficiency of solar cells by converting UV and high-energy particles in space that would otherwise be wasted to visible/IR light. This innovation is a generic technique that can be readily implemented to significantly increase efficiencies of both space and terrestrial solar cells, without incurring much cost, thus bringing a broad base of economical, social, and environmental benefits. The key to this approach is that the "fluorescent" material must be very efficient, and cannot block or attenuate the "desirable" and unconverted" waveband of solar radiation (e.g. Vis-NIR) from reaching the cells. Some nano-phosphors and novel organometallic complex materials have been identified that enhance the energy efficiency on some state-of-the-art commercial silicon and thin-film-based solar cells by over 6%.

Sun, Xiadong↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2025

Multiphysics Meshfree Degradation Modeling of Energy Storage Materials with Kernel Enrichment

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. Reference: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

25 ENERGY STORAGE↗
DOE OSTI2021

Theory and application of the vector pair correlation function for real-space crystallographic analysis of order/disorder correlations from STEM images

Deviations of local structure and chemistry from the average crystalline unit cell are increasingly recognized to have a significant influence on the properties of many technologically important materials. Here, we present the vector pair correlation function (vPCF) as a new real-space crystallographic analysis method, which can be applied to atomic-resolution scanning transmission electron microscopy (STEM) images to quantify and analyze structural order/disorder correlations. Our STEM-based vPCFs have several advantages over radial PCFs and/or 3D pair distribution functions from x-ray total scattering: vPCFs explicitly retain crystallographic orientation information, are spatially resolved, can be applied directly on a sublattice basis, and are suitable for any material that can be imaged with STEM. To show the utility of our approach, we measure partial vPCFs in Ba 5 SmSn 3 Nb 7 O 30 (BSSN), a tetragonal tungsten bronze (TTB) structured complex oxide. Many TTBs are known to be classical or relaxor ferroelectrics, and these properties have been correlated with the presence of superlattice ordering. BSSN, specifically, exhibits relaxor behavior and an incommensurate structural modulation. From the vPCF data, we observe that, of the cation sites, only the Ba (A2) sublattice is structurally modulated. We then infer the local modulation vector and reveal a marked anisotropy in its correlation length. Finally, short-range correlated polar displacements on the B2 cation sites are observed. This work introduces the vPCF as a powerful real-space crystallography technique, which enables direct, robust quantification of short-to-long range order on a sublattice-specific basis and is applicable to a wide range of complex material types.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Preliminary Thermoluminescent Dosimeter Glow Curve Analysis with Automated Glow Peak Identification for LiF Mg,Ti

When appropriately analyzed, thermoluminescent dosimeter glow curve analysis allows for improved quantification of thermoluminescent material behavior while flagging abnormalities. The mathematical separation of a glow curve into contributions from energetically unique trap states, or glow curve analysis, may be used to remove undesired effects of signal fading for complex materials. A generalized glow curve analysis software for the separation of glow curves is presented in this paper. Written in C ++ , the software uses the first-order kinetics model with automatic peak identification. The automatic identification of peaks is achieved through a unique peak-finding algorithm. Here, the program was performance tested using experimental glow curve data from LiF:Mg,Ti, and comparative results are presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

COWALKER:EFFECTIVE TRANSPORT PROPERTIES OF COMPOSITE MATERIALS

SF-23-026 This software computes effective transport properties of composite materials involving fibers and nanoparticles using a random-walk algorithm that efficiently scales to an arbitrary number of processes and cores. Effective transport properties (thermal, electrical) are key to bridge the microstructure of complex materials with its macroscopic behavior. Traditional approaches either use effective medium approximations (closed mathematical expressions that are approximation for certain conditions) or continuum simulation models such as finite element or finite volume, which require the generation of a mesh for each configuration explored. cowalker leverages the equivalence between laplacian or heat equation-based models and random walks to compute the asymptotic transport properties from an ensemble of first sojourn times of a random walker moving through the composite material. This allows us to directly define a composite material as a collection of particles and use algorithms developed for molecular dynamics to quickly compute the intersection of the walker with the different interfaces in the material. cowalker is developed in C++, and it relies on the GNU Scientific Library for random generation. cowalker is currently delivered as source code, so the GSL library is not included in cowalker's distribution. A more userfriendly version, cowalker.jl is currently in development and will be released as part of cowalker.

YANGUAS-GIL, ANGEL↗
DOE OSTI2026

Kernel Enriched Meshfree Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Laboratory of the Rockies (NLR), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. References: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Probabilistic learning and updating of a digital twin for composite material systems

This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.

42 ENGINEERING↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2020

Atomic fluctuations in electronic materials revealed by dephasing

The microscopic origin and timescale of the fluctuations of the energies of electronic states has a significant impact on the properties of interest of electronic materials, with implication in fields ranging from photovoltaic devices to quantum information processing. Spectroscopic investigations of coherent dynamics provide a direct measurement of electronic fluctuations. Modern multidimensional spectroscopy techniques allow the mapping of coherent processes along multiple time or frequency axes and thus allow unprecedented discrimination between different sources of electronic dephasing. Exploiting modern abilities in coherence mapping in both amplitude and phase, we unravel dissipative processes of electronic coherences in the model system of CdSe quantum dots (QDs). The method allows the assignment of the nature of the observed coherence as vibrational or electronic. The expected coherence maps are obtained for the coherent longitudinal optical (LO) phonon, which serves as an internal standard and confirms the sensitivity of the technique. Fast dephasing is observed between the first two exciton states, despite their shared electron state and common environment. This result is contrary to predictions of the standard effective mass model for these materials, in which the exciton levels are strongly correlated through a common size dependence. In contrast, the experiment is in agreement with ab initio molecular dynamics of a single QD. Electronic dephasing in these materials is thus dominated by the realistic electronic structure arising from fluctuations at the atomic level rather than static size distribution. The analysis of electronic dephasing thereby uniquely enables the study of electronic fluctuations in complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MXenoids: Generalization of MXene-Inspired Covalent Surface Modifications Across Two-Dimensional Materials

The ability to perform versatile covalent surface modifications in two-dimensional (2D) inorganic materials marks a significant advance in the functionalization of this broad family of materials. One particularly successful example of 2D materials with chemically modifiable surfaces are 2D transition metal carbides and nitrides (MXenes). MXenes' strong in-plane metal-carbon bonds and labile surface metal-halide bonds create altogether unprecedented opportunities for versatile postsynthetic modifications and assembling complex materials, including various organic-inorganic hybrids. Here, we demonstrate the general applicability of this surface modification strategy to non-MXene halide-terminated 2D materials, termed MXenoids. These surface modifications enable compositional and electronic structure engineering, introduce chiral hybrid organic-inorganic structures, and photoluminescence ranging from near-IR to blue. This study highlights the avenue of surface chemistry-driven materials design, enhancing the functional capabilities of 2D materials.

Zhou, Chenkun [University of Chicago, IL (United S↗
DOE OSTI2020

Natural Resources Assessment for the SNS Second Target Station, Oak Ridge, Tennessee

The US Department of Energy’s Oak Ridge National Laboratory (ORNL) is a leading institution in advanced materials, supercomputing, neutrons, and nuclear science. As a research laboratory managed by UT-Battelle, LLC for DOE, ORNL has national priorities in energy, security, and scientific discovery that necessitate facility improvements and expansions. DOE is also committed to environmental stewardship. The laboratory is located on the ~32,000-acre Oak Ridge Reservation (ORR), much of which is categorized as a National Environmental Research Park and a state Wildlife Management Area. DOE works with the Tennessee Wildlife Resources Agency (TWRA), Tennessee Department of Environment and Conservation (TDEC), US Fish and Wildlife Service (USFWS), US Department of Agriculture, and other agencies to serve as an effective steward of the ORR. Accordingly, project managers must conform to environmental regulations, agreements, and policies at the federal, state, and institutional levels. Per 40 CFR (Code of Federal Regulations) 1508.14, potential effects on research and science education on the National Environmental Research Park represent potential impacts of federal actions. Moreover, federal actions that affect the quantity and quality of hunting opportunities and deer reduction harvest on the Oak Ridge Wildlife Management Area must be considered whenever other aspects of the human environment (as defined by NEPA) are affected. The Spallation Neutron Source (SNS) is a premiere facility at ORNL that provides advanced capabilities in neutron scattering to promote new discoveries and research opportunities in material sciences, physics, chemistry, biological sciences, and others. A conceptual design for a Second Target Station (STS) has been in consideration for several years. The STS is intended to complement and enhance existing ORNL capabilities, notably research and exploration of complex materials. The proposed STS will involve development of existing natural areas on the ORR, which might contain sensitive resources that require mitigation or avoidance in accordance with existing policies and regulation. This report summarizes current knowledge of natural and cultural resources within the STS project area. At the time of this report, the proposed STS project consisted of an operations area comprising 55.4 acres (22.4 ha) and a total review area for potential construction comprising ~224 acres (90.6 ha). The review area is located primarily within forested natural areas of the ORR with minor development in the form of power-line rights-of-way and secondary/graveled roads (Figure 1). The primary goal was to evaluate potential effects on sensitive resources that might result from development of the STS. In addition to onthe-ground surveys during summer 2009 and fall 2019 to summer 2020 by ORNL Natural Resources Management Program and Aquatic Ecology Group staff, this report makes use of historical (pre-1995) and contemporary (1995 to present) data from additional confirmed sources (e.g., TDEC). Likewise, forest conditions were compiled primarily from a 2013 forest inventory effort for Forest Management Compartment 17 and supplemented with limited ground observations in 2019. The individuals who obtained and compiled the data that are presented here are familiar with and routinely assess sensitive resources on the ORR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Prototype Thick-Target Bremsstrahlung Model with Angularly-Dependent Emission in the MCNP6 ® Code

This document summarizes the current thick-target bremsstrahlung (TTB) model in MCNP and provides test results for an alternative implementation to improve the accuracy with reduced cost compared to full electron transport. It has been observed that the current TTB model produces inaccurate results in problems where the medium is thick with respect to electrons, but the photon distribution in the problem has a strong directionality. An example of such a simulation is detectors surrounding a metal target irradiated with a radiographic beam of high energy photons. The primary cause of this discrepancy is the current TTB method emits all bremsstrahlung photons in the same direction as the primary electron produced from each (γ, e ± ) interaction, leading to artificially forward peaked photon distributions for intermediate to high-energy incident photons. To improve the TTB model, we have implemented an angularly-dependent TTB model in a developer version of the MCNP6 ® code; for developers, this was done on the branch prototype/angular_ttb in the mcnp6 repo on bitbucket. The angularly-dependent TTB model accounts for the energy and scattering of electrons as they slow down in the current material, but does not sample the computationally expensive energy straggling, secondary electron events, and tracking electrons; this approach is significantly less computationally expensive than full electron transport and can be comparable to the original TTB method for problems with sufficiently complex materials and geometry. To evaluate the method, we have modeled a simple problem of a beam of 5 MeV photons incident on a sphere of plutonium surrounded by detectors at different deflection angles. For this problem, the angularly-dependent TTB produces a photon flux within 8.0% for a 90 degree deflection angle and 0.8% along the beam axis, as compared to the electron transport solution. This is an improvement compared to a 43% and 81% discrepancy with the original TTB method, respectively. The rest of this work includes the following: the first section details the current TTB treatment in MCNP, which has not been well documented elsewhere. Then, the modified TTB algorithm is detailed and the approximations compared to the condensed history algorithm are compared. Results are given comparing the two TTB methods to the condensed history transport algorithm. The appendix includes details for code developers on relevant electron transport implementation details and potential code improvements for future work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Mixed Ionic Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen

The innovative research conducted by Arizona State University and Princeton University in the project "Mixed Ionic-Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen" marks a significant stride forward in thermochemical water splitting. Through an intricate blend of computational design and experimental validation, the project delved into the promising potential of Mixed Ionic Electronic Conducting (MIEC) perovskites. These complex materials, characterized by their unique redox-active nature and adaptability in stoichiometry, present a promising frontier for efficient solar thermochemical hydrogen production. Firstly, the research enhanced the science by utilizing state-of-the-art computational methodologies to unravel the nuanced chemical potentials of MIEC perovskites. By simulating various off-stoichiometric scenarios and redox conditions, the team was able to predict material behaviors under diverse environmental conditions, a feat unachievable through conventional experimental methodologies alone. This approach not only fast-tracks the material screening process, significantly reducing the time from laboratory re-search to practical application, but also uncovers trends and correlations that are pivotal for future materials innovation. Regarding technical effectiveness, the project stands out in its economic feasibility. Traditional methods of materials discovery are often marred by high costs and extensive timeframes, owing to the iterative nature of experimental processes. However, by employing theoretical computations and validating these findings with targeted experiments, the project introduced a cost-effective paradigm for materials discovery and the first ever prediction, synthesis, and preliminary validation of a material solely from computational and theoretical considerations. This synergy between computation and experimentation expedites the discovery of optimal materials conducive to high-efficiency solar-to-hydrogen conversion processes. Furthermore, the public stands to benefit substantially from this research. The success of MIEC perovskites in solar thermochemical applications heralds a shift towards lower cost and lower electricity input for clean hydrogen production, hence potentially impacting climate and energy resilience. By improving the efficiency of solar-to-hydrogen conversions, the research paves the way for reduced dependency on fossil fuels, addressing the urgent global need for accessible and renewable energy sources. Moreover, the project's advancements contribute to scientific literacy in renewable energy technologies, empowering society through knowledge and spurring future innovations. In essence, this research project demonstrates significant progress in the realm of advanced water splitting through solar thermochemistry. Through its groundbreaking approaches in computational materials science and its implications for real-world applications, it holds the promise of a cleaner, more energy-resilient future.

