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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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Multi-physics modeling of injected nanoparticles effect on remediation of CO 2 leakage through cracks
One potential risk in CO 2 sequestration is the leakage of carbon dioxide, which can result in contamination of underground water, creating potential threats to existing ecosystems. The common leakage pathway is through the pre-existing fractures or discontinuities within cement in the wellbore, incurred by the environmental conditions imposed on the cement. Injecting nanoparticles into pre-existing cracks is one of the most recently proposed ideas for mitigating fracture propagation in cement CO 2 sequestration. To demonstrate the feasibility of this new technology, a numerical approach was taken in this work, as it is challenging to investigate it in a laboratory setting. We proposed a coupled ALE (Arbitrary Lagrangian–Eulerian)–DEM (Discrete Element Method)–peridynamic modeling strategy within the LS-Dyna package to investigate the intertwined interaction among the CO 2 fluid flow, native fluid (e.g., brine), particle clusters, and cracks within the cement. The numerical results demonstrate that injected nanoparticles can effectively reduce the pressure exerted on the crack surface. Accordingly, the potential fracture propagation at the crack tip would be reduced compared to corresponding cases without nanoparticels as pressurized by fluid flow. This result verifies the effectiveness of proposed nanoparticle injection technology. Finally, using this established modeling strategy, the effect of filling particles on the fracture mitigation for different crack geometries (e.g. particle cluster patterns, aspect ratio of crack aperture and length) and CO 2 reservoir pressure are examined. The result shows that injected particles successfully reduce the fracture propagation in these scenarios.
Physics model validation of propane and methane for Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+)
HyRAM+ is a toolkit that includes fast-running models for the unconstrained (i.e., no wall interactions) dispersion and flames for non-premixed fuels. The models were developed for use with hydrogen, but the toolkit was expanded to include propane and methane in a recent release. Here, in this work we validate the dispersion and flame models for these additional fuels, based on reported literature data. The validation efforts spanned a range of release conditions, from subsonic to underexpanded jets and flames for a range of mass flow rates. In general, the dispersion model works well for both propane and methane although the width of the jet/plume is predicted to be wider than observed in some cases. The flame model tends to over-predict the induced buoyancy for low-momentum flames, while the radiative heat flux agrees with the experimental data reasonably well, for both fuels. The models could be improved but give acceptable predictions for propane and methane behavior for the purposes of risk assessment.
Simulation of Hubbard model physics in WSe2/WS2 moiré superlattices
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Bayesian optimization of laser-plasma accelerators assisted by reduced physical models
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Efficient kinetic particle simulations of space charge limited emission in magnetically insulated transmission lines using reduced physics models
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Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models
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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.
Reactor Physics Model Development and Depletion for HFIR 85 MW and 100 MW Studies
Explore the source record for details and available documents.
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.
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.
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.
Precision early universe simulations to constrain nuclear reactions and beyond standard model physics [Slides]
Abstract not provided.
ExaWind: Exascale Predictive Wind Plant Flow Physics Modeling.
Abstract not provided.
Measured and simulated high-energy x-ray photoelectron spectra for validation of radiation transport physics models.
Abstract not provided.