Inverse design optimization framework via a two-step deep learning
approach: application to a wind turbine airfoil
- URL: http://arxiv.org/abs/2108.08500v1
- Date: Thu, 19 Aug 2021 05:00:08 GMT
- Title: Inverse design optimization framework via a two-step deep learning
approach: application to a wind turbine airfoil
- Authors: Sunwoong Yang, Sanga Lee, Kwanjung Yee
- Abstract summary: inverse design is computationally efficient in aerodynamic design as the desired target performance distribution is specified.
The proposed framework applies active learning and transfer learning techniques to improve accuracy and efficiency.
The results of the optimizations show that this framework is sufficiently accurate, efficient, and flexible to be applied to other inverse design engineering applications.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Though inverse approach is computationally efficient in aerodynamic design as
the desired target performance distribution is specified, it has some
significant limitations that prevent full efficiency from being achieved.
First, the iterative procedure should be repeated whenever the specified target
distribution changes. Target distribution optimization can be performed to
clarify the ambiguity in specifying this distribution, but several additional
problems arise in this process such as loss of the representation capacity due
to parameterization of the distribution, excessive constraints for a realistic
distribution, inaccuracy of quantities of interest due to theoretical/empirical
predictions, and the impossibility of explicitly imposing geometric
constraints. To deal with these issues, a novel inverse design optimization
framework with a two-step deep learning approach is proposed. A variational
autoencoder and multi-layer perceptron are used to generate a realistic target
distribution and predict the quantities of interest and shape parameters from
the generated distribution, respectively. Then, target distribution
optimization is performed as the inverse design optimization. The proposed
framework applies active learning and transfer learning techniques to improve
accuracy and efficiency. Finally, the framework is validated through
aerodynamic shape optimizations of the airfoil of a wind turbine blade, where
inverse design is actively being applied. The results of the optimizations show
that this framework is sufficiently accurate, efficient, and flexible to be
applied to other inverse design engineering applications.
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