Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

McAllister, Charles D.; Simpson, Timothy W.

doi:10.1115/detc2001/dac-21124

Cited by 11 publications

(10 citation statements)

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“…23,24 RDO studies seek to find optimal designs which are less sensitive to the perturbations in the design space, or analysis errors that can be introduced into the design process at various stages. 25 RDO has been studied earlier by many researchers 6,[26][27][28] and has found many successful applications in engineