  An evolutionary multi-objective aerodynamic design optimization method using
the computational fluid dynamics (CFD) simulations incorporating deep neural
network (DNN) to reduce the required computational time is proposed. In this
approach, the DNN infers the flow field from the grid data of a design and the
CFD simulation starts from the inferred flow field to obtain the steady-state
flow field with a smaller number of time integration steps. To show the
effectiveness of the proposed method, a multi-objective aerodynamic airfoil
design optimization is demonstrated. The results indicate that the
computational time for design optimization is suppressed to 57.9% under 96
cores processor conditions.
