Dynamic Relaxation Coordination Based Collaborative Optimization for Optimal Design of Multi-physics Systems

2018 ◽  
pp. 92-100
Author(s):  
Hamda Chagraoui ◽  
Mohamed Soula
Keyword(s):  
Optimal Design ◽  
Collaborative Optimization ◽  
Dynamic Relaxation

Multidisciplinary collaborative optimization based on relaxation method for solving complex problems

2020 ◽  
Vol 28 (4) ◽  
pp. 280-289
Author(s):  
Hamda Chagraoui ◽  
Mohamed Soula
Keyword(s):  
Optimization Problems ◽  
Inequality Constraints ◽  
Relaxation Method ◽  
Multidisciplinary Optimization ◽  
Collaborative Optimization ◽  
System Level ◽  
Dynamic Relaxation ◽  
Dynamic Relaxation Method ◽  
Feasible Solutions ◽  
The Impact

The purpose of the present work is to improve the performance of the standard collaborative optimization (CO) approach based on an existing dynamic relaxation method. This approach may be weakened by starting design points. First, a New Relaxation (NR) method is proposed to solve the difficulties in convergence and low accuracy of CO. The new method is based on the existing dynamic relaxation method and it is achieved by changing the system-level consistency equality constraints into relaxation inequality constraints. Then, a Modified Collaborative Optimization (MCO) approach is proposed to eliminate the impact of the information inconsistency between the system-level and the discipline-level on the feasibility of optimal solutions. In the MCO approach, the impact of the inconsistency is treated by transforming the discipline-level constrained optimization problems into an unconstrained optimization problem using an exact penalty function. Based on the NR method, the performance of the MCO approach carried out by solving two multidisciplinary optimization problems. The obtained results show that the MCO approach has improved the convergence of CO significantly. These results prove that the present MCO succeeds in getting feasible solutions while the CO fails to provide feasible solutions with the used starting design points.

Multi-level design of an isolation transformer using collaborative optimization

2014 ◽  
Vol 33 (3) ◽  
pp. 1038-1050 ◽  
Author(s):  
Alexandru C. Berbecea ◽  
Frédéric Gillon ◽  
Pascal Brochet
Keyword(s):  
Optimal Design ◽  
Design Process ◽  
Low Voltage ◽  
Electrical Engineering ◽  
Multidisciplinary Optimization ◽  
Collaborative Optimization ◽  
Content Type ◽  
Multi Level ◽  
Level Design ◽  
Isolation Transformer

Purpose – The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer. Design/methodology/approach – The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method – sequential quadratic programming – carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer. Findings – Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers. Originality/value – The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.

APPLICATION OF IMPROVED COLLABORATIVE OPTIMIZATION METHOD TO MECHANICAL MULTI-OBJECTIVE OPTIMAL DESIGN PROBLEM

2012 ◽  
Vol 11 (02) ◽  
pp. 151-157 ◽  
Author(s):  
FENGTAO WEI ◽  
LI SONG ◽  
YAN LI ◽  
KUN SHI
Keyword(s):  
Optimal Design ◽  
Efficient Method ◽  
Design Problem ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Flow Chart ◽  
Optimal Design Problem ◽  
Design Problems ◽  
Multi Objective ◽  
Result Show

In order to solve the mechanical multi-objective optimal design problems, the basic idea and flow chart of collaborative optimization method are introduced in this paper. In view of the shortcomings that exist in standard collaborative optimization method, this method has been improved by applying the dynamic slack factor method. Taking a mechanical multi-objective optimal design of spring as an example, the multi-objective optimal design problem has been solved by the improved collaborative optimization method. The process and result show that the improved collaborative optimization method has higher accuracy and efficiency. This paper has provided an efficient method to solve the complicated mechanical multi-objective optimal design problems.

scholarly journals Multidisciplinary Lightweight Optimization for Front Impact Structure of Body Frame Based on Active and Passive Safety

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 907
Author(s):  
Tingting Wang ◽  
Mengjian Wang ◽  
Xia Li ◽  
Dongchen Qin
Keyword(s):  
Aerodynamic Characteristics ◽  
Collaborative Optimization ◽  
System Level ◽  
Vehicle Body ◽  
Passive Safety ◽  
Feasible Region ◽  
Dynamic Relaxation ◽  
Body Frame ◽  
Relaxation Factor ◽  
Front Impact

The Analytic Target Cascading (ATC) is an effective method for solving hierarchical Multidisciplinary Design Optimization (MDO) problems. At the same time, this method suffers from poor convergence and low accuracy, which is caused by the inconsistency of system constraints. In this paper, a novel ATC method based on dynamic relaxation factor is proposed. The dynamic relaxation factor of consistency constraint is added in the system level and is adjusted by the deviation of the linking variables between the levels to ensure the feasible region of the design space. The effectiveness and accuracy of this method are verified by a mathematical example. This method is used to solve the lightweight problem of the trussed front part of the vehicle body frame based on active and passive safety to achieve the collaborative optimization of lightweight trussed frame, crash safety, and aerodynamic characteristics. The important value of the novel ATC method based on dynamic relaxation factor in engineering applications is proven.

Crack divergence phenomena in tempered glass

2020 ◽  
Vol 13 (3) ◽  
pp. 115-129
Author(s):  
Shin’ichi Aratani
Keyword(s):  
Optimal Design ◽  
High Speed ◽  
Glass Plate ◽  
Acute Angle ◽  
Divergence Angle ◽  
High Speed Photography ◽  
Tempered Glass ◽  
Thick Glass

High speed photography using the Cranz-Schardin camera was performed to study the crack divergence and divergence angle in thermally tempered glass. A tempered 3.5 mm thick glass plate was used as a specimen. It was shown that two types of bifurcation and branching existed as the crack divergence. The divergence angle was smaller than the value calculated from the principle of optimal design and showed an acute angle.

G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity

2018 ◽  
pp. 150-155
Author(s):  
Muklas Rivai
Keyword(s):  
Electrical Conductivity ◽  
Optimal Design ◽  
Linear Models ◽  
Silicon Oxide ◽  
Information Matrix ◽  
Relevant Information ◽  
Non Linear

Optimal design is a design which required in determining the points of variable factors that would be attempted to optimize the relevant information so that fulfilled the desired criteria. The optimal fulfillment criteria based on the information matrix of the selected model.

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