A Systematic Review of Integrated Multi-Physics and AI-Based Modeling of Photovoltaic–Thermal (PV/T) Systems Enhanced with Porous Fins Using ANSYS, Python, and MATLAB for Improved Energy Performance

Tamadhur Thamer Hashim Al_Salihi

doi:10.71229/gf59wy32

Authors

Tamadhur Thamer Hashim Al_Salihi Department of Medical Instrumentation Engineering, Al-Yarmouk University College, Diyala, Iraq

DOI:

https://doi.org/10.71229/gf59wy32

Keywords:

PV/T systems, porous fins, MATLAB optimization

Abstract

Photovoltaic-thermal (PV/T) hybrid systems are among the most promising opportunities for high density solar energy conversion, as they can simultaneously extract electrical and thermal energy from a single collector area. Despite a long history of research, coupled thermo-fluid and photovoltaic transport processes underlying PV/T operation remain difficult to model accurately due to highly non-linear, multi-scale interactions between radiation absorption, charge carrier transport, heat transfer in the cooling channels, and deformation. This scoping review collates and critically synthesizes the latest advances in the integration of three computational approaches - finite-volume/finite-element-based multi-physics simulation (ANSYS), data-driven and physics-informed artificial intelligence (Python), and numerical optimization and signal processing (MATLAB) - for PV/T systems incorporating porous fin structures.

Adhering to the PRISMA-2020 framework, 178 research articles from 2010-2024 were reviewed, with 94 primary studies included for comprehensive analysis. Major insights include that porous fin configurations, such as metal-foam and lattice-structured fins, can boost overall PV/T system energy efficiency by 8-22 percentage points relative to traditional channel designs, largely by raising the convective heat transfer coefficient on the absorber plate, while lowering parasitic pump power. ANSYS Fluent-Mechanical simulations show that thermal-mechanical stresses at fin-absorber interfaces can reduce PV/T durability without gradient porosity optimization. Python-based AI surrogates (neural networks, Gaussian processes, XGBoost) predict thermal and electrical outcomes with less than 2% error within six orders of magnitude faster than CFD simulations, allowing real-time design optimization. MATLAB genetic algorithms and particle swarm optimizers reliably converge to Pareto-optimal designs that achieve 8-31% higher exergy efficiency compared to single-objective designs. The review highlights key research opportunities, including the lack of standardized uncertainty-quantification methods, lack of experimental testing of AI surrogates under degradation conditions, and lack of multiyear durability experiments, and outlines a vision for digital-twin PV/T systems integrating all three simulation platforms

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