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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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At least 217 records · Page 12

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NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators Using Micro and Macro-Scale Modeling

Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.

Thermal Protection Systems↗

NASA NTRS2013

Detecting Nanophase Weathering Products with CheMin: Reference Intensity Ratios of Allophane, Aluminosilicate Gel, and Ferrihydrite

X-ray diffraction (XRD) data collected of the Rocknest samples by the CheMin instrument on Mars Science Laboratory suggest the presence of poorly crystalline or amorphous materials [1], such as nanophase weathering products or volcanic and impact glasses. The identification of the type(s) of X-ray amorphous material at Rocknest is important because it can elucidate past aqueous weathering processes. The presence of volcanic and impact glasses would indicate that little chemical weathering has occurred because glass is highly susceptible to aqueous alteration. The presence of nanophase weathering products, such as allophane, nanophase iron-oxides, and/or palagonite, would indicate incipient chemical weathering. Furthermore, the types of weathering products present could help constrain pH conditions and identify which primary phases altered to form the weathering products. Quantitative analysis of phases from CheMin data is achieved through Reference Intensity Ratios (RIRs) and Rietveld refinement. The RIR of a mineral (or mineraloid) that relates the scattering power of that mineral (typically the most intense diffraction line) to the scattering power of a separate mineral standard such as corundum [2]. RIRs can be calculated from XRD patterns measured in the laboratory by mixing a mineral with a standard in known abundances and comparing diffraction line intensities of the mineral to the standard. X-ray amorphous phases (e.g., nanophase weathering products) have broad scattering signatures rather than sharp diffraction lines. Thus, RIRs of X-ray amorphous materials are calculated by comparing the area under one of these broad scattering signals with the area under a diffraction line in the standard. Here, we measured XRD patterns of nanophase weathering products (allophane, aluminosilicate gel, and ferrihydrite) mixed with a mineral standard (beryl) in the CheMinIV laboratory instrument and calculated their RIRs to help constrain the abundances of these phases in the Rocknest samples.

Rampe, E. B.↗

DOE OSTI2026

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

Atomistic models↗

DOE OSTI2025

Bridging the length scales in ionic separations via data-driving machine learning

We pursued a data science driven machine learning (ML) approach that blended molecular scale attributes informed from molecular dynamics (MD) simulation and materials properties to the selectivity and energy efficiency in targeted ionic separations using electric fields. The model mixtures investigated for ionic separations are pH sensitive and include organic acids, silica and boron, transition metals, such as copper and chromium. There were two major research thrusts of this project. Firstly, we investigated surrogate models and deep learning that relate material chemistries and structures to selective transport of ionic species under applied electric fields. Secondly we investigated how the bipolar junction interfacial design and water dissociation catalyst in bipolar membranes affect reverse bias polarization behavior and pH modulation in deionization platforms as a function of the platform operating parameters (e.g., cell voltage, residence time, and salt feed concentration). As a result of this work, we also were able to start a new direction, namely ML models for molecular design of surfactants.

36 MATERIALS SCIENCE↗

NASA NTRS2000

NASA Tech Briefs, January 2000

Topics include: Data Acquisition; Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Bio-Medical; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Information Sciences; Books and reports.

Source record↗

NASA NTRS2000

NASA Tech Briefs, July 2000

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

Source record↗

NASA NTRS1995

LDEF materials results for spacecraft applications: Executive summary

To address the challenges of space environmental effects, NASA designed the Long Duration Exposure Facility (LDEF) for an 18-month mission to expose thousands of samples of candidate materials that might be used on a space station or other orbital spacecraft. LDEF was launched in April 1984 and was to have been returned to Earth in 1985. Changes in mission schedules postponed retrieval until January 1990, after 69 months in orbit. Analyses of the samples recovered from LDEF have provided spacecraft designers and managers with the most extensive data base on space materials phenomena. Many LDEF samples were greatly changed by extended space exposure. Among even the most radially altered samples, NASA and its science teams are finding a wealth of surprising conclusions and tantalizing clues about the effects of space on materials. Many were discussed at the first two LDEF results conferences and subsequent professional papers. The LDEF Materials Results for Spacecraft Applications Conference was convened in Huntsville to discuss implications for spacecraft design. Already, paint and thermal blanket selections for space station and other spacecraft have been affected by LDEF data. This volume synopsizes those results.

