Multidisciplinary Design Optimization Framework for the Pre Design Stage

  • Authors:

  • Marin Guenov;Paolo Fantini;Libish Balachandran;Jeremy Maginot;Mattia Padulo;Marco Nunez

  • Affiliations:

  • Advanced Engineering Design Group, Dept. of Aerospace Engineering, Cranfield University, Bedfordshire, UK MK43 0AL;Aircraft Research Association Ltd., Bedford, UK MK41 7PF;Advanced Engineering Design Group, Dept. of Aerospace Engineering, Cranfield University, Bedfordshire, UK MK43 0AL;Advanced Engineering Design Group, Dept. of Aerospace Engineering, Cranfield University, Bedfordshire, UK MK43 0AL;Advanced Engineering Design Group, Dept. of Aerospace Engineering, Cranfield University, Bedfordshire, UK MK43 0AL;Advanced Engineering Design Group, Dept. of Aerospace Engineering, Cranfield University, Bedfordshire, UK MK43 0AL

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2010

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Abstract

Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows: The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously. The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front. The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step. The work demonstrated for the first time that such capabilities can be used in a coordinated way to enhance the efficiency of the computational process and the effectiveness of the decision making at preliminary design stage.