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Results for “Fidelity”
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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Quantifying the Impact of Simulation Frequency Fidelity on Waveform-Based Bayesian Inference for Seismic Monitoring Using Bayesian Experimental Design.
Abstract not provided.
High-Fidelity Particle-in-Cell Simulations of Thermionic Converters.
Abstract not provided.
A Validation Study of Hypersonic Aerodynamics with Multiple Physics-Fidelity Models.
Abstract not provided.
High-Fidelity, Low-Loss Detection of Alkali Atoms in Optical Tweezers.
Abstract not provided.
Enhancing high-fidelity nonlinear solver with reduced order model.
Abstract not provided.
High-Fidelity Processing of Instantaneous Line-of-Sight Returns from Nacelle-Mounted Lidar including Supervised Machine Learning.
Abstract not provided.
Turbulent Inflow Data Assimilation with a 2.8 MW Wind Turbine to Validate Mid-Fidelity Aeroservoelastic Modeling Techniques .
Abstract not provided.
Thermal Modeling of Hypersonic Wind Tunnel Heater Using Low-Fidelity Fluid Model.
Abstract not provided.
Increasing Biological Fidelity of Arctic Greenhouse Gas Emissions from Permafrost to Improve Climate Modeling.
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Increasing Biological Fidelity of Arctic Greenhouse Gas Emissions from Permafrost to Improve Climate Modeling.
Abstract not provided.
High Fidelity Modeling of WELL58-32 DFITS
Between 2017 and 2019, a series of injection tests were performed at pilot vertical well 58-32 at the Utah FORGE site. These injection activities were conducted in three distinct zones, isolated with packers and bridge plugs during the tests. Zone 1 consists of an open-hole section at the toe of the well, whereas Zones 2 and 3 are three-meter long cased and perforated sections. Several injection cycles were conducted in each zone. A detailed description of the geology of the FORGE site, of well 58-32 and of all injection cycles can be found in (Xing et al., 2020). The injection cycles included Diagnostic Fracture Injection Tests (DFITs). DFITs comprise two primary stages: an injection (pump-in) phase, where controlled fluid injection is employed to create a small hydraulic fracture in the surrounding rock, followed by a shut-in phase, where injection ceases, allowing observation of the formation’s pressure response. Utilizing methodologies such as the G-function analysis, DFITs provide valuable insights, including fracture closure pressure and the Instantaneous Shut-in Pressure (ISIP) and can be employed to estimate the minimum horizontal stress.
High Fidelity CFD Simulations Supporting the KP-FHR
Kairos Power, LLC, is developing its version of the Fluoride-cooled High-temperature Reactor, the KP-FHR. The design uses a pebble bed core with fluoride salt as a coolant. The pebbles used in the KP-FHR have a diameter of 4 cm, with a shell fuel region where TRISO particles are embedded. A Pebble bed core design is adopted by several Gen IV reactors, They boast many benefits, such as fuel integrity, highly efficient heat transfer, and passive safety. However, it is challenging to accurately predict temperature and flow inside a pebble bed. Traditional approaches use the porous media model, which regards the pebble bed as a continuous medium, but with different temperature fields representing different levels, such as the fluid temperature, pebble surface temperature, and pebble center temperature. Empirical heat transfer correlations are adopted to calculate the heat transfer coefficient between different phases. However, empirical correlations are usually validated with experimental data, which usually lacks detail inside the pebble bed. The available experimental data is also generally at a high Reynolds number, which falls outside of the conditions of KP-FHR. Explicit computational fluid dynamics (CFD) simulations of randomly packed pebble beds have only become feasible recently. This is thanks to the rapid development of computational power and scalable algorithms. In this work, we used the Spectral Element Method (SEM) CFD code NekRS to simulate the randomly packed pebble bed in a cylindrical container. NekRS, which is the GPU variant of Nek5000, but refactored to utilize the computational power of GPUs using the OCCA library to run on hybrid architecture high performance computing systems. It was initially developed with the libParamunal library, but truncated and tuned for large-scale turbulence simulation. As a result, the SEM reaches higher precision with the same degrees of freedom by using a high-order Lagrange polynomial basis distributed on Gauss-Lobatto-Legendre quadrature inside each element, compared to lower-order methods, such the Finite Volume Method and Finite Element Method. The report is divided into five parts. We start with a general discussion of the pebble bed reactor, along with a specific investigation into the KP-FHR. The second part presents the numerical methodology. In the third part, we study a modular pebble bed with 1741 pebbles in a container of 7 pebble-diameter radius. Beyond LES simulations done by NekRS, we also leveraged the thermal radiation model in OpenFOAM to study heat transfer under no-forced-flow scenarios. Then, in the fourth part we simulated a pebble bed similar to the size of the Hermes Test Reactor. The total number of pebbles is in these simulations is 34,374. The container radius is 14 pebble-diameters. Finally, the report concludes in part five, with a discussion of future work.
Assessing and benchmarking the fidelity of posterior inference methods for astrophysics data analysis
In this era of large and complex astronomical survey data, interpreting, validating, and comparing inference techniques becomes increasingly difficult. This is particularly critical for emerging inference methods like Simulation-Based Inference (SBI), which offer significant speedup potential and posterior modeling flexibility, especially when deep learning is incorporated. We present a study to assess and compare the performance and uncertainty prediction capability of Bayesian inference algorithms – from traditional MCMC sampling of analytic functions to deep learning-enabled SBI. We focus on testing the capacity of hierarchical inference modeling in those scenarios. Before we extend this study to cosmology, we first use astrophysical simulation data to ensure interpretability. We demonstrate a probabilistic programming implementation of hierarchical and non-hierarchical Bayesian inference using simulations derived from the DeepBench software library, a benchmarking tool developed by our group that generates simple and controllable astrophysical objects from first principles. This study will enable astronomers and physicists to harness the inference potential of these methods with confidence.
Developing nonlinear laser-plasma instability models for high-fidelity, multi-physics simulation capability for ICF/HED (IC Report) [Slides]
Modeling nonlinear effects of laser plasma instabilities (LPI) is critically important for ICF/HED experiments. Our project has the following goals: • Develop physics-based nonlinear LPI models using PIC simulations • Couple the nonlinear LPI effects to macroscopic modeling of ICF/HED exp. through laser ray-tracing (LRT).
High-Fidelity Accelerated Design of High-performance Electrochemical Systems
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. To accelerate this process, our team has developed the AutoMat platform. AutoMat can accelerate development of new electrochemical materials along two avenues: first, automated input generation and management of simulations at multiple lengthscales as well as “handoff” of outputs from one lengthscale as inputs to the next; and second, replacement of the most computationally intensive simulation processes with machine-learned surrogate models. The crux of our team’s effort was not “reinventing the wheel” by developing entirely new techniques, but rather building a “superhighway” that allows existing state-of-the-art techniques to run faster and more smoothly than before. AutoMat can utilize tools spanning from first-principles quantum chemistry computations to automated robotic experimentation, and is driven by design space search techniques to reduce the number of iterations through the full simulation loop by rapidly targeting promising regions of design spaces such as single-atom alloy catalysts or blends of liquid electrolytes.
High-fidelity multiscale model development for accelerated fuel qualification for high discharge burnup [Poster]
Reactor vendors seek economic benefits associated with increasing nuclear fuel service lifetime in the existing light-water reactors fleet. FFRD phenomena represent a major safety concern which still needs to be addressed. Formation of high burnup structure (HBS) in conjunction with LOCA can lead to relocating fuel to escape the fuel pin and get dispersed into the primary coolant system.