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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Educating HPC Users in the use of advanced computing technology

We examine a multi-modal approach to educating and training users of an advanced computing technology testbed at the Institute for Advanced Computational Science at Stony Brook University. Ookami provides researchers worldwide with access to 176 Fujitsu A64FX compute nodes, this being the same processor technology powering the Japanese Fugaku supercomputer, the fastest computer in the world since June 2020. However, achieving high-performance on this Arm-based, leadership computing technology requires that users be familiar with details of computer architecture, performance analysis and modeling, and high-performance programming models that are commonly omitted in introductory programming courses. Indeed, regardless of their seniority, many of the testbed users are surprisingly unfamiliar with basic concepts such as vectorization, pipelining, latency/bandwidth, roofline models, computing energy/power, threads, and non-uniform memory access. These same concepts also pervade mainstream x86 technologies, so this is of widespread concern. Due to the national/global nature of our user community that is also very diverse in both discipline and experience, the inability to offer formal classes, and our experience that most people do not tend to read online documentation or training materials in sufficient depth, we have consciously employed multiple approaches that heavily emphasize (online) personal interactions and transfer of skills. Online documentation has been organized around best-practices and FAQs; twice-weekly hackathons and office hours via Zoom enable deep dives by both the team and the user community with multiple broad benefits; a Slack channel provides both real time and archived answers and discussions; and workshops, training and webinars target community needs as they arise. Furthermore, the perspective that these tools are being used in an educational setting rather than just for project communication makes them more effective and contributes to community success.

A64FX↗
DOE OSTI2013

Alternative Methods for Tritium Recovery from the Life Lithium Blanket: FY13 Final Report

One of the critical components for the Laser Inertial Fusion Energy (LIFE) power plant is the recovery of tritium from the reactor lithium cooling blanket. In the process proposed by Maroni, high temperature halide salts extract tritium from the lithium using a high-speed centrifugal contactor. Because of high-temperature fluid mixture is very corrosive, this system is not practical. The LIFE team has proposed an alternative process that utilizes a halide-free salt (e.g., LiOH) with a non-centrifuge separation process to extract tritium from the lithium cooling blanket, thus eliminating the need for high-temperature, halide corrosion-resistant materials. To facilitate the design of the contactor and separator, Colorado State University (CSU) was subcontracted by LLNL to conduct the following initial steps in the design process during FY13: • Develop preliminary designs for the separator and Plexiglas models for flow visualization and computational model development, • Select surrogate fluids and flow conditions for the separator, and • Select an appropriate flow visualization fluorescent dye for the surrogate tritium solute.

42 ENGINEERING↗
DOE OSTI2014

Comparison of Fission Product Release Predictions Using PARFUME with Results from the AGR-1 Irradiation Experiment

This report documents comparisons between post-irradiation examination measurements and model predictions of silver (Ag), cesium (Cs), and strontium (Sr) release from selected tristructural isotropic (TRISO) fuel particles and compacts during the first irradiation test of the Advanced Gas Reactor program that occurred from December 2006 to November 2009 in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The modeling was performed using the particle fuel model computer code PARFUME (PARticle FUel ModEl) developed at INL. PARFUME is an advanced gas-cooled reactor fuel performance modeling and analysis code (Miller 2009). It has been developed as an integrated mechanistic code that evaluates the thermal, mechanical, and physico-chemical behavior of fuel particles during irradiation to determine the failure probability of a population of fuel particles given the particle-to-particle statistical variations in physical dimensions and material properties that arise from the fuel fabrication process, accounting for all viable mechanisms that can lead to particle failure. The code also determines the diffusion of fission products from the fuel through the particle coating layers, and through the fuel matrix to the coolant boundary. The subsequent release of fission products is calculated at the compact level (release of fission products from the compact) but it can be assessed at the particle level by adjusting the diffusivity in the fuel matrix to very high values. Furthermore, the diffusivity of each layer can be individually set to a high value (typically 10-6 m2/s) to simulate a failed layer with no capability of fission product retention. In this study, the comparison to PIE focused on fission product release and because of the lack of failure in the irradiation, the probability of particle failure was not calculated. During the AGR-1 irradiation campaign, the fuel kernel produced and released fission products, which migrated through the successive layers of the TRISO-coated particle and potentially through the compact matrix. The release of these fission products was measured in PIE and modeled with PARFUME.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Uranium Hydride Nucleation and Growth Model FY'16 ESC Annual Report

