基于Isight平台的房车空调送风参数优化

Optimization Based on Isight Platform for Air Inlet Parameters in RV Air Conditioner

为了提高房车空调夏季降温效果,对房车空调的送风温度、速度、角度进行优化研究。利用CFD方法对房车内流场进行数值模拟。应用k-e RNG湍流模型,合理的设置热力学边界条件,并引入太阳辐射模型。以Isight软件为优化平台集成Fluent软件搭建房车夏季降温多目标优化设计流程。通过最优拉丁超立方试验设计方法设计样本点,采用径向基函数神经网络建立近似模型,并结合NCGA遗传算法对不同送风参数进行多目标优化。在Pareto最优解集中选取最优折衷解,并通过CFD仿真和房车静态温度实验对比验证。结果表明:建立的近似模型能很好取代内流场仿真进行多目标优化,优化后的结果使房车内部能够获得更好的气流组织形式,温度场、速度场更加合理。

In order to improve cooling effect of RV air conditioning in summer, air supply temperature, speed and angle of RV air conditioning are optimized and studied. We use the method of CFD to run a numerical simulation of flow field in RV. Applying a k-e RNG turbulence model, we reasonably set thermodynamic boundary conditions and introduce a solar radiation model. We take the software Isight as the optimization platform and integrate the software Fluent to build a multi-objective optimization design process for RV summer cooling. Sample points are designed by the method of optimal Latin hypercube test. The radial basis function neural network is used to establish an approximate model, which is combined with the NCGA genetic algorithm to optimize different air supply parameters with multiple objectives. The optimal compromise solution is selected in the Pareto optimal solution set and verified by CFD simulation and RV static temperature experiment. The results show that the approximate model can replace internal flow field simulation for multi-objective optimization. The optimized results enable better airflow organization in the RV, and it makes the temperature field and velocity field more reasonable.