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.

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 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.

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.

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.

On the Use of Multi-Disciplinary Optimisation Methods for Road Vehicle Passive Suspension Design

2019 ◽  
Author(s):  
Liunan Yang ◽  
Federico Ballo ◽  
Giorgio Previati ◽  
Massimiliano Gobbi ◽  
Gianpiero Mastinu
Keyword(s):  
Total Mass ◽  
Decomposition Methods ◽  
Analytical Target Cascading ◽  
Spring Stiffness ◽  
Objective Functions ◽  
Road Vehicle ◽  
Passive Suspension ◽  
Design Variables ◽  
Target Cascading ◽  
Optimisation Methods

Abstract Two widely used decomposition-based multi-disciplinary optimisation (MDO) methods, namely analytical target cascading (ATC) and collaborative optimisation (CO), are applied to the design of the suspension system of a road vehicle. Instead of directly optimising the spring stiffness and the damping coefficient, three parameters of the spring and three parameters of the damper are selected as design variables. Discomfort, road holding, and the total mass of the spring-damper system, are considered as objective functions. An investigation is completed to analyse the performance of the two decomposition methods compared with the conventional all-in-one (AiO) formulation in terms of efficiency and applicability.

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.

Analytical Target Cascading in Automotive Vehicle Design

2003 ◽  
Vol 125 (3) ◽  
pp. 481-489 ◽  
Author(s):  
Hyung Min Kim ◽  
D. Geoff Rideout ◽  
Panos Y. Papalambros ◽  
Jeffrey L. Stein
Keyword(s):  
Solution Process ◽  
Ride Quality ◽  
Analytical Target Cascading ◽  
Multilevel Optimization ◽  
Efficient Manner ◽  
Trade Offs ◽  
Systematic Effort ◽  
Target Cascading ◽  
Analysis Models ◽  
Hierarchical Multilevel Optimization

Target cascading in product development is a systematic effort to propagate the desired top-level system design targets to appropriate specifications for subsystems and components in a consistent and efficient manner. If analysis models are available to represent the consequences of the relevant design decisions, analytical target cascading can be formalized as a hierarchical multilevel optimization problem. The article demonstrates this complex modeling and solution process in the chassis design of a sport-utility vehicle. Ride quality and handling targets are cascaded down to systems and subsystems utilizing suspension, tire, and spring analysis models. Potential incompatibilities among targets and constraints throughout the entire system can be uncovered and the trade-offs involved in achieving system targets under different design scenarios can be quantified.

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