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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

UNR Grant Proposal Contribution

The work of Carrier et. al. (citation needed for Matt’s effect of surface roughness on phase transitions/ETI paper) demonstrates that 1D and 2D resistive magnetohydrodynamic (MHD) simulations can reliably model exploding aluminum rods driven by megaampere currents. Close agreement between simulation results and photonic Doppler velocimetry data from the Mykonos electrothermal instability (METI-II) campaign builds confidence in predictive modelling capabilities for pulsed-power HED experiments. Furthermore, 2D MHD simulations show how machined features and micro-scale surface roughness can seed the electrothermal instability (ETI). Surface roughness was observed to reduce the time of melt by 19%, with ETI growth driving the enhanced heating of the rod surface.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Prototype Common Bus Spacecraft: Hover Test Implementation and Results

In order to develop the capability to evaluate control system technologies, NASA Ames Research Center (Ames) began a test program to build a Hover Test Vehicle (HTV) - a ground-based simulated flight vehicle. The HTV would integrate simulated propulsion, avionics, and sensors into a simulated flight structure, and fly that test vehicle in terrestrial conditions intended to simulate a flight environment, in particular for attitude control. The ultimate purpose of the effort at Ames is to determine whether the low-cost hardware and flight software techniques are viable for future low cost missions. To enable these engineering goals, the project sought to develop a team, processes and procedures capable of developing, building and operating a fully functioning vehicle including propulsion, GN&C, structure, power and diagnostic sub-systems, through the development of the simulated vehicle.

Hine, Butler Preston↗
DOE OSTI2020

GEOPHIRES v2.0

GEOPHIRES v2.0 is a geothermal techno-economic simulation tool upgraded from GEOPHIRES v1.2. The name stands for "GEOthermal energy for Production of Heat and electricity (“IR”) Economically Simulated". GEOPHIRES combines reservoir, wellbore, and surface plant technical models with cost correlations and levelized cost models to estimate the capital and operation and maintenance costs, instantaneous and lifetime energy production, and overall levelized cost of energy of a geothermal plant. In addition to electricity generation, direct-use heat applications and combined heat and power or cogeneration can be modeled. Various upgrades have been implemented to develop GEOPHIRES v2.0, including: updating the built-in cost correlations, coupling to the external reservoir simulator TOUGH2, enhancing the built-in wellbore simulator, converting the programming language to Python, and making the code open-source. Users are encouraged to build upon the tool, e.g. by implementing their own correlations or coupling to other simulators.

Beckers, Koenraad↗
NASA NTRS1992

Simulator for concurrent processing data flow architectures

A software simulator capability of simulating execution of an algorithm graph on a given system under the Algorithm to Architecture Mapping Model (ATAMM) rules is presented. ATAMM is capable of modeling the execution of large-grained algorithms on distributed data flow architectures. Investigating the behavior and determining the performance of an ATAMM based system requires the aid of software tools. The ATAMM Simulator presented is capable of determining the performance of a system without having to build a hardware prototype. Case studies are performed on four algorithms to demonstrate the capabilities of the ATAMM Simulator. Simulated results are shown to be comparable to the experimental results of the Advanced Development Model System.

Malekpour, Mahyar R.↗
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↗
NASA NTRSDate not supplied

Lunar Dust level sensor & Effects on Surfaces (LDES): Percent Area Coverage of Lunar Simulant on a Surface and Fairy Castle Structures

With NASA’s goal of returning humans to the Moon, it has become increasingly important to understand the impact of lunar regolith on spacecraft systems. The ability to measure the level of dust coverage on a surface is essential. Developing an accurate technique to determine percent area coverage is challenging due to the unique and complex layering of the dust particles on the surface, creating fairy castle structures that form complex towers, bridges, and buttresses using the smallest of dust particles as building blocks. For this study, these structures have been simulated using a unique dust distributor device and imaged with a scanning electron microscope. The results show the dusted surface’s complex topology, thus demonstrating the difficulty with determining the percent area of dust coverage. Implications for dust buildup on spacecraft, even at the microscopic level, can include degraded heat transfer and altered optical properties, making continuation of this research critical to support future human exploration of the Moon.

