Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “material science”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Polymeric materials science in the microgravity environment

The microgravity environment presents some interesting possibilities for the study of polymer science. Properties of polymeric materials depend heavily on their processing history and environment. Thus, there seem to be some potentially interesting and useful new materials that could be developed. The requirements for studying polymeric materials are in general much less rigorous than those developed for studying metals, for example. Many of the techniques developed for working with other materials, including heat sources, thermal control hardware and noncontact temperature measurement schemes should meet the needs of the polymer scientist.

Coulter, Daniel R.↗
NASA NTRS2005

How We Used NASA Lunar Set in Planetary Material Science Analog Studies on Lunar Basalts and Breccias with Industrial Materials of Steels and Ceramics

Analog studies play important role in space materials education. Various aspects of analogies are used in our courses. In this year two main rock types of NASA Lunar Set were used in analog studies in respect of processes and textures with selected industrial material samples. For breccias and basalts on the lunar side, ceramics and steels were found as analogs on the industrial side. Their processing steps were identified on the basis of their textures both in lunar and in industrial groups of materials.

Berczi, S.↗
DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗
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.↗
DOE OSTI2024

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Publishing Challenges in Energetic Materials Science

The editorial addressed the ethical dilemma that energetic materials scientists face in advancing their field for societal good and not providing information that could be used for nefarious purposes. The reaction to the editorial was heated, both pro and con. Here we concluded that PEP had done a good job in catalyzing a thoughtful debate in our community about what information should or should not be published.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