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At least 55 records · Page 3

NASA GRC ICME Schema for Materials Data Management: An Executive Summary

Integrated Computational Materials Engineering (ICME) has received a growing emphasis in attention due its potential impact on rapid material design, reduction in cost and time to market for new applications, and the promise of ‘fit-for-purpose’ materials coupled with recent advances in high performance computing and material characterization tools. However, for an organization to implement ICME practices for material discovery and design, a series of both technical and cultural challenges must be overcome to foster an environment that enables efficient, traceable, and predictive multiscale simulations of material behavior to enable virtual design of materials. In 2016, NASA sponsored a 2040 Vision study to define the potential 25-year future state required for integrated multiscale modeling of materials and systems to improve both the associated time and cost for aerospace and aeronautical innovation. The study envisions a cyber-physical-social ecosystem of experimentally validated computational models, tools, and techniques, along with the associated digital tapestry, that can enable rapid, optimized, ‘fit-for-purpose’ design of materials, components, and systems. A key requirement for such an ecosystem is the development of a robust information management system for materials across their full lifecycle, including material pedigree, experimental (real) and virtual (simulation) data, developed material models, and the implementation of models in engineering applications, such that process-structure-property-performance relationships can be established, thereby enabling the virtual design and optimization of materials. Such an information management system must be able to effectively capture: i) material information at each length scale; ii) test data and analysis; iii) associated material models; and iv) material and model deployment in engineering applications. These systems must also provide traceability between experimental and virtual representations of the material to ensure, when appropriate, the material digital twin is maintained. Additionally, this robust material information management system must be able to seamlessly connect with both commercial and an organization’s in-house software tools, be they analysis tools, other material databases, product lifecycle management (PLM) or simulation data management (SDM) tools, etc., such that automation of the design and analysis of a material across multiple length scales is possible. In this paper, an executive summary of the NASA GRC ICME Schema for materials information management is presented. The database best practices and schema design philosophy specifically for ICME materials data management and an overview description of each element in the schema is given, along with its associated role in an ICME workflow. Additionally, auxiliary tools that interact with the database and provide judicious automation with regards to importing, exporting, and analyzing materials data are presented. Such tools are critical to an ICME ecosystem, not only for their role in enabling optimization, but also in relieving users of tedious manual tasks, thus helping to promote adoption and combat the cultural challenges organizations face in enabling ICME.

Materials↗

Thermal Protection Materials and Systems at NASA Ames Research Center

Thermal Protection Systems (TPS) are critical for enabling NASA missions involving high-speed atmospheric flight where the entries usually include descending into the atmosphere followed by a trajectory that aims to burn off energy and result in a controlled landing. NASA Ames focuses on qualifying and certifying TPS for current missions, sustaining TPS for future missions, and developing new TPS for upcoming missions where a heritage solution is not viable. More recently there is also a focus on advancing and transferring technologies that can benefit both commercial and government space needs.Developing mature thermal protection systems is a lengthy process involving advanced tools, extensive research, and testing. Design and analysis tools are used to predict aerothermal environments, aid the design of test and flight hardware, and support the testing for the thermal/mechanical response of thermal protection systems. More recently, advances in computational methods help reduce the time and cost of technological advances, aid in optimized material architecture design, and improve material properties and performance. While high-enthalpy testing that simulates the conditions of space flight remains essential for the evaluation and development of TPS materials, computational tools are already showing promise in reducing the need for widespread testing and can help fast-trackthe design cycle.With the exploration of new destinations EDL instrumentation remains an important element of the heatshield and NASA Ames and partners have developed and delivered instrumentation flight hardware in support of recent Mars missions,including Mars Science Lab (MSL) and Mars 2020 (M2020),as well as Artemis Orion. Sensors installed on the heatshield and backshell of spacecraft provide coveted information about the aerodynamic and aerothermal environment during entry.Overthe years NASA Ames has brought several reusable and ablative TPS materials to a level of readiness to hand off to missions and the Thermal Protection Materials branch continues to serve as a TPS steward for the agency.

Thermal protection materials↗

Thermal Protection Materials and Systems at NASA Ames Research Center

Thermal Protection Systems (TPS) are critical for enabling NASA missions involving high-speed atmospheric flight where the entries usually include descending into the atmosphere followed by a trajectory that aims to burn off energy and result in a controlled landing. NASA Ames focuses on qualifying and certifying TPS for current missions, sustaining TPS for future missions, and developing new TPS for upcoming missions where a heritage solution is not viable. More recently there is also a focus on advancing and transferring technologies that can benefit both commercial and government space needs. Developing mature thermal protection systems is a lengthy process involving advanced tools, extensive research, and testing. Design and analysis tools are used to predict aerothermal environments, aid the design of test and flight hardware, and support the testing for the thermal/mechanical response of thermal protection systems. More recently, advances in computational methods help reduce the time and cost of technological advances, aid in optimized material architecture design, and improve material properties and performance. While high-enthalpy testing that simulates the conditions of space flight remains essential for the evaluation and development of TPS materials, computational tools are already showing promise in reducing the need for widespread testing and can help fast-track the design cycle. With the exploration of new destinations EDL instrumentation remains an important element of the heatshield and NASA Ames and partners have developed and delivered instrumentation flight hardware in support of recent Mars missions (MSL and M2020) as well as Artemis Orion. Sensors installed on the heatshield and backshell of spacecraft provide coveted information about the aerodynamic and aerothermal environment during entry. Over the years NASA Ames has brought several reusable and ablative TPS materials to a level of readiness to hand off to missions and the branch continues to serve as a TPS steward for the agency. This presentation will cover current developments in the above areas that have enabled recent missions, and look to future TPS needs for missions such as Ice Giants.

