scholarly journals An Enhanced Analytical Target Cascading and Kriging Model Combined Approach for Multidisciplinary Design Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Jiang ◽  
Jianzhuang Wang ◽  
Qi Zhou ◽  
Xiaolin Zhang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Kriging Model ◽  
Multidisciplinary Design ◽  
High Approximation ◽  
Engineering Systems ◽  
Analytical Target Cascading ◽  
Combined Approach ◽  
Coordination Strategy ◽  
Target Cascading

Multidisciplinary design optimization (MDO) has been applied widely in the design of complex engineering systems. To ease MDO problems, analytical target cascading (ATC) organizes MDO process into multilevels according to the components of engineering systems, which provides a promising way to deal with MDO problems. ATC adopts a coordination strategy to coordinate the couplings between two adjacent levels in the design optimization process; however, existing coordination strategies in ATC face the obstacles of complicated coordination process and heavy computation cost. In order to conquer this problem, a quadratic exterior penalty function (QEPF) based ATC (QEPF-ATC) approach is proposed, where QEPF is adopted as the coordination strategy. Moreover, approximate models are adopted widely to replace the expensive simulation models in MDO; a QEPF-ATC and Kriging model combined approach is further proposed to deal with MDO problems, owing to the comprehensive performance, high approximation accuracy, and robustness of Kriging model. Finally, the geometric programming and reducer design cases are given to validate the applicability and efficiency of the proposed approach.

A Note on the Convergence of Analytical Target Cascading With Infinite Norms

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Jeongwoo Han ◽  
Panos Y. Papalambros
Keyword(s):  
Linear Programming ◽  
Multidisciplinary Design Optimization ◽  
Optimization Method ◽  
Multidisciplinary Design ◽  
Strong Monotonicity ◽  
Sequential Linear Programming ◽  
Analytical Target Cascading ◽  
Hierarchical Systems ◽  
Coordination Strategy ◽  
Target Cascading

Analytical target cascading (ATC) is a multidisciplinary design optimization method for multilevel hierarchical systems. To improve computational efficiency, especially for problems under uncertainty or with strong monotonicity, a sequential linear programming (SLP) algorithm was previously employed as an alternate coordination strategy to solve ATC and probabilistic ATC problems. The SLP implementation utilizes L∞ norms to maintain the linearity of SLP subsequences. This note offers a proof that there exists a set of weights such that the ATC algorithm converges when L∞ norms are used. Examples are also provided to illustrate the effectiveness of using L∞ norms as a penalty function to maintain the formulation linear and differentiable. The examples show that the proposed method provides more robust results for linearized ATC problems due to the robustness of the linear programming solver.

Multidisciplinary Optimization of Autonomous Rendezvous Spacecraft Using Updated Analytical Target Cascading Method

2012 ◽  
Vol 195-196 ◽  
pp. 801-806
Author(s):  
Bei Bei Wu ◽  
Hai Huang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Optimization ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Analytical Target Cascading ◽  
Analysis Model ◽  
Autonomous Rendezvous ◽  
Target Cascading ◽  
Practical Engineering

For the multidisciplinary design optimization (MDO) question of autonomous rendezvous spacecraft, first, the analysis model and coupling relations of payload, propulsion and structure discipline are discussed; then the updated analytical target cascading (UATC) method is introduced and compared with the widely used collaborative optimization (CO). Results prove that the UATC method requires 54.8% fewer average subspace iterations than the CO and is more efficient for practical engineering MDO problem. Finally, based on the UATC method, a MDO problem of autonomous rendezvous spacecraft is solved and gets reasonable and effective results. The process proves the effectiveness of UATC method solving spacecraft MDO problem.

Comprehensive Study of Typical Metamodel Methods Applied in Aircraft Multidisciplinary Design Optimization

2011 ◽  
Vol 110-116 ◽  
pp. 3031-3039 ◽  
Author(s):  
Teng Long ◽  
Li Liu
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Least Squares Method ◽  
Numerical Test ◽  
Kriging Model ◽  
Multidisciplinary Design ◽  
Test Problems ◽  
Comparison Results ◽  
Moving Least Squares Method ◽  
Comprehensive Study

—Metamodels have been widely applied in aircraft multidisciplinary design optimization (MDO) to alleviate the computation burden and improve the optimization efficiency. At present, there are various metamodel methods available, such as response surface method, Kriging model, moving least squares method, radial basis function, neural networks and so on. However, it is difficult to confirm which metamodel method is more promising, which is also an interesting question puzzled most designers. In this article, by using a series of numerical test problems with different inherent features, a comprehensive study is employed to compare five typical metamodel methods commonly applied in aircraft MDO under multiple metrics. In term of the comparison results, the conclusions are drawn and recommendations for selecting suitable metamodels in aircraft MDO practice are also summarized.

scholarly journals 1114 Multidisciplinary Design Optimization of Wing Shape for a Small Jet Aircraft Using Kriging Model

2005 ◽  
Vol 2005.15 (0) ◽  
pp. 66-69
Author(s):  
Takayasu Kumano ◽  
Shinkyu Jeong ◽  
Shigeru Obayashi ◽  
Yasushi Ito ◽  
Keita Hatanaka ◽  
...  
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Kriging Model ◽  
Wing Shape ◽  
Multidisciplinary Design

A Sequential Linear Programming Coordination Algorithm for Analytical Target Cascading

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Jeongwoo Han ◽  
Panos Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Test Problems ◽  
Analytical Target Cascading ◽  
Step Size ◽  
Coordination Strategy ◽  
Target Cascading ◽  
Solution Accuracy

Decomposition-based strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC that coordinates interactions among subproblems using sequential linearizations. The linearity of subproblems is maintained using infinity norms to measure deviations between targets and responses. A subproblem suspension strategy is used to suspend temporarily inclusion of subproblems that do not need significant redesign, based on trust region and target value step size. An individual subproblem trust region method is introduced for faster convergence. The proposed strategy is intended for use in design optimization problems where sequential linearizations are typically effective, such as problems with extensive monotonicities, a large number of constraints relative to variables, and propagation of probabilities with normal distributions. Experiments with test problems show that, relative to standard ATC coordination, the number of subproblem evaluations is reduced considerably while the solution accuracy depends on the degree of monotonicity and nonlinearity.

Variable-Fidelity Multidisciplinary Design Optimization Based on Analytical Target Cascading Framework

2012 ◽  
Vol 544 ◽  
pp. 49-54 ◽  
Author(s):  
Jun Zheng ◽  
Hao Bo Qiu ◽  
Xiao Lin Zhang
Keyword(s):  
Multidisciplinary Design Optimization ◽  
Model Building ◽  
Computational Cost ◽  
Simulation Models ◽  
Multidisciplinary Design ◽  
High Fidelity ◽  
Analytical Target Cascading ◽  
Approximation Model ◽  
Target Cascading ◽  
Variable Fidelity

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.

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