Non-hierarchical coordination for multidisciplinary design optimization in an industrial environment

This paper presents a numerical investigation of the non-hierarchical formulation of Analytical Target Cascading (ATC) for coordinating multidisciplinary design optimization (MDO) problems. More specifically, we have... [ view full abstract ]

This paper presents a numerical investigation of the non-hierarchical formulation of Analytical Target Cascading (ATC) for coordinating multidisciplinary design optimization (MDO) problems. More specifically, we have implemented this formulation for use within the preliminary MDO (PMDO) framework of Bombardier Aerospace to coordinate the multidisciplinary analyses required in aircraft design. Since the computational cost of the analyses can be high and/or assymetric, it is beneficial to understand the impact of the number of ATC iterations required for coordination and the number of iterations required for disciplinary feasibility on the quality of the obtained MDO solution. At each ``outer" ATC iteration, the disciplinary optimization subproblems are solved for a predefined maximum number of ``inner" loop iterations. The numerical experiments consider different numbers of maximum outer iterations while keeping the total budget of analyses constant. Solution quality is quantified by optimality (objective function value) and consistency (violation of consistency constraints).
The impact of the values of two parameters involved in the alternating directions updating scheme of the augmented Lagrangian penalty functions (aka method of multipliers) on solution quality is also investigated. Numerical results are provided for a variety of analytical MDO test problems and a high-fidelity, computationally-expensive aerostructural wing design problem. The results indicate consistently that a balanced modest number of outer and inner iterations is more effective; moreover, there seems to be a specific combination of parameter value ranges that yield better results.