Prediction of inner pinch for supercritical CO₂ heat exchanger using Artificial Neural Network and evaluation of its impact on cycle design

Supercritical CO₂ (S-CO₂) power cycles have received much attention due to their desired advantages in various applications. Due to the substantial difference in specific heat of S-CO₂ at different pressure, unique characteristics inside heat exchangers, especially in the recuperator, are observed and affect the cycle design. In this research, the problem of inner pinch occurring inside the S-CO₂ recuperator is identified and resolved by suggesting a new framework for the definition of heat exchanger effectiveness using the point of zero inner pinch. The design methodology of S-CO₂ cycles is improved using this definition by developing a module to calculate the zero inner pinch using artificial neural network (ANN) to undergo a learning process. As a result, the computation time required for cycle analysis is reduced by an order of 10³ in the case of simple recuperated Brayton cycle, under the accuracy of 10^-7 error bound. As the readers gain the access to the developed module for calculating the zero inner pinch, the procedure for S-CO₂ cycle optimization will become far more manageable for researchers, and as a result, this result can allow the conceptualization of even further complex layouts in S-CO₂ cycle research.