MULTI-SCALE THERMO-FLUID OPTIMIZATION OF HIGH-EFFICIENCY ENERGY CONVERSION SYSTEMS USING COUPLED COMPUTATIONAL AND EXPERIMENTAL FRAMEWORK

Abstract

High-efficiency energy conversion systems operating under dynamic and off-design conditions demand integrated multi-scale thermo-fluid optimization that seamlessly bridges molecular interfacial science, pore-scale transport, continuum-level conjugate heat transfer, and full-system performance. This review presents a comprehensive coupled computational-experimental framework for the design and optimization of next-generation technologies, including low-temperature thermal energy conversion systems (LT-TECS), proton exchange membrane and solid oxide fuel cells (PEMFC/SOFC), supercritical CO₂ power cycles, and nuclear microreactors. Advanced methodologies such as topology optimization with improved Darcy-penalty formulations, physics-informed reduced-order models (ROMs), machine learning surrogates, and data assimilation techniques are shown to resolve nonlinear multi-physics interactions while significantly reducing computational cost. Experimental validation relies on high-resolution laser-based diagnostics (micro-PIV/LIF, thermographic PIV, hybrid IR/seed-gas techniques) and uncertainty-quantified measurements. The framework achieves notable performance gains: up to 15% reduction in peak temperatures and 40% lower thermal resistance in microelectronic cooling, >2% improvement in electrical output for LT-TECS, and >70% electrical efficiency potential in hybrid SOFC systems. The synthesis of high-fidelity modeling, intelligent digital twins, and real-time experimental feedback enables autonomous control, adaptive operation, and rapid design iteration, providing a transformative pathway toward resilient, decarbonized, and high-density energy conversion in the era of variable renewables and industrial waste-heat recovery.