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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Improving the Effectiveness of Integral Property Calculation in a CSG Solid Modeling System by Exploiting Predictability

Integral property calculation is an important application for solid modeling systems. Algorithms for computing integral properties for various solid representation schemes are fairly well known. It is important to deigners and users of solid modeling systems to understand the behavior of such algorithms. Specifically the trade-off between execution time and accuracy is critical to effective use of integral property calculation. The average behavior of two algorithms for Constructive Solid Geometry (CSG) representations is investigated. Experimental results from the PADL-2 solid modeling system show that coarse decompositions can be used to predict execution time and error estimates for finer decompositions. Exploiting this predictability allow effective use of the algorithms in a solid modeling system.

Clark, A. L.↗
DOE OSTI2021

Next Generation Co-Molded One-Piece Automotive Parts

The purpose of this project was to determine the feasibility of co-molding Class A Sheet Molding Compound (SMC), structural SMC, and continuous fiber prepreg materials to produce a single piece co-molded automotive part, such as a hood. The combination of the three molding materials and the incorporation of selective design features (ribs, flanges, corrugations) was expected to eliminate the need for inner reinforcement panels, significantly reducing tooling costs and simplifying the manufacturing process. A multi-material solution would also result in significant weight savings. The scope of this project included the material characterization of the three materials, development of cure and flow simulation models, and validation of the models against parts molded with different combinations of the materials on an 11”x11” plaque tool with rib features. The intent of this project was to apply the learnings obtained from co-molding a part with simplified geometry to a Phase 2 project that would produce a single piece hood, co-molded with the same materials. Resins from INEOS Composites were provided to IDI to produce SMC and continuous fiber prepregs. Purdue University and INEOS Composites characterized the rheological and curing behavior of the different materials as well as the mechanical properties of the co-molded parts. This data was used by Purdue University to create simulation models. Models were created to predict both flow patterns and predict mechanical properties of different laminate constructions in and around the ribs. Michigan State University - Corktown validated Purdue’s models by co-molding the three materials in different combinations on a tool containing rib features provided by Century Tool. The co-molded parts were evaluated against the simulation models by Purdue, Corktown and INEOS. The project team demonstrated the following: • The ability to obtain a cohesive co-molded structure made up of Class A SMC, Structural SMC, and continuous fiber prepreg. • The ability to obtain a co-molded part with a Class A surface • Modeling of flow and fiber orientation • Modeling to predict mechanical properties of multi-material co-molded structures The predicted flow behavior and fiber orientation from simulations were then compared with the molded samples. Exact local orientation state was difficult to compare between microscopy and flow simulation, but captured general trends. Multi-material flow behavior was generally modeled well with SMC materials, but the introduction of woven material sheets requires further model development. Purdue compared the predicted versus actual mechanical properties, and INEOS Composites evaluated the surface appearance of unpainted and painted co-molded parts. The models developed by Purdue demonstrated that the mechanical properties can be predicted for parts with varying material configurations. The resulting models can be applied to future co-molded part design, tooling design, and molding conditions.

36 MATERIALS SCIENCE↗
NASA NTRS2019

A Comparison of Different Modeling Strategies for Predicting Effective Properties of 3D Woven Composites

Three-dimensional (3D) woven composites are an attractive means of achieving superior mechanical performance in aerospace structures. Limited analysis capability currently exists to predict both effective elastic and strength properties for these complex composites. In this study, a comparison of three modeling strategies was performed to assess the ability of the different methods to predict the effective elastic properties of four distinct 3D orthogonal woven composites. Two finite element techniques (in-plane and triply-periodic boundary conditions) and one method of cells technique, the Multiscale Generalized Method of Cells, were considered.

