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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Mars Exploration Rover Heatshield Observation Campaign

For the first time ever, engineers were able to observe a heatshield on the surface of another planet after a successful entry through the atmosphere. A three-week heatshield observation campaign was conducted in December 2004 after the Mars Exploration Rover Opportunity exited "Endurance Crater." By utilizing the rover's scientific instruments, data was collected to make a qualitative assessment of the performance of the heatshield. This data was gathered to gain a better understanding of how the heatshield performed during entry through the Martian atmosphere. In addition, this unprecedented look at the heatshield offered engineers the opportunity to assess if any unexpected anomalies occurred. Once a survey of the heatshield debris was completed, multiple targets of interest were chosen for the collection of imaging data. This data was then used to assess the char depth of the thermal protection material, which compared well with design and post-flight computational predictions. Extensive imaging data was collected and showed the main seal in pristine conditions, and no observable indications of structure overheating. Additionally, unexpected vehicle dynamics during the atmospheric entry were explained by the observation of thermal blanket remnants attached to the heatshield.

Thermal Protection System (TPS)↗
NASA NTRS2011

Mars Exploration Rover Heatshield Observation Campaign

For the first time ever, engineers were able to observe a heatshield on the surface of another planet after a successful entry through the atmosphere. A three-week heatshield observation campaign was conducted in December 2004 after the Mars Exploration Rover Opportunity exited "Endurance Crater." By utilizing the rover's scientific instruments, data was collected to make a qualitative assessment of the performance of the heatshield. This data was gathered to gain a better understanding of how the heatshield performed during entry through the Martian atmosphere. In addition, this unprecedented look at the heatshield offered engineers the opportunity to assess if any unexpected anomalies occurred. Once a survey of the heatshield debris was completed, multiple targets of interest were chosen for the collection of imaging data. This data was then used to assess the char depth of the thermal protection material, which compared well with design and post-flight computational predictions. Extensive imaging data was collected and showed the main seal in pristine conditions, and no observable indications of structure overheating. Additionally, unexpected vehicle dynamics during the atmospheric entry were explained by the observation of thermal blanket remnants attached to the heatshield.

Mars Exploration Rovers↗
DOE OSTI2021

Computational design of passivants for CdTe grain boundaries

CdTe is the second most-widely deployed photovoltaic (PV) material due to its high efficiency and low manufacturing costs. Currently, polycrystalline CdTe (poly-CdTe) has a record efficiency of similar to 22%, which is still well below the theoretical limit (similar to 30%). Polycrystalline CdTe films that have incorporated Cl or Se show higher efficiency, possibly due to the segregation of these ions to the grain boundaries (GBs), where they may passivate the dangling bonds. However, the efficiency enhancement mechanisms of passivants in CdTe GBs, and the feasibility of employing alternative passivants, have not been well explored. Here, we present a systematic computational study of CdTe GBs with potential passivants, namely S, P, As, Se, and Sb on Te sites, and Na, Mg, Al, Sc, Cu, and Zn on Cd sites. Density functional theory (DFT) calculations were performed on GB dislocation core structures derived from scanning transmission electron microscopy (STEM) images of a model GB (bicrystal). We computed the segregation thermodynamics, electronic density of states, and charge variations near doped CdTe GBs. We find that segregation of impurities to GBs is thermodynamically favorable. For a Te-terminated core, Se, S, and P on Te sites effectively reduce midgap states. For both Cd-and Te-terminated dislocation cores, Sc and Al reduce midgap states when substituted for Cd atoms. Overall, the greatest improvement was achieved with co-doping, i.e. simultaneously substituting Te with Se and substituting Cd with Cu or Al. The elimination of midgap states is predicted to increase the photovoltaic efficiency of CdTe by reducing the recombination at grain boundaries.

14 SOLAR ENERGY↗
DOE OSTI2025

Metal additive manufacturing simulation across length, time, and computing scales

Metal additive manufacturing (AM) offers a unique opportunity for production of advanced materials and complex geometries. However, variability in microstructure and properties challenges conventional approaches to design, process optimization, qualification, and materials selection. Modeling and simulation can improve understanding of AM processing and materials, but also poses major challenges for existing computational methods. Simultaneously, modern scientific computing hardware has become increasingly complex, most notably with the adoption of hybrid architectures such as Graphical Processing Units (GPUs). If appropriately utilized, emerging computational capabilities provide an opportunity to reveal new insight into AM processing and the resulting material structure and properties. In this review we describe the computational AM landscape, identify critical gaps, and highlight opportunities to impact the development and application of AM. First, the requirements and challenges of representative AM problem statements will be defined. Here, these problems range from scientific studies to industrial applications and are designed to capture the breadth of challenges facing the AM community. Next, the current state of AM modeling and simulation is evaluated, broken down by enabling hardware and software, process simulation, microstructure simulation, and property simulation. Each section describes the diversity of simulation approaches and associated trade-offs in physical fidelity and computational expense. Each area is then assessed based on their suitability and readiness for current and developing computational architectures. Lastly, the greatest opportunities for future research and application are highlighted, including gaps in modeling capabilities, opportunities for near-term application, and key scientific challenges.

