Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2003 ◽  
Vol 125 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2001 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

Abstract In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed approach, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design

2018 ◽  
Vol 233 (3) ◽  
pp. 918-936
Author(s):  
Mohsen Bidoki ◽  
Mehdi Mortazavi ◽  
Mehdi Sabzehparvar
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Robust Design ◽  
Multidisciplinary Design Optimization ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Robust Design Optimization ◽  
Multidisciplinary Design ◽  
Optimization Framework ◽  
Design Variables

The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines.

scholarly journals Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shujuan Wang ◽  
Qiuyang Li ◽  
Gordon J. Savage
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Robust Design ◽  
Structural Reliability ◽  
Computational Cost ◽  
Optimization Process ◽  
Robust Design Optimization ◽  
Global Approximation ◽  
Design Variables ◽  
Uncertainty In Design

This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO) method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

Probabilistic Tolerance Allocation for the Dynamics of an Internal Combustion Engine With a Flexible Connecting Rod

1997 ◽  
Author(s):  
F. Zhang ◽  
B. J. Gilmore ◽  
A. Sinha
Keyword(s):  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Equations Of Motion ◽  
Optimization Procedure ◽  
Internal Combustion ◽  
Tolerance Allocation ◽  
Radial Clearance ◽  
Connecting Rod ◽  
Link Length ◽  
Design Variables

Abstract Tolerance allocation standards do not exist for mechanical systems with flexibility and whose response are time varying, subjected to discontinuous forcing functions. Previous approaches based on optimization and numerical integration of the dynamic equations of motion encounter difficulty with determining sensitivities around the force discontinuity. The Alternating Frequency/Time approach is applied here to capture the effect of the discontinuity. The effective link length model is used to model the system and to account for the uncertainties in the link length, radial clearance and pin location. Since the effective link length model is applied, the equations of motion for the nominal system can be applied for the entire analysis. Optimization procedure is applied to the problem where the objective is to minimize the manufacturing costs and satisfy the constraints imposed on mechanical errors and design variables. Examples of tolerance allocation are presented for a single cylinder internal combustion engine with a flexible connecting rod.

The Application of Genetic Algorithm in Valve Spring Optimization Design of Internal Combustion Engine

2015 ◽  
Vol 741 ◽  
pp. 810-813
Author(s):  
Shao Ni Wang
Keyword(s):  
Genetic Algorithm ◽  
Internal Combustion Engine ◽  
Optimization Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Technical Problems ◽  
Intelligent Computing ◽  
Application Range ◽  
Valve Spring ◽  
Design Variables

This paper described examples of genetic algorithm in valve spring optimization design of internal combustion engine, which solve the optimal values of the design variables by constructing multi-objective function. The technical problems that complex performance can't be considered in the calculation were solved in designing the spring, the method has many advantages such as simple universal, strong robustness, and wide application range; it is an essential and critical intelligent computing technology in the future mechanical optimization design.

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