The collaboration pursuing method is a sampling-based multidisciplinary design optimization method, which does not rely on sensitivity analysis. It was found that the collaboration pursuing method is constrained by the effectiveness of sampling in a design space when solving larger multidisciplinary design optimization problems. Three new modules, that is, discrete sampling, new initialization process, and active design variable control, are developed in this work to extend the collaboration pursuing method's capability in dealing with larger multidisciplinary design optimization problems. Using the collaboration pursuing method wi,th the new modules, called extended collaboration pursuing method, a conceptual aircraft design problem involving structures, aerodynamics, and propulsion is successfully solved. The extended collaboration pursuing method is a promising new multidisciplinary design optimization method to solve larger multidisciplinary design optimization problems with better accuracy and comparable efficiency, when compared with other multidisciplinary design optimization methods.

Multidisciplinary design optimization problems are dominated by couplings among subsystems formulated from different disciplines. Effective and efficient collaboration between subsystems is always desirable when solving multidisciplinary design optimization problems. This work proposes a new sampling-based methodology, named the collaboration pursuing method, for multidisciplinary design optimization problems. In the collaboration pursuing method, a new collaboration model, reflecting both physical and mathematical characteristics of couplings in multidisciplinary design optimization problems, is formulated to guide the search of feasible design solutions. The interdisciplinary consistency among coupled state parameters in multidisciplinary design optimization problems is reflected and maintained by the collaboration model. An adaptive sampling strategy is also developed to speed up the search of local optimal solutions. The new method is implemented using MATLAB (R) 6.0 and successfully applied to four test problems, including an engineering design application.

One major challenge in multidisciplinary design optimization (MDO) is the presence of couplings among state parameters, which demands an iterative and often expensive system analysis (SA) process for each function evaluation in optimization. This paper offers a new perspective and proposes a corresponding method for solving MDO problems. The proposed method, named the boundary search and simplex decomposition method (BSSDM), geometrically captures the relation among coupled state parameters with a feasible state parameter region. Given the feasible state parameter region, the SA can be avoided during the optimization of the system objective function. To identify the feasible state parameter region, a search strategy is developed to find boundary points of the region. In the boundary search process, a collaboration model (CM) is applied to maintain the feasibility of samples with respect to the SA. In search of the system optimum in the feasible region, a robust simplex decomposition algorithm is developed for convex and star-like feasible state parameter regions. The BSSDM is tested with two numerical cases, one of which is an MDO problem constrained by a convex state parameter region, and the other is a SA problem with a star-like state parameter region. All results are then validated, and the results show the promising capability of the proposed BSSDM.

This article develops an advancement of the Collaboration Pursuing Method (CPM). Three new modules, i.e., discrete sampling, new initialization process, and active design variable control, were developed and applied to the framework of the CPM. A conceptual aircraft design problem involving structures, aerodynamics, and propulsion is successfully solved with the CPM. The CPM is a promising new MDO method to deal with relatively large-scale MDO problems, based on comparisons between the CPM and other MDO methods, for solving the conceptual aircraft design problem.

Multidisciplinary Design Optimization (MDO) problems are dominated by couplings among subsystems formulated from different disciplines. Effective and efficient collaboration between subsystems is always desirable when solving MDO problems. This work proposes a new sampling-based methodology, named the Collaboration Pursuing Method (CPM), for MDO problems. In the CPM, a new collaboration model, reflecting both physical and mathematical characteristics of couplings in MDO problems, is formulated to guide the search of feasible design solutions. The interdisciplinary consistency among coupled state parameters in MDO problems is reflected and maintained by the collaboration model. An adaptive sampling strategy is also developed to speed up the search of local optimal solutions. The new method is implemented using MATLAB ® 6.0 and successfully applied to four test problems including an engineering design application.

In this paper, a global optimization technique based on the Adaptive Response Surface Method (ARSM) is integrated with a Control Volume Finite-Element Method (CVFEM) for thermofluid optimization. The objective of the optimization is to improve the thermal effectiveness of an aircraft de-icing strategy by re-designing the cooling-bay surface shape. The design optimization combines the modeling of heat conduction and potential fluid flow to investigate the complex thermofluid phenomena at the engine cooling bay. Based on the comparison between the ARSM predicted results and the plotted objective function, it is observed that the integrated technique provides an effective method for thermofluid optimization. The current method of integrating heat conduction, fluid flow, and optimization techniques shows a promising potential for subsequent extensions to more complex Multidisciplinary Design Optimization problems. In addition, a novel pre-optimization technique is developed, and optimization results are presented and validated for the helicopter engine cooling-bay problem.