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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs
Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...
Beckers, K. et al National Renewable Energy Laboratory
Feb 18, 2021
1 Resources
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1 Resources
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TriFrame Mount Structural Performance Period A
Long-Term TriFrame Monitoring System (LTTFMS) data collected during Period A (13 days 10/22/20 11/4/20). The central component of the Verdant Power Free Flow System is a three-bladed horizontal-axis turbine. The turbine is equipped with a composite rotor with three fixed-pitch bl...
Colby, J. et al Verdant Power Inc.
Nov 12, 2021
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Seismic Survey 2016 Metadata at San Emidio, Nevada
1301 Vertical Component seismic instruments were deployed at San Emidio Geothermal field in Nevada in December 2016. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in o...
Lord, N. et al University of Wisconsin
Dec 05, 2016
10 Resources
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10 Resources
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3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA
In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydr...
Siler, D. and Pepin, J. United States Geological Survey
Oct 01, 2021
4 Resources
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4 Resources
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Utah FORGE Project 2439: A Multi-Component Approach to Characterizing In-Situ Stress
Core-based in-situ stress estimation, Triaxial Ultrasonic Velocity (labTUV) data, and Deformation Rate Analysis (DRA) data for Utah FORGE well 16A(78)-32 using triaxial ultrasonic velocity and deformation rate analysis. Report documenting a multi-component approach to characterizi...
Bunger, A. et al Battelle Memorial Institute
Dec 13, 2022
4 Resources
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4 Resources
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EGS Collab Experiment 2: 4100 Foot Level Elastic Property Data
Static and dynamic elastic properties (Young's modulus, Shear Modulus, P-wave Modulus, and Poisson's Ratio) of amphibolites and rhyolites from the TV4100 and TH4100 boreholes at the Sanford Underground Research Laboratory (SURF). Elastic properties include Young's modulus, Poisson...
Jahnke, B. et al Lawrence Berkeley National Laboratory
Dec 18, 2020
4 Resources
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4 Resources
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Additional Data Focused on Phase 1 Geared Toward Computational Fluid Dynamics (CFD) validation
**Overview**
A new validation campaign was developed within the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) to better understand the complex interactions between components of a floating wind system (e.g., columns, pontoons, etc.) in ...
Robertson, A. Wind Energy Technologies Office (WETO)
Apr 08, 2021
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Python Codebase and Jupyter Notebooks Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, a...
Brown, S. and Smith, C. Nevada Bureau of Mines and Geology
Jun 30, 2022
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4 Resources
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Data for Known Geothermal Resource Areas (KGRA) and Identified Hydrothermal Resource Areas (IHRA) in Southern Idaho and Southeastern Oregon
The presented database includes water chemistry data and structural rating values for various geothermal features used for performing principal component (PC) and cluster analyses work to identify promising KGRAs and IHRAs in southern Idaho and southeastern Oregon. A brief note on...
Neupane, G. et al Idaho National Laboratory
Oct 09, 2017
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Field Testing Unvented Roofs with Asphalt Shingles in Cold and Hot-humid Climates
BSC TO5 Task 7.1 Field Testing Unvented Roofs with Asphalt Shingles in Cold Climates Bolingbrook, IL 60440
BSC TO5 Task 7.1 Field Testing Unvented Roofs with Asphalt Shingles in Hot-Humid Climates Friendswood, TX 77546
To understand the long-term moisture performance of unvented ...
Ueno, K. and Lstiburek, J. Building Science Corporation
Apr 27, 2016
3 Resources
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Utah FORGE Friction-Permeability-Seismicity Laboratory Experiments with Non-Linear Acoustics
Laboratory experimental data on saw-cut interface of Westerly Granite and Utah Forge granitoid rocks. Experiments include velocity-stepping and fluid pressure stepping experiments. Mechanical data from 3 ISCO pumps connected to a Temco pressure vessel measure axial, confining and ...
Eijsink, A. and Elsworth, D. Penn State University
Jul 08, 2022
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Advanced TidGen Power System Composites Structural Testing
Seawater aging response was investigated in marine-grade glass/epoxy, glass/vinyl ester, carbon/epoxy and carbon/vinyl ester composites with respect to water uptake, interlaminar shear strength, flexural strength, tensile strength, and tensile fracture surface observations. The re...
Krumpe, A. ORPC Inc.
Jan 06, 2020
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2 Resources
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Radar ARL Wind Profilerwith RASS, Boardman Raw Data
**Overview**
**Winds**
A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities ...
Wilczak, J. and King, C. Wind Energy Technologies Office (WETO)
Oct 28, 2015
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Radar ESRL Wind Profiler with RASS, Condon Raw Data
**Overview**
**Winds**
A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities ...
Wilczak, J. and King, C. Wind Energy Technologies Office (WETO)
Oct 28, 2015
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Radar ESRL Wind Profiler with RASS, Prineville Raw Data
**Overview**
**Winds**
A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities ...
Wilczak, J. and King, C. Wind Energy Technologies Office (WETO)
Oct 28, 2015
1 Resources
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Radar ESRL Wind Profiler with RASS, Troutdale Raw Data
**Overview**
**Winds**
A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities ...
Wilczak, J. and King, C. Wind Energy Technologies Office (WETO)
Dec 01, 2015
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Radar ESRL Wind Profiler with RASS, Wasco Airport Raw Data
**Overview**
**Winds**
A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities ...
Wilczak, J. and King, C. Wind Energy Technologies Office (WETO)
Oct 28, 2015
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Microwave Radiometer CU Radiometrics MWR, Condon Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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Microwave Radiometer CU Radiometrics MWR, Condon Reviewed Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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Microwave Radiometer ESRL Radiometrics MWR, Troutdale Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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1 Resources
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Microwave Radiometer ESRL Radiometrics MWR, Troutdale Reviewed Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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Microwave Radiometer ESRL Radiometrics MWR, Wasco Airport Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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1 Resources
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Microwave Radiometer ESRL Radiometrics MWR, Wasco Airport Reviewed Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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1 Resources
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Microwave Radiometer UND Radiometrics MWR, Rufus Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
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1 Resources
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Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions
This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal str...
Kelley, M. et al Battelle Memorial Institute
Jun 19, 2023
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