Multidisciplinary design optimization of mechanisms

The optimization of mechanisms is usually done in the field of mechanism itself. As a result the structural safety of the mechanism is neglected. To ensure the safety and improve the dynamic characteristics of mechanisms, a multidisciplinary design optimization procedure is proposed in this paper to synthesize optimum mechanisms. Two disciplines are involved in the multidisciplinary design optimization. They are the mechanism and the structure. The multi-level decomposition approach is chosen to generate optimum mechanisms. The optimized mechanisms not only satisfy mechanism and structural constraints but also have optimum objective function values in the two disciplines. In order to solve general mechanism design problems two widely used commercial softwares MSC/NASTRAN and MSC/ADAMS are integrated in the procedure to do the structural and the mechanism analysis, respectively. When the structural optimization is performed, a compromised approach is introduced to treat multiple configurations of mechanisms during operation. Two mechanism design problems are given to test the proposed method.     

LOcal Uncertainty Processing (LOUP) method for multidisciplinary robust design optimization

In this paper, we develop an easy-to-implement approximate method to take uncertainties into account during a multidisciplinary optimization. Multidisciplinary robust design usually involves setting up a full uncertainty propagation within the system, requiring major modifications in every discipline and on the shared variables. Uncertainty propagation is an expensive process, but robust solutions can be obtained more easily when the disciplines affected by uncertainties have a significant effect on the objectives of the problem. A heuristic method based on local uncertainty processing (LOUP) is presented here, allowing approximate solving of specific robust optimization problems with minor changes in the initial multidisciplinary system. Uncertainty is processed within the disciplines that it impacts directly, without propagation to the other disciplines. A criterion to verify a posteriori the applicability of the method to a given multidisciplinary system is provided. The LOUP method is applied to an aircraft preliminary design industrial test case, in which it allowed to obtain robust designs whose performance is more stable than the one of deterministic solutions, relatively to uncertain parameter variations.     

三维薄膜型FLNG储舱晃荡载荷的数值仿真

针对浮式液化天然气(Floating Liquefied Natural Gas,FLNG)船大型储舱内的液体晃荡问题,分别以薄膜型储舱的1∶20模型及原型为研究对象,采用CFD仿真方法分析百年一遇生存海况下液舱内晃荡载荷的基本规律和危险工况.结果表明:在真实海况下,现有FLNG储舱设计中的晃荡冲击载荷主要来源于船体纵向运动,可能出现的最大冲击载荷约为0.4 MPa,主要发生在纵舱壁与横向隔水舱的拐角位置,危险载液率为20%~30%及90%.     

Multidisciplinary design optimization of a small solid propellant launch vehicle using system sensitivity analysis

Multidisciplinary design optimization approaches have significant effects on aerospace vehicle design methodology. In designing next generation of space launch systems, MDO processes will face new and greater challenges. This study develops a system sensitivity analysis method to optimize multidisciplinary design of a two-stage small solid propellant launch vehicle. Suitable design variables, technological, and functional constraints are considered. Appropriate combinations of disciplines such as propulsion, weight, geometry, and trajectory simulation are used. A generalized sensitivity equation is developed and solved. These results are basis for optimization. Comparison of the developed approach with gradient optimization methods reveals that developed approach requires less computation time.     

Model reduction for multidisciplinary optimization - application to a 2D wing

Multidisciplinary optimization (MDO) is a growing field in engineering, with various applications in aerospace, aeronautics, car industry, etc. However, the presence of multiple disciplines leads to specific issues, which prevent MDO to be fully integrated in industrial design methodology. In practice, the key issues in MDO lie in the management of the interconnections between disciplines, along with the high number of simulations required to find a feasible multidisciplinary (optimal) solution. Therefore, in this paper, a novel approach is proposed, combining proper orthogonal decomposition to decrease the amount of data exchanged between disciplines, with surrogate models based on moving least squares to reduce disciplines. This method is applied to an original 2D wing demonstrator involving two disciplines (fluid and structure). The numerical results obtained for an optimization task show its benefits in diminishing both the interfaces between disciplines and the overall computational time.     