Uranium hydride corrosion is of great interest to the nuclear industry. Uranium reacts with water and/or hydrogen to form uranium hydride which adversely affects material performance. Hydride nucleation is influenced by thermal history, mechanical defects, oxide thickness, and chemical defects. Information has been gathered from past hydride experiments to formulate a uranium hydride model to be used in a Canned Subassembly (CSA) lifetime prediction model. This multi-scale computer modeling effort started in FY’13, and the fourth generation model is now complete. Additional high-resolution experiments will be run to further test the model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Topology Optimization for Nonlinear Transient Applications Using a Minimally Invasive Approach (LDRD Final Report)

The purpose of this project was to devise, implement, and demonstrate a method that can use Sandia's existing analysis codes (e.g., Sierra, Alegra, the CTH hydro code) with minimal modification to generate objective function gradients for optimization-based design in transient, non-linear, coupled-physics applications. The approach uses a Moving Least Squares representation of the geometry to substantially reduce the number of geometric degrees of freedom. A Multiple-Program Multiple-Data computing model is then used to compute objective gradients via finite differencing. Details of the formulation and implementation are provided, and example applications are presented that show effectiveness and scalability of the approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Basic Research in Edge and Scrape-Off Layer Fusion Plasmas (Final Report)

U.S. Department of Energy (DOE) supported work at Lodestar Research Corporation under grant DE-FG02-97ER54392 is summarized in this final report. The research conducted under this grant addressed key scientific issues associated with boundary plasmas in fusion energy experimental devices. Harnessing the energy that is released in fusion reactions would provide a safe and abundant source of power to meet the growing energy needs of the world population. Lodestar investigated the important cm-scale region near the edge of these devices where hot plasma, close to one million degrees, meets material surfaces in what is known as a scrape-off layer (SOL). This critical region is subject to plasma turbulence and plasma flows which must be understood to optimize fusion performance on future machines. Strong interactions of applied RF waves is also observed to occur in this region, and must be understood to mitigate unwanted effects and enhance overall performance. The work employed theoretical models and theory-based computational models for validation. The models were applied to understand experimental results and to carry out basic theoretical investigations relevant to the production of fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1971

Project MAC

On-line programming for the following topics is discussed: (1) artificial intelligence, (2) automata theory, (3) cellular automata, (4) computation structures, (5) computer systems research, (6) dynamic modeling, computer graphics, and computer networks, (7) education, (8) implicit computation, (10) interactive management systems, (11) interactive algebraic manipulation, and (12) programming languages.

Licklider, J. C. R.↗
NASA NTRS1990

Gaussian quadrature exponential sum modeling of near infrared methane laboratory spectra obtained at temperatures from 106 to 297 K

Transmission measurements made on near-infrared laboratory methane spectra have previously been fit using a Malkmus band model. The laboratory spectra were obtained in three groups at temperatures averaging 112, 188, and 295 K; band model fitting was done separately for each temperature group. These band model parameters cannot be used directly in scattering atmosphere model computations, so an exponential sum model is being developed which includes pressure and temperature fitting parameters. The goal is to obtain model parameters by least square fits at 10/cm intervals from 3800 to 9100/cm. These results will be useful in the interpretation of current planetary spectra and also NIMS spectra of Jupiter anticipated from the Galileo mission.

Giver, Lawrence P.↗
NASA NTRS1996

Development of Minimally-Invasive Aortic Pressure and Flow Instrumentation

To better understand the mechanisms underlying the effects of microgravity on the cardiovascular system, cardiovascular models have been developed. These computational models estimate changes in cardiovascular parameters such as total peripheral resistance and systemic arterial compliance, and require high quality aortic pressure and flow measurements as their input. Many of these measurements are obtained in experimental animals and therefore the invasiveness of the instrumentation must be as kept to a minimum. These considerations are the primary motivation behind this work.

Ewert, Dan↗
NASA NTRS2014

Numerical Simulations For the F-16XL Aircraft Configuration

Numerical simulations of flow around the F-16XL are presented as a contribution to the Cranked Arrow Wing Aerodynamic Project International II (CAWAPI-II). The NASA Tetrahedral Unstructured Software System (TetrUSS) is used to perform numerical simulations. This CFD suite, developed and maintained by NASA Langley Research Center, includes an unstructured grid generation program called VGRID, a postprocessor named POSTGRID, and the flow solver USM3D. The CRISP CFD package is utilized to provide error estimates and grid adaption for verification of USM3D results. A subsonic high angle-of-attack case flight condition (FC) 25 is computed and analyzed. Three turbulence models are used in the calculations: the one-equation Spalart-Allmaras (SA), the two-equation shear stress transport (SST) and the kε turbulence models. Computational results, and surface static pressure profiles are presented and compared with flight data. Solution verification is performed using formal grid refinement studies, the solution of Error Transport Equations, and adaptive mesh refinement. The current study shows that the USM3D solver coupled with CRISP CFD can be used in an engineering environment in predicting vortex-flow physics on a complex configuration at flight Reynolds numbers.

