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.

Collaborative Optimization Algorithm Based on the Penalty Function

2014 ◽  
Vol 538 ◽  
pp. 447-450
Author(s):  
Zhi Xia Jiang ◽  
Pin Chao Meng ◽  
Yan Zhong Li ◽  
Wei Shi Yin
Keyword(s):  
Penalty Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Collaborative Optimization ◽  
System Level ◽  
Feasible Region ◽  
Relaxation Factor ◽  
Algorithm Stability ◽  
Calculation Process ◽  
Relaxation Factors

The paper discusses the collaborative optimization problems with bounded. Based the penalty function the system-level optimization convert to a unconstraint programming. To the discipline-level optimization, the normalized weighted coefficients are used and combine relaxation factors to solve. It uses the relaxation factor to expand the feasible region, and possibly makes the iteration in the calculation process run inside feasible region. The data have shown that the algorithm has expanded the choice range of the initial points with high calculation accuracy and better algorithm stability.

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.

scholarly journals Development of virtual benches for testing the digital twin of a vehicle

2021 ◽  
Vol 284 ◽  
pp. 06006
Author(s):  
Pavel Cvetkov ◽  
Elena Zhilenkova ◽  
Anton Zhilenkov
Keyword(s):  
Power Plant ◽  
Automotive Industry ◽  
Test Bench ◽  
Vehicle Body ◽  
Passive Safety ◽  
Body Frame ◽  
Cooling Systems ◽  
Digital Platform ◽  
Digital Twin ◽  
Virtual Test

The article analyzes the approaches to the creation of the concept of a virtual test bench for testing the digital twin of the automotive industry product. Such components of the digital platform as suspension area, power plant, braking and cooling systems are being investigated. The problem of the implementation in the digital twin of such important units as the attachment points of units and assemblies on the vehicle body frame, even surface, driver dummy, the scheme of attachment of body elements, etc is studied. The importance of the implementation of such benches as platforms for assessing the indicators of the vehicle passive safety or as stand for assessing the water tightness of the vehicle body is considered. A number of results that illustrate development areas and success of the authors of the article in these areas are presented. It is shown that the digital platform can be used for certification and rating tests, assessing the comfort and visibility of the driver's cab.

Decision-Based Collaborative Optimization Under Uncertainty

2000 ◽  
Author(s):  
Xiaoyu Gu ◽  
John E. Renaud ◽  
Leah M. Ashe ◽  
Stephen M. Batill ◽  
Amarjit S. Budhiraja ◽  
...  
Keyword(s):  
Systems Design ◽  
Collaborative Optimization ◽  
Optimization Under Uncertainty ◽  
System Level ◽  
Optimization Strategy ◽  
Business Decisions ◽  
Single Level ◽  
Optimization Framework ◽  
Multidisciplinary Systems ◽  
Decision Based Design

Abstract In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg (1996a, 1998) is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming the Hazelrigg framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

Probability Collectives with Collaborative Optimization (PCCO): A Novel Framework for Handling Complex Optimization Problems

2013 ◽  
Vol 373-375 ◽  
pp. 1036-1044
Author(s):  
Wei Zhao ◽  
Nan Wang
Keyword(s):  
Optimization Problems ◽  
Level Structure ◽  
Collaborative Optimization ◽  
System Level ◽  
Coordination Ability ◽  
Complex Optimization ◽  
Pc Method

In this paper, a novel framework as a combination of Probability Collectives (PC) and Collaborative Optimization (CO) is proposed and detailed illustrated. The framework has a two-level structure which is similar to that of CO, but with the system level replaced by distributed PC based agents. This formulation maintains the advantage of CO while enhances the optimization and coordination ability at the system level. For better implementation, some adaption and improvement has been made to the origin PC method. The resultant PCCO framework shows satisfied performance in handling complex optimization problems with both efficiency and accuracy.

Research in Multidisciplinary Collaborative Optimization Base on Symmetric Fuzzy Satisfaction

2012 ◽  
Vol 616-618 ◽  
pp. 2175-2181
Author(s):  
Nan Li ◽  
Bao Wei Song
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
System Optimization ◽  
Collaborative Optimization ◽  
System Level ◽  
Academic Level ◽  
Multi Level ◽  
The Impact

Multidisciplinary collaborative optimization (CO) is a multi-level optimization algorithm, which include system-level and academic level. Because these are many complications in Engineering, it will exist the fuzzy in Discipline-level optimization devices. If the impact of discipline-level ambiguity was ignored, it will lead to some big errors in the system optimization. In order to solve this problem, this paper introduced the optimal fuzzy satisfaction into multidisciplinary collaborative optimization design, and made it as optimal targets of sub-discipline optimization devices. Then a method of multidisciplinary collaborative optimization base on Fuzzy Satisfaction was get., which can solve the fuzzy problem in multidisciplinary collaborative optimization. Meanwhile this paper used this method in multidisciplinary collaborative optimization of torpedo, and compared the results that was get by this method with the results which was get by the method that did not base on the fuzzy satisfaction, finally get the conclusion that the method is fit to Multidisciplinary collaborative optimization of torpedo.

