Finned-absorber solar air heaters: A PRISMA-based systematic review of thermohydraulic performance, hybrid integration, predictive modeling, and a strategic development roadmap

Solar air heaters are limited by the inherently low convective heat transfer of air, which constrains their efficiency in space heating, industrial drying, and agricultural applications. Finned absorbers are an effective passive remedy, yet the evidence is scattered across fin geometries, hybrid integrations, and operating conditions, leaving no common basis for comparison. This review consolidates that evidence to quantify the thermohydraulic performance of different fin geometries, evaluate the synergistic gains of hybridizing fins with other technologies, assess the accuracy of predictive models, and set out a strategic development roadmap. Following PRISMA principles, the 2015–2026 literature in Scopus, ScienceDirect, and Web of Science was screened, yielding 1176 records (Scopus, 600; ScienceDirect, 353; Web of Science, 223), which after the removal of 551 duplicates left 625 unique records, of which 42 eligible studies were grouped into four thematic clusters for quantitative comparison. The synthesis shows that double-pass transverse fins reach 62.5% thermal efficiency, wavy fins deliver 2.98 times the thermal performance of plain designs, and discrete fins attain 82.5% energy efficiency at a thermohydraulic performance factor of 2.01. Pairing fins with phase change materials extends useful operation by up to four hours after sunset and raises daily efficiency by 10.8–13.6%, while Random Forest machine-learning models predict performance with R² up to 0.98. Beyond these benchmarks, the review explains the fluid-dynamic and heat-transfer mechanisms that render each geometry optimal for its operating regime and converts them into configuration-specific design and material-selection guidance, with aluminium identified as the preferred fin material on conductivity, weight, and cost grounds. On this basis, the review proposes the first comprehensive, evidence-based three-phase roadmap for next-generation systems: empirical correlations (1–3 years), AI-driven hybridization (3–7 years), and adaptive, IoT-enabled fins (7–15 years).