ABSTRACT Driven by the energy crisis and carbon neutrality goals, high‐efficiency heat exchange equipment has become a core demand in the industrial sector. The spiral grooved double‐pipe heat exchanger exhibits significant heat transfer enhancement advantages, yet it requires balancing heat transfer efficiency and pressure loss. This study uses water and supercritical carbon dioxide (sCO 2 ) as working fluids, focusing on four key parameters: inner/outer tube Reynolds numbers ( Re i , Re o ), spiral groove pitch ( p ), and diameter ratio ( i ). A coupled CFD‐AI optimization method is adopted to maximize the performance evaluation criterion (PEC) within an extended parameter range. The k ‐ ω SST model and finite element method (FEM) were employed to solve the fluid–solid heat transfer control equations. THe 42 operating conditions were designed via Response Surface Methodology (RSM) to obtain a dataset, and a high‐precision prediction model was established based on artificial neural network (ANN) (training set R 2 ≈1.0). Genetic algorithm (GA) was integrated to achieve PEC maximization. The optimization results show that within the specified parameter range, the maximum PEC reached 1.416, which was 37.86% higher than the initial optimal value. After expanding the parameter space, the maximum PEC reached 1.746, which was 23.31% higher than the initial optimal value and even more than 50% higher compared to the baseline smooth tube structure. The CFD verification error was 6.6%, confirming the reliability of this method. This study clarifies the heat transfer enhancement effect of the spiral grooved double‐pipe heat exchanger in sCO 2 high‐temperature heat pumps and the influence law of key parameters. The significance of this work lies in its pioneering application of a deeply coupled CFD‐AI optimization framework to spiral grooved tubes in transcritical CO 2 systems, which provides accurate references for engineering design and opens up a new path for the optimization of supercritical fluid heat transfer equipment.
Yang et al. (Fri,) studied this question.