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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

PPW 2022 Short Course Introduction to Aerocapture, Entry, Descent, and Landing: Computational Materials

Computational materials science encompasses modeling and simulation techniques that are used to improve our understanding of materials from the atomic to macroscopic scales. This talk will focus on recent applications of computational materials science to ablative thermal protection materials, including heritage PICA and state-of-the-art woven materials like HEEET. Development of advanced molecular, microscopic, and mesoscopic models will be discussed.

phenolic↗
DOE OSTI2022

Artificial intelligence in computational materials science

In this themed collection we aim to broadly review some of the critical, recent progress in the application of AI/ML to various aspects of computational materials science and materials science more broadly. In this collection spread across two issues, we have assembled a collection of articles from leaders in the broad domain of applying AI/ML, which we collectively refer to as ML, in computational materials science. Together these articles curate the critical, recent progress in the application of ML to various aspects of materials science. Furthermore, these include ML approaches for understanding and driving electron microscopy, designing energy materials and the discovery of principles and materials relevant to the design of materials for the future, studying crystal nucleation and growth, the use of ML to describe force fields governing material and molecular behavior, and other topics.

36 MATERIALS SCIENCE↗
NASA NTRS2026

Computational Materials for Qualification and Certification (CM4QC) Strategy Document: Maturation of Computational Materials Methods for Aviation-Focused Qualification and Certification of Metal Additive Manufacturing (as an Example of Process-Intensive Materials)

Although considerable scientific and technological advances have been made in recent years in additive manufacturing (AM) processes, these advances have not translated into significant market penetration of AM parts within the aviation industry. It is broadly acknowledged that using traditional qualification and certification (Q&C) approaches for AM components is one of the most significant barriers to broader adoption of AM, resulting in high costs, long product development and certification timelines, and complex design iterations during the product development cycle. A new approach is urgently needed. This document lays out a vision for a new Q&C paradigm with increased use of computational materials (CM) methods aimed at decreasing the time and cost of Q&C of process-intensive material (PIM) approaches in the aviation industry, with AM as the immediate use case. This vision was developed with substantial input from industry, regulatory agencies, government research organizations, and academia.

Edward H Glaessgen↗
NASA NTRS1996

Computational Materials Research

Computational Materials aims to model and predict thermodynamic, mechanical, and transport properties of polymer matrix composites. This workshop, the second coordinated by NASA Langley, reports progress in measurements and modeling at a number of length scales: atomic, molecular, nano, and continuum. Assembled here are presentations on quantum calculations for force field development, molecular mechanics of interfaces, molecular weight effects on mechanical properties, molecular dynamics applied to poling of polymers for electrets, Monte Carlo simulation of aromatic thermoplastics, thermal pressure coefficients of liquids, ultrasonic elastic constants, group additivity predictions, bulk constitutive models, and viscoplasticity characterization.

Hinkley, Jeffrey A.↗
NASA NTRS2005

Computational Materials Program for Alloy Design

The research program sponsored by this grant, "Computational Materials Program for Alloy Design", covers a period of time of enormous change in the emerging field of computational materials science. The computational materials program started with the development of the BFS method for alloys, a quantum approximate method for atomistic analysis of alloys specifically tailored to effectively deal with the current challenges in the area of atomistic modeling and to support modern experimental programs. During the grant period, the program benefited from steady growth which, as detailed below, far exceeds its original set of goals and objectives. Not surprisingly, by the end of this grant, the methodology and the computational materials program became an established force in the materials communitiy, with substantial impact in several areas. Major achievements during the duration of the grant include the completion of a Level 1 Milestone for the HITEMP program at NASA Glenn, consisting of the planning, development and organization of an international conference held at the Ohio Aerospace Institute in August of 2002, finalizing a period of rapid insertion of the methodology in the research community worlwide. The conference, attended by citizens of 17 countries representing various fields of the research community, resulted in a special issue of the leading journal in the area of applied surface science. Another element of the Level 1 Milestone was the presentation of the first version of the Alloy Design Workbench software package, currently known as "adwTools". This software package constitutes the first PC-based piece of software for atomistic simulations for both solid alloys and surfaces in the market.Dissemination of results and insertion in the materials community worldwide was a primary focus during this period. As a result, the P.I. was responsible for presenting 37 contributed talks, 19 invited talks, and publishing 71 articles in peer-reviewed journals, as detailed later in this Report.

