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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Microstructural Influence on Deformation and Fatigue Life of Composites Using the Generalized Method of Cells

A fully coupled deformation and damage approach to modeling the response of composite materials and composite laminates is presented. It is based on the semi-­‐analytical generalized method of cells (GMC) micromechanics model as well as its higher fidelity counterpart, HFGMC, both of which provide closed-form constitutive equations for composite materials as well as the micro scale stress and strain fields in the composite phases. The provided constitutive equations allow GMC and HFGMC to function within a higher scale structural analysis (e.g., finite element analysis or lamination theory) to represent a composite material point, while the availability of the micro fields allow the incorporation of lower scale sub­‐models to represent local phenomena in the fiber and matrix. Further, GMC's formulation performs averaging when applying certain governing equations such that some degree of microscale field accuracy is surrendered in favor of extreme computational efficiency, rendering the method quite attractive as the centerpiece in a integrated computational material engineering (ICME) structural analysis; whereas HFGMC retains this microscale field accuracy, but at the price of significantly slower computational speed. Herein, the sensitivity of deformation and the fatigue life of graphite/epoxy PMC composites, with both ordered and disordered microstructures, has been investigated using this coupled deformation and damage micromechanics based approach. The local effects of fiber breakage and fatigue damage are included as sub‐models that operate on the microscale for the individual composite phases. For analysis of laminates, classical lamination theory is employed as the global or structural scale model, while GMC/HFGMC is embedded to operate on the microscale to simulate the behavior of the composite material within each laminate layer. A key outcome of this study is the statistical influence of microstructure and micromechanics idealization (GMC or HFGMC) on the overall accuracy of unidirectional and laminated composite deformation and fatigue response.

Microscale Models↗
NASA NTRS2018

Modeling-Driven Damage Tolerant Design of Graphene Nanoplatelet/Carbon Fiber/Epoxy Hybrid Composite Panels for Full-Scale Aerospace Structures

The objective of this study is to design a new nano graphenecarbon fiberpolymer hybrid composite that can be used for the NASA SLS Composite Exploration Upper Stage (CEUS) forward skirt structure. The new material will improve the resistance to open-hole compression failure of the structure relative to traditional polymer fiber composites. The material is designed rapidly and with little cost using the Integrated Computational Materials Engineering (ICME) approach. Multiscale modeling and experiments are used to synergistically optimize the material design to yield improved properties and performance by controlling key processing parameters for manufacturing nano-enhanced materials. Specifically, the nanocomposite panel showed a 22 reduction in mass relative to the traditional composite panel, while both designs are equal in terms of ease of manufacture. This potential mass savings corresponds to an estimated 45 savings in materials and manufacturing costs. The multiscale ICME workflow developed for this project can be readily applied to the development of nano-enhanced composite materials and large aerospace structures. In addition, all key aspects of ICME were employed to complete this project including multiscale modeling, experimental characterization and visualization, data management, visualization, error and uncertainty quantification, and education. The results presented herein indicate a dramatic level of success, as well as the power and potential of ICME approach and multiscale modeling for composite materials.

computational mechanics↗
NASA NTRS2022

Topical. Permanent Low-Earth Orbit Testbed for Welding and Joining: A Path Forward for the Commercialization of Space

NASA, defense, academia, and commercial space companies seek to develop orbital manufacturing to enhance mission capabilities. In-space welding and joining are needed for space-based manufacturing. We propose a permanent low-earth orbital welding and joining testbed for scientific study of relevant physical phenomena and development of welding and joining processes to technical maturity. Such data will enable integrated computational materials engineering(ICME)to elucidate the underlying physical and metallurgical processes needed to achieve critical space welding and joining operations.

In-space manufacturing↗
NASA NTRSDate not supplied

An Integrated Recursive Framework for Arbitrarily Multiscale and Multi-fidelity Modeling

The NASA Multiscale Analysis Tool (NAMSAT) serves as a state-of-the-art, “plug and play,” massively multiscale modeling (M3) platform for hierarchical materials and structures. The development of NASMAT has focused on modularity, upgradability and maintainability, interoperability, and utility. Application Program Interfaces (APIs) have been developed to facilitate the integration NASMAT into other programs (commercial, research, and user-defined) as well as the integration of other codes into NASMAT. NASMAT is an integrated, recursive platform that seamlessly allows for an arbitrary number of scales, and a variety of modeling fidelity, in a single, non-linear analysis. Thus, the macroscopic response of a material can be directly linked to the behavior of the microstructure(s), including subscale defects, making it a suitable tool for integrated computational materials engineering (ICME) because . Examples using NASMAT for simulation of polycrystalline metals at elevated temperatures will be presented.

