AIAA Journal, Ahead of Print.
This work presents a fully coupled electrothermal motor analysis and optimization framework, where the motor’s electromagnetic performance depends on its temperature distribution, and the temperature distribution simultaneously depends on its electromagnetic performance. Notably, we analytically compute derivatives through this nonlinear, multidisciplinary analysis using coupled adjoints, which enables the exploration of high-dimensional design spaces. We demonstrate the framework on a series of optimization problems that investigate the effect of feedback coupling and serve to highlight the framework’s utility and flexibility. Furthermore, our results indicate that modeling the motor’s electrothermal coupling is critical to accurate performance prediction. We show that designs optimized using a feedforward-coupled model are found to be either infeasible or nonoptimal under the fully coupled feedback analysis; both conditions are unacceptable for aircraft applications, in which performance is critical.
Fast and accurate evaluation of aerodynamic characteristics is essential for aerodynamic design optimization because aircraft programs require many years of design and optimization. Therefore, it is imperative to develop sufficiently fast, robust, and accurate computational tools for industry routine analysis. This paper presents a nonintrusive machine-learning method for building reduced-order models (ROMs) using an autoencoder neural network architecture. An optimization framework was developed to identify the optimal solution by exploring the low-dimensional subspace generated by the trained autoencoder. To demonstrate the convergence, stability, and reliability of the ROM, a subsonic inverse design problem and a transonic drag minimization problem of the airfoil were studied and validated using two different parameterization strategies. The robustness and accuracy demonstrated by the method suggest that it is valuable in parametric studies, such as aerodynamic design and optimization, and requires only a small fraction of the cost of full-order modeling.
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