additive manufacturing↗
DOE OSTI2020

SNL-NJOY-2016

This is a wrapper around the LANL NJOY-2016 code that interfaces with extended code capabilities and minor modification of the LANL NJOY-2016 code that supports modeling radiation damage to materials. The NJOY Nuclear Data Processing System is a modular computer code designed to read evaluated data in ENDF format, transform the data in various ways, and output the results as libraries designed to be used in various applications. The wrapper provided here permits Sandia-specific control parameters to be used in the input data file. The modifications incorporated here enhance the ability of NJOY to address material damage response functions for many materials, e.g. to include the NRT and arc-dpa forms of the damage energy in addition to the default NJOY-2016 implementation of the sharp-threshold Kinchin-Pease threshold energy treatment. All of the modifications provided here are being made available to the GitHub-based NJOY-2016 code. As useful enhancements found here are incorporated into the baseline NJOY-2016 code, they will be eliminated from this version so as to maintain our compatibility with the baseline NJOY-2016 code. SAND2020-13060 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA00035

Griffin, Patrick↗
DOE OSTI2022

Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Equilibrium Core Model for Micro Pebble Bed Reactors Using OpenMC

Estimating the equilibrium state for pebble bed reactors (PBRs) presents complex challenges as it requires simultaneous consideration of changes in the pebbles’ movement as well as their fuel compositions. Whereas traditional approaches use multigroup diffusion codes for neutronics calculations of PBRs’ equilibrium state, the double-heterogeneity of PBRs complicates neutron cross-section generation. Continuous-energy Monte Carlo (MC) methods are better suited for detailed PBR analysis because of their natural handling of double-heterogeneity, but they demand substantially more computational resources. Here, this study introduces a novel method for efficiently estimating the equilibrium state in small and micro PBRs with reduced computational cost. The method is anticipated to accelerate the processes of core design and performing parametric studies for utilizing advanced fuel and structural materials. The HTR-10 reactor design was used for validating the method’s predictions and evaluating its computational efficiency. When compared to reference calculation values from the literature, criticality (k-effective) was predicted to be approximately within the margin of error of the MC transport calculation, average core power density (in megawatts per cubic meter) was predicted within 2.5% relative error, and maximum thermal flux (10 13 n/cm 2 .s −1 ) was predicted within 1.8% relative error. The calculated inventory of fission products and fuel composition in the equilibrium core were within 15% and 16.6%, respectively, when compared to reported values from the literature. The difference is attributed to variance in the considered values of the core temperature, which was found to significantly affect the depletion analyses.

Equilibrium core↗
DOE OSTI2025

Informing ARC divertor design and plasma facing material selection through integrated modeling

This INFUSE 2023 project between UCLA and Commonwealth Fusion Systems used computer modeling to test whether tungsten materials can survive in CFS's ARC fusion reactor divertor. The team simulated plasma conditions and material responses, finding that tungsten-rhenium alloys resist grain growth better than pure tungsten, and that hydrogen buildup depends more on particle flux than temperature. The work helps CFS design durable plasma-facing components for their fusion power plant.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Physics of debris clouds from hypervelocity impacts

The protection scheme developed for long duration space platforms relies primarily upon placing thin metal plates or 'bumpers' around flight critical components. The effectiveness of this system is highly dependent upon its ability to break up and redistribute the momentum of any particle which might otherwise strike the outer surface of the spacecraft. Therefore it is of critical importance to design the bumpers such that maximum dispersion of momentum is achieved. This report is devoted to an in-depth study into the design and development of a laboratory instrument which would permit the in-situ monitoring of the momentum distribution as the impact event occurs. A series of four designs were developed, constructed and tested culminating with the working instrument which is currently in use. Each design was individually tested using the Space Environmental Effects Facility (SEEF) at the Marshall Space Flight Center in Huntsville, Alabama. Along with the development of the device, an experimental procedure was developed to assist in the investigation of various bumper materials and designs at the SEEF. Preliminary results were used to compute data which otherwise were not experimentally obtainable. These results were shown to be in relative agreement with previously obtained values derived through other methods. The results of this investigation indicated that momentum distribution could in fact be measured in-situ as the impact event occurred thus giving a more accurate determination of the effects of experimental parameters on the momentum spread. Data produced by the instrument indicated a Gaussian-type momentum distribution. A second apparatus was developed and it was placed before the shield in the line of travel utilized a plate to collect impact debris scattered backwards. This plate had a passage hole in the center to allow the particle to travel through it and impact the proposed shield material. Applying the law of conservation of angular momentum a backward momentum vector was determined from the angular velocity of the plate. The forward scattered and backward scattered momentum values were then analyzed to judge the distribution of debris. Loss of momentum was attributed to the inaccuracies of the means of measurement. Assumptions of symmetrical debris for the forward and backward scattered directions also contributed to this loss.