COLLABORATIVE OPTIMIZATION WITH DIMENSION REDUCTION

2010 ◽  
Vol 01 (02) ◽  
pp. 179-198 ◽  
Author(s):  
ZHENXIAO GAO ◽  
TIANYUAN XIAO ◽  
WENHUI FAN
Keyword(s):  
Dimension Reduction ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problem ◽  
Convergence Property ◽  
Extreme Points ◽  
Optimization Methods ◽  
Collaborative Optimization ◽  
System Level ◽  
Design Variables ◽  
Feasible Domain

Collaborative optimization (CO) method is widely used in solving multidisciplinary design optimization (MDO) problems, yet its computation requirement has been an obstacle to the applications, leading to doubts about CO's convergence property. The feasible domain of CO problem is first examined and it is proven that feasible domain remains the same during the CO formulation. So is the same with extreme points. Then based on contemporary research conclusion that the system-level optimization problem suffers from inherent computational difficulties, it is further pointed out that the employment of meta-heuristic optimization methods in CO could eliminate these difficulties. To make CO more computational feasible, a new method collaborative optimization with dimension reduction (CODR) is proposed. It focused on optimization dimension reduction and lets local copy of common shared design variables equal system shared design variables directly. Thus, the number of dimensions that CODR could reduce equal the number of common shared design variables. Numerical experiment suggests that CODR reduces computations greatly without losing of optimization accuracy.

An Alternative Formulation of Collaborative Optimization Based on Geometric Analysis

2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Xiang Li ◽  
Changan Liu ◽  
Weiji Li ◽  
Teng Long
Keyword(s):  
Design Problem ◽  
Optimization Problems ◽  
Geometric Analysis ◽  
Equality Constraints ◽  
Collaborative Optimization ◽  
System Level ◽  
Alternative Formulation ◽  
Linear Approximations ◽  
Engineering Design Problems ◽  
Level Problem

Collaborative optimization (CO) is a multidisciplinary design optimization (MDO) method with bilevel computational structure, which decomposes the original optimization problem into one system-level problem and several subsystem problems. The strategy of decomposition in CO is a useful way for solving large engineering design problems. However, the computational difficulties caused by the system-level consistency equality constraints hinder the development of CO. In this paper, an alternative formulation of CO called CO with combination of linear approximations (CLA-CO) is presented based on the geometric analysis of CO, which is more intuitive and direct than the previous algebraic analysis. In CLA-CO, the consistency equality constraints in CO are replaced by linear approximations to the subsystem responses. As the iterative process goes on, more linear approximations are added into the system level. Consequently, the combination of these linear approximations makes the system-level problem gradually approximate the original problem. In CLA-CO, the advantages of the decomposition strategy are maintained while the computational difficulties of the conventional CO are avoided. However, there are still difficulties in applying the presented CLA-CO to problems with nonconvex constraints. The application of CLA-CO to three optimization problems, a numerical test problem, a composite beam design problem, and a gear reducer design problem, illustrates the capabilities and limitations of CLA-CO.

Application of Multidisciplinary Design Optimization to a Resource Satellite

2012 ◽  
Vol 195-196 ◽  
pp. 1066-1077
Author(s):  
Wen Rui Wu ◽  
Hai Huang ◽  
Bei Bei Wu
Keyword(s):  
Design Optimization ◽  
System Design ◽  
Multidisciplinary Design Optimization ◽  
Research Work ◽  
Thermal Control ◽  
Satellite System ◽  
Collaborative Optimization ◽  
System Level ◽  
Multidisciplinary Design ◽  
Satellite Mission

Satellite system design is a process involving various branches of knowledge, in which the designer usually needs to tradeoff many essentials and takes remarkable time. While multidisciplinary design optimization (MDO) method provides an effective approach for complicated system design, it seems especially suitable for such kind design purpose. By applying MDO in satellite system design, the efficiency of design can be expected to be improved and powerful technical supports can be obtained, which means better performance, faster design process and lower cost. According to the Resource satellite mission, width of ground cover and ground resolution are taken as the performance measurement, which combined with total mass of satellite is accounted in the optimization objective in system level. The design variables and constraints of the problem are dealt with disciplines or subsystems such as GNC, power, structure and thermal control. Corresponding analysis modules close to practical engineering are modeled. A MDO program system is developed by integrating collaborative optimization (CO) methods in iSIGHT. The result shows that the comprehensive objective can be improved, which also indicates MDO is feasible and efficient to solve the spacecraft design problem. The technology can be consulted for further research work.

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