Papers by Author: Hao Bo Qiu

Abstract: Reliability-based design optimization (RBDO) evaluates variation of output induced by uncertainties of design variables and results in an optimal design while satisfying the reliability requirements. However, its use in practical applications is hindered by the huge computational cost during the evaluation of structure reliability. In this paper, the reliability index based decoupling method is developed to improve the efficiency of probabilistic optimization. The reliability index is used to calculate the shifting vector in the decoupling process, due to its efficiency in evaluating violated probabilistic constraints. The computation capability of the proposed method is demonstrated using two examples, which are widely used to test RBDO methods. The comparison results show that the proposed method has the same accuracy as the existing methods, and it is also very efficient.

Abstract: Nowadays, computer-based engineering design is becoming widely used in the design of products. In the field of engineering, CAD model, FEM method and surrogate model are used to reduce the time and computational cost comparing with the traditional engineering design. But when facing with computationally expensive tasks, such design method mentioned above seems unable to deal with such tasks. In that case, surrogate model is gradually used and shows great potential in dealing with the computationally complex tasks. Although computing power and speed are rapidly growing, the use of the computer simulation analysis is limited in doing engineering design and some other analysis such as reliability analysis for complex product, so that it limits the use of metamodeling techniques. In that case, we use space-filling DOE sample method to support the construction of surrogate model. In this paper, we consider both Hammersley sequences and SVM as sampling method and surrogate model to construct the simulation design model, aiming at reduce computational costs. SVR achieves more accuracy and shows great potential in application in the design of complex and computationally expensive tasks.

Abstract: This paper suggests a design space exploration method using Artificial Neural Networks and metamodeling to systematically reduce the design space to a relatively small region. This method consists of three major steps. Firstly, self-organizing maps is employed to analyze design variables and objective function(s) with the original samples as preliminary reduction optimization of the initial large design space. Successively, resampling within the preliminary reduction space, clustering sample points using the fuzzy c-means clustering method with the given number of cluster, and choosing the most attractive cluster to construct kriging model and identify the design optimum within the reduced design space in the last step. The accuracy and validity of proposed methodology is proved by a heat exchanger design problem. It is found that the proposed method can intuitively capture promising design regions in which it is efficient to acquire the global or near-global desigm optimum.

Abstract: ATC provides a systematic approach in solving decomposed large scale systems that has solvable subsystems. However， complex engineering system usually has a high computational cost , which result in limiting real-life applications of ATC based on high-fidelity simulation models. To address these problems, this paper aims to develop an efficient approximation model building techniques under the analytical target cascading (ATC) framework, to reduce computational cost associated with multidisciplinary design optimization problems based on high-fidelity simulations. An approximation model building techniques is proposed: approximations in the subsystem level are based on variable-fidelity modeling (interaction of low- and high-fidelity models). The variable-fidelity modeling consists of computationally efficient simplified models (low-fidelity) and expensive detailed (high-fidelity) models. The effectiveness of the method for modeling under the ATC framework using variable-fidelity models is studied. Overall results show the methods introduced in this paper provide an effective way of improving computational efficiency of the ATC method based on variable-fidelity simulation models.