为解决当前汽车气动减阻设计中基于工程师经验的试凑法所带来的盲目性与低效性,以及车身曲面难于参数化的问题,作者将离散伴随法和试验设计(Design of Experiment,DOE)引入汽车减阻优化流程,以阶梯背式MIRA模型为研究对象,通过对外流场...为解决当前汽车气动减阻设计中基于工程师经验的试凑法所带来的盲目性与低效性,以及车身曲面难于参数化的问题,作者将离散伴随法和试验设计(Design of Experiment,DOE)引入汽车减阻优化流程,以阶梯背式MIRA模型为研究对象,通过对外流场进行数值计算,根据外流场特性和车辆表面灵敏度分析,确定优化变量;通过DOE创建样本空间,并采用网格变形技术对各优化变量进行参数化;通过CFD仿真获取各样本点的风阻系数;采用Kriging空间插值法来建立近似模型;使用自适应模拟退火算法对模型优化。优化后模型风阻系数较原模型共计降低16 counts,减阻5.4%,表明该方法在汽车气动减阻优化中有较好的减阻效果和可行性。展开更多
Efficient method to handle the geometric constraints in the optimization of turbomachinery blade profile is required. Without constraints on the blade thickness, optimal designs typically yield thinner blade to reduce...Efficient method to handle the geometric constraints in the optimization of turbomachinery blade profile is required. Without constraints on the blade thickness, optimal designs typically yield thinner blade to reduce the friction loss, however, at the risk of degraded strength and stiffness. This issue is seldom discussed and existing literature always treat the blade thickness constraint in an indirect manner. In this work, two different geometric constraints on the blade thickness are proposed and applied in the adjoint optimization: one is on the maximum blade thickness and the other is on the blade area. Methods to compute sensitivities of both constraints are proposed and they are integrated into an optimization system based on a finite volume code and a solver for the discrete adjoint equation. Adjoint optimization is conducted to minimize the entropy production in a transonic compressor stage. Results for the adjoint optimization without geometry constraint and two comparative cases are detailed. It is indicated that three cases yield similar performance improvement;however, if geometry constraints are properly handled, the optimal designs have almost the same maximum thickness as the original design, compared to a thinner blade profile with 14% reduction of maximum thickness in the case without geometry constraint. The cases considering geometry constraints also consume slightly reduced Central Processing Unit(CPU) cost. Result of this work verifies the effectiveness of the proposed method to treat geometric constraints in adjoint optimization.展开更多
文摘为解决当前汽车气动减阻设计中基于工程师经验的试凑法所带来的盲目性与低效性,以及车身曲面难于参数化的问题,作者将离散伴随法和试验设计(Design of Experiment,DOE)引入汽车减阻优化流程,以阶梯背式MIRA模型为研究对象,通过对外流场进行数值计算,根据外流场特性和车辆表面灵敏度分析,确定优化变量;通过DOE创建样本空间,并采用网格变形技术对各优化变量进行参数化;通过CFD仿真获取各样本点的风阻系数;采用Kriging空间插值法来建立近似模型;使用自适应模拟退火算法对模型优化。优化后模型风阻系数较原模型共计降低16 counts,减阻5.4%,表明该方法在汽车气动减阻优化中有较好的减阻效果和可行性。
基金financially supported by National Natural Science Foundation of China, under project No. 51876098 and No. 51506107
文摘Efficient method to handle the geometric constraints in the optimization of turbomachinery blade profile is required. Without constraints on the blade thickness, optimal designs typically yield thinner blade to reduce the friction loss, however, at the risk of degraded strength and stiffness. This issue is seldom discussed and existing literature always treat the blade thickness constraint in an indirect manner. In this work, two different geometric constraints on the blade thickness are proposed and applied in the adjoint optimization: one is on the maximum blade thickness and the other is on the blade area. Methods to compute sensitivities of both constraints are proposed and they are integrated into an optimization system based on a finite volume code and a solver for the discrete adjoint equation. Adjoint optimization is conducted to minimize the entropy production in a transonic compressor stage. Results for the adjoint optimization without geometry constraint and two comparative cases are detailed. It is indicated that three cases yield similar performance improvement;however, if geometry constraints are properly handled, the optimal designs have almost the same maximum thickness as the original design, compared to a thinner blade profile with 14% reduction of maximum thickness in the case without geometry constraint. The cases considering geometry constraints also consume slightly reduced Central Processing Unit(CPU) cost. Result of this work verifies the effectiveness of the proposed method to treat geometric constraints in adjoint optimization.
