  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.
