Deep neural network model for predicting thermal-hydraulic performance of a solar air heater with artificial roughness: Sensitivity, generalization capacity, and computational efficiency

Siddharth Suman*, Sarvapriya Singh, Santanu Mitra, Manish Kumar

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
35 Downloads (Pure)

Abstract

A novel coupled computational fluid dynamics-deep neural network approach is proposed to accurately predict the performance of an artificially roughened solar air heater. Computational fluid dynamics data sets are used to develop an optimized neural network model for the prediction of thermal-hydraulic performance factor. The optimized neural network model with softmax transfer function in hidden layers has an architecture of 3‐37‐37‐1 and it predicts the thermal-hydraulic performance factor of the artificially roughened solar air heater with maximum, mean, and minimum error of 2.54 %, 0.245 %, and 0.0009 %, respectively. The concept of Shapley values is adopted to evaluate the global sensitivity analysis of the optimized deep neural network model and it is found that rib height is the most influential design parameter for the artificially roughened solar air heater. The generalization capacity of the neural network model is evaluated by comparing its predictions with experimental data, demonstrating superior predictive performance over traditional computational fluid dynamics simulations. The optimized artificial neural network model achieves a computational time gain of approximately 100 % against computational fluid dynamics.
Original languageEnglish
Pages (from-to)821-833
JournalProcess Safety and Environmental Protection
Volume188
DOIs
Publication statusPublished - Aug 2024
MoE publication typeA1 Journal article-refereed

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Keywords

  • Artificial intelligence
  • Artificial neural network
  • Computational fluid dynamics
  • Nusselt number
  • Shapley values
  • Solar energy

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