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Coupling Subsurface and Above-Surface Models for Optimizing the Design of Borefields and District Heating and Cooling Systems
Accurate dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange for seasonal energy storage. Current modeling approaches in building and district energy simulation tools typically consider heat conduct...
Hu, J. et al Lawrence Berkeley National Laboratory
Jan 31, 2022
9 Resources
0 Stars
Curated
9 Resources
0 Stars
Curated
An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Geothermal Behavior with Example Simulations on The Treasure Island and UC Berkeley Campus
This submission contains the source code of the Hydrothermal Finite Element Simulator used for the Treasure Island and UC Berkeley campus geothermal simulation. It contains a report that summarizes the development and validation of this Hydrothermal Finite Element Simulator, with ...
Chen, K. et al Lawrence Berkeley National Laboratory
Aug 01, 2021
5 Resources
0 Stars
Publicly accessible
5 Resources
0 Stars
Publicly accessible
Matlab Scripts and Sample Data Associated with Water Resources Research Article
Scripts and data acquired at the Mirror Lake Research Site, cited by the article submitted to Water Resources Research:
Distributed Acoustic Sensing (DAS) as a Distributed Hydraulic Sensor in Fractured Bedrock
M. W. Becker(1), T. I. Coleman(2), and C. C. Ciervo(1)
1 California St...
Becker, M. and Coleman, T. California State University
Jul 18, 2015
2 Resources
0 Stars
Publicly accessible
2 Resources
0 Stars
Publicly accessible
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
Publicly accessible
3 Resources
0 Stars
Publicly accessible