Data underlying the publication: Multi-Objective Optimization of Energy Efficiency and Geomechanical Safety in High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Systems Based on Coupled Thermo-Hydro-Mechanical (THM) Analysis

DOI:10.4121/5770abff-df68-4e9e-900c-b3add1e3d210.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/5770abff-df68-4e9e-900c-b3add1e3d210

Datacite citation style

Zhang, Le; Hermans, Thomas (2025): Data underlying the publication: Multi-Objective Optimization of Energy Efficiency and Geomechanical Safety in High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Systems Based on Coupled Thermo-Hydro-Mechanical (THM) Analysis. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/5770abff-df68-4e9e-900c-b3add1e3d210.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This repository contains the complete code and dataset for a multi-objective optimization framework developed for the design of High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems. The research focuses on achieving a balanced design that enhances energy production while minimizing geomechanical risks. Our approach involves building surrogate models using XGBoost to approximate high-fidelity THM simulation outputs and integrating these models with a NSGA-II based optimization algorithm. This framework efficiently explores the trade-offs among competing objectives, enabling the identification of optimal design configurations. The implementation is done in Python and leverages libraries such as pymoo (0.6.1.3), XGBoost (2.1.3), and scikit-learn (1.2.2).

History

  • 2025-05-06 first online, published, posted

Publisher

4TU.ResearchData

Format

npy/py

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Engineering
Ghent University, Department of Geology

DATA

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