Zee, Ralph↗
DOE OSTI2023

Design of an Integrated Solids Handling System to Maximize Syngas Process Reliability (Cooperative Research and Development Final Report)

The National Renewable Energy Laboratory (NREL), in partnership with Wonderful Renewable Energy (WRE) and Idaho National Laboratory (INL), plans to develop a general methodology for designing integrated biorefinery solids preprocessing, handling, and feeding systems based on the chemical, physical, and mechanical attributes of the starting biomass material. This attribute-driven approach will include detailed feedstock property measurements, iterative computational modeling, and bench-scale testing to design systems for preprocessing, handling, and reactor in-feed, up to and including the selection of the conversion reactor. The initial tests and system design will be conducted using waste material from almond and pistachio growing and production operations (shells, hulls, and wood), targeting the conversion of this material to syngas for electricity production. The methodology will then be generalized to other feedstocks. The purpose of this project is to design an integrated solids handling system to maximize the process reliability of converting almond and pistachio waste to electricity. The design methodology and workflow developed from this example will then be applied to the Feedstock Conversion Interface Consortium (FCIC) benchmark loblolly pine residues, thus demonstrating the robustness of the overall design approach and providing insight and guidance for future conversion systems. V-Grid Energy Systems was brought on as a subcontractor to provide gasifiers and labor to complete gasifier runs.

09 BIOMASS FUELS↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
DOE OSTI2021

Inverse design of two-dimensional materials with invertible neural networks

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS 2 . Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition in MoS 2 which are important for memristive neuromorphic applications, among others. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Computational Nanotechnology at NASA Ames Research Center, 1996

Some forms of nanotechnology appear to have enormous potential to improve aerospace and computer systems; computational nanotechnology, the design and simulation of programmable molecular machines, is crucial to progress. NASA Ames Research Center has begun a computational nanotechnology program including in-house work, external research grants, and grants of supercomputer time. Four goals have been established: (1) Simulate a hypothetical programmable molecular machine replicating itself and building other products. (2) Develop molecular manufacturing CAD (computer aided design) software and use it to design molecular manufacturing systems and products of aerospace interest, including computer components. (3) Characterize nanotechnologically accessible materials of aerospace interest. Such materials may have excellent strength and thermal properties. (4) Collaborate with experimentalists. Current in-house activities include: (1) Development of NanoDesign, software to design and simulate a nanotechnology based on functionalized fullerenes. Early work focuses on gears. (2) A design for high density atomically precise memory. (3) Design of nanotechnology systems based on biology. (4) Characterization of diamonoid mechanosynthetic pathways. (5) Studies of the laplacian of the electronic charge density to understand molecular structure and reactivity. (6) Studies of entropic effects during self-assembly. Characterization of properties of matter for clusters up to sizes exhibiting bulk properties. In addition, the NAS (NASA Advanced Supercomputing) supercomputer division sponsored a workshop on computational molecular nanotechnology on March 4-5, 1996 held at NASA Ames Research Center. Finally, collaborations with Bill Goddard at CalTech, Ralph Merkle at Xerox Parc, Don Brenner at NCSU (North Carolina State University), Tom McKendree at Hughes, and Todd Wipke at UCSC are underway.

Globus, Al↗
DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
NASA NTRS1992

A brief overview of computational structures technology related activities at NASA Lewis Research Center

The presentation gives a partial overview of research and development underway in the Structures Division of LeRC, which collectively is referred to as the Computational Structures Technology Program. The activities in the program are diverse and encompass four major categories: (1) composite materials and structures; (2) probabilistic analysis and reliability; (3) design optimization and expert systems; and (4) computational methods and simulation. The approach of the program is comprehensive and entails exploration of fundamental theories of structural mechanics to accurately represent the complex physics governing engine structural performance, formulation, and implementation of computational techniques and integrated simulation strategies to provide accurate and efficient solutions of the governing theoretical models by exploiting the emerging advances in computer technology, and validation and verification through numerical and experimental tests to establish confidence and define the qualities and limitations of the resulting theoretical models and computational solutions. The program comprises both in-house and sponsored research activities. The remainder of the presentation provides a sample of activities to illustrate the breadth and depth of the program and to demonstrate the accomplishments and benefits that have resulted.

Hopkins, Dale A.↗
NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators

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↗