percent area coverage↗
NASA NTRS1984

Characterization of Surface Reflectance Variation Effects on Remote Sensing

The use of Monte Carlo radiative transfer codes to simulate the effects on remote sensing in visible and infrared wavelengths of variables which affect classification is examined. These variables include detector viewing angle, atmospheric aerosol size distribution, aerosol vertical and horizontal distribution (e.g., finite clouds), the form of the bidirectional ground reflectance function, and horizontal variability of reflectance type and reflectivity (albedo). These simulations are used to characterize the sensitivity of observables (intensity and polarization) to variations in the underlying physical parameters both to improve algorithms for the removal of atmospheric effects and to identify techniques which can improve classification accuracy. It was necessary to revise and validate the simulation codes (CTRANS, ARTRAN, and the Mie scattering code) to improve efficiency and accommodate a new operational environment, and to build the basic software tools for acquisition and off-line manipulation of simulation results. Initial calculations compare cases in which increasing amounts of aerosol are shifted into the stratosphere, maintaining a constant optical depth. In the case of moderate aerosol optical depth, the effect on the spread function is to scale it linearly as would be expected from a single scattering model. Varying the viewing angle appears to provide the same qualitative effect as modifying the vertical optical depth (for Lambertian ground reflectance).

Pearce, W. A.↗
NASA NTRS1992

A Center for Extraterrestrial Engineering and Construction (CETEC)

A group of knowledgeable scientists and engineers in New Mexico has recognized the need for such a testing capability and has proposed a project to evelop an extraterrestrial surface simulation facility. A group of universities, national laboratories, and private industrial firms is proposing to establish a Center for Extraterrestrial Engineering and Construction (CETEC) and to develop large extraterrestrial surface simulation facilities in which this needed testing can be realistically performed. The CETEC is envisioned to be both a center of knowledge and data regarding engineering, construction, mining, and material process operations on extraterrestrial bodies and a set of extraterrestrial surface simulation facilities. The primary CETEC facility is proposed to be a large domed building made of steel reinforced concrete with more than one acre of test floor area covered with several feet of simulated lunar soil and dust. Various aspects of the project are presented in viewgraph form.

Leigh, Gerald G.↗
DOE OSTI2021

QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA

Understanding the best known parameters, performance, and systematic behavior of the Quantum Approximate Optimization Algorithm (QAOA) remain open research questions, even as the algorithm gains popularity. We introduce QAOAKit, a Python toolkit for the QAOA built for exploratory research. QAOAKit is a unified repository of preoptimized QAOA parameters and circuit generators for common quantum simulation frameworks. We combine, standardize, and cross-validate previously known parameters for the MaxCut problem, and incorporate this into QAOAKit. We also build conversion tools to use these parameters as inputs in several quantum simulation frameworks that can be used to reproduce, compare, and extend known results from various sources in the literature. We describe QAOAKit and provide examples of how it can be used to reproduce research results and tackle open problems in quantum optimization.

Shaydulin, Ruslan↗
NASA NTRS1990

Space Station Freedom Disturbance Simulation and Management Tool

The design and functions of the Disturbance Simulation and Management Tool, (DMST), a software package developed to evaluate the effects of design changes and disturbance inputs on the Space Station dynamic environment, are described. DMST is a menu-driven interactive package, written in the PRO-MATLAB, which incorporates methods such as FEM model reduction, mode synthesis, and discrete-time numerical simulation. Particular attention is given to the main-menu options, including building a payload and payload-pointing-system (PPS) configuration, selecting disturbance inputs, running PPS and lab-module microgravity-environment (MGE) simulations, calculating limits to meet PPS and MGE requirements, and performing MGE spectral analyses using power-spectral density or shock spectra. Typical numerical results are presented in graphs.

Lisman, Sima S.↗
NASA NTRSDate not supplied

Determination of Percent Area Coverage of Lunar Simulant on a Surface and Observations of Fairy Castle Structures

With NASA’s goal of returning humans to the Moon, it has become increasingly important to understand the impact of lunar regolith on spacecraft systems. The ability to measure the level of dust coverage on a surface is essential. Developing an accurate technique to determine percent area coverage is challenging due to the unique and complex layering of the dust particles on the surface, creating fairy castle structures that form complex towers, bridges, and buttresses using the smallest of dust particles as building blocks. For this study, these structures have been simulated using a unique dust distributor device and imaged with a scanning electron microscope. The results show the dusted surface’s complex topology, thus demonstrating the difficulty with determining the percent area of dust coverage. Implications for dust buildup on spacecraft, even at the microscopic level, can include degraded heat transfer and altered optical properties, making continuation of this research critical to support future human exploration of the Moon.

lunar dust↗
NASA NTRS2024

Determination of Percent Area Coverage of Lunar Simulant on a Surface and Observations of Fairy Castle Structures

With NASA’s goal of returning humans to the Moon, it has become increasingly important to understand the impact of lunar regolith on spacecraft systems. The ability to measure the level of dust coverage on a surface is essential. Developing an accurate technique to determine percent area coverage is challenging due to the unique and complex layering of the dust particles on the surface, creating what is called fairy castle structures that form complex towers, bridges, and buttresses using the smallest of dust particles as building blocks. For this study, these structures have been simulated using a unique dust distributor device and imaged with a scanning electron microscope. The results show the dusted surface’s complex topology, thus demonstrating the difficulty with determining the percent area of dust coverage. Implications for dust buildup on spacecraft can include issues for heat transfer, making continuation of this research critical to support future human exploration of the Moon.