Multiscale Modeling↗
NASA NTRSDate not supplied

Advancement of Extreme Environment Additively Manufactured Alloys for Next Generation Space Propulsion Applications

The National Aeronautics and Space Administration (NASA) has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into propulsion development and flight applications. NASA matured commonly used aerospace alloys from various alloy families (Nickel, Copper, Stainless and Steel, Aluminum, and Titanium-based) through detailed AM process and heat treatment characterization, in addition to mechanical and thermophysical testing. While these alloys are actively used in many propulsion applications, there is a need for ongoing AM optimized alloys using integrated computational materials engineering (ICME) and process development for high performance applications. The applications targeted are liquid rocket engines; advanced propulsion systems; and in-space propulsion with high heat fluxes, high pressure, and/or that use propellants that can degrade alloys (e.g., hydrogen). This paper highlights the characterization and physical properties of the more common AM alloys using laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes. Additionally, this paper discusses some of the ongoing novel alloy development and maturation using AM for use in these harsh environments, such as GRCop42, GRCop-84, NASA HR-1, GRX-810, and C-103. The results from these processes demonstrated that AM could enable rapid development and ongoing efforts for optimized alloys using ICME, yielding higher performances. These alloys have undergone modeling, fundamental metallurgical evaluations, heat treatment studies, detailed microstructure characterization, and mechanical testing campaigns. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL) through high duty-cycle testing. A background and overview of these novel AM-enabled alloys and AM processing developments including metallurgical and mechanical property studies is presented here. The latest advancement in the parallel component development and hot-fire testing and future developments for these alloys is also discussed.

Additive Manufacturing↗
NASA NTRSDate not supplied

Advancement of Extreme Environment Additively Manufactured Alloys for Next Generation Space Propulsion Applications

The National Aeronautics and Space Administration (NASA) has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into propulsion development and flight applications. NASA matured commonly used aerospace alloys from various alloy families (Nickel, Copper, Stainless and Steel, Aluminum, and Titanium-based) through detailed AM process and heat treatment characterization, in addition to mechanical and thermophysical testing. While these alloys are actively used in many propulsion applications, there is a need for ongoing AM optimized alloys using integrated computational materials engineering (ICME) and process development for high performance applications. The applications targeted are liquid rocket engines; advanced propulsion systems; and in-space propulsion with high heat fluxes, high pressure, and/or that use propellants that can degrade alloys (e.g., hydrogen). This paper highlights the characterization and physical properties of the more common AM alloys using laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes. Additionally, this paper discusses some of the ongoing novel alloy development and maturation using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, GRX-810, and C-103. The results from these processes demonstrated that AM could enable rapid development and ongoing efforts for optimized alloys using ICME, yielding higher performances. These alloys have undergone modeling, fundamental metallurgical evaluations, heat treatment studies, detailed microstructure characterization, and mechanical testing campaigns. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL) through high duty-cycle testing. A background and overview of these novel AM-enabled alloys and AM processing developments including metallurgical and mechanical property studies is presented here. The latest advancement in the parallel component development and hot-fire testing and future developments for these alloys is also discussed.

Additive Manufacturing↗
NASA NTRSDate not supplied

Maturation of Additive Manufactured Aerospace Alloys and Development of Mechanical and Thermophysical Properties for Space Applications

The National Aeronautics and Space Administration (NASA) has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts have focused on the understanding of AM processes through design optimization, component fabrication, material characterization, testing, standards development, and infusion into propulsion development for flight applications. NASA and partners have matured commonly used aerospace alloys from various alloy families (Nickel, Steel, Iron, Aluminum, and Titanium) and AM processes. An extensive effort has been ongoing between NASA, industry partners, and academia to complete detailed AM process and heat treatment characterization, in addition to generating temperature-dependent mechanical and thermophysical properties. This presentation highlights the characterization and property results using laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes among various alloys. In addition to commonly used alloys, there is a need for ongoing AM optimized alloys using integrated computational materials engineering (ICME) and process development for high performance applications. The applications targeted are launch vehicles, liquid rocket engines, advanced propulsion systems, advanced power systems, and in-space propulsion with high heat fluxes, high pressure, and that utilize propellants such as hydrogen, which can degrade alloys. This presentation will also discuss some of the ongoing novel alloy development and maturation using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, and C-103. The results from these processes have demonstrated that AM can enable rapid development and new AM optimized alloys can yield higher performance. These alloys have undergone modeling, fundamental metallurgical evaluations, heat treatment studies, and microstructure characterization and mechanical testing campaigns. This, combined with direct application-specific component fabrication and hot-fire testing, can enable the increase of the Technology Readiness Level (TRL) through high duty-cycle testing. This presentation provides a background and overview of these various common and AM-enabled alloys and processing developments including the initial effort related to metallurgical, mechanical, and thermophysical property studies. It also covers the latest advancement in the parallel component development, hot-fire testing, and future developments for these alloys.