Ricks, Trenton M.↗
DOE OSTI2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction

Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure–property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

The effect of interface properties on nickel base alloy composites

This program was performed to assess the extent to which mechanical behavior models can predict the properties of sapphire fiber/nickel aluminide matrix composites and help guide their development by defining improved combinations of matrix and interface coating. The program consisted of four tasks: 1) selection of the matrices and interface coating constituents using a modeling-based approach; 2) fabrication of the selected materials; 3) testing and evaluation of the materials; and 4) evaluation of the behavior models to develop recommendations. Ni-50Al and Ni-20AI-30Fe (a/o) matrices were selected which gave brittle and ductile behavior, respectively, and an interface coating of PVD YSZ was selected which provided strong bonding to the sapphire fiber. Significant fiber damage and strength loss was observed in the composites which made straightforward comparison of properties with models difficult. Nevertheless, the models selected generally provided property predictions which agreed well with results when fiber degradation was incorporated. The presence of a strong interface bond was felt to be detrimental in the NiAI MMC system where low toughness and low strength were observed.

Groves, M.↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗
NASA NTRSDate not supplied

Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.

anode↗
DOE OSTI2020

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor's performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Phonon gas model for thermal conductivity of dense, strongly interacting liquids

Developing predictive thermal property models for liquids based on microscopic principles has been elusive. The difficulty is that liquids have gas-like and solid-like attributes that are at odds when considering the frameworks of microscopic models: Models for gases are simple due to randomness and low density, whereas models for crystalline solids rely on symmetry and long-range order for easier calculation. The short-range order in liquids does, however, provide structure to neighboring molecules similar to amorphous solids, and there have been recent advances indicating that collective vibrational modes store heat in liquids. Models combining Debye approximations from solid-state physics and Frenkel’s theory of liquids can accurately predict the heat capacity of liquids. Phonon-like dispersions in liquids have also been widely observed in neutron scattering experiments. These developments motivate us to propose a model where high-frequency vibrational modes, which travel at the speed of sound and have a mean free path on the order of the average intermolecular distance, conduct heat in liquids. We use this liquid phonon gas model to calculate the thermal conductivity of liquids with varying intermolecular interaction energies from strongest to weakest—Coulomb, hydrogen-bonding, Keesom, and London dispersion energy. Generally, the model is more accurate as the intermolecular interaction energy and density of liquids increase. The calculated thermal conductivity of Coulombic-bound molten sodium nitrate and hydrogen-bonded water is within 1.46% and 2.98% of the experimentally measured values, respectively, across their entire temperature ranges. Further modal analysis of the velocity and the mean free path of collective vibrations could establish the liquid phonon gas model as an accurate model for weakly interacting liquids as well.

36 MATERIALS SCIENCE↗
NASA NTRS1978

The inertial properties of the body and its segments

Mass distribution properties of the adult human body are summarized. The summary is user-oriented for design engineers and mathematical modeling. Properties are dicussed in terms of the musculoskeletal linkage system, axes systems, mass, volume, center of mass, and inertial properties. Data and prediction equations or coefficients for modeling these properties are provided. Predictive formulas use total body weight and stature as independent variables. The data are based on small samples of living and cadaveric subjects typical of the white European male.

Reynolds, H. M.↗
DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
NASA NTRS2002

A Particle and Energy Balance Model of the Orificed Hollow Cathode

A particle and energy balance model of orificed hollow cathodes was developed to assist in cathode design. The model presented here is an ensemble of original work by the author and previous work by others. The processes in the orifice region are considered to be one of the primary drivers in determining cathode performance, since the current density was greatest in this volume (up to 1.6 x 10(exp 8) A/m2). The orifice model contains comparatively few free parameters, and its results are used to bound the free parameters for the insert model. Next, the insert region model is presented. The sensitivity of the results to the free parameters is assessed, and variation of the free parameters in the orifice dominates the calculated power consumption and plasma properties. The model predictions are compared to data from a low-current orificed hollow cathode. The predicted power consumption exceeds the experimental results. Estimates of the plasma properties in the insert region overlap Langmuir probe data, and the predicted orifice plasma suggests the presence of one or more double layers. Finally, the model is used to examine the operation of higher current cathodes.

Domonkos, Matthew T.↗