A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis

Abstract

The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and reliable. For such problems, minimum computational cost while providing global optimality and extensive design information at an early conceptual stage is desired. However, such a complex problem consisting of various objectives and interacting disciplines is associated with a challenging design space. This provides a large pool of possible designs, requiring an efficient exploration scheme with the ability to provide sufficient feedback early in the design process. This paper demonstrates a generalized optimization framework with rapid design space exploration capabilities in which a Multifidelity approach is directly adjusted to the emerging needs of the design. The methodology is developed to be easily applicable and efficient in computationally expensive multidisciplinary problems. To accelerate such a demanding process, Surrogate Based Optimization methods in the form of both Radial Basis Function and Kriging models are employed. In particular, a modification of the standard Kriging approach to account for Multifidelity data inputs is proposed, aiming to increasing its accuracy without increasing its training cost. The surrogate optimization problem is solved by a Particle Swarm Optimization algorithm and two constraint handling methods are implemented. The surrogate model modifications are visually demonstrated in a 1D and 2D test case, while the Rosenbrock and Sellar functions are used to examine the scalability and adaptability behaviour of the method. Our particular Multiobjective formulation is demonstrated in the common RAE2822 airfoil design problem. In this paper, the framework assessment focuses on our infill sampling approach in terms of design and objective space exploration for a given computational cost.

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

“…A nonwell defined design space runs the risk of including physically and/or geometrically unnecessary search regions, wasting search time (Sóbester and Powell 2013). A large pool of possible designs need an efficient exploration scheme to provide sufficient feedback (Kontogiannis and Savill 2020).…”

Comparative Design Improvement of the Growing Rod for the Scoliosis Treatment Considering the Mechanical Complications

“…The algorithm can find a diverse set of solutions by using a concept called "crowding distance" to measure the diversity of solutions in the population and ensure that a wide range of solutions is explored. As a result, MOPSO can identify the non-dominated solutions in the set and generate the Pareto front [31][32][33].…”

Multi-fidelity Multi-Objective Optimization of a High-Altitude Propeller

“…Then different criteria are used to better resolve the Pareto front approximation and choose the solver fidelity for each iteration [38][39][40]. Similarly, Kontogiannis et al [41] used the Expected Improvement (EI) of each objective to find a non-dominated front of EIs with the help of a population-based multi-objective algorithm. The approach is similar to the one proposed by Jeong S. et al [42] for multi-objective EGO, but it further chooses the solver's fidelity by evaluating the multi-fidelity metamodel error prediction.…”