Model selection and performance evaluation of solar chimney systems using CFD and multivariate analysis with Chernoff faces

Abstract

This paper presents an integrated study combining computational fluid dynamics (CFD) and multivariate statistical analysis for solar chimney optimization. The Manzanares plant was adopted as a reference model and simulated in ANSYS Fluent to generate a dataset for parametric evaluation. Eleven regression models were examined to describe air velocity as a function of the scale parameter, and their formulations were validated using statistical metrics and graphical multivariate analysis. The methodology introduces Chernoff faces as a visual–statistical tool to encode multiple performance indicators per model, enabling a structured comparison beyond conventional criteria. The powerlaw model proved the most suitable, showing strong goodness-of-fit, physical consistency, and stable behavior across scenarios. Specific equations were derived for different irradiance levels, and system efficiency and power output were estimated and compared with previous studies. The results confirm that geometric down-scaling significantly reduces performance. The proposed visual–statistical framework provides a robust and interpretable approach for model selection and performance assessment in solar energy systems, offering a novel and reproducible multivariate strategy for engineering design and optimization.

Abstract by the authors, reproduced under the CC BY-NC 4.0 licence. Original publication via the link below.

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