Dataset for surrogate-assisted multi-objective optimization of fin geometries in hydrate-based thermochemical energy storage

Published: 25 December 2025| Version 1 | DOI: 10.17632/69k294mhkh.1
Contributors:
Yiling Li, Xilei Sun, Benwen Li

Description

This dataset contains 320 simulation cases generated using COMSOL Multiphysics for training and validation of a machine-learning surrogate model. Each case includes geometric parameters of branched-fin structures and the corresponding thermal performance metrics, including total heat storage capacity and thermal power. The dataset is intended to support transparency, reproducibility, and comparative studies of surrogate-assisted optimization in hydrate-based thermochemical energy storage systems.

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Institutions

  • Dalian University of Technology

Categories

Machine Learning, Multi-Objective Optimization, Energy Storage, Surrogate Modeling

Funders

  • National Natural Science Foundation of China
    China
    Grant ID: 51976021
  • National Key Research and Development Program of China
    Grant ID: 2024YFB2408300
  • International Science and Technology Cooperation Plan of Liaoning Province
    Grant ID: 2023JH2/10700001
  • High-Level International Collaboration Project of Dalian University of Technology
    Grant ID: DU-TIO-ZG-202306

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