Search NASA⌕ Search

SEARCH · Search NASA

Results for “materials availability”

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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data

The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that include mechanistic information, such as would be available from partial differential equations, with a strong focus on the quantification of uncertainty associated with data, process, and parameters. Here, we give a brief review of several paradigms and focus our attention on Bayesian implementations given they naturally accommodate uncertainty quantification. We then show that it is straight-forward to include the Bayesian PINN (B-PINN) within the Bayesian hierarchical model (BHM) framework that has long been considered for modeling dynamic spatio-temporal processes. Such a BHM-PINN is illustrated via a simulation study in which a latent nonlinear Burgers’ equation PDE governs the dynamics of Poisson distributed spatio-temporal data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian↗
DOE OSTI2015

A generalized method for high throughput in-situ experiment data analysis: An example of battery materials exploration

A generalized method is introduced to extract critical information from series of ranked correlated data. The method is generally applicable to all types of spectra evolving as a function of any arbitrary parameter. This approach is based on correlation functions and statistical scedasticity formalism. Numerous challenges in analyzing high throughput experimental data can be tackled using the herein proposed method. We applied this method to understand the reactivity pathway and formation mechanism of a Li-ion battery cathode material during high temperature synthesis using in-situ highenergy X-ray diffraction. We demonstrate that Pearson's correlation function can easily unravel all major phase transition and, more importantly, the minor structural changes which cannot be revealed by conventionally inspecting the series of diffraction patterns. Furthermore, a two-dimensional (2D) reactivity pattern calculated as the scedasticity along all measured reciprocal space of all successive diffraction pattern pairs unveils clearly the structural evolution path and the active areas of interest during the synthesis. The methods described here can be readily used for on-the-fly data analysis during various in-situ operando experiments in order to quickly evaluate and optimize experimental conditions, as well as for post data analysis and large data mining where considerable amount of data hinders the feasibility of the investigation through point-by-point inspection.

25 ENERGY STORAGE↗
DOE OSTI2021

Enabling deeper learning on big data for materials informatics applications

Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

The pursuit of scalable and manufacturable all-solid-state batteries continues to intensify, motivated by the rapidly increasing demand for safe, dense electrical energy storage. Here, we describe the numerous, often conflicting implications of materials choices that have been made in the search for effective mitigations to the interfacial instabilities plaguing solid-state batteries. Specifically, we show that the manufacturing scalability of solid-state batteries can be governed by at least three principal consequences of materials selection: (1) the availability, scaling capacity, and price volatility of the chosen materials’ constituents, (2) the manufacturing processes needed to integrate the chosen materials into full cells, and (3) the cell performance that may be practically achieved with the chosen materials and processes. While each of these factors is, in isolation, a pivotal determinant of manufacturing scalability, we show that consideration and optimization of their collective effects and trade-offs is necessary to more completely chart a scalable pathway to manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2019

Methylmercury sorption onto engineered materials

Four commercially available sorbents (BioChar (BC), ThiolSAMMS® (TS), SediMite (SM), and Organoclay™ PM-199 (OC-199)) were tested for their ability to sorb methylmercury (MeHg) and MeHg complexed with dissolved organic matter (DOM). Testing sorption behavior with DOM is more representative of the environmental conditions and mercury speciation expected during in-situ remediation efforts. Isotherms were fit using a robust, iterative re-weighting scheme. This fitting method improves upon the traditionally used indirect sorption method by removing the dependence between aqueous and solid phase concentrations in isotherm fitting. Developed isotherms reflect that without DOM, BC, TS, and SM adsorbed similar amounts of MeHg while OC-199 sorbed substantially less MeHg. Below an equilibrium concentration of 5.6 ng L -1 BC was the best performing sorbent, between 5.6 and 20. ng L -1 SM sorbed the most MeHg, and above an equilibrium concentration of 20.9 ng L -1 TS outperformed the other sorbents. BC and OC-199 showed indication of MeHg sorption saturation over the tested concentration range of 3.5–680 ng L -1 . With DOM, SM outperformed the other sorbents at equilibrium concentrations less than 0.98 ng L -1 and TS was the superior MeHg:DOM sorbent at higher concentrations. MeHg:DOM sorption was controlled by DOM-sorbent interactions. DOM decreased MeHg sorption onto BC and SM whereas TS exhibited similar sorption with and without DOM. OC-199 had slightly higher MeHg uptake with DOM. East Fork Poplar Creek (EFPC), an industrially Hg contaminated site, was used as a case study example to build a relationship between aqueous and fish MeHg concentrations and subsequently compare the cost of sorbent materials required to meet regulatory objectives. For this case study, SM provided the most cost-effective sorbent option for in-situ remediation efforts to reduce aqueous MeHg concentrations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Discovery of AMPX Thermal Scattering Law Processing Issue for Solid Moderators

