SST k-ω湍流模型参数优化及其在离心风机性能模拟中的应用

Optimization of SST k-ω Turbulence Model Parameters and Its Application in Performance Simulation of Centrifugal Fans

摘要: 为了提高离心通风机在仿真模拟中的精准度，本研究构建了一种适用于风机流场的湍流模型参数组。以G4-73型8号离心式通风机为研究对象建模绘制网格，首先使用正交分析评估了SST k-ω湍流模型中六个核心参数对风机全压升的敏感性；其次通过拉丁超立方采样构建采样点，使用CFX软件计算各样本点的全压升值；再构建Kriging回归的代理模型，并将代理模型和智能算法相结合，以全压升值最大为目标进行函数迭代寻找最优解。在最优参数组下，风机全压升与理论值的相对误差仅为0.56%，验证了仿真结果精度的显著提升；最后对比分析得出优化湍流模型参数不仅有效改善了小流量工况下的流场预测精度，同时在其他工况点下也显著降低了仿真误差，验证了该方法对风机全流量工况的适用性与可靠性。

Abstract: To enhance the accuracy of the simulation of centrifugal fans, a set of turbulence model parameters suitable for the flow field of the fan was constructed. Taking the G4-73 No. 8 centrifugal fan as the research object, the model was established, and the grid was drawn. Firstly, the sensitivity of six core parameters in the SST k-ω turbulence model to the total pressure rise of the fan was evaluated by orthogonal analysis. Secondly, the sampling points were constructed by Latin hypercube sampling, and the total pressure rise values of each sample point were calculated using CFX software. Then, a Kriging regression surrogate model was constructed, and combined with an intelligent algorithm, the function was iterated to find the optimal solution with the maximum total pressure rise as the objective. Under the optimal parameter set, the relative error between the total pressure rise of the fan and the theoretical value was only 0.56%, verifying the significant improvement in the simulation result accuracy. Finally, through comparative analysis, it was concluded that optimizing the turbulence model parameters not only effectively improved the prediction accuracy of the flow field under low flow conditions, but also significantly reduced the simulation error at other operating points, verifying the applicability and reliability of this method for the full flow conditions of the fan.