scholarly journals Bounded Target Cascading in Hierarchical Design Optimization

2014 ◽  
Vol 6 ◽  
pp. 790620
Author(s):  
Xiaoling Zhang ◽  
Debiao Meng ◽  
Ruan-Jian Yang ◽  
Zhonglai Wang ◽  
Hong-Zhong Huang
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
System Level ◽  
Weighted Sum ◽  
Analytical Target Cascading ◽  
Hierarchical Design ◽  
Subsystem Level ◽  
Target Cascading ◽  
The Common ◽  
Single Objective

For large scale systems, as a hierarchical multilevel decomposed design optimization method, analytical target cascading coordinates the inconsistency between the assigned targets and response in each level by a weighted-sum formulation. To avoid the problems associated with the weighting coefficients, single objective functions in the hierarchical design optimization are formulated by a bounded target cascading method in this paper. In the BTC method, a single objective optimization problem is formulated in the system level, and two kinds of coordination constraints are added: one is bound constraint for the design points based on the response from each subsystem level and the other is linear equality constraint for the common variables based on their sensitivities with respect to each subsystem. In each subsystem level, the deviation with target for design point is minimized in the objective function, and the common variables are constrained by target bounds. Therefore, in the BTC method, the targets are coordinated based on the optimization iteration information in the hierarchical design problem and the performance of the subsystems, and BTC method will converge to the global optimum efficiently. Finally, comparisons of the results from BTC method and the weighted-sum analytical target cascading method are presented and discussed.

Bounded Target Cascading in Hierarchical Design Optimization

2011 ◽  
Author(s):  
Xiao-Ling Zhang ◽  
Po Ting Lin ◽  
Hae Chang Gea ◽  
Hong-Zhong Huang
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
System Level ◽  
Weighted Sum ◽  
Analytical Target Cascading ◽  
Hierarchical Design ◽  
Design Variables ◽  
Target Cascading ◽  
The Cost ◽  
Weighting Coefficients

Analytical Target Cascading method has been widely developed to solve hierarchical design optimization problems. In the Analytical Target Cascading method, a weighted-sum formulation has been commonly used to coordinate the inconsistency between design points and assigned targets in each level while minimizing the cost function. However, the choice of the weighting coefficients is very problem dependent and improper selections of the weights will lead to incorrect solutions. To avoid the problems associated with the weights, single objective functions in the hierarchical design optimization are formulated by a new Bounded Target Cascading method. Instead of point targets assigned for design variables in the Analytical Target Cascading method, bounded targets are introduced in the new method. The target bounds are obtained from the optimal solutions in each level while the response bounds are updated back to the system level. If the common variables exist, they are coordinated based on their sensitivities with respect to design variables. Finally, comparisons of the results from the proposed method and the weighted-sum Analytical Target Cascading are presented and discussed.

scholarly journals Multi-Disciplinary Design Optimization for Large-Scale Reverse Osmosis Systems

2014 ◽  
Author(s):  
Bo Yang Yu ◽  
Tomonori Honda ◽  
Syed Zubair ◽  
Mostafa H. Sharqawy ◽  
Maria C. Yang
Keyword(s):  
Optimal Design ◽  
Complex Systems ◽  
Design Optimization ◽  
Reverse Osmosis ◽  
Large Scale ◽  
Analytical Target Cascading ◽  
Total Water ◽  
Target Cascading ◽  
Desalination Plants ◽  
Functional Components

Large-scale desalination plants are complex systems with many inter-disciplinary interactions and different levels of sub-system hierarchy. Advanced complex systems design tools have been shown to have a positive impact on design in aerospace and automotive, but have generally not been used in the design of water systems. This work presents a multi-disciplinary design optimization approach to desalination system design to minimize the total water production cost of a 30,000m3/day capacity reverse osmosis plant situated in the Middle East, with a focus on comparing monolithic with distributed optimization architectures. A hierarchical multi-disciplinary model is constructed to capture the entire system’s functional components and subsystem interactions. Three different multi-disciplinary design optimization (MDO) architectures are then compared to find the optimal plant design that minimizes total water cost. The architectures include the monolithic architecture multidisciplinary feasible (MDF), individual disciplinary feasible (IDF) and the distributed architecture analytical target cascading (ATC). The results demonstrate that an MDF architecture was the most efficient for finding the optimal design, while a distributed MDO approach such as analytical target cascading is also a suitable approach for optimal design of desalination plants, but optimization performance may depend on initial conditions.

