Material Data Management - State of the Art and Recent Developments
Vision 2040: A roadmap for integrated, multiscale modeling and simulation of materials and systems.
Steve Arnold↗
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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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Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread
Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.
Machine Learning↗
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↗
Popnet : computer vision based deep learning model for forecasting gridded population
Here, this study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial variables and population data from 2000 to 2020, Popnet predicts South Korea’s population trends by age groups (under 14, 15-64 and over 65) up to 2040. In validation, it outperforms traditional machine learning and state-of-the-art computer vision models. The output of this model discovered significant polarisation: population growth in urban areas, especially the capital region, and severe depopulation in rural areas. Popnet is a robust tool for offering significant insights to policymakers and related stakeholders about the detailed future population, which allows them to establish detailed, localised planning and resource allocations.
computer vision↗
A Vision for Ice Giant Exploration
From Voyager to a Vision for 2050: NASA and ESA have just completed a study of candidate missionsto Uranus and Neptune, the so-called ice giant planets. It is a Pre-Decadal Survey Study, meant to inform the next Planetary Science Decadal Survey about opportunities for missions launching in the 2020's and early 2030's. There have been no space flight missions to the ice giants since the Voyager 2 flybys of Uranus in 1986 and Neptune in 1989. This paper presents some conclusions of that study (hereafter referred to as The Study), and how the results feed into a vision for where planetary science can be in 2050. Reaching that vision will require investments in technology andground-based science in the 2020's, flight during the 2030's along with continued technological development of both ground- and space-based capabilities, and data analysis and additional flights in the 2040's. We first discuss why exploring the ice giants is important. We then summarize the science objectives identified by The Study, and our vision of the science goals for 2050. We then review some of the technologies needed to make this vision a reality.
2050↗