Adjoint-Response Surface Method in Aerodynamic Shape Optimization of Turbomachinery Blades

TL;DR: In this paper, an adjoint response surface method is developed to provide efficient surrogate model in a parametrized design space for aerodynamic optimization of turbomachinery blades to improve the adiabatic efficiency or equivalently reduce the entropy generation through blade row with a mass flow rate constraint.

TL;DR: A review on aerodynamic optimization of turbomachinery using adjoint method summarizes the significant studies on the subject since the 21st century. It covers various aspects of the adjoint approach in turbomachinery optimization, including shape optimization, multi-objective optimization, and uncertainty quantification.

TL;DR: This study improves the U-Net algorithm for predicting turbine endwall film cooling effectiveness, achieving >99.5% correlation and <0.3% first-order error, and demonstrates its potential as a surrogate model for accelerating design procedures in film cooling.

TL;DR: A generative surrogate model that can be applied on multi-objective optimization problems and the generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost.