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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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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Development of Probabilistic Life Prediction Methodologies and Testing Strategies for MEMS

This effort is to investigate probabilistic life prediction methodologies for MicroElectroMechanical Systems (MEMS) and to analyze designs that determine stochastic properties of MEMS. This includes completion of a literature survey regarding Weibull size effect in MEMS and strength testing techniques. Also of interest is the design of a proper test for the Weibull size effect in tensile specimens. The Weibull size effect is a consequence of a stochastic strength response predicted from the Weibull distribution. Confirming that MEMS strength is controlled by the Weibull distribution will enable the development of a probabilistic design methodology for MEMS - similar to the GRC developed CARES/Life program for bulk ceramics. Another potential item of interest is analysis and modeling of material interfaces for strength as well as developing a strategy to handle stress singularities at sharp corners, filets, and material interfaces. The ultimate objective of this effort is to further develop and verify the ability of the Ceramics Analysis and Reliability Evaluation of Structuredlife (CARES/Life) code to predict the time-dependent reliability of MEMS structures subjected to multiple transient loads. Along these lines work may also be performed on transient fatigue life prediction methodologies.

Jadaan, Osama M.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27686-DND

Accurate flight prediction methods are critical for all aspects of vehicle performance, handling characteristics, and structural integrity. The first NASA/Industry/DoD workshop on wind tunnel/flight correlation was held in 1981, when discrepancies arising from Reynolds number effects, aeroelastic effects, and tunnel wall and sting interference effects posed significant barriers to experimentally-based prediction methods in use at that time. Significant advances in ground-based experimental capabilities and facilities, CFD methods, and flight test technology in the next 20 years significantly improved the tools available for predictions. In addition, numerous new production and experimental military and civil aircraft completed flight testing, offering an unprecedented experience base and data for assessing the accuracy of current prediction methods and practices. Thus, the first Flight Prediction Workshop (FPW) was held in 2002. The objective of the 2002 workshop and the current 3 day workshop for technical specialists is to provide a forum for the exchange of: experiences and lessons learned, critical assessments of the state of the art, and recommendations for further developments in flight prediction methodology. Information and opinions provided by the workshop will be incorporated in R&T planning for future government activities in aerodynamics, aeroelasticity, aeroacoustics, propulsion/air frame integration, etc.

Joseph H Morrison↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗
DOE OSTI2026

Virtual Growth of SRF Materials: A Machine Learning Approach to Predict the Crystalline Structural Ordering in Nb Surface Oxides

Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.

Tilkin, Anthony [Unlisted, US, IL; Fermilab]↗
DOE OSTI2021

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High Entropy Alloys: Advanced Synchrotron X-Ray and Neutron Scattering Studies

Synchrotron high energy X-rays and neutrons are powerful complementary tools for structure-property relationship studies by probing materials under complex external stimuli and measuring the bulk-average materials properties and response mechanism, thanks to the high brightness and advanced instrumentations. The research of using the high energy X-ray or neutron scattering instruments at large facilities in high entropy alloys has greatly increased in alloy design, synthesis, property evaluation, performance testing, and predictive model validation, and thus accelerates the next generation structural material discoveries.

An, Ke↗
NASA NTRS2008

Optimized Non-Obstructive Particle Damping (NOPD) Treatment for Composite Honeycomb Structures

Non-Obstructive Particle Damping (NOPD) technology is a passive vibration damping approach whereby metallic or non-metallic particles in spherical or irregular shapes, of heavy or light consistency, and even liquid particles are placed inside cavities or attached to structures by an appropriate means at strategic locations, to absorb vibration energy. The objective of the work described herein is the development of a design optimization procedure and discussion of test results for such a NOPD treatment on honeycomb (HC) composite structures, based on finite element modeling (FEM) analyses, optimization and tests. Modeling and predictions were performed and tests were carried out to correlate the test data with the FEM. The optimization procedure consisted of defining a global objective function, using finite difference methods, to determine the optimal values of the design variables through quadratic linear programming. The optimization process was carried out by targeting the highest dynamic displacements of several vibration modes of the structure and finding an optimal treatment configuration that will minimize them. An optimal design was thus derived and laboratory tests were conducted to evaluate its performance under different vibration environments. Three honeycomb composite beams, with Nomex core and aluminum face sheets, empty (untreated), uniformly treated with NOPD, and optimally treated with NOPD, according to the analytically predicted optimal design configuration, were tested in the laboratory. It is shown that the beam with optimal treatment has the lowest response amplitude. Described below are results of modal vibration tests and FEM analyses from predictions of the modal characteristics of honeycomb beams under zero, 50% uniform treatment and an optimal NOPD treatment design configuration and verification with test data.

