In engineering design, uncertainties related to geometries, material properties, manufacturing processes and operating conditions are inevitable factors which should be accurately quantified and included while designing and optimizing a realistic system for a required level of reliability and efficiency. In this paper, a reliability based multidisciplinary optimization framework is constructed by coupling high-fidelity commercial solvers for aeroelastic analysis and an in-house code developed for reliability analysis. In this computational framework, finite volume based flow solver Fluent is used to solve inviscid 3D Euler equations and Catia is used as a parametric 3D solid modeler.
Abaqus, a structural finite element method solver, is used to compute the structural response of the aeroelastic system. Mpcci, mesh based parallel code coupling interface, is used to exchange the pressure and displacement information between Fluent and Abaqus to perform a loosely coupled aeroelastic analysis.
modeFRONTIER is employed as a multi-objective and multidisciplinary optimization driver to control the optimization workflow. The optimization criteria include both deterministic and probabilistic constraints with both structural and aerodynamic uncertainties such as in allowable stress, Mach number and angle of attack. To optimize the probability of failure for the probabilistic constraints, a first order reliability analysis method, Hassofer-Lind iteration method is implemented in Matlab to compute MPP (Most Probable failure Point) solution.
The integrated framework is validated with academic and structural problems and then extended to more realistic wing configurations with aeroelastic criteria. The presented reliability based multidisciplinary optimization process is proven to be fully-automatic, modular and practical which could find potential applications in industrial problems.