The supercritical carbon dioxide (sCO2) power generation system holds tremendous potential in nuclear, chemical and renewable energy utilization fields due to its compactness, security and high efficiency. However, the dramatic variation in the physical property of sCO2 complicates the system analysis and optimization. Recent researches usually took simple stack of all governing equations of individual components as the physical model of system. Besides, based on the traditional heat transfer modeling method, some researches apply the segmentation method to take fluid property variation into consideration. These methods exacerbate the multivariate nonlinearity of the system and are not suitable to analyze complex sCO2 thermal systems. Moreover, taking the consideration of the strong nonlinearity of sCO2 system, most researches adopt single parameter analysis to obtain the optimum solution, which may not achieve global optimization. In this contribution, introduction of a new definition of thermal resistance of heat exchanger disassembles the original implicit nonlinear properties of heat transfer processes as the linear relation between inlet temperature difference of fluids and heat flow rate, and the explicit nonlinear expression of thermal resistance. For the nonlinearity caused by the variable properties of sCO2, segmentation is also used in heat exchanger modeling. However differently, the introduction of new defined thermal resistance enables the elimination of most intermediate variables produced by segmentation, which contributes to the connection of all segments in heat exchanger into a heat exchanger network. Furthermore, based on the system layout, the equivalent power flow diagram of the system is built to derive the corresponding governing equations revealing the overall transfer and conversion laws of heat. Combining the flow resistance balance equations of all components and the accompanying power flow processes constraints offers the inherent physical constraints among operating parameters. Benefit from the conciseness of system model, the genetic algorithm can be used for the model optimization. Taking thermal efficiency of the system as the optimization objective, the optimal matching of the operating parameters under variable working conditions is obtained.

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