Bozzolo, Guillermo↗
DOE OSTI2021

Lightweight Materials - Carbon Fiber and Polymer Composites, Integrated Computational Materials Engineering (ICME) Predictive Tools Development for Low-Cost Carbon Fiber for Lightweight Vehicles (University of Virginia) (Final Report)

This project aimed to develop low-cost alternative precursors and processing techniques through the implementation of an integrated computational materials engineering (ICME) framework to evaluate precursor conversion kinetics. Once established, this framework was then used to down-select precursors for laboratory- and pilot-scale production to validate resulting carbon fiber (CF) material properties and process cost-savings. The two most promising approaches to reduce the cost of CFs were to (1) use new low-cost precursors and (2) reduce the energy requirements of CF processing. These approaches were investigated over the course of three phases of work, spanning 3.5 years. This report describes the overall project objectives, approach, and the key accomplishments in each phase.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Computational Materials Techniques for Thermal Protection Solutions: Materials and Process Design

Integrated computational materials techniques that span the atomistic and continuum scales have the potential to aid the design and manufacturing of thermal protection materials. Two cases demonstrating the practical application of these methods are discussed. Case one examines the selection of a high temperature coating for carbon/carbon, with the target application being a solar thermal propulsion heat exchanger. The performance of various refractory metal and metal-carbide coatings is characterized considering extreme thermal (3500 K) and chemical (hydrogen flows) conditions. The recession rate, hydrogen leakage, and likelihood of mechanical failure are characterized and provide directions for further experimental investigation. Case two examines the process optimization of a heat shield material composed of a woven silica fiber preform and cyanate ester resin. Frequently, internal voids were found to be present in this composite after the resin infusion and curing stages of manufacturing. Using the manufacturing conditions, computations indicate that both water adsorption and resin cure shrinkage are contributing factors to void formation. The results suggest an alternative process configuration for curing that would mitigate voids.

thermal protection↗
NASA NTRS2019

Computational Materials Techniques for Thermal Protection Solutions: Materials and Process Design

Integrated computational materials techniques that span the atomistic and continuum scales have the potential to aid the design and manufacturing of thermal protection materials. Two cases demonstrating the practical application of these methods are discussed. Case one examines the selection of a high temperature coating for carbon/carbon, with the target application being a solar thermal propulsion heat exchanger. The performance of various refractory metal and metal-carbide coatings is characterized considering extreme thermal (3500 degrees Kelvin) and chemical (hydrogen flows) conditions. The recession rate, hydrogen leakage, and likelihood of mechanical failure are characterized and provide directions for further experimental investigation. Case two examines the process optimization of a heat shield material composed of a woven silica fiber preform and cyanate ester resin. Frequently, internal voids were found to be present in this composite after the resin infusion and curing stages of manufacturing. Using the manufacturing conditions, computations indicate that both water adsorption and resin cure shrinkage are contributing factors to void formation. The results suggest an alternative process configuration for curing that would mitigate voids.

Thermal Protection↗
NASA NTRSDate not supplied

Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance

The maturation of computational materials approaches for fatigue performance prediction in a qualification and certification process is stifled by the ability to validate complex, microstructure-based simulations. Such a validation strategy bears immediate challenges including generating accurate virtual microstructures, efficiently solving physics-based mechanical simulations over relevant spatial and temporal scales, and acquiring high-fidelity calibration and validation data at the appropriate length scale. This presentation will overview these common challenges and present a case study to demonstrate a computational materials workflow for additively manufactured metallic materials. In this study, process-specific defects are characterized using segmented X-Ray micro-computed tomography measurements and overlaid on virtual microstructures. Accelerated crystal plasticity-based fatigue simulations are performed to demonstrate cyclic evolution and localization of mechanical fields in the vicinity of defects in response to their precise spatial configuration. An example of how this computational materials workflow may support next-generation qualification is discussed.

computational materials↗
DOE OSTI2024

7th World Congress on Integrated Computational Materials Engineering (ICME 2023) (Final Technical Report)

Integrated Computational Materials Engineering (ICME) has received international attention due to its potential to shorten product development time, while lowering cost and improving design and manufacturing outcomes. ICME is an approach to designing materials solutions for specific applications that use computer modeling programs to predict the behavior of materials and integrate this information into the overall materials, processing, and manufacturing design cycle. The 7th World Congress on Integrated Computational Materials Engineering (ICME 2023) was held in Orlando, Florida from May 21–25, 2023 with the goal to convene stakeholders from across all areas of modeling and simulation, experimental specialization, and design, as well as from across academia, government, and industry, to address ICME tools and techniques and their integration, as well as to examine their application in engineering. This atmosphere facilitated rich interactions between the experimentalists, modelers, and computational and design, from academia, government, and industry, to discuss ICME tools and techniques and their application in engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Computational Materials Techniques for Thermal Protection Solutions: Materials and Process Design