Emma J Quick↗
DOE OSTI2021

Development of a metamodelling framework for building energy models with application to fifth-generation district heating and cooling networks

Fully defined physics-based building energy models can accurately represent building systems; however, generating models based on high-level parameters is time consuming and simulation time of complex models can be slow. This article discusses the development of a Metamodelling Framework to create metamodels from a building energy modelling dataset. The framework generates metamodels using either linear regression, random forests, or support vector regressions. A fifth-generation district heating and cooling system analysis use case was used to motivate the development of the framework. The use case required quick and accurate representations of annual building loads reported hourly. Typical annual building modelling approaches can result in a runtime of 10 min. The metamodels runtime was reduced to less than 10 s to load and run an annual simulation with user-defined covariates. The results of the metamodel performance and an abbreviated topology analysis based on the motivating use case will be presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modeling rarefied gas chemistry with QuiPS, a novel quasi-particle method

The goal of this work is to build up the capability of quasi-particle simulation (QuiPS), a novel flow solver, such that it can adequately model the rarefied portion of an atmospheric reentry trajectory. Direct simulation Monte Carlo (DSMC) is the conventional solver for such conditions, but struggles to resolve transient flows, trace species, and high-level internal energy states due to stochastic noise. Quasi-particle simulation (QuiPS) is a novel Boltzmann solver that describes a system with a discretized, truncated velocity distribution function. The resulting fixed-velocity, variable weight quasi-particles enable smooth variation of macroscopic properties. The distribution function description enables the use of a variance-reduced collision model, greatly minimizing expense near equilibrium. This work presents the addition of a neutral air chemistry model to QuiPS and some demonstrative 0D simulations. The explicit representation of internal distributions in QuiPS reveals some of the flaws in existing physics models. Overall, variance reduction, a key feature of QuiPS, can greatly reduce expense of multi-dimensional calculations, but is only cheaper when the gas composition is near chemical equilibrium.

42 ENGINEERING↗
DOE OSTI2023

Residual estimation for grid modification in wall-modeled large eddy simulation using unstructured high-order methods

Here, the accuracy and computational cost of a large eddy simulation are highly dependent on the computational grid. Building optimal grids manually from a priori knowledge is not feasible in most practical use cases; instead, solution-adaptive strategies can provide a robust and cost-efficient method to generate a grid with the desired accuracy. We adapt the residual estimation algorithm developed by Toosi and Larsson for Discontinuous Galerkin Spectral Elements Methods (DGSEM) to guide the grid-adaptation process. The core of the method is the computation of the estimated modeling residual using the polynomial basis functions used in DGSEM, and the averaging of the estimated residual over each element. The final method is assessed in multiple channel flow test cases and for the transonic flow over an airfoil, in both cases making use of mortar interfaces between elements with hanging nodes. The method is found to be robust and reliable, and to provide solutions on grids with significantly fewer elements at comparable accuracy compared to when using human-generated grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗
DOE OSTI2025

MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8) for advanced open-source tritium transport and fuel cycle modeling

Tritium management is critical for the safety, sustainability, and economics of fusion energy systems, and advanced and reliable modeling tools help accelerate the development of tritium technologies. This paper presents the Tritium Migration Analysis Program, Version 8 (TMAP8), an open-source, MOOSE-based application developed to provide state-of-the-art tritium transport and fuel cycle modeling capabilities. TMAP8 aims to expand the capabilities of previous versions (i.e., TMAP4 and TMAP7) by leveraging modern computational techniques, ensuring high software quality assurance standards (key to building trust), and enabling multispecies, multiscale, and multiphysics simulations for integrated tritium transport modeling in complex geometries. This paper outlines TMAP8’s scope and rigorous development practices, emphasizing its transparency, accessibility, modularity, and reliability. We present the current suite of verification and validation cases based on those from TMAP4, demonstrating TMAP8’s accuracy and reliability against analytical solutions and experimental data. Additionally, the paper showcases TMAP8’s integrated fuel cycle modeling capabilities, highlighting its applicability at various scales and levels. The TMAP8 code and documentation are openly available, promoting collaborative development and widespread adoption within the fusion community. Future work will soon expand TMAP8’s verification and validation suite to include those from TMAP7 and other recent experimental studies for validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