Research On The Decomposition, Coordination And Uncertainty Of The Multidisciplinary Design Optimization

In today's engineering design, the number of design variables involved and the amount of data handled becomes so large that attempting to optimize the design by traditionally sequential approach is both intractable and costly. Multidisciplinary design optimization (MDO) emerges as an enabling methodology for the design of complex systems. The increasing economic competition of industrial markets and growing complexity of engineering problems has sparked increasing interest to MDO in recently years.Comprehensively and systemically the present thesis studies the theories and methods of MDO. Broadly speaking, the investigation focuses on the following four aspects.The sensitivity analysis (SA) is an important element of MDO research field. It is used not only for derivatives computation in single disciple but also for system decomposition. The first research area of thesis focuses on the sensitivity analysis. Firstly, The SA methods that fit MDO are studied systemically. Several individual disciplinary SA methods are introduced, also the details of each method. And comparisons of these methods are presented. On this basis of global sensitivity equation, the second order global sensitivity equation is derived. Based on the second order global sensitivity equation, the multidisciplinary system could be decomposed into several independent subsystems.Surrogate models are an effective approach for alleviating some of the problems associated with the direct use of modern computerized analysis techniques in MDO environment. Surrogate models shift the computational burden from the optimization problem to the problem of building the surrogate models. Additionally, surrogate models filter out the noise inherent to most numerical analysis procedures by providing smooth approximate functions. The second research area of thesis is two-fold. One, a large-scale comparison of five popular means for building approximation models is presented. Second, Based on Kriging surrogate of computer simulations and experiments, a new means enables the creation of surrogate models that are advanced Kriging surrogate fitted with data obtained from deterministic numerical models and/or experimental data using optimal sampling. The statistical theory of Bayesian inference is used to support the building of surrogates. Information from computer simulations of different levels of accuracy and detail is integrated, updating surrogates sequentially to improve their accuracy. Surrogates are updated in sequential stages. They are flexible in their accuracy level and can be reusable because they can be updated as information is gathered.The primary goal of MDO is to decompose a large multidisciplinary system into a related grouping of smaller, more tractable, coupled subsystems. The third research area involves on the decomposition of the MDO. Based on the second order global sensitivity equations and advanced Kriging approximation, a new MDO method is proposed. The method optimizes decomposed subspaces concurrently, followed by a coordination procedure of global approximation. The global approximation used in the coordination procedure is formed using the advanced Kriging approximation strategy. Optimization of a global approximation problem is used as a coordination procedure for directing system convergence and resolving subspace conflicts. The global approximation problem is formed using information generated during concurrent subspace optimization. At each subspace, non-local analyses are approximated at the subspaces using second order global sensitivity equations. Local analyses are performed using analyses packages available to the subspace design team. System coupling is maintained and updated using second order global sensitivity equations approach.Finally, the fourth area of research involves the uncertainty in multidisciplinary design optimization. This study examines sources of uncertainty and classifies these errors into two categories, that is, the bias error associated with the disciplinary design tool and the precision error of the input data. For the formulation presented in this thesis, we employ probability methods to uncertainty. In this research an investigation of how uncertainty propagates through a multidisciplinary system analysis subject to the bias errors and the precision errors is undertaken. Though the usefulness of multidisciplinary design optimization formulation to evaluate uncertainty encountered in the design process is widely acknowledged, its implementation is rare. One of the reasons is due to the complexity and computational burden when evaluating the uncertainty in MDO. The approach proposed to this problem is to employ Kriging approximation to create a uncertain surrogate, and then perform the evaluation of uncertainty in MDO.