thermal protection materials↗

Experimental Characterization and Simulation of Slip Transfer at Grain Boundaries and Microstructurally-Sensitive Crack Propagation

A systematic study of crack tip interaction with grain boundaries is critical for improvement of multiscale modeling of microstructurally-sensitive fatigue crack propagation and for the computationally-assisted design of more durable materials. In this study, single, bi- and large-grain multi-crystal specimens of an aluminum-copper alloy are fabricated, characterized using electron backscattered diffraction (EBSD), and deformed under tensile loading and nano-indentation. 2D image correlation (IC) in an environmental scanning electron microscope (ESEM) is used to measure displacements near crack tips, grain boundaries and within grain interiors. The role of grain boundaries on slip transfer is examined using nano-indentation in combination with high-resolution EBSD. The use of detailed IC and EBSD-based experiments are discussed as they relate to crystal-plasticity finite element (CPFE) model calibration and validation.

Gupta, Vipul↗

Database Design Strategies for Coordinated Simulation and Testing in Additive Manufacturing

The qualification and certification (Q&C) process presents a significant challenge for widespread adoption of additive manufacturing (AM) materials and processes for aerospace applications. A relational database framework will be presented as a tool for data curation of coordinated experimental and computational materials modeling research activities. A comparison of relational and hierarchical data structures in this domain will be emphasized through the evolution of a database design strategy. This framework’s mission is to support the advancement of computational materials-informed Q&C by providing the necessary data infrastructure to trace reliability and reproducibility measures through unified AM materials simulation and experimental testing. FAIR (findable, accessible, interoperable, and reusable) data will be highlighted as a necessary precursor for automation of specific actions, which ultimately reduces the time and expense burden for Q&C. The discussion will be mostly limited to back-end design elements, though a few front-end user experience examples will also be shared.

Qualification↗

Alloy Design Workbench-Surface Modeling Package Developed

NASA Glenn Research Center's Computational Materials Group has integrated a graphical user interface with in-house-developed surface modeling capabilities, with the goal of using computationally efficient atomistic simulations to aid the development of advanced aerospace materials, through the modeling of alloy surfaces, surface alloys, and segregation. The software is also ideal for modeling nanomaterials, since surface and interfacial effects can dominate material behavior and properties at this level. Through the combination of an accurate atomistic surface modeling methodology and an efficient computational engine, it is now possible to directly model these types of surface phenomenon and metallic nanostructures without a supercomputer. Fulfilling a High Operating Temperature Propulsion Components (HOTPC) project level-I milestone, a graphical user interface was created for a suite of quantum approximate atomistic materials modeling Fortran programs developed at Glenn. The resulting "Alloy Design Workbench-Surface Modeling Package" (ADW-SMP) is the combination of proven quantum approximate Bozzolo-Ferrante-Smith (BFS) algorithms (refs. 1 and 2) with a productivity-enhancing graphical front end. Written in the portable, platform independent Java programming language, the graphical user interface calls on extensively tested Fortran programs running in the background for the detailed computational tasks. Designed to run on desktop computers, the package has been deployed on PC, Mac, and SGI computer systems. The graphical user interface integrates two modes of computational materials exploration. One mode uses Monte Carlo simulations to determine lowest energy equilibrium configurations. The second approach is an interactive "what if" comparison of atomic configuration energies, designed to provide real-time insight into the underlying drivers of alloying processes.

Abel, Phillip B.↗

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↗

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 Tech Briefs, March 1996

Topics: Computer-Aided Design and Engineering; Electronic Components and Cicuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information; Books and Reports.

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NASA Tech Briefs, April 1996

Topics covered include: Advanced Composites and Plastics; Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information; Books and Reports.

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NASA Tech Briefs, March 1997

Topics: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports.

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NASA Tech Briefs, September 1999

Topics discussed include: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences;

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NASA Tech Briefs, February 2000

Topics covered include: Test and Measurement; Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Bio-Medical; Mathematics and Information Sciences; Computers and Peripherals.

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NASA Tech Briefs, March 2000

Topics include: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports.

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NASA Tech Briefs, March 1993

Topics include: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences;

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NASA Tech Briefs, March 1994

Topics include: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports

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NASA Tech Briefs, July 1994

Topics covered include: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports

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NASA Tech Briefs, October 1994

Topics: Data Acquisition and Analysis; Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences; Books and Reports

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