Implementation of set-based design in multidisciplinary design optimization

Set-based design is a design approach where feasible regions for the design variables are determined from different disciplines, with the goal of locating and working with the areas of feasible overlap. During the process the constraints are adjusted in order to accommodate conflicting requirements between disciplines. The main objective of set-based design is to narrow the design space, while delaying the pursuit of a single point design as much as possible. This process avoids finalizing decisions early and allows for flexibility in dealing with requirement creep. This paper presents the development and application of a new multidisciplinary design optimization (MDO) algorithm inspired by the principles of set-based design. The new MDO algorithm was developed with the core concept of describing the design using sets to incorporate features of set-based design and achieve greater flexibility than with a single-point optimization. The MDO algorithm was applied to a ship design problem and the ship design application demonstrated the value of utilizing set-based design as a space-reducing technique before approaching the problem with a point-based optimization. Furthermore, incorporating flexibility in the constraints allowed the optimization to handle a problem with very strict constraints in a rational manner and minimize the necessary constraint violation.     

A hybrid multilevel approach for aeroelastic optimization of composite wing-box

The quest for finding optimum solutions to engineering problems has been existing for a long time. In the last decade several optimization techniques have been applied to the structural design of composite wing structures. Generally many of these proposed procedures have dealt with different disciplines such as aerodynamics, structures, or dynamics separately. However an aeronautical design process is multidisciplinary since it involves strong couplings and interactions among, for instance, aerodynamics, dynamics, flight mechanics and structures. The main problem in a multidisciplinary aircraft design is usually the development of an efficient method to integrate structures or structural properties, which can be considered both as “global” and “local” design variables. This paper describes an integrated aerodynamic / dynamic / structural optimization procedure for a composite wing-box design. The procedure combines an aeroelastic optimization of a composite wing based on a general purpose optimizer such as the Sequential Quadratic Programming (SQP) and a composite optimization using Genetic Algorithm (GA). Both the optimizations are implemented through a hybrid multilevel decomposition technique.     

Collaborative robust optimization under uncertainty based on generalized dynamic constraints network

Uncertainties exist in every aspect of a collaborative multidisciplinary design process. These uncertainties will have a great influence on design negotiations between various disciplines and may force designers to make conservative decisions. In this paper, a novel collaborative robust optimization (CRO) method based on constraints network under uncertainty is presented. The generalized dynamic constraints network (GDCN) is developed to analysis and management of uncertainties, and to ensure the parameter consistency in the collaborative design process. Given the feasible consistent parameter region, The CRO is formulated as a multi-criteria optimization problem, which brings both the objective robustness and the feasibility robustness of the constraint into account simultaneously. The CRO based on GDCN could bring both the design parameters dynamic consistency management and robust optimization into account simultaneously, which assures a product’s reliability and quality robustness. The efficiency of proposed method is evaluated in the design of crank and connecting rod in one V6 engine.     

Multidisciplinary design optimization on vehicle tailor rolled blank design

Tailor rolled blank (TRB) is an emerging steel rolling process to produce lightweight vehicle components. It allows continuous metal thickness changes, and as a result, it offers opportunities for automotive design in weight reduction, part complexity reduction, reduced capital investment, yet, maintains equal to or better strength characteristics. The objective of this research is to take advantages of the TRB manufacturing technology and combine with the advanced multidisciplinary design optimization (MDO) methodology to optimize vehicle structure. The process begins with noise vibration and harshness (NVH) optimization. The outputs of the optimal NVH response sensitivities are employed to build the first order response surface models. Uniform Latin Hypercube sampling and subset selection regression methods are used to construct the response surface models for the highly nonlinear impact and seatbelt pull responses. The optimal NVH design is then used as the starting point for MDO to obtain the optimal thickness profiles for the TRB parts. A vehicle application considering multiple impact modes, seatbelt pulls, and NVH, is used to demonstrate the proposed process for vehicle underbody TRB design. Results of this MDO TRB study is presented and discussed.