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Multi-Fidelity Aerodynamic Optimization Using Treed Meta-Models
 

Summary: Multi-Fidelity Aerodynamic Optimization Using Treed
Meta-Models
Andrea Nelson,
Juan J. Alonso,
and Thomas H. Pulliam
Stanford University, Stanford, CA 94305
The multi-fidelity Treed Meta-Model (TMM) framework developed and applied here
creates a tree-based partitioning of an aerodynamic design space and employs indepen-
dent Kriging surfaces in each partition to globally model computationally inexpensive low-
fidelity analysis data. Using the low-fidelity meta-model to select points for evaluation
with a high-fidelity analysis tool, a multi-fidelity global model is created from collocated
high and low fidelity data points. A steady-state genetic algorithm is used to construct an
optimal partitioning scheme based on the number of points in each partition and the com-
bined predictive capabilities of the Kriging surfaces, as measured through cross-validation.
The TMM framework mitigates the effects of the "curse of dimensionality" associated with
surrogate modeling of large datasets, allows for parallelization of design space searches, and
increases model flexibility by using a number of smaller Kriging surfaces. Uniform incre-
mental sampling is incorporated to build the low-fidelity database progressively while the
GA optimizes the partitioning scheme using available data. High-fidelity data points are
chosen using a Least Angle Regression Scheme (LARS) to approximate the sensitivity of

  

Source: Alonso, Juan J. - Department of Aeronautics and Astronautics, Stanford University

 

Collections: Engineering