Power flow simulations of the Saturn accelerator using 1D multi-mode transmission line models.
Abstract not provided.
Sirajuddin, David↗
SEARCH · Engineering Papers
Results for “accelerator modeling”
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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Modeling and characterization of α-U to accelerate metallic fuels development [Poster]
This is a poster summarizing the results of LDRD 20A44-121FP at close-out.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Advanced Surface Physics Model for Power Flow simulations of the Z Accelerator
Explore the source record for details and available documents.
Bennett, Nichelle Lee↗
High tip speed fan inlet noise reduction using treated inlet splitters and accelerating inlets (quiet engine program fan C scale model)
A series of inlet suppression tests were run on a supersonic tip speed fan which employed an acoustically treated cowl wall, treated splitters and elevated average throat Mach numbers in various combinations. Results show appreciable fan noise reductions at high fan speeds; 15-18 PNdB. On the basis of inlet total pressure recovery loss per PNdb of noise reduction, an inlet with no splitters produced the most efficient design. However, greater reduction in noise was achieved with one splitter in the inlet. It was also noted that moderate increases in inlet Mach number increased noise in the acoustically treated inlets and that Mach numbers in excess of 0.65 were required before net noise reduction was realized.
Kazin, S. B.↗
Neutral Orbital Altitude Density Effects on the International Space Station
One of the design requirements of the International Space Station (ISS) is that each year accelerations of one micro-g cannot be exceeded at the ISS internal payload location for 6 periods of not less than 30 consecutive days. Although there are other causes, this study deals only with the accelerations caused by atmospheric drag. The critical ambient neutral density, computed using the Marshall Engineering Thermosphere Model, required to produce accelerations of one micro-g on the ISS, is estimated using an atmospheric drag acceleration equation. Results show that the design requirements may be difficult to meet during periods of extremely high solar activity; the planned reboost and altitude strategies for the ISS may have to be revised to allow for the uncertainty in the prediction of neutral atmospheric density within the 100-day period established for orbital decay before reboost.
Smith, O.E.↗
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Dingreville, Remi Philippe Michel↗
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Dingreville, Remi Philippe Michel↗
Degradation of Leakage Currents and Reliability Prediction for Tantalum Capacitors
Two types of failures in solid tantalum capacitors, catastrophic and parametric, and their mechanisms are described. Analysis of voltage and temperature reliability acceleration factors reported in literature shows a wide spread of results and requires more investigation. In this work, leakage currents in two types of chip tantalum capacitors were monitored during highly accelerated life testing (HALT) at different temperatures and voltages. Distributions of degradation rates were approximated using a general log-linear Weibull model and yielded voltage acceleration constants B = 9.8 +/- 0.5 and 5.5. The activation energies were Ea = 1.65 eV and 1.42 eV. The model allows for conservative estimations of times to failure and was validated by long-term life test data. Parametric degradation and failures are reversible and can be annealed at high temperatures. The process is attributed to migration of charged oxygen vacancies that reduce the barrier height at the MnO2/Ta2O5 interface and increase injection of electrons from the MnO2 cathode. Analysis showed that the activation energy of the vacancies' migration is ~ 1.1 eV.
acceleration factors↗
Data-driven electrolyte design for lithium metal anodes
Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.
25 ENERGY STORAGE↗
Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM
Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.
54 ENVIRONMENTAL SCIENCES↗
COLD-SAT dynamic model
This report discusses the development and underlying mathematics of a rigid-body computer model of a proposed cryogenic on-orbit liquid depot storage, acquisition, and transfer spacecraft (COLD-SAT). This model, referred to in this report as the COLD-SAT dynamic model, consists of both a trajectory model and an attitudinal model. All disturbance forces and torques expected to be significant for the actual COLD-SAT spacecraft are modeled to the required degree of accuracy. Control and experimental thrusters are modeled, as well as fluid slosh. The model also computes microgravity disturbance accelerations at any specified point in the spacecraft. The model was developed by using the Boeing EASY5 dynamic analysis package and will run on Apollo, Cray, and other computing platforms.
Adams, Neil S.↗
Nonresonant particle acceleration in strong turbulence: Comparison to kinetic and MHD simulations
Collisionless, magnetized turbulence offers a promising framework for the generation of nonthermal high-energy particles in various astrophysical sites. Yet, the detailed mechanism that governs particle acceleration has remained subject to debate. By means of 2D and 3D particle-in-cell, as well as 3D (incompressible) magnetohydrodynamic (MHD) simulations, we test here a recent model of nonresonant particle acceleration in strongly magnetized turbulence, which ascribes the energization of particles to their continuous interaction with the random velocity flow of the turbulence, in the spirit of the original Fermi model. To do so, we compare, for a large number of particles that were tracked in the simulations, the predicted and the observed histories of particles momenta. The predicted history is that derived from the model, after extracting from the simulations, at each point along the particle trajectory, the three force terms that control acceleration: the acceleration of the field line velocity projected along the field line direction, its shear projected along the same direction, and its transverse compressive part. Overall, we find a clear correlation between the model predictions and the numerical experiments, indicating that this nonresonant model can successfully account for the bulk of particle energization through Fermi-type processes in strongly magnetized turbulence. Additionally we also observe that the parallel shear contribution tends to dominate the physics of energization in the particle-in-cell simulations, while in the magnetohydrodynamic incompressible simulation, both the parallel shear and the transverse compressive term provide about equal contributions.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Cosmic-ray shock acceleration in oblique MHD shocks
A one-dimensional, steady-state hydrodynamical model of cosmic-ray acceleration at oblique MHD shocks is presented. Upstream of the shock the incoming thermal plasma is subject to the adverse pressure gradient of the accelerated particles, the J x B force, as well as the thermal gas pressure gradient. The efficiency of the acceleration of cosmic-rays at the shock as a function of the upstream magnetic field obliquity and upstream plasma beta is investigated. Astrophysical applications of the results are briefly discussed.
Webb, G. M.↗
[A Predictive Model for the Magnetic Field in the Heliosphere and Acceleration of Suprathermal Particles in the Solar Wind]
The purpose of this grant was to develop a theoretical understanding of the processes by which open magnetic flux undergoes large-scale transport in the solar corona, and to use this understanding to develop a predictive model for the heliospheric magnetic field, the configuration for which is determined by such motions.
Fisk, L. A.↗
A Semi-Empirical Model for Forecasting Relativistic Electrons at Geostationary Orbit
We developed a new prediction model for forecasting relativistic (>2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/Interplanetary Magnetic Field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is about 0.9. The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible. The correlation coefficient between predicted and actual electron fluxes is stable and incredibly high.
Lyatsky, Wladislaw↗
Relativistic Electrons at Geostationary Orbit: Modeling Results
We developed a new prediction model for forecasting relativistic (greater than 2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/interplanetary magnetic field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is stable and incredibly high (about 0.9). The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible.
Khazanov, George V.↗
Prediction Model for Relativistic Electrons at Geostationary Orbit
We developed a new prediction model for forecasting relativistic (greater than 2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/interplanetary magnetic field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is stable and incredibly high (about 0.9). The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible.
Khazanov, George V.↗
Accelerated, scalable and reproducible AI-driven gravitational wave detection
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.
79 ASTRONOMY AND ASTROPHYSICS↗