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EGS Collab Experiment 1: Microseismic Monitoring

The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first expe...

Schoenball, M. et al Lawrence Berkeley National Laboratory

Jul 29, 2019
45 Resources
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BUTTER Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...

Tripp, C. et al National Renewable Energy Laboratory

May 20, 2022
4 Resources
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ALFA Station Keeping Results for Seabotix vLBV300 Underwater Vehicle near Newport, OR

This data set presents results testing the station keeping abilities of a tethered Seabotix vLBV300 underwater vehicle equipped with an inertial navigation system. These results are from an offshore deployment on April 20, 2016 off the coast of Newport, OR (44.678 degrees N, 124.1...

Hollinger, G. Oregon State University

Apr 20, 2016
8 Resources
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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
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BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...

Tripp, C. et al National Renewable Energy Laboratory

Dec 30, 2022
9 Resources
1 Stars
Curated

G2Aero Database of Airfoils Curated Airfoils

This dataset contains a curated set of 19,164 airfoil shapes from various applications and the data-driven design space of separable shape tensors (PGA space), which can be used as a parameter space for machine-learning applications focused on airfoil shapes. We constructed the a...

Doronina, O. et al National Renewable Energy Lab NREL

Sep 24, 2024
3 Resources
0 Stars
Curated

Modeling the Integration of Marine Energy into Microgrids Wave Resource Assessment

This submission has wave resource assessments which were conducted for six locations based on IEC requirements using the DOE WPTO Hindcast data and MHKiT. The locations are chosen to provide varying wave climates and include PacWave South, OR; Wave Energy Testing Site (WETS), HI; ...

Mankle, H. and Robertson, B. University of Alaska Fairbanks

Jan 19, 2023
9 Resources
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Airfoil Computational Fluid Dynamics 2k shapes, 25 AoA's, 3 Re numbers

This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes w...

Ramos, D. et al National Renewable Energy Laboratory (NREL)

Feb 10, 2023
3 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...

Beckers, K. et al National Renewable Energy Laboratory

Oct 20, 2021
6 Resources
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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
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