Additive Manufacturing↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
NASA NTRSDate not supplied

A Robust Schema for Storing and Managing Machine Learning Data and Models

- Machine Learning (ML) has enabled models that can improve efficiency and decrease computational cost - ML models are crucial in enabling Integrated Computational Materials Engineering (ICME) - Large data sets require robust means of storing ML data and models

Brandon L. Hearley↗
NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗
NASA NTRSDate not supplied

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing fit-for-purpose materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of automation in the material decision process, implementing some optimization algorithms, to truly enable the full benefits of ICME, particularly when considering materials at multiple length/time scales. In this work, we will demonstrate how the GRC ICME schema and Python framework automates a workflow that captures, analyzes, maintains, and disseminates the digital footprint in the context of tailoring resin material at the nanoscale of a woven composite Y-joint at the macroscale for an Aurora D8 double bubble fuselage. This digital footprint incorporates the interaction of both structural digital twins and material twins at various length scales.

Brandon L. Hearley↗
NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗
NASA NTRSDate not supplied

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗
NASA NTRS2024

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing ‘fit-for-purpose’ materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of judicious automation in the material decision process; that is implementing some optimization algorithms to truly enable the benefits of ICME, particularly when considering materials at multiple length and time scales. Furthermore, the ability to effectively store developed material models, experimental data used for validation, and link models at multiple length and time scales must be implemented to ensure traceability across the material design process, such that the data gathered can be leveraged towards efficient material design. To enable such an optimization scheme a robust framework must exist: (1) that can capture changes made at a given length scale, (2) automatically propagate changes upstream to the highest scale, and (3) evaluate the material’s performance at the structural level. In this work, a developed framework for tracking material changes, automatically running the necessary simulations to determine the properties at the next highest scale, and saving each iteration of the design process to maintain the application’s digital thread is presented for polymer matrix composites (PMCs). The Automated Information Management Across Organizations and Scales (AIMAOS) program offers users an interactive graphical user interface (GUI) for defining constituent materials, building lamina and laminates, and applying effective laminate properties to finite element and composite optimization third party software. At each length scale, the necessary input files are automatically written, and subsequent analysis tools are called to solve for effective properties at the next scale, which are then read by the AIMAOS tool and displayed to the user. As changes are made to the material at lower length scales, information is automatically propagated upstream to higher length scales, and changes made are automatically tracked and versioned to maintain traceability during the design process. The AIMAOS tools serves as the first step in enabling optimized design of composites from the nano to the macroscale for a given application.

Brandon L Hearley↗
NASA NTRSDate not supplied

Maturation of In-space Welding in Reduced Gravity and Reduced Pressure Environments Through Progression to Suborbital Flight Experiments

Development of in-space welding (ISW) is a key and enabling manufacturing technology as welding is currently involved in producing approximately 90% of durable goods, and this trend is expected to hold for in-space manufacturing as well. ISW is critical to a sustainable space economy. To mature in-space welding, numerous gaps must be closed since NASA has not made a weld in space in 50 years. Advancing laser beam welding (LBW) towards a suborbital flight demonstration will help mature the process for in-space assembly & manufacturing (ISAM) through demonstration in relevant environments and achieving technology readiness milestones. LBW technologies and science improved by this work will benefit in-space manufacturing, long-term sustainability of space structures & operations, and the overall space economy. Ground-based welding of aerospace hardware requires an Edisonian approach to qualify a welding process; however, this approach is infeasible for ISW qualification due to mass, volume, logistic, and cost challenges inherent to the space environment. To address this challenge, we are conducting ground and suborbital tests with partners performing parabolic flights to gather data on LBW processes and infuse them into NASA & partner missions. This progression from ground to flight experiments will elucidate relevant physics during LBW: 1) reduced gravity, 2) reduced pressure, and 3) extreme temperature. Data from post-flight evaluation and in situ instrumentation will feed integrated computational materials engineering (ICME) tools to reduce the burden of ISW infusion by establishing a link between space and terrestrial environments, ultimately accelerating the qualification of LBW in space.

