Abstract
Variable-fidelity optimization (VFO) has emerged as an attractive method of performing, both, high-speed and high-fidelity optimization. VFO uses computationally inexpensive low-fidelity models, complemented by a surrogate to account for the difference between the high-and low-fidelity models, to obtain the optimum of the function efficiently and accurately. To be effective, however, it is of prime importance that the low fidelity model be selected prudently. This paper outlines the requirements for selecting the low fidelity model and shows pitfalls in case the wrong model is chosen. It then presents an efficient VFO framework and demonstrates it by performing transonic airfoil drag optimization at constant lift, subject to thickness constraints, using several low fidelity solvers. The method is found to be efficient and capable of finding the optimum that closely agrees with the results of high-fidelity optimization alone.
Highlights
Given the high cost of CFD and optimization, a prominent area of research today is to find ways to reduce the computational time while retaining the high-fidelity of the analysis
Variable-fidelity optimization (VFO) uses computationally inexpensive low-fidelity models, complemented by a surrogate to account for the difference between the high- and low-fidelity models, to obtain the optimum of the function efficiently and accurately
In the area of aerodynamic optimization, the variable fidelity method has quickly grown in popularity [1,2,3,4,5,6,7]
Summary
Given the high cost of CFD and optimization, a prominent area of research today is to find ways to reduce the computational time while retaining the high-fidelity of the analysis. Variable-fidelity optimization has emerged as an attractive method of performing, both, high-speed and highfidelity optimization [1,2,3,4,5,6,7,8,9] These algorithms attempt to leverage information from computationally inexpensive lowfidelity models to reduce the time required to converge to the optimum of the high-fidelity function. This is usually accomplished by building a computationally inexpensive surrogate for the high-fidelity model. In variable-fidelity algorithms, the surrogate for the high-fidelity model usually consists of the low-fidelity model plus a correction term that models the difference between the high- and lowfidelity models, calibrated at selected sample points in the design space [7]. The VF optimization results are compared to the results of direct optimization—where the HF solver, alone, was coupled to the optimizer to find the optimum result
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
