Enhancing box-wing design efficiency through machine learning based optimization

The optimization of wings typically relies on computationally intensive high-fidelity simulations, which restrict the quick exploration of design spaces. To address this problem, this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach. This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique, specifically designed for early-stage wing configuration. By employing surrogate model trained on small dataset derived from low-fidelity simulations, our method aims to predict outputs within an acceptable range. This strategy significantly mitigates computational costs and expedites the design exploration process. The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL, serving as a benchmark, while the primary focus remains on optimizing the newly designed box wing by Bionica. Applying this method to the Bionica configuration led to a notable 14% improvement in overall aerodynamic efficiency. Furthermore, all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation. This methodology introduces innovative approach that aims to streamline computational processes, potentially reducing the time and resources required compared to traditional optimization methods.