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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, the Chemora framework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.

Blazewicz, Marek↗
DOE OSTI2024

Physics Model For The Agn-201 Dt

The surrogate model is a Gaussian Process Regression model based on sci-kit learn. The model takes the coarse and fine control rod position, along with the temperature of the reactor, and produces a corresponding k-eff value. Given k-eff over time, deviations can be determine and flagged for review at a later date.

Stewart, RyanH. [Idaho National Laboratory (INL), ↗
DOE OSTI2022

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Physical Modeling of Coupled Thermohydraulic Behavior of Compacted MX80 Bentonite during Heating

Abstract This paper describes a tank-scale test setup and experimental methodology used to investigate coupled heat transfer and water flow processes during heating of compacted MX80 bentonite to high temperatures. Specifically, a temperature of 200°C was maintained by a cylindrical heating element at the center of a compacted bentonite layer containing an array of temperature, dielectric, and relative humidity sensors. In addition to providing an evaluation of the spatiotemporal variations in temperature, relative humidity, degree of saturation, and global volume, the coupled thermohydraulic properties of the bentonite were assessed. A wetting front was initially observed to move away from the central heater, followed by a drying process until reaching thermohydraulic equilibrium. The soil–water retention curve (SWRC) of the bentonite followed a wetting scanning path before following the primary drying path exhibiting a shift in water retention with elevated temperature. Results from the tank-scale test can be used for validation of numerical simulations of drying processes in the engineered barrier system of a high-level radioactive waste geological disposal repository and confirm that a temperature-dependent hysteretic SWRC with scanning paths is required to accurately capture the bentonite response.

Engineering↗
DOE OSTI2012

Improving the Monitoring, Verification, and Accounting of CO 2 Sequestered in Geologic Systems with Multicomponent Seismic Technology and Rock Physics Modeling

Research done in this study showed that P-SV seismic data provide better spatial resolution of geologic targets at our Appalachian Basin study area than do P-P data. This finding is important because the latter data (P-P) are the principal seismic data used to evaluate rock systems considered for CO 2 sequestration. The increase in P-SV 1 resolution over P-P resolution was particularly significant, with P-SV 1 wavelengths being approximately 40-percent shorter than P-P wavelengths. CO 2 sequestration projects across the Appalachian Basin should take advantage of the increased resolution provided by converted-shear seismic modes relative to P-wave seismic data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Physical Models for Particle Tracking Simulations in the RF Gap

This document describes the algorithms that are used in the PyORBIT code to track the particles accelerated in the Radio-Frequency cavities. It gives the mathematical description of the algorithms and the assumptions made in each case. The derived formulas have been implemented in the PyORBIT code. The necessary data for each algorithm are described in detail.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

Modeling Physical Processes at Galactic Scales and Above

What should these lectures be? The subject is so broad that many books can be written about it. I decided to prepare these lectures as if I were teaching my own graduate student. Given my research interests, I selected what the student would need to know to be able to discuss science with me and to work on joint research projects. So, the story presented below is both personal and incomplete, but it does cover several subjects that are poorly represented in the existing textbooks (if at all). Some of topics I focus on below are closely connected, others are disjoint, some are just side detours on specific technical questions. There is an overlapping theme, however. Our goal is to follow the cosmic gas from large scales, low densities, (relatively) simple physics to progressively smaller scales, higher densities, closer relation to galaxies, and more complex and uncertain physics. We follow a "yellow brick road" from the gas well beyond any galaxy confines to the actual sites of star formation and stellar feedback. On the way we will stop at some places for a tour and run without looking back through some others. So, the road will be uneven. The organization of the material is as follows: physics of the intergalactic medium, from intergalactic medium to circumgalactic medium, interstellar medium: gas in galaxies, star formation, and stellar feedback.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A Templated Approach for Multi-Physics Modeling of Hybrid Energy Systems in Modelica

A prototypical hybrid energy system (HES) couples a primary thermal power generator (i.e., nuclear power plant) with one or more additional subsystems beyond the traditional balance of plant electricity generation system. The definition and architecture of an HES can be adapted based on the needs and opportunities of a given local market. For example, locations in need of potable water may be best served by coupling a desalination plant to the HES. A location near an oil refinery may have a need for emission-free hydrogen production. The flexible, multidomain capabilities of Modelica are being used to investigate the dynamics (e.g., thermal hydraulics and electrical generation/consumption) of such a hybrid system. This paper examines the simulation infrastructure created to enable the coupling of multiphysics subsystem models for HES studies. A demonstration of a tightly coupled nuclear hybrid energy system implemented using the Modelica based infrastructure is presented for two representative cases. An appendix is also included providing a step-by-step procedure for using the template-based infrastructure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Institutional Computing: Annual Progress Report, Precision early universe simulations to constrain nuclear reactions and beyond standard model physics

The current one-year allocation (w20 qburst) is the first year in a two year allocation of the same name. The project has supported two publications in peer-review journals, another manuscript in preparation for publication, a white paper for the Astro2020 Decadal Survey, and a number of talks (see below). Code development continues on implementing a neutrino quantum kinetic equation solver into BURST, now called QBURST. While implementing coherent and forward-scattering processes into the dynamical neutrino evolution equations, multiple numerical issues have appeared. We have remedied a number of these issues through extensive debugging and testing, which include streamlining interpolation and implementing new ODE integration techniques. Further testing is required to solve the problem at a precision of better than one part in 10 6 . We expect that we will understand the numerical difficulties and implement solutions by end of year 2021.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Fast Reactor Physics Model Verification Studies using ARC and PyARC Workflows

PyARC was recently developed at Argonne National Laboratory to automate many of the tasks required in the ARC (Argonne Reactor Computation) fast reactor simulation workflow, from input file generation, code execution, data transfer between ARC codes, and output postprocessing. PyARC will likely be the path forward to train new users of the ARC codes with the goal of wide adoption by the national laboratories, academia, and industry. In particular, for the ANL-JAEA collaboration under the Civil Nuclear Working Group (CNWG) project agreement NE-01, PyARC will be used to model the Joyo and EBR-II reactors for comparisons with measured data and calculated results from JAEA (Task 3: Fast Reactor Fuel and Core). As an additional avenue for verification and validation, this report investigates the use of PyARC towards a variety of existing ARC-based reactor models, in order to understand its efficacy in replicating the behavior of base ARC codes and better understand any limitations within modeling realistic fast reactor problems. To this end, PyARC was used to model the Joyo MKI, RBEC Benchmark-M, PRISM Mod-B, and EBR-II Run 138B cores, and its results were compared to those from existing ARC-based models. It was found that for hexagonal-based geometries PyARC was able to replicate the behavior of ARC codes to within 10 pcm for small reactor cores, and ~150pcm difference in eigenvalue for larger cores. These discrepancies are attributed primarily to differences in local mesh refinement options between ARC and PyARC, which currently cannot be resolved with PyARC’s latest version (1.6.0). In some of these cases, PyARC was used to model steady-state problems with initial core compositions originating from a prior REBUS depletion calculation. While PyARC was not designed to support such steady-state calculations, workarounds were applied to replicate the behavior of ARC-based calculations as closely as possible. Thus, these results demonstrate the wide extent to which they can be applied to fast reactor problems while still providing immense benefit to the user in terms of automating and standardizing common routines within the fast reactor analysis workflow. This study concluded that PyARC will be suitable for modeling the steady-state conditions of the EBR-II and Joyo fast reactors as part of the CNWG project agreement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