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GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information...

Vesselinov, V. Los Alamos National Laboratory

Mar 29, 2021
4 Resources
0 Stars
Publicly accessible

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework ...

Buster, G. et al Upflow

Jun 30, 2021
11 Resources
0 Stars
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TEAMER: Dynamometer Testing of a Magnetically Geared Electrical Generator

Ocean Renewable Power Company's (ORCP) RivGen device is a horizontal cross-flow turbine featuring a proprietary power takeoff unit. This system has been proven in real-world conditions in Alaska and is now the longest operating hydrokinetic project in the Americas. ORPC systems ty...

Bassett, C. and Gibbs, P. University of Washington Applied Physics Lab

Aug 14, 2025
5 Resources
0 Stars
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Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...

Siler, D. et al United States Geological Survey

Oct 01, 2021
6 Resources
0 Stars
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Multiple Scalar Mixing

This dataset contains high-fidelity numerical simulation data of three-component passive scalar mixing in statistically isotropic turbulence. This dataset is designed to study the fundamental physics governing industrial combustion systems. The temporal evolution of the mixture is...

Yellapantula, S. et al National Renewable Energy Laboratory (NREL)

Sep 03, 2026
1 Resources
0 Stars
Awaiting curation

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 Di...

Ahmmed, B. Stanford University

Apr 04, 2022
3 Resources
0 Stars
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Wind Speed Dataset with Observation And Copula-based Bias Correction and Multi-Model Fusion at 23 U.S. Locations

This dataset provides two related products of bias-corrected hourly wind speed at 23 U.S. sites spanning coastal, inland, mountainous, and plains regions, designed for energy applications and wind-related research. Physics-based models carry systematic biases relative to observati...

Zhang, W. et al National Laboratory of the Rockies (NLR)

Jun 15, 2026
4 Resources
0 Stars
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TEAMER: Acoustics UW Tidal Turbine

This dataset contains underwater acoustic measurements collected around the University of Washington's pilot-scale cross-flow tidal turbine deployed at the entrance to Sequim Bay, WA through November, 2023 and February, 2024. Measurements are a combination of stationary observatio...

Raghukumar, K. et al Integral Consulting Inc.

Mar 25, 2025
7 Resources
0 Stars
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Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...

Jin, W. et al Idaho National Laboratory

Apr 15, 2022
4 Resources
0 Stars
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