08 HYDROGEN↗
NASA NTRS1979

Acoustic behavior of a fibrous bulk material

A semiempirical model is presented describing the acoustic behavior of Kevlar 29, a bulk absorbing material. The model is based on an approximate solution to the one-dimensional equations representing conservation of fluctuating mass, momentum and energy. By treating the material as a momentum sink, theoretical expressions of the material complex propagation constants and characteristic impedance were derived in terms of a single constant. Evaluating the constant at a single frequency for a particular specimen, excellent agreement between prediction and measurement was achieved for a large range of sound frequencies and material porosities and thicknesses. Results show that Kevlar 29 absorbs sound efficiently even at low frequencies. This is explained in terms of a frequency dependent material phase speed.

Hersh, A. S.↗
NASA NTRS1980

Time-temperature-stress capabilities of composite materials for advanced supersonic technology application, phase 1

Implementation of metal and resin matrix composites into supersonic vehicle usage is contingent upon accelerating the demonstration of service capacity and design technology. Because of the added material complexity and lack of extensive service data, laboratory replication of the flight service will provide the most rapid method of documenting the airworthiness of advanced composite systems. A program in progress to determine the time temperature stress capabilities of several high temperature composite materials includes thermal aging, environmental aging, fatigue, creep, fracture, and tensile tests as well as real time flight simulation exposure. The program has two parts. The first includes all the material property determinations and aging and simulation exposures up through 10,000 hours. The second continues these tests up to 50,000 cumulative hours. Results are presented of the 10,000 hour phase, which has now been completed.

Kerr, J. R.↗
NASA NTRS2014

Robust Informatics Infrastructure Required For ICME: Combining Virtual and Experimental Data

With the increased emphasis on reducing the cost and time to market of new materials, the need for robust automated materials information management system(s) enabling sophisticated data mining tools is increasing, as evidenced by the emphasis on Integrated Computational Materials Engineering (ICME) and the recent establishment of the Materials Genome Initiative (MGI). This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and or multi-scale models requires both the processing of large volumes of test data and complex materials data necessary to establish processing-microstructure-property-performance relationships. Fortunately, material information management systems have kept pace with the growing user demands and evolved to enable: (i) the capture of both point wise data and full spectra of raw data curves, (ii) data management functions such as access, version, and quality controls;(iii) a wide range of data import, export and analysis capabilities; (iv) data pedigree traceability mechanisms; (v) data searching, reporting and viewing tools; and (vi) access to the information via a wide range of interfaces. This paper discusses key principles for the development of a robust materials information management system to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets to enable ICME. In particular, NASA Glenn's efforts towards establishing such a database for capturing constitutive modeling behavior for both monolithic and composites materials

Mutli-scale models↗
DOE OSTI2024

Systematic Improvement of Quantum Monte Carlo Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gap Prediction

Accurate determination of the electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly in predicting band gaps. We introduce a systematic methodology to enhance the accuracy of diffusion Monte Carlo (DMC) simulations for both ground and excited states, focusing on LiCoO 2 as a case study. By employing a selected configuration interaction (sCI) approach, we demonstrate the capability to optimize wavefunctions beyond the constraints of single-reference DFT+U trial wavefunctions. Here, we show that the sCI framework enables accurate prediction of band gaps in LiCoO 2 , closely aligning with experimental values and substantially improving traditional computational methods. The study uncovers a nuanced mixed state of t 2g and e g orbitals at the band edges that is not captured by conventional single-reference methods, further elucidating the limitations of PBE+U in describing d-d excitations. Our findings advocate for the adoption of beyond-DFT methodologies, such as sCI, to capture the essential physics of excited-state wavefunctions in strongly correlated materials. The improved accuracy in band gap predictions and the ability to generate more reliable trial wavefunctions for DMC calculations underscore the potential of this approach for broader applications in the study of correlated oxides. This work not only provides a pathway for more accurate simulations of electronic structures in complex materials but also suggests a framework for future investigations of the excited states of other challenging systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