English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Evolutionary Multi-Objective Aerodynamic Design Optimization Using CFD Simulation Incorporating Deep Neural Network