Panossian, H.↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Hypersonic wing test structure design, analysis, and fabrication

An investigation to provide the analyses, data, and hardware required to experimentally validate the beaded panel concept and demonstrate its usefulness as a basis for design of a Hypersonic Research Airplane (HRA) wing is reported. Combinations of the beaded panel structure, heat shields, channel caps and corrugated webs for ribs and spars were analyzed for the wing of a specified HRA to operate at Mach 8 with a lifespan of 150 flights. Detailed analyses were conducted in accordance with established design criteria and included aerodynamic heating and load predictions, transient structural thermal calculations, extensive NASTRAN computer modeling, and structural optimization. Optimum beaded panel tests at 922 K (1200 F) were performed to verify panel performance. Close agreement of predicted and actual critical loads permitted use of design procedures and equations for the beaded panel concept without modification.

Plank, P. P.↗
NASA NTRS1994

Experimental validation of finite element and boundary element methods for predicting structural vibration and radiated noise

This research report is presented in three parts. In the first part, acoustical analyses were performed on modes of vibration of the housing of a transmission of a gear test rig developed by NASA. The modes of vibration of the transmission housing were measured using experimental modal analysis. The boundary element method (BEM) was used to calculate the sound pressure and sound intensity on the surface of the housing and the radiation efficiency of each mode. The radiation efficiency of each of the transmission housing modes was then compared to theoretical results for a finite baffled plate. In the second part, analytical and experimental validation of methods to predict structural vibration and radiated noise are presented. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and boundary element method (BEM) models of the apparatus were used to predict the noise level radiated from the box. The FEM was used to predict the vibration, while the BEM was used to predict the sound intensity and total radiated sound power using surface vibration as the input data. Vibration predicted by the FEM model was validated by experimental modal analysis; noise predicted by the BEM was validated by measurements of sound intensity. Three types of results are presented for the total radiated sound power: sound power predicted by the BEM model using vibration data measured on the surface of the box; sound power predicted by the FEM/BEM model; and sound power measured by an acoustic intensity scan. In the third part, the structure used in part two was modified. A rib was attached to the top plate of the structure. The FEM and BEM were then used to predict structural vibration and radiated noise respectively. The predicted vibration and radiated noise were then validated through experimentation.

Seybert, A. F.↗
NASA NTRS1989

Modelling and design of high performance indium phosphide solar cells

A first principles pn junction device model has predicted new designs for high voltage, high efficiency InP solar cells. Measured InP material properties were applied and device parameters (thicknesses and doping) were adjusted to obtain optimal performance designs. Results indicate that p/n InP designs will provide higher voltages and higher energy conversion efficiencies than n/p structures. Improvements to n/p structures for increased efficiency are predicted. These new designs exploit the high absorption capabilities, relatively long diffusion lengths, and modest surface recombination velocities characteristic of InP. Predictions of performance indicate achievable open-circuit voltage values as high as 943 mV for InP and a practical maximum AM0 efficiency of 22.5 percent at 1 sun and 27 C. The details of the model, the optimal InP structure and the effect of individual parameter variations on device performance are presented.

Rhoads, Sandra L.↗
DOE OSTI2023

ReaxFF reactive force field model enables accurate prediction of physiochemical and mechanical properties of crystalline and amorphous shape‐memory polyurethane

Abstract We investigate reactive force field (ReaxFF) prediction of physiochemical and mechanical properties, and stimuli response behavior of a shape‐memory polyurethane (SMPU) rigid segment. We used SMPUs as a platform to link the rigid/hard segment domain crystallinity with properties that can also be experimentally measured. Specifically, we developed models for crystalline and amorphous forms of a common rigid segment of SMPUs, for example, 4,4′‐diphenylmethane diisocyanate (MDI) with n‐butanediol (BDO), denoted as MDI‐BDO. ReaxFF molecular dynamics simulations with equilibrium dynamics are performed by controlling mass, temperature, and pressure or volume to predict the structural and physiochemical properties of crystalline and amorphous rigid segments of SMPU systems, followed by simulating their XRD and FTIR patterns, respectively. Non‐equilibrium ReaxFF simulations are implemented by using uniaxial box deformation technique to study response‐behavior of crystalline SMPU under tensile loading and during stress‐relaxation. The model and simulation scheme presented in this study demonstrates a robust theoretical pathway toward application‐dictated, on‐demand design of high‐performance SMPUs in applications such as soft robotics.

