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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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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↗

NASA NTRSDate not supplied

Overview of LaRC Structures and Materials Capabilities Computational Materials & Additive Manufacturing

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Computational Materials↗

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↗

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↗

NASA NTRS2018

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

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↗

NASA NTRS2021

Leveraging Scalable Computational Materials Packages and High-Performance Computing Resources to Simulate Deformation and Damage in Polycrystalline Metallic Materials

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Saikumar "Sai" Yeratapally↗

NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗

NASA NTRS2018

Aladyn - Adaptive Neural Network Molecular Dynamics Simulation Code: Computational Materials Mini-Application

This report provides an overview and commands description of the Computational Materials mini-application, Aladyn. Aladyn is a simple molecular dynamics code written in FORTRAN 2008, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of quantum mechanics-based energy calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. The Aladyn code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing many core architectures based on graphic processing units (GPUs). The effort is related to the High Performance Computing Incubator (HPCI) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗

NASA NTRS2003

Computational Materials: Modeling and Simulation of Nanostructured Materials and Systems

The paper provides details on the structure and implementation of the Computational Materials program at the NASA Langley Research Center. Examples are given that illustrate the suggested approaches to predicting the behavior and influencing the design of nanostructured materials such as high-performance polymers, composites, and nanotube-reinforced polymers. Primary simulation and measurement methods applicable to multi-scale modeling are outlined. Key challenges including verification and validation of models are highlighted and discussed within the context of NASA's broad mission objectives.

Gates, Thomas S.↗

NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗

NASA NTRS2026

Integrated Computational Materials Engineering (ICME) Capability Maturity Levels for Ecosystems Enabling Digital Transformation

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

ICME↗