Convergence Analysis and Experimental Validation of a Fused Numerical/Experimental Active System Optimization Framework

Abstract

This paper presents a convergence analysis and experimental validation of an iterative design optimization framework that fuses numerical simulations with experiments. At every iteration, a G-optimal design generates a set of simulations and experiments that are used to characterize response surfaces. A subset of the experiments termed as the training points are used to fit a combined numerical/experimental response. This numerical response is obtained as a result of numerical model correction via experiments. The quality of fit for this combined response is evaluated using the remaining validation points. Based on the quality of fit, the feasible design space is reduced for a given confidence interval using hypothesis testing. A convergence analysis of the framework quantifies the closeness of the corrected numerical model to the true system as a function of response estimation error. This design optimization framework, along with the convergence result, is validated through an airborne wind energy (AWE) application using a lab-scale water channel setup. The quality of flight is greatly improved by optimizing the center of mass location, pitch angle set point, horizontal and vertical stabilizer areas using an effective experimental infusion as compared to a pure numerically optimized design.