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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Advances in fatigue life prediction methodology for metallic materials

The capabilities of a plasticity-induced crack-closure model to predict small- and large-crack growth rates, and in some cases total fatigue life, for four aluminum alloys and three titanium alloys under constant-amplitude, variable-amplitude, and spectrum loading are described. Equations to calculate a cyclic-plastic-zone corrected effective stress-intensity factor range from a cyclic J-integral and crack-closure analysis of large cracks were reviewed. The effective stress-intensity factor range against crack growth rate relations were used in the closure model to predict small- and large-crack growth under variable-amplitude and spectrum loading. Using the closure model and microstructural features, a total fatigue life prediction method is demonstrated for three aluminum alloys under various load histories.

Newman, J. C., Jr.↗
NASA NTRS1986

Life prediction and constitutive models for engine hot section

The purpose of this program is to develop life prediction models for coated anisotropic materials used in gas turbine airfoils. In the program, two single crystal alloys and two coatings are being tested. These include PWA 1480, Alloy 185, overlay coating (PWA 286), and aluminide coating (PWA 273). Constitutive models are also being developed for these materials to predict the time independent (plastic) and time dependent (creep) strain histories of the materials in the lab tests and for actual design conditions. This nonlinear material behavior is particularly important for high temperature gas turbine applications and is basic to any life prediction system. Some of the accomplishments of the program are highlighted.

Swanson, G. A.↗
NASA NTRS1987

A high temperature fatigue and structures testing facility

As man strives for higher levels of sophistication in air and space transportation, awareness of the need for accurate life and material behavior predictions for advanced propulsion system components is heightened. Such sophistication will require complex operating conditions and advanced materials to meet goals in performance, thrust-to-weight ratio, and fuel efficiency. To accomplish these goals will require that components be designed using a high percentage of the material's ultimate capabilities. This serves only to complicate matters dealing with life and material behavior predictions. An essential component of material behavior model development is the underlying experimentation which must occur to identify phenomena. To support experimentation, the NASA Lewis Research Center's High Temperature Fatigue and Structures Laboratory has been expanded significantly. Several new materials testing systems have been added, as well as an extensive computer system. The intent of this paper is to present an overview of the laboratory, and to discuss specific aspects of the test systems. A limited discussion of computer capabilities will also be presented.

Bartolotta, Paul A.↗
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model

The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
NASA NTRS1995

Recent results from Long Duration Exposure Facility materials testing

The overall goals of the Long Duration Exposure Facility (LDEF) investigations, established by the Materials Special Investigation Group (MSIG) prior to LDEF retrieval, are to provide useful engineering data to people designing and building spacecraft, and secondarily, to obtain data of potential interest to materials researchers. The specific objectives are to support predictions of materials lifetimes under the various low earth orbit (LEO) environments to determine how long the material will physically survive; to estimate the engineering performance lifetimes of these same materials under specific LEO exposures; to identify materials and processes by which given materials degrade; and to provide insights into development of new, more inherently LEO environmentally resistant materials. To achieve the established objectives, two criteria were established to select which materials had the highest priorities for analyses. The first criteria was to examine materials which are still being used on new spacecraft. The second priority was to examine materials with multiple exposure locations on LDEF, because this provided the opportunity to develop predictions on how a material will behave as the exposure environment varies. The goals as defined led to the identification of silverized Teflon (Ag/FEP), chromic acid anodized aluminum, and certain thermal control paints used on LDEF as the material types of highest priority for examination. Ag/FEP was chosen because of an excellent a/e ratio (extremely low), and extensive flight history. Also influencing the decision was the fact that the Hubble Space Telescope uses Ag/FEP as its primary passive thermal control system (and was launched shortly after LDEF retrieval) and that a number of other spacecraft are using or considering this material for use. The various forms of Ag/FEP were used on virtually every side of the LDEF except the earth end. The potential uses of this material, and the location distribution and therefore, exposure conditions, have provided the rationale and opportunity for a comprehensive study of this material.

Pippin, H. Gary↗
DOE OSTI2023

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events

Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.

Jung, Gang Seob↗
DOE OSTI2021

Common workflows for computing material properties using different quantum engines

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Separation of airborne and structureborne noise radiated by plates constructed of conventional and composite materials with applications for prediction of interior noise paths in propeller driven aircraft

The anticipated application of advanced turboprop propulsion systems and use of composite materials in primary structure is expected to increase the interior noise of future aircraft to unacceptability high levels. The absence of technically and economically feasible noise source-path diagnostic tools has been a primer obstacle in the development of efficient noise control treatments for propeller driven aircraft. A new diagnostic method which permits the separation and prediction of the fully coherent airborne and structureborne components of the sound radiated by plates or thin shells has been developed. Analytical and experimental studies of the proposed method were performed on plates constructed of both conventional and composite materials. The results of the study indicate that the proposed method can be applied to a variety of aircraft materials, could be used in flight, and has fewer encumbrances than the other diagnostic tools currently available. The study has also revealed that the noise radiation of vibrating plates in the low frequency regime due to combined airborne and structureborne inputs possesses a strong synergistic nature. The large influence of the interaction between the airborne and structureborne terms has been hitherto ignored by researchers of aircraft interior noise problems.