The 2020 edition of The International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook includes a newly produced plastic-moderated evaluation, identified as PU-MET-MIXED-002 and referred to as PMM002. The 2021 edition should include another new plastic-moderated experiment, HEU-MET-THERM-004, referred to as HMT-004. These evaluations are of particular interest, as PMM-002 is moderated with polyethylene, and HMT-004 is moderated with polymethyl methacrylate (Lucite), allowing for investigation of the differences in the thermal scattering laws (TSLs) available for these materials. ENDF/B-VIII.0 includes newly produced data for 1 H-based scattering materials, including Lucite and yttrium hydride. Other available 1 H TSLs include polyethylene, light water, zirconium hydride. The unbound 1 H (free-gas) cross sections were also used. ENDF/B-VIII.0 also includes revisions made to the ENDF/B-VII.1 light water and polyethylene evaluations. Zirconium hydride was unchanged from ENDF/B-VII.1. As of this writing, there are no benchmarks included in the Verified, Archived Library of Inputs and Data (VALID) that are primarily moderated with any solid moderator, so validation that includes the PMM-002 and HMT-004 evaluations would expand the coverage to two new moderators. A study was undertaken at Oak Ridge National Laboratory with two purposes: to provide validation data based on systems that are primarily moderated with polyethylene and Lucite, and to demonstrate the reactivity changes that can result from use of different 1 H TSLs in these plastic-moderated systems. The first objective was mainly to test data and code for SCALE and AMPX, whereas the second objective was to provide useful data for practitioners on the potential variability of predicted $k_{eff}$ based on TSL changes. The use of an exactly correct TSL is often not possible given the materials involved (e.g., lubricants), but this study was intended to provide some indication of the magnitude of the changes among similar materials that can manifest through application of different TSLs in hydrogenous systems over a range of different neutron energy spectra. Unfortunately, the nominal $k_{eff}$ results exposed deviations between SCALE and Monte Carlo N-Particle (MCNP). Some of the results using different TSLs were also unexpected and difficult to explain. An investigation revealed a processing issue in AMPX that was caused by an ambiguous description of the data for incoherent elastic scattering in the ENDF manual. This issue is discussed in detail in a SCALE User Notification, and it affects some solid moderators, but it does not apply to 1 H bound in water. This issue highlights the importance of validating all TSLs used in safety analysis calculations. The remainder of this paper provides a more detailed examination of the results that triggered the investigation and a summary of the findings of that investigation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2017

Hypervelocity Impact Testing of Materials for Additive Construction: Applications on Earth, the Moon, and Mars

Additive Construction is the process of building infrastructure such as habitats, garages, roads, berms, etcetera layer by layer (3D printing). The National Aeronautics and Space Administration (NASA) and the United States Army Corps of Engineers (USACE) are pursuing additive construction to build structures using resources available in-situ. Using materials available in-situ reduces the cost of planetary missions and operations in theater. The NASA team is investigating multiple binders that can be produced on planetary surfaces, including the magnesium oxide-based Sorel cement; the components required to make Ordinary Portland Cement (OPC), the common cement used on Earth, have been found on Mars. The availability of OPC-based concrete on Earth drove the USACE to pursue additive construction for base housing and barriers for military operations. Planetary and military base structures must be capable of resisting micrometeoroid impacts with velocities ranging from 11 to 72km/s for particle sizes 200 micrometers or more (depending on protection requirements) as well as bullets and shrapnel with a velocity of 1.036km/s with projectiles 5.66mm diameter and 57.40mm in length, respectively.

Hypervelocity Impact↗