A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics
Not provided.
Nuclear Science & Technology↗
SEARCH · Engineering Papers
Results for “model data”
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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Search for gravitational waves from Scorpius X-1 with a hidden Markov model in O3 LIGO data
Not Available
Abbott, R.↗
Data-driven and model-agnostic approach to solving combinatorial assignment problems in searches for new physics
We present a novel approach to solving combinatorial assignment problems in particle physics. The correct assignment of decay products to parent particles is achieved in a model-agnostic fashion by introducing a neural network architecture, asswd-, which combines a custom layer based on attention mechanisms and dual autoencoders. We demonstrate how the network, trained purely on background events in an unsupervised setting, is capable of reconstructing correctly hypothetical new particles regardless of their mass, decay multiplicity, and substructure, and produces simultaneously an anomaly score that can be used to efficiently suppress the background. This model allows the extension of the suite of searches for localized excesses to include nonresonant particle pair production where the reconstruction of the two resonant masses is thwarted by combinatorics. Published by the American Physical Society 2024
Astronomy & Astrophysics↗
Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling
Not Available
97 MATHEMATICS AND COMPUTING↗
Tolerance Bound Calculation for Compact Model Calibration Using Functional Data Analysis.
Abstract not provided.
Reza, Shahed↗
Integration of Physics- and Data-Driven Power System Models in Transient Analysis After Major Disturbances
Not provided.
Computer Science↗
Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy
The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.
Mckee, Evan↗
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↗
SBML Level 3: an extensible format for the exchange and reuse of biological models
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluated Nuclear Structure Data File (ENSDF)
Contains recommended nuclear structure and decay data for all the known nuclides, which are obtained following a critical review of all available experimental data, supplemented with systematic trend studies and theoretical models. ENSDF data include (i) nuclear level properties, such as observation source, energy, half-life, decay modes, spin and parity; (ii) gamma ray energies, intensities, multipolarities, mixing ratios and conversion coefficients; (iii) nuclear radiation energy and intensity as well as radiation-specific data for different radiation types, such as such as gammas, alphas, betas and neutrons. Data for about 220 nuclides are fully evaluated each year; additionally, many datasets are partially updated each year. Nearly all the evaluation work is supported by the US Nuclear Data Program. The National Nuclear Data Center at Brookhaven National Laboratory is responsible for coordinating the evaluation effort as well as its web and journal dissemination.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Quillinan, et al 2018 DOE Geothermal Technology Office REE Report for NEWTS Database and Case Studies
Produced water data processed into the NEWTS data format for easy input into aqueous chemistry modeling software, including oil & gas and coal bed methane produced waters, and geothermal waters. Includes information on rare earth elements (REEs) and critical minerals (CMs). Case studies are included to demonstrate solved streams using aqueous chemistry software. An input template is provided for modeling stream data in OLI Studio. Original data from: Quillinan, Scott, Nye, Charles, Engle, Mark, Bartos, Timothy T., Neupane, Ghanashyam, Brant, Jonathan, Bagdonas, Davin, McLing, Travis, McLaughlin, J. Fred, Phillips, Erin, Hallberg, Laura L., Shahabadi, Mahdi, and Johnson, Matthew. Assessing rare earth element concentrations in geothermal and oil and gas produced waters: A potential domestic source of strategic mineral commodities (Final Report). United States: N. p., 2018. Web. doi:10.2172/1509037.
Aqueous Chemistry↗
A physics-informed data-driven low order model for the wind velocity deficit at the wake of isolated buildings
Detailed simulations of the Atmospheric boundary layer (ABL) provides information on wind velocity, direction and turbulence for optimizing the design of wind turbines and wind farms and their operations. Distributed wind turbines have been considered for use in urban or suburban areas for covering energy needs using a sustainable energy solution. Simulations of high Reynolds number turbulent ABL flows past obstacles can be used for the prediction of the velocity deficit and turbulence characteristics in the leeward side of building-like obstacles, allowing for the estimation of power losses and fatigue loads in wind turbines. However, fully three-dimensional simulations of high Reynolds number ABL flows using Large Eddy Simulation (LES) or Reynolds Average Navier-Stokes Simulations (RANS) continue to require large computational/ execution times. As a result, these 3D simulations may not be ideal tools for the rapid assessment of wind energy potential. In the present study, 3D numerical results from wall resolved RANS simulations were used for the development of a low order model that allows the rapid assessment of wind velocity and turbulence in the leeward side of an isolated building.
17 WIND ENERGY↗
Interpolation data transfer between the models before and after a partial drawdown leach in BH
It has been recognized that as cavern operations become more frequent due to oil sales, field conditions may arise which require a faster turnaround time of analysis to address potential cavern impacts. This letter describes attempts to implement a strategy of transferring an intermediate solution of a Big Hill (BH) geomechanical model from a previous finite element mesh with a specified cavern geometry, to a new mesh with a new cavern geometry created by leaching from an oil sale operation.
02 PETROLEUM↗
Approximating and incorporating model uncertainty in an inversion for seismic source functions: Preliminary results
We present preliminary work on propagating model uncertainty into the estimation of the time domain source time functions of the seismic source. Our method is based on an estimated model covariance function, which we estimate from the data. The model covariance function is then used to construct a suite of surrogate Greens functions which we use in a Monte Carlo type inversion scheme. The result is a probability density function of the six independent source time functions, each of which corresponds to an individual component of the seismic moment tensor. We compare the results of our method with those obtained using a computationally expensive finite difference Monte Carlo method and find that our new method produces results that are deficient in low frequencies. The advantage of our new method, which we term the Karhunen-Loeve Monte Carlo (KLMC) method, is that is several orders of magnitude faster than our current method, which uses a finite difference scheme to produce the suite of forward models.
42 ENGINEERING↗
FY21 Status Report: Probabilistic SCC Model for SNF Dry Storage Canisters
Stress corrosion cracking (SCC) is an important failure degradation mechanism for storage of spent nuclear fuel. Since 2014, Sandia National Laboratories has been developing a probabilistic methodology for predicting SCC. The model is intended to provide qualitative assessment of data needs, model sensitivities, and future model development. In fiscal year 2021, improvement of the SCC model focused on the salt deposition, maximum pit size, and crack growth rate models.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Analysis Program Annual Progress Report (FY2020)
During fiscal year 2020 (FY 2020), the U.S. Department of Energy Vehicle Technologies Office (VTO) funded analysis projects supportive of VTO’s goals to pursue early stage research in vehicle and mobility system technologies to reduce petroleum dependence, increase energy reliability and security, improve transportation affordability, and promote economic growth. VTO analysis projects result in a foundation of data, analytical models, and applied analyses that provide insights into critical transportation energy problems and assist in research investment prioritization and portfolio planning. This document presents a brief overview of VTO analysis efforts and progress for projects funded in FY 2020. Each of the progress reports includes project objectives, approach, and highlights of the technical results that were accomplished during the fiscal year.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