Implementation of Deep Neural Networks for performance prediction and optimization of a porous volumetric solar receiver considering mechanical safety

Abstract

The volumetric solar receiver is a major component of concentrating solar thermal systems. Proper selection of design and operating parameters is necessary to obtain the best receiver performance. The current work models and optimizes the hydraulic, thermal, and mechanical performance of a porous volumetric solar receiver using the Deep Neural Network (DNN) technique. The dataset for DNN training is generated by numerical simulations and porosity and pore size of foam structure, inlet fluid velocity and absorber length are selected as the input parameters. The pressure drop, outlet fluid temperature and maximum failure index are selected as the output variables. The trained model demonstrates high accuracy in performance prediction for any combination of input variables in the trained range. Further, the present work proposes the integration of the DNN technique with Genetic Algorithm for the constrained multi-objective optimization of the absorber performance with a novel constraint of maximum failure index to select configurations preventing mechanical failure. Such integration is found to be nearly 524 times faster than the conventional direct approach, demonstrating a significant decrease in the time needed for optimization. Furthermore, the potential of the Technique for Order of Preference by Similarity to Ideal Solution in selecting the desired configuration from the Pareto optimal solutions as per individual requirements is also investigated. The optimization results indicate that the optimum values for porosity, pore size, and absorber length are in the range of 0.9–0.93, 2–4 mm, and 0.01–0.06 m, respectively, when all variables are optimized simultaneously. Furthermore, the optimal input velocity is observed to be close to the lower bound of 0.4 m/s while the corresponding maximum failure index varies from 0.346 to 0.836, indicating mechanically safe configurations. The high accuracy and faster optimization results obtained in the present work demonstrate the ability of the proposed approach in implementing quick and computationally cheap optimization studies for improved and mechanically safe absorber designs.