Multidisciplinary Design Optimization of a Turbofan Engine Using pyCycle and OpenMDAO

The aero-engine design has been a compound of complex and multidisciplinary processes that include trade-offs and couplings between disciplines. The multidisciplinary design optimization of the turbine engine during the conceptual design phase is of prime importance due to its influence on the designs that follow. These include overall engine performance, as well as other major disciplines, such as engine economy and weight. In the present work, the engine performance code ”pyCycle” is used to create the engine model, and verification against NPSS is performed for the JT9D engine, which is chosen for all the disciplinary and optimization studies. The OpenMDAO framework serves as a foundation for modeling other disciplines (i.e., weight and cost) and the optimization process. Other disciplines consist of cost and weight, in which the objective functions to be evaluated are acquisition cost and weight of the whole engine, and are calculated using the available conceptual performance data at hand. Finally, the multi-objective optimization with the SLSQP method and ϵ -constraint approach is leveraged to form the pareto front of the multidisciplinary multi-objective design optimization process. The results showed the improvement of the performance of the selected engine by relatively minor changes to a few selected design variables.