Hydraulic imbalance and uneven cooling and heating in the building heating systems often result from the difficulty in precisely controlling the flow through control valves. Traditional turbulence models, such as the shear stress transport (SST) model, often exhibit significant errors in predicting flow behavior, leading to a reduction in system performance. This study aims to enhance the predictive accuracy of the SST turbulence model through a novel correction method. A DE-Bayesian model, integrating Bayesian and differential evolution algorithms, was applied to recalibrate the SST k−ω model. The process involved Latin hypercube sampling to generate a sample library of turbulence model constants, followed by flow field simulations under various valve openings. Principal component analysis (PCA) was used to identify key constants that influence the valve's flow characteristics, which were then recalibrated using experimental data. The performance of the modified model was evaluated using mean absolute percentage error (MAPE) and root mean square error (RMSE). Results demonstrate that the modified SST model reduced errors by 3.69 %, 3.82 %, 5.8 %, and 3.88 % at valve openings of 20 %, 40 %, 60 %, and 80 %, respectively, with all errors kept under 6 %. Additionally, the modified model outperformed the large eddy simulation (LES) model, especially at higher valve openings, showcasing greater accuracy and engineering potential. This research provides a novel solution for improving turbulence model predictions, with significant implications for enhancing the performance of building heating systems.

中文翻译：

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基于 DE-Bayesian 修正湍流模型的控制阀流量特性数值模拟


建筑供暖系统中的水力不平衡和冷却和加热不均匀通常是由于难以精确控制通过控制阀的流量造成的。传统的湍流模型，如剪切应力传输 （SST） 模型，在预测流动行为时经常表现出重大误差，从而导致系统性能下降。本研究旨在通过一种新的校正方法提高 SST 湍流模型的预测准确性。应用集成贝叶斯和差分进化算法的 DE-Bayesian 模型来重新校准 SST k−ω 模型。该过程涉及拉丁超立方体采样，以生成湍流模型常数的样本库，然后对各种阀门开口下的流场进行仿真。主成分分析 （PCA） 用于确定影响阀门流量特性的关键常数，然后使用实验数据重新校准这些常数。使用平均绝对百分比误差 （MAPE） 和均方根误差 （RMSE） 评估修改模型的性能。结果表明，修改后的 SST 模型在阀门开度为 20 %、40 %、60 % 和 80 % 时分别减少了 3.69 %、3.82 %、5.8 % 和 3.88 %，所有误差均保持在 6 % 以下。此外，修改后的模型优于大涡模拟 （LES） 模型，尤其是在较高的阀门开口处，展示了更高的准确性和工程潜力。这项研究为改进湍流模型预测提供了一种新颖的解决方案，对提高建筑供暖系统的性能具有重要意义。