Geometric Design of Micro Scale Volumetric Receiver Using System-Level Inputs: An Application of Surrogate-Based Approach

TL;DR

This paper introduces a surrogate-based optimization method for designing micro-scale volumetric solar receivers, significantly improving efficiency and accuracy in the conceptual design process compared to traditional models.

Contribution

It develops and validates a surrogate modeling approach for optimizing the geometry of micro-scale solar receivers using system-level inputs.

Findings

Surrogate models achieved over 98% R² fit.

Optimization with surrogates was faster and more effective.

Surrogate models outperformed the base model in objective value.

Abstract

Concentrating solar thermal power is an emerging renewable technology with accessible storage options to generate electricity when required. Central receiver systems or solar towers have the highest commercial potential in large-scale power plants because of reaching the highest temperature. With the increasing solar chemistry applications and new solar thermal power plants, various receiver designs require in micro or macro-scale, in materials, and temperature limits. The purpose of the article is computing the geometry of the receiver in various conditions and provide information during the conceptual design. This paper proposes a surrogate-based design optimization for a micro-scale volumetric receiver model in the literature. The study includes creating training data using the Latin Hypercube method, training five different surrogate models, surrogate model validation, selection procedure, and surrogate-based design optimization. Selected surrogates have over $98\%$ R\textsuperscript{2} fit and less than $4\%$ root mean square error. In final step, optimization performance compared with the base model. Because of the model complexity, surrogate models reached better objective values in a significantly shorter time.