Whitaker, A. F.↗

NASA NTRS2005

NASA Earth Observations (NEO): Data Access for Informal Education and Outreach

The NEO (NASA Earth Observations) web space is currently under development with the goal of significantly increasing the demand for NASA remote sensing data while dramatically simplifying public access to georeferenced images. NEO will target the unsophisticated, nontraditional data users who are currently underserved by the existing data ordering systems. These users will include formal and informal educators, museum and science center personnel, professional communicators, and citizen scientists and amateur Earth observers. Users will be able to view and manipulate georeferenced browse imagery and, if they desire, download directly or order the source HDF data from the data provider (e.g., NASA DAAC or science team) via a single, integrated interface. NE0 will accomplish this goal by anticipating users expectations and knowledge level, thus providing an interface that presents material to users in a more simplified manner, without relying upon the jargon/technical terminology that make even the identification of the appropriate data set a significant hurdle. NEO will also act as a gateway that manages users expectations by providing specific details about images and data formats, developing tutorials regarding the manipulation of georeferenced imagery and raw data, links to software tools and ensuring that users are able to get the image they want in the format they want as easily as possible.

Ward, Kevin↗

DOE OSTI2024

MatCal Users Guide: Release 1.3.0

Any continuum mechanics model will require three components: (1) a discretized geometry of the boundary value problem being studied, (2) the partial differential equations to be solved, and (3) the initial conditions and boundary conditions for the problem. To describe material behavior in these computational models, material models contribute to (2) the underlying equations and, occasionally, to (3) the initial conditions for the simulation. These material models can exhibit a mathematical form that is empirically based, based on first principles, or developed from both empirical observations and known physics. In general, these models are meant to represent a class of materials with well understood behavior. As a result, material models have parameters that must be tuned or calibrated so that the model response matches characterization data available for the specific material it is intended to represent when used to simulate a specific system. For simple models, such as isotropic, linear elastic materials in solid mechanics, this calibration process can be a simple analytical calculation directly extracting the parameters from experimental measurements. For complex models that have many inputs and require many characterization datasets to adequately identify the material behavior, the model calibration process can require an inverse problem approach where an optimization is performed to tune the model parameters to the available data.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Beyond the Hype: Navigating the Promise and Pitfalls of Multi-Modal Models for Materials Science

Multi-modal models offer great potential for accelerating discovery in materials and chemical systems, but their adoption raises crucial questions: What materials science challenges are best addressed by multi-modal approaches? How do we weigh the benefits against the resource investment required for multi-modal data acquisition? And critically, how can we optimize experimental workflows to leverage these models effectively? In this presentation, I will delve into the development of multi-modal characterization and analytics, focusing on their application in the demanding fields of next-generation microelectronics and energy storage materials. I will share challenges encountered in designing these workflows, highlighting lessons learned and posing questions that remain unanswered.

AI↗

NASA NTRSDate not supplied

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

The NASA Mars Science Laboratory (MSL) and Mars 2020 entry vehicles included temperature measurements inside and direct heat flux on the surface of the thermal protection system (TPS) material. The instrumentation was included to begin addressing predictive capabilities for aerodynamic heating and ablative TPS material response. The MSL aeroshell included temperature measurements at seven heatshield locations, from which total aerodynamic heating was reconstructed. The Mars 2020 instrumentation suite included eleven heatshield measurement locations and new backshell measurements at nine locations: six with temperature sensors, two with sensors to directly measure total heat flux, and one to measure radiative heat flux. Both instrumentation suites returned full sets of data, with all temperature sensors nearest to the surface surviving entry, indicating minimal material recession. Heatshield boundary layer transition (BLT) was observed for both vehicles prior to the time of peak heating, with transition starting near the shoulder and progressing towards the stagnation area. MSL’s higher speed on approach to Mars resulted in higher total heating compared to Mars 2020, with the maximum occurring near the heatshield’s leeside shoulder. The Mars 2020 backshell heating data indicate significant contributions from shock layer radiation. Heating from Navier-Stokes flowfield calculations are in family with reconstructed heating. The paper covers the instrumentation, BLT and reconstructed heating on both heatshields, backshell heating for Mars 2020, TPS performance, and predicted versus reconstructed heat fluxes. Opportunities for improved design approaches for future Mars missions are discussed, especially for the upcoming Mars Sample Return mission.