Elmiligui, Alaa A.↗
DOE OSTI2013

A study of grout flow pattern analysis

A new disposal unit, designated as Salt Disposal Unit no. 6 (SDU6), is being designed for support of site accelerated closure goals and salt nuclear waste projections identified in the new Liquid Waste System plan. The unit is cylindrical disposal vault of 380 ft diameter and 43 ft in height, and it has about 30 million gallons of capacity. Primary objective was to develop the computational model and to perform the evaluations for the flow patterns of grout material in SDU6 as function of elevation of grout discharge port, and slurry rheology. A Bingham plastic model was basically used to represent the grout flow behavior. A two-phase modeling approach was taken to achieve the objective. This approach assumes that the air-grout interface determines the shape of the accumulation mound. The results of this study were used to develop the design guidelines for the discharge ports of the Saltstone feed materials in the SDU6 facility. The focusing areas of the modeling study are to estimate the domain size of the grout materials radially spread on the facility floor under the baseline modeling conditions, to perform the sensitivity analysis with respect to the baseline design and operating conditions such as elevation of discharge port, discharge pipe diameter, and grout properties, and to determine the changes in grout density as it is related to grout drop height. An axi-symmetric two-phase modeling method was used for computational efficiency. Based on the nominal design and operating conditions, a transient computational approach was taken to compute flow fields mainly driven by pumping inertia and natural gravity. Detailed solution methodology and analysis results are discussed here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗
NASA NTRSDate not supplied

The Additive Manufacturing Moment Measure (AM3) Approach to Predictions of Solid Cooling Rate and Time Above Melt

Qualification of a laser powder bed fusion additive manufacturing (LPBF-AM) process requires knowledge of the multi-scale material physics during the process, per part. As the LPBF-AM build occurs, each moment is influenced by the process history. Knowledge of the build sequence can be used to generate a discretized time-space-condition point field that when coupled with a nearest neighbors’ calculation results in a generalized and fully parallel process model computation. This GPU accelerated approach was developed for part-scale analysis of build files along with in-situ process monitoring sensor data and is termed the “Additive Manufacturing Moment Measure” (AM3). The AM3 approach will be presented and then used to evaluate an AM Bench relevant geometry with synchronized in-situ process data, ex-situ nondestructive evaluation, and optical microscopy observations. These comparisons permit a better understanding of how the process actions can affect the LPBF-AM build quality and the signals generated during in-situ process monitoring.

Additive Manufacturing↗
DOE OSTI2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a highly optimized implementation that scales to {\herogpucount} NVIDIA Volta GPUs. We develop a hierarchical scheme based on a multi-player game-theoretic approach for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. Our implementation scales to {\heronodecount} nodes on the Summit supercomputer with a \weakscalingeff\% scaling efficiency, achieving peak and sustained half-precision rates of {\heropeakperf} PF/s and {\herosustainedperf} PF/s.

physics informed ML, machine learning, generative ↗
DOE OSTI2014

Methods for SAXS-Based Structure Determination of Biomolecular Complexes

Measurements from small-angle X-ray scattering (SAXS) are highly informative to determine the structures of bimolecular complexes in solution. Here, we describe current and recent SAXS-driven developments, with an emphasis on computational modeling. In particular, accurate methods to computing one theoretical scattering profile from a given structure model are discussed, with a key focus on structure factor coarse-graining and hydration contribution. Methods for reconstructing topological structures from an experimental SAXS profile are currently under active development. We report on several modeling tools designed for conformation generation that make use of either atomic-level or coarse-grained representations. Furthermore, since large, flexible biomolecules can adopt multiple well-defined conformations, a traditional single-conformation SAXS analysis is inappropriate, so we also discuss recent methods that utilize the concept of ensemble optimization, weighing in on the SAXS contributions of a heterogeneous mixture of conformations. These tools will ultimately posit the usefulness of SAXS data beyond a simple space-filling approach by providing a reliable structure characterization of biomolecular complexes under physiological conditions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1988

Composite panel development at JPL

Parametric computer studies can be use in a cost effective manner to determine optimized composite mirror panel designs. An InterDisciplinary computer Model (IDM) was created to aid in the development of high precision reflector panels for LDR. The materials properties, thermal responses, structural geometries, and radio/optical precision are synergistically analyzed for specific panel designs. Promising panels designs are fabricated and tested so that comparison with panel test results can be used to verify performance prediction models and accommodate design refinement. The iterative approach of computer design and model refinement with performance testing and materials optimization has shown good results for LDR panels.

Mcelroy, Paul↗