in-space welding↗
NASA NTRSDate not supplied

A Combined Computational, Experimental, and Technology Development Approach to In-Space Laser Manufacturing Maturation at NASA Marshall Space Flight Center

In-space manufacturing (ISM) is emerging as a field vital to continued access and capabilities in the space environment. NASA Marshall Space Flight Center (MSFC) is advancing the frontier of in-space laser manufacturing (ISLM) techniques through work initially focused on maturing laser beam welding (LBW) and laser forming (LF) for use in space. Such techniques proffer the ability to assemble and join structures in space from sheet metal or other stock – extant satellites, in situ resource utilization of Lunar regolith, etc. – by forming to desired shapes and then joining via in-space welding (ISW). ISLM processes are useful for assembly, joining, modification, and repair of structures in free space and on the Lunar surface such as large observatories, antennas, trusses, blast/thermal/radiation shields, pressure vessels, and more. However, these techniques are not yet qualified & certified (Q&C) for regular application in space. It would be prohibitively expensive, laborious, and time-consuming to perform Q&C via traditional experimental approaches as data collection & experimentation in space is resource-intensive. As such, benchmark experiments and focused, properly instrumented technology demonstration efforts in space can collect sufficient data that – when combined with verified computational models in an integrated computational materials engineering (ICME) approach – can validate ICME tools capable of translating more readily obtained ground data to in-space, in situ, computationally informed Q&C of ISLM techniques. Several ISLM projects at MSFC are obtaining the data required to validate ICME tools through both ground and flight experiments. A parabolic flight experiment of LBW under vacuum is manifested for August 2024, including both microgravity and Lunar gravity profiles. This collaboration with the Ohio State University is investigating common aerospace alloys such as 316L stainless steel, 2219 aluminum alloy, and Ti64 titanium alloy. In situ data collection includes videography, thermography, and reference thermocouples to build a thermal model of the welds. This will elucidate the relevant physics when combined with post-flight microstructural examination and mechanical testing. MSFC is also progressing towards a suborbital flight experiment of LBW under vacuum, which could provide reams of data on ISW during sustained, high-quality reduced gravity. The effect of combined thermal (cryogenic and high-temperature) and vacuum exposure on both LBW (NASA-funded) and LF (DARPA-funded) is being investigated through ground experiments. In addition to the copious data collected during these ground experiments, ruggedization of LBW hardware will also be pursued. The datasets from these experiments will be used to validate computational models which will inform future ISLM efforts in an ICME framework. A variety of techniques across lengths scales, from CALPHAD-driven thermodynamics & kinetics to phase field modeling of solidification to kinetic Monte Carlo simulations of grain evolution at the mesoscale, will be employed to accelerate the infusion and eventual Q&C of LBW and LF for use in space. The development of data-driven surrogate models to bridge ground to flight experiments and thereby reduce the need for resource-intensive experiments in space will also be investigated. These ICME techniques, surrogate models, and datasets from ground testing can also be employed to advance manufacturing in terrestrial environments.

in-space welding↗
DOE OSTI2024

An integrated modeling framework with open architecture for phase field simulation of multi-component alloys

An integrated modeling framework (PanPhaseField) has been developed, which enables a direct and fast coupling between CALPHAD calculations and large-scale phase field simulations for multi-component alloys. Further, it adopts an open architecture allowing for integration of user-defined phase field models in a plug-and-play manner by taking full advantage of the user-friendly graphical interface of Pandat software. The developed modeling platform becomes an enabling tool that can be used to simulate the evolution of spatially varying microstructures of industrial complex alloys for various engineering applications.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Li-O2 Batteries for NASA Electric Aircraft (LiON)

Li-Air batteries are a unique fit for electric aircraft due to their high theoretical energy densities and their potential to leverage on-board oxygen systems. We will investigate novel "electrolyte engineering" concepts integrating computation, material fabrication and cell/pack design to obtain Li-Air batteries with high energy densities, rechargeability and safety.

Lawson, John↗