Mcgary, M. C.↗
NASA NTRS1983

Creep fatigue life prediction for engine hot section materials (isotropic)

The Hot Section Technology (HOST) program, creep fatigue life prediction for engine hot section materials (isotropic), is reviewed. The program is aimed at improving the high temperature crack initiation life prediction technology for gas turbine hot section components. Significant results include: (1) cast B1900 and wrought IN 718 selected as the base and alternative materials respectively; (2) fatigue test specimens indicated that measurable surface cracks appear early in the specimen lives, i.e., 15% of total life at 871 C and 50% of life at 538 c; (3) observed crack initiation sites are all surface initiated and are associated with either grain boundary carbides or local porosity, transgrannular cracking is observed at the initiation site for all conditions tested; and (4) an initial evaluation of two life prediction models, representative of macroscopic (Coffin-Mason) and more microscopic (damage rate) approaches, was conducted using limited data generated at 871 C and 538 C. It is found that the microscopic approach provides a more accurate regression of the data used to determine crack initiation model constants, but overpredicts the effect of strain rate on crack initiation life for the conditions tested.

Moreno, V.↗
NASA NTRS1977

Aeronautics and Space Engineering Board: Aeronautics Assessment Committee

High temperature engine materials, fatigue and fracture life prediction, composite materials, propulsion noise pollution, propulsion components, full-scale engine research, V/STOL propulsion, advanced engine concepts, and advanced general aviation propulsion research were discussed.

Source record↗
DOE OSTI2026

A multimodal large language model for materials science

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

Tang, Yingheng [Lawrence Berkeley National Laborat↗
NASA NTRS2013

Unit-Sphere Multiaxial Stochastic-Strength Model Applied to Anisotropic and Composite Materials

Models that predict the failure probability of brittle materials under multiaxial loading have been developed by authors such as Batdorf, Evans, and Matsuo. These "unit-sphere" models assume that the strength-controlling flaws are randomly oriented, noninteracting planar microcracks of specified geometry but of variable size. This methodology has been extended to predict the multiaxial strength response of transversely isotropic brittle materials, including polymer matrix composites (PMCs), by considering (1) flaw-orientation anisotropy, whereby a preexisting microcrack has a higher likelihood of being oriented in one direction over another direction, and (2) critical strength, or K (sub Ic) orientation anisotropy, whereby the level of critical strength or fracture toughness for mode I crack propagation, K (sub Ic), changes with regard to the orientation of the microstructure. In this report, results from finite element analysis of a fiber-reinforced-matrix unit cell were used with the unit-sphere model to predict the biaxial strength response of a unidirectional PMC previously reported from the World-Wide Failure Exercise. Results for nuclear-grade graphite materials under biaxial loading are also shown for comparison. This effort was successful in predicting the multiaxial strength response for the chosen problems. Findings regarding stress-state interactions and failure modes also are provided.

Nemeth, Noel, N.↗
NASA NTRS1993

Prediction of nonlinear optical properties of organic materials. General theoretical considerations

The prediction of nonlinear optical properties of organic materials is geared to assist materials scientists in the selection of good candidate molecules. A brief summary of the quantum mechanical methods used for estimating hyperpolarizabilities will be presented. The advantages and limitations of each technique will be discussed. Particular attention will be given to the finite-field method for calculating first and second order hyperpolarizabilities, since this method is better suited for large molecules. Corrections for dynamic fields and bulk effects will be discussed in detail, focusing on solvent effects, conformational isomerization, core effects, dispersion, and hydrogen bonding. Several results will be compared with data obtained from third-harmonic-generation (THG) and dc-induced second harmonic generation (EFISH) measurements. These comparisons will demonstrate the qualitative ability of the method to predict the relative strengths of hyperpolarizabilities of a class of compounds. The future application of molecular mechanics, as well as other techniques, in the study of bulk properties and solid state defects will be addressed. The relationship between large values for nonlinear optical properties and large conjugation lengths is well known, and is particularly important for third-order processes. For this reason, the materials with the largest observed nonresonant third-order properties are conjugated polymers. An example of this type of polymer is polydiacetylene. One of the problems in dealing with polydiacetylene is that substituents which may enhance its nonlinear properties may ultimately prevent it from polymerizing. A model which attempts to predict the likelihood of solid-state polymerization is considered, along with the implications of the assumptions that are used. Calculations of the third-order optical properties and their relationship to first-order properties and energy gaps will be discussed. The relationship between monomeric and polymeric third-order optical properties will also be considered.

Cardelino, B.↗
NASA NTRS2018

Towards the Prediction of the Mars 2020 Heatshield Material Response

Introduction: NASA’s next mission to Mars, the Mars 2020, will use the same heatshield of the Mars Science Laboratory (MSL) for thermal protection during entry, descent and landing. The heatshield is a tiled system made of Phenolic Impregnated Carbon Ablators (PICA) blocks [1]. PICA is a lightweight carbon fiber/polymeric resin material that offers excellent performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of atmospheric entry and material response. MEDLI recorded, among others, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP). The objective of this work is to compare the thermal response of the MSL heatshield to the MISP flight data. In preparation to Mars 2020 post-flight analysis, the predictive material response capability is benchmarked against MEDLI flight data.

heatshield↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗
NASA NTRS2023

PMT F7 Silver Standard Material Properties Characterization

PMT F7 epoxy resin was characterized to capture the material's thermochemical and thermo-mechanical properties. DSC testing was performed and the data was used to develop a cure kinetics model which predicts the material cure response and specific heat capacity. TMA and DMA testing were performed and the data was used to develop thermo-mechanical models which predict the material CTE, cure shrinkage, and modulus evolution with cure. Convergent Manufacturing Technologies US determined test parameters, analyzed data, and performed model fitting. NASA completed tests and provided data to Convergent Manufacturing Technologies US.

Epoxy↗