Aerodynamic Design Applying Automatic Differentiation and Using Robust Variable Fidelity Optimization
In modern aerospace engineering, the physics-based computational design method is becoming more important. However, high-fidelity models require longer computational time, so the advantage of efficiency is partially lost. This problem has been overcome with the development of the approximation management framework (AMF).
In the AMF, objective and constraint functions of a low-fidelity model are scaled at a design point so that the scaled functions, referred to as gsurrogate functions,h match those of a high-fidelity model. Since scaling functions and the low-fidelity model constitutes surrogate functions, evaluating the surrogate functions is faster than evaluating the high-fidelity model. Therefore, in the optimization process of the AMF, the surrogate functions are used to obtain a new design point.
However, the author found that 1) the AMF is very vulnerable when the computational analysis models have numerical noise, and that 2) the AMF terminates optimization prematurely when the optimization problems have constraints.
In order to solve the first problem, automatic differentiation (AD) technique is applied. If derivatives are computed with the generated derivative code, they are analytical, and the computational time is independent of the number of design variables. However, if analysis models implement iterative computations such as computational fluid dynamics (CFD), computing derivatives through the AD requires a massive memory size. The author solved this deficiency by modifying the AD approach and developing a more efficient implementation with CFD.
In order to solve the second problem, the governing equation of the trust region ratio is modified so that it can accept the violation of constraints within some tolerance. By accepting violations of constraints during the optimization process, the AMF can continue optimization without terminating immaturely and eventually find the true optimum design point.
With these modifications, the AMF is referred to as gRobust AMF,h and it is applied to airfoil and wing designs using Euler CFD software. The proposed AD method computes derivatives more accurately and faster than the finite differentiation method, and the Robust AMF successfully optimizes shapes of the airfoil and the wing in a much shorter time than the sequential quadratic programming with only high-fidelity models.
Advisor:Lakshmi, Sankar; Mavris, Dimitri; Alley, Nicholas; Stephen, Ruffin; Sriram, Rallabhandi
School:Georgia Institute of Technology
School Location:USA - Georgia
Source Type:Master's Thesis
Date of Publication:09/03/2008