Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

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 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 Design Optimization: A Survey of Architectures

“…Examples include the design of launch vehicles [39], rocket engines [40], satellite constellations [147], flight trajectories [72,148], and flight control systems [149], as well as the preliminary design of complete aircraft [17,18] and aircraft family design [150]. Beyond aerospace engineering, CO has been applied to problems involving automobile engines [28], bridge design [22], and railway cars [26], and even the design of a scanning optical microscope [27].…”

“…McAllister et al [151] present a multiobjective approach using linear physical programming. Available robust design formulations incorporate the decision-based models of Gu et al [152] and McAllister and Simpson [28], the implicit uncertainty propagation method of Gu et al [153], and the fuzzy computing models of Huang et al [154]. Zadeh and Toropov [118] integrated multiple model fidelities into CO for an aircraft design problem.…”

“…One of the first applications of MDO was aircraft wing design, where aerodynamics, structures, and controls are three strongly coupled disciplines [9,10,11,12,13,14,15,16]. Since then, the application of MDO has been extended to complete aircraft [17,18,19,20,21] and a wide range of other engineering systems, such as bridges [22], buildings [23,24], railway cars [25,26], microscopes [27], automobiles [28,29], ships [30,31], propellers [32,33], rotorcraft [34,35], wind turbines [36,37,38], and spacecraft [39,40].…”

“…The KKT conditions require the availability of function gradients, so we assume that the objective and constraint functions are differentiable. However, various architectures have been developed specifically for multiobjective problems [43,44,45,28] or problems with discrete variables [46,47]. Other architectures determine optimality without using gradients [48,49] or use optimality concepts from game theory [50,51,52].…”

Multiobjective optimization of a two-piece aluminum beverage bottle considering tactile sensation of heat and embossing formability

“…In order to optimize effectively a design problem with many design variables in complex geometrical relations, the Design Variable Progressive Optimization method based on RSA is proposed and applied to optimize the bottom of 2-Pc aluminum beverage bottles (Han et al 2005). Since practical optimization in engineering design problems usually have more than one objective and disciplinary, multi-objective / multidisciplinary optimization techniques have been developed at a rapidly increasing pace (Kim and Weck 2004;Yun et al 2001;McAllister and Simpson 2003;Manning 2004). However, no attempts in multi-objective and multidisciplinary optimum designs are tried to improve the beverage cans / bottles considering simultaneously the human feeling and sheet-metal formability.…”