Optimization of rectangular micro-channel heat exchangers for use in HCPV panels through genetic algorithm

The present work seeks to develop, through a genetic algorithm (GA), a procedure for optimization of the geometry of micro-channel heat exchangers used to dissipate the residual heat from high concentration photovoltaic panels (HCPV) to prevent the cells from overheating. In addition to the proper thermal management of the photovoltaic cell, the possibility of reuse of the dissipated thermal energy in a secondary process (e.g. desalination through membrane distillation) was also included during the optimization process. The methodology combines the open-source computational fluid dynamics software, OpenFOAM, for computational simulation of conjugate flow and heat transfer within the micro-channel heat sink, and the Python programming language, for the development of the procedure that couples the HCPV panel model with that of the heat sink, including the genetic algorithm to solve the optimization problem. In addition to the thermal parameters, the pressure drop inside the micro-heat exchanger is also considered in the optimization process. Although only single-phase flows are handled now, the algorithm can be extended to other flow regimes. As key findings, one can stress that the algorithm devised in-house works well in the optimization of the number vs dimensions of straight microchannels, for single-phase flows. The results also show that the number of channels should be maximized when the maximum electric power generation is required. On the other hand, when heat recovery is also a concurrent objective, the algorithm leads to the opposite solution, i.e. , the number of channels should be minimized, so the efficiency of the secondary process can be attended.