Integrated computational materials techniques that span the atomistic and continuum scales have the potential to aid the design and manufacturing of thermal protection materials. Two cases demonstrating the practical application of these methods are discussed. Case one examines the selection of a high temperature coating for carbon/carbon, with the target application being a solar thermal propulsion heat exchanger. The performance of various refractory metal and metal-carbide coatings is characterized considering extreme thermal (3500 K) and chemical (hydrogen flows) conditions. The recession rate, hydrogen leakage, and likelihood of mechanical failure are characterized and provide directions for further experimental investigation. Case two examines the process optimization of a heat shield material composed of a woven silica fiber preform and cyanate ester resin. Frequently, internal voids were found to be present in this composite after the resin infusion and curing stages of manufacturing. Using the manufacturing conditions, computations indicate that both water adsorption and resin cure shrinkage are contributing factors to void formation. The results suggest an alternative process configuration for curing that would mitigate voids.

Haskins, Justin↗
NASA NTRS2004

Theory, Modeling, Software and Hardware Development for Analytical and Computational Materials Science

The focus of this Cooperative Agreement between the Computational Materials Laboratory (CML) of the Processing Science and Technology Branch of the NASA Glenn Research Center (GRC) and the Department of Theoretical and Applied Mathematics at The University of Akron was in the areas of system development of the CML workstation environment, modeling of microgravity and earth-based material processing systems, and joint activities in laboratory projects. These efforts complement each other as the majority of the modeling work involves numerical computations to support laboratory investigations. Coordination and interaction between the modelers, system analysts, and laboratory personnel are essential toward providing the most effective simulations and communication of the simulation results. Toward these means, The University of Akron personnel involved in the agreement worked at the Applied Mathematics Research Laboratory (AMRL) in the Department of Theoretical and Applied Mathematics while maintaining a close relationship with the personnel of the Computational Materials Laboratory at GRC. Network communication between both sites has been established. A summary of the projects we undertook during the time period 9/1/03 - 6/30/04 is included.

Young, Gerald W.↗
NASA NTRS2021

NASA / NIST / FAA Technical Interchange Meeting on Computational Materials Approaches for Qualification by Analysis for Aerospace Applications

This report documents the goals, organization and outcomes of a Technical Interchange Meeting (TIM) on Computational Materials Approaches for Qualification by Analysis, co-organized by NASA, NIST and the FAA. The TIM was held at NASA Langley Research Center on January 15-16, 2020. Approximately 60 subject matter experts (SMEs) representing 8 aerospace manufacturers, 7 government organizations and 2 universities participated. Expertise of the SMEs spanned the Technology Readiness Level (TRL) scale from the low-to-mid TRL focus of government laboratories and universities to the high TRL perspective of the regulatory organizations and aerospace manufacturers. During this TIM, the future needs of the government regulators and manufacturers motivated the overall discussion and framed the input given by the participants. Hence, the key objectives of the TIM were to understand existing gaps in model-based, e.g., computational materials, processing and performance predictions for aerospace materials and components and forecast how they can be matured to support material, process and part-level qualification and certification (Q&C). The TIM focused on process-intensive metallic materials technologies, including, but not limited to, additive manufacturing. Participation was roughly evenly divided among the processing and performance tracks, suggesting that both topic areas are generally perceived as being both valuable and requiring additional investment. The output of this TIM may be used by both participating and other organizations, in part, as guidance for future national efforts on maturing computational materials capabilities for use in the Q&C of advanced metallic material systems in aerospace applications.

Edward H Glaessgen↗
NASA NTRSDate not supplied

Computational Materials for Qualification and Certification of Process-Intensive Metallic Materials (CM4QC)

NASA and the FAA established the Computational Materials (CM) for Qualification and Certification (Q&C) steering group comprised of representatives from industry, government regulatory and research organizations, and academia with the following goals: - Inform the research and development investments made by U.S. industry and the U.S. government toward maturation of CM-based approaches for Qualification and Certification (Q&C) of process intensive metallic materials (PIM) including additive manufacturing. - Identify key considerations and enablers required to increase airworthiness / certifying authorities’ acceptance of CM methods use for Q&C of structural or flight-critical parts produced with PIM technologies. - Identify key CM technology gaps and maturation paths (in the context of Q&C applications). - Increase dialogue among stakeholder organizations. This presentation will summarize the technology and regulatory considerations that motivate this national effort, discuss progress and provide a summary and status of a multi-year roadmap that is being developed to guide technology development in this important area.

Computational Materials↗