Multi-Disciplinary Design Optimization of Transonic Fan Blade Design Using Analytical Target Cascading

2014 ◽  
Author(s):  
Saima Naz ◽  
Christophe Tribes ◽  
J.-Y. Trépanier ◽  
Jason Nichols ◽  
Eddy Petro
Keyword(s):  
Design Optimization ◽  
Solution Process ◽  
System Level ◽  
Analytical Target Cascading ◽  
Structure And Dynamics ◽  
Design Variables ◽  
Key Points ◽  
Target Cascading ◽  
Fan Blade ◽  
Transonic Fan

Analytical Target Cascading (ATC), a multilayer multidisciplinary design optimization (MDO) formulation employed on a transonic fan design problem. This paper demonstrates the ATC solution process including the specific way of initializing the problem and handling system level and discipline level targets. High-fidelity analysis tools for aerodynamics, structure and dynamics disciplines have been used. A multi-level parameterization of the fan blade is considered for reducing the number of design variables. The overall objective is the transonic fan efficiency improvement under structure and dynamics constraints. This design approach is applied to the redesign of the NASA Rotor 67. The overall study explores the key points of implementation of ATC on transonic fan design practical problem.

scholarly journals Multidisciplinary Design of Electric Vehicles Based on Hierarchical Multi-Objective Optimization

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Kesavan Ramakrishnan ◽  
Gianpiero Mastinu ◽  
Massimiliano Gobbi
Keyword(s):  
Complex Systems ◽  
Electric Vehicles ◽  
Optimal Solution ◽  
System Level ◽  
Optimization Approach ◽  
Analytical Target Cascading ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Subsystem Level ◽  
Target Cascading

A method for the optimal design of complex systems is developed by effectively combining multi-objective optimization and analytical target cascading techniques. The complex systems with high dimensionality are partitioned into manageable subsystems that can be optimized using dedicated algorithms. The multiple objective functions in each subsystem are treated simultaneously, and the interactions between subsystems are managed using linking variables and shared variables. The analytical target cascading algorithm ensures the convergence of the optimal solution that meets the system level targets while complying with the subsystem level constraints. A design optimization of electric vehicles with in-wheel motors is formulated as a two-level hierarchical scheme where the top level has a model representing the electric vehicle and the bottom level contains models of battery and suspension. The vehicle model includes an electric motor model and a power electronics model. Pareto-optimal solutions are derived holistically. The effectiveness of the proposed method for optimizing the complex systems is compared against the conventional all-in-one optimization approach.

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.

Cutting Plane Methods for Analytical Target Cascading With Augmented Lagrangian Coordination

2013 ◽  
Vol 135 (10) ◽  
Author(s):  
Wenshan Wang ◽  
Vincent Y. Blouin ◽  
Melissa K. Gardenghi ◽  
Georges M. Fadel ◽  
Margaret M. Wiecek ◽  
...  
Keyword(s):  
Large Scale ◽  
Augmented Lagrangian ◽  
Optimization Problems ◽  
Cutting Plane ◽  
Analytical Target Cascading ◽  
Cutting Plane Methods ◽  
Subgradient Optimization ◽  
Decomposition Approach ◽  
Target Cascading ◽  
Augmented Lagrangian Coordination

Analytical target cascading (ATC), a hierarchical, multilevel, multidisciplinary coordination method, has proven to be an effective decomposition approach for large-scale engineering optimization problems. In recent years, augmented Lagrangian relaxation methods have received renewed interest as dual update methods for solving ATC decomposed problems. These problems can be solved using the subgradient optimization algorithm, the application of which includes three schemes for updating dual variables. To address the convergence efficiency disadvantages of the existing dual update schemes, this paper investigates two new schemes, the linear and the proximal cutting plane methods, which are implemented in conjunction with augmented Lagrangian coordination for ATC-decomposed problems. Three nonconvex nonlinear example problems are used to show that these two cutting plane methods can significantly reduce the number of iterations and the number of function evaluations when compared to the traditional subgradient update methods. In addition, these methods are also compared to the method of multipliers and its variants, showing similar performance.

scholarly journals Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Debiao Meng ◽  
Xiaoling Zhang ◽  
Hong-Zhong Huang ◽  
Zhonglai Wang ◽  
Huanwei Xu
Keyword(s):  
Interaction Design ◽  
Large Scale ◽  
Optimization Problems ◽  
Collaborative Optimization ◽  
System Level ◽  
Design Parameters ◽  
Large Scale Systems ◽  
Interaction Prediction ◽  
Design Variables ◽  
Subsystem Level

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

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