A machine learning-based study of sCO2 cycle precooler's design and performance with straight and zigzag channels

This research addresses a gap by investigating pumping power reduction through efficient channel geometries, an area underexplored due to complexities in pseudo-critical flow and heat transfer assessment. The study evaluates straight and zigzag channeled printed circuit heat exchangers' potential as sCO2-BC precoolers. A comprehensive approach integrates CFD, machine learning ML, in-house codes for heat exchanger (RPDAC) design and cycle simulation (CDPC) and multi-objective genetic algorithm (MOGA) techniques. CFD assesses thermohydraulic performance and informs an ML model, which guides RPDAC design. MOGA identifies optimal designs minimizing precooler size and maximizing cycle performance. PDAC findings reveal that straight channel precoolers can halve pumping power compared to zigzag channels, albeit at the cost of a threefold increase in precooler length. Optimization results emphasize that peak efficiency is attainable through straight-channel designs, despite their size being up to eight times larger than zigzag-channel counterparts. Concurrently, precooler designs featuring zigzag channels, aligned with compressor inlet temperatures of 305 K to 306 K and channel Reynolds numbers spanning 17,000 to 18,000, strike a balance between cycle efficiency and precooler size. While cycle efficiency in the proposed zigzag designs slightly lags behind that of optimal straight channel precoolers, the recommended configuration presents a threefold reduction in size.