Reactor Physics Model Development and Depletion for HFIR 85 MW and 100 MW Studies
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The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.
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Residential buildings account for significant energy consumption, creating opportunities to offer grid services. As electric utilities seek to implement effective system operation strategies, understanding residential energy consumption patterns becomes essential; However, the time intervals of load profiles measured by utilities' smart meters are typically from 15 minutes to 60 minutes. The low-resolution data make it hard to extract appliance-level load information, which is critical for providing grid services. This paper presents a load profile generator designed to produce synthetic load profiles for residential buildings that emphasizes the importance of accurate representations of realistic energy consumption patterns. The generator takes realistic low-resolution residential load measurements and weather data as inputs, producing 1-minute interval profiles that match the characteristics of the original profiles. Further, this generator can be used to populate load profiles in areas where actual measurements are limited to improve the ability of utilities to analyze their distribution systems. By providing more high-resolution residential building load profiles, this tool supports electric utilities to enhance their residential building load control strategies and improve overall grid stability.
Tutorial at the upcoming BIRS workshop https://www.birs.ca/events/2025/5-day-workshops/25w5381/schedule
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Mathematical models for IBM 7094 computer program prediction of neutron induced activation
Shock formation of tektites during meteor impact on earth
Model for statistically isotropic homogeneous turbulence in incompressible fluid, representing turbulence as superposition of individual vortex sheets
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Global models of the neutral constituents are considered relevant to ion density models and improved knowledge of the ion chemistry. Information provided on the pressure gradients that control the wind system and the electric field systems due to balloon, satellite, and incoherent scatter measurements is discussed along with the implication of these results to the development of global ionospheric models. The current state of knowledge of the factors controlling the large day to day variations in the ionosphere and possible approaches for operational models are reviewed.
Auger spectra and work function measurements are used to study the surface reactions between tungsten surface and adsorbed layers of barium, and barium and oxygen. The barium on an impregnated tungsten cathod seems to be an intermediate state, probably a coadsorbed barium-oxygen layer on tungsten. A slightly revised version of the previously suggested (1976) impregnated tungsten cathode model is proposed. This revised model assumes that the cathode surface during life has an adsorbed surface layer of a monolayer or less of both barium and oxygen on the surface. At end of life, steep drop in electron emission and resultant cathode failure occur. Recent NASA life test results on TWT type tubes are reported and explained by the proposed model.
A semi-empirical theory was developed, tying the hydrogen production rate of a comet to its visual light curve. Two unknown constants, which are essentially the efficiencies of these two processes, are derived from the comets for which quantitative data exist. This theory has been applied to P/Halley, using the light curve of Yeomans, which was derived from the original 1910 observations, with aperture corrections applied. Given the hydrogen production rate, it is assumed that N(H20) = 0.5 N(H) and that there is 0.2 N(H20) of other species. From several converging lines of evidence the mass of dust is taken as half the mass of gas. The Sekanina-Miller distribution function of particle size is used. Particle velocities are calculated using Delsemmes version of the Probstein theory. From these, near nucleus dust density, flux, etc. are calculated. Dust envelope sizes are calculated from the old mechanical theory of tails. Extreme models are calculated from the old mechanical theory of tails. Extreme models are also furnished based upon a "sum-of-negative-tolerance" approach.
The use of simulation data bases for the examination of turbulent flows is an effective research tool. Studies of the structure of turbulence have been hampered by the limited number of probes and the impossibility of measuring all desired quantities. Also, flow visualization is confined to the observation of passive markers with limited field of view and contamination caused by time-history effects. Computer flow fields are a new resource for turbulence research, providing all the instantaneous flow variables in three-dimensional space. Simulation data bases also provide much-needed information for phenomenological turbulence modeling. Three dimensional velocity and pressure fields from direct simulations can be used to compute all the terms in the transport equations for the Reynolds stresses and the dissipation rate. However, only a few, geometrically simple flows have been computed by direct numerical simulation, and the inventory of simulation does not fully address the current modeling needs in complex turbulent flows. The availability of three-dimensional flow fields also poses challenges in developing new techniques for their analysis, techniques based on experimental methods, some of which are used here for the analysis of direct-simulation data bases in studies of the mechanics of turbulent flows.
A theory is proposed to explain the generation mechanism of type II solar radio bursts. It is suggested that the shock wave formed at the leading edge of a coronal transient can accelerate electrons. Because of the nature of the acceleration process, the energized electrons can possess a 'hollow-beam' type distribution function. When the electron beam propagates along the ambient magnetic field to lower altitudes and attains larger pitch angles, a synchrotron-maser instability can set in. This instability leads to the amplification of unpolarized or weakly polarized radiation. The present discussion incorporates a model which describes the ambient magnetic field and background plasma by means of MHD simulation. The potential emission regions may be located approximately, according to the time-dependent MHD simulation. Since the average local plasma frequency in the source region can be evaluated from the MHD model, the frequent drift associated with the radiation may be estimated. The result seems to be in good agreement with that derived from observations.
Sea ice remote sensing and estimation of concentrations of each of several ice types from passive microwave satellite data is described. The approach is based on the Kalman filter; it incorporates surface temperature, ice advection, and ice deformation data derived from drifting buoys and uses the whole temporal microwave record to make a smoothed estimate of ice concentration. The method allows resolution of previously ambiguous surface types. An example using time histories of two SMMR measurements to resolve the fractional areas of four surface types: open water, first-year, second-year and older multiyear ice is shown.