Mars Science Laboratory↗

NASA NTRSDate not supplied

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

Mars Science Laboratory↗

NASA NTRS2022

Solar Wind Ion Sputtering of Sodium from Silicates Using Molecular Dynamics Calculations of Surface Binding Energies

For nearly 40 yr, studies of exosphere formation on airless bodies have been hindered by uncertainties in our understanding of the underlying ion collisional sputtering by the solar wind (SW). These ion impacts on airless bodies play an important role in altering their surface properties and surrounding environment. Much of the collisional sputtering data needed for exosphere studies come from binary collision approximation (BCA) sputtering models. These depend on the surface binding energy (SBE) for the atoms sputtered from the impacted material. However, the SBE is not reliably known for many materials important for planetary science, such as plagioclase feldspars and sodium pyroxenes. BCA models typically approximate the SBE using the cohesive energy for a monoelemental solid. We use molecular dynamics (MD) to provide the first accurate SBE data we are aware of for Na sputtered from the above silicate minerals, which are expected to be important for exospheric formation at Mercury and the Moon. The MD SBE values are ∼8 times larger than the Na monoelemental cohesive energy. This has a significant effect on the predicted SW ion sputtering yield and energy distribution of Na and the formation of the corresponding Na exosphere. We also find that the SBE is correlated with the coordination number of the Na atoms within the substrate and with the cohesive energy of the Na-bearing silicate. Our MD SBE results will enable more accurate BCA predictions for the SW ion sputtering contribution to the Na exosphere of Mercury and the Moon.

Lunar surface↗

NASA NTRS1992

LDEF: 69 Months in Space. First Post-Retrieval Symposium, part 1

This document is a compilation of papers presented at the First Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium. The papers represent the preliminary data analysis of the 57 experiments flown on the LDEF. The experiments include materials, coatings, thermal systems, power and propulsion, science (cosmic ray, interstellar gas, heavy ions, and micrometeoroid), electronics, optics, and life

Space experiment↗

NASA NTRS1991

LDEF: 69 Months in Space. First Post-Retrieval Symposium, part 3

Space experiment↗

NASA NTRS1992

Second LDEF Post-Retrieval Symposium Abstracts

These abstracts from the symposium represent the data analysis of the 57 experiments flown on the LDEF. The experiments include materials, coatings, thermal systems, power and propulsion, science, (cosmic ray, interstellar gas, heavy ions, micrometeoroids, etc.), electronics, optics, and life science.

Levine, Arlene S.↗

NASA NTRS1993

LDEF: 69 Months in Space. Part 3: Second Post-Retrieval Symposium

Papers presented at the Second Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium are included. The papers represent the data analysis of the 57 experiments flown on the LDEF. The experiments include materials, coatings, thermal systems, power and propulsion, science (cosmic ray, interstellar gas, heavy ions, micrometeoroid, etc.), electronics, optics, and life science.

Levine, Arlene S.↗

NASA NTRS1993

LDEF: 69 Months in Space. Second Post-Retrieval Symposium, part 2

This document is a compilation of papers presented at the Second Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium. The papers represent the data analysis of the 57 experiments flown on the LDEF. The experiments include materials, coatings, thermal systems, power and propulsion, science (cosmic ray, interstellar gas, heavy ions, micrometeoroid, etc.), electronics, optics, and life science.

Levine, Arlene S.↗