Afroz, Mohammad M.↗
DOE OSTI2022

Dynamic Performance Comparison and Prediction based on Distribution-level Phasor Measurement Units

This paper introduces a new distribution level Phasor Measurement Unit (PMU) which adopts advanced hardware components and structure. The hardware parameters from the new PMU and the existing PMU are used to build a simulation model to predict the PMU performance. Therefore, a real-world testbench is built and four distribution level PMUs are tested under the steady-state and dynamic tests. The quantitative experiment result confirms the prediction model which could guide future PMU design, and also verifies the accuracy of the new PMU on the synchrophasor and frequency measurements in multiple scenarios.

Wu, Yuru↗
NASA NTRS1985

Test results of a 40 kW Stirling engine and comparison with the NASA-Lewis computer code predictions

A Stirling engine was tested without auxiliaries at NASA-Lewis. Three different regenerator configurations were tested with hydrogen. The test objectives were (1) to obtain steady-state and dynamic engine data, including indicated power, for validation of an existing computer model for this engine; and (2) to evaluate structurally the use of silicon carbide regenerators. This paper presents comparisons of the measured brake performance, indicated mean effective pressure, and cyclic pressure variations with those predicted by the code. The measured data tended to be lower than the computer code predictions. The silicon carbide foam regenerators appear to be structurally suitable, but the foam matrix tested severely reduced performance.

Allen, D.↗
NASA NTRS2000

Slow Crack Growth Analysis of Advanced Structural Ceramics Under Combined Loading Conditions: Damage Assessment in Life Prediction Testing

Slow crack growth analysis was performed with three different loading histories including constant stress-rate/constant stress-rate testing (Case I loading), constant stress/constant stress-rate testing (Case II loading), and cyclic stress/constant stress-rate testing (Case III loading). Strength degradation due to slow crack growth arid/or damage accumulation was determined numerically as a Function of percentage of interruption time between the two loading sequences for a given loading history. The numerical solutions were examined with the experimental data determined at elevated temperatures using four different advanced ceramic materials, two silicon nitrides, one silicon carbide and one alumina for the Case I loading history, and alumina for the Case II loading history. The numerical solutions were in reasonable agreement with the experimental data, indicating that notwithstanding some degree of creep deformation presented for some test materials slow crack growth was a governing mechanism associated with failure for all the test material&

Choi, Sung R.↗
NASA NTRS2000

Slow Crack Growth Analysis of Advanced Structural Ceramics Under Combined Loading Conditions: Damage Assessment in Life Prediction Testing

Slow crack growth analysis was performed with three different loading histories including constant stress-rate/constant stress-rate testing (Case 1 loading), constant stress/constant stress-rate testing (Case 2 loading), and cyclic stress/constant stress-rate testing (Case 2 loading). Strength degradation due to slow crack growth and/or damage accumulation was determined numerically as a function of percentage of interruption time between the two loading sequences for a given loading history. The numerical solutions were examined with the experimental data determined at elevated temperatures using four different advanced ceramic materials, two silicon nitrides, one silicon carbide and one alumina for the Case 1 loading history, and alumina for the Case 3 loading history. The numerical solutions were in reasonable agreement with the experimental data, indicating that notwithstanding some degree of creep deformation presented for some test materials slow crack growth was a governing mechanism associated with failure for all the test materials.

Choi, Sung R.↗
NASA NTRS2001

Slow Crack Growth Analysis of Advanced Structural Ceramics Under Combined Loading Conditions: Damage Assessment in Life Prediction Testing

Slow crack growth analysis was performed with three different loading histories including constant stress- rate/constant stress-rate testing (Case I loading), constant stress/constant stress-rate testing (Case II loading), and cyclic stress/constant stress-rate testing (Case III loading). Strength degradation due to slow crack growth and/or damage accumulation was determined numerically as a function of percentage of interruption time between the two loading sequences for a given loading history. The numerical solutions were examined with the experimental data determined at elevated temperatures using four different advanced ceramic materials, two silicon nitrides, one silicon carbide and one alumina for the Case I loading history, and alumina for the Case II loading history. The numerical solutions were in reasonable agreement with the experimental data, indicating that notwithstanding some degree of creep deformation presented for some test materials slow crack growth was a governing mechanism associated with failure for all the rest materials.

Choi, S. R.↗