Adjustment of the MDAO Problem Formulation using Sensitivity Analysis to Reduce the Computational Cost within Aircraft Design

Abstract

The increasing complexity of aeronautical systems and the prevailing focus to develop sustainable technological innovations for future aircraft configurations and technologies have emphasized a particular demand on advancing the Multidisciplinary Design Analysis and Optimization (MDAO) technique. MDAO addresses the complex synergy within aircraft design where multiple coupled disciplines and domains need to be efficiently analyzed and optimized. Considerable developments within MDAO have already generated a significant time reduction for setup and solving of an MDAO problem and consequently in lead time and costs of aircraft design. For example, through the introduction of the software KADMOS (Knowledge-and graph-based Agile Design for Multidisciplinary Optimization System), that can automatically formulate MDAO workflows based on a repository of tools. The aims of this thesis are to further enhance the current application of MDAO for aircraft design and to alleviate the significant computational expense of MDAO problems through the consideration of sensitivity data during the MDAO formulation phase. Therefore, two approaches where the generation and evaluation of sensitivity information play a key role, were evaluated within this thesis, with the subsequent goal to shorten the MDAO execution time. Through the development and the following implementation of a sensitivity analysis tool into the sequencing and decomposition algorithms of KADMOS, this thesis demonstrated a new contribution to investigate on how to adjust the MDAO problem formulation. Sequencing is defined as the execution order and decomposition is the distribution of disciplines over various partitions. Furthermore, sensitivity information was used to identify the non-influential design variables respective to the objective during the MDAO problem formulation. Thus, during this thesis two independent methodologies were created in which the first methodology used a global sensitivity method to identify non-influential design variables, while the second methodology used a local sensitivity method to enhance the sequencing and decomposition algorithms of KADMOS. The methodologies were tested on an aircraft design problem and efficiently demonstrated the identification and removal of low sensitivity design variables from the problem formulation, with minor impact on accuracy. Overall, this thesis demonstrated that although incorporating sensitivity information in the MDAO problem formulation phase is advantageous, the computational effort to perform the sensitivity analysis may diminish the benefits.