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Solar thermal collectors are a practical solution for low and medium-temperature heat for buildings and industry, with flat-plate collectors remaining prevalent due to their high simplicity and low cost. However, their performance is tightly coupled to absorber-plate geometry. To address this issue, the present study established a parametric mathematical framework for comparative analysis of geometrically divergent collector configurations. Rigorous computational methodologies had been employed to evaluate critical performance determinants—including effective surface area, inclination dynamics, and curvature-dependent radiative capture efficiency—across multiple archetypal designs (planar, conical, pyramidal, and curved). Subsequent experimental validation provided the model's predictive fidelity through empirical benchmarking. Additionally, further computational intelligence paradigms, notably Artificial Neural Networks (ANN) and Genetic Algorithms (GA), has been synergistically deployed for multi-objective optimization of geometric parameters. Initial parametric assessment achieved conical and pyramidal collectors conducted 8–15.2% superior thermal energy absorption relative to planar configurations, attributable to enhanced surface area utilization. However, optimization convergence revealed that a parabolic-curved collector (governed by the equation y=9.92x 2 ) constituted the global optimum, yielding ≥15% efficiency gains over conical designs. Crucially, curvature angle variation conducted analogous influence profiles in conical and pyramidal geometries, while exhibiting attenuated impact in curved collectors—quantified by a statistically significant 2.25% performance differential at the critical 15° threshold. This geometric sensitivity underscored the necessity for configuration-specific design optimization in maximizing radiative energy capture.
Skheel et al. (Thu,) studied this question.