论文标题
空气动力学优化中伴随矢量的机器学习
Machine learning for adjoint vector in aerodynamic shape optimization
论文作者
论文摘要
伴随方法被广泛用于空气动力学设计中，因为伴随方法仅需要一次解决所有设计变量的梯度。但是，伴随矢量的计算成本大约等于流量计算的计算成本。为了加速伴随矢量的解决方案并提高基于伴随的优化效率，提出了伴随向量建模的机器学习。深度神经网络（DNN）用于构建伴随矢量与局部流量变量之间的映射。 DNN可以有效地预测伴随矢量，并通过减少NACA0012机翼的跨音速阻力来检查其概括。结果表明，伴随矢量的计算成本可忽略不计，提出的基于DNN的伴随方法可以实现与传统伴随方法相同的优化结果。
Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for adjoint method to obtain the gradients of all design variables. However, the calculation cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the adjoint-based optimization efficiency, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction about NACA0012 airfoil. The results indicate that with negligible calculation cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.
