The inverse optimization of exhaust hood by using intelligent algorithms and CFD simulation
The inverse optimization of exhaust hood by using intelligent algorithms and CFD simulation
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DOI：
10.1016/j.powtec.2017.04.019
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发表时间：
2017-06
影响因子：
5.2
通讯作者：
Weixue Cao;You Xueyi
中科院分区：
文献类型：
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作者：
Weixue Cao;You Xueyi
提出了一种人工神经网络（ANN）、遗传算法（GA）和计算流体力学（CFD）耦合的方法来逆向优化排气罩的几何结构和运行参数。排烟罩优化的最佳目标是限制出口排放浓度和烟丝在排烟罩壁上的沉积最少。设计变量是空气流量、阀门开启值和测试点的压力。选择基于 3-D CFD 模型的 12 个不同的正交案例来训练和验证 ANN，以获得最佳目标和设计变量之间的关系。遗传算法（GA）使用CFD案例和ANN案例来寻找满足设计目标的最优设计变量。采用CFD和实验方法验证了优化后排气罩的性能。结果表明，通过优化排烟罩内的压力分布，出口排放和烟丝壁沉积明显减少。得出了最佳的排烟罩结构和运行工况。
A method that couples artificial neural network (ANN), genetic algorithm (GA) and computational fluid mechanics (CFD) was proposed to inversely optimize the geometric configuration of exhaust hood and operation parameters. The optimal objectives of exhaust hood optimization are the limitation concentration of emission at the exit and the minimum deposition on the exhaust hood walls of cut tobacco. The design variables are air flow rate, valve open values and the pressures at the test points. Twelve different orthogonal cases based on the 3-D CFD model were chosen to train and validate the ANN in order to obtain the relationship between the optimal objectives and the design variables. The CFD cases and those of ANN were used by the genetic algorithm (GA) to find the optimal design variables satisfying the design objectives. Both CFD and experimental methods were used to verify the performance of the optimized exhaust hood. The results showed that the emission at the exit and the wall deposition of cut tobacco was significantly reduced by optimizing the pressure distribution in the exhaust hood. The optimal exhaust hood structure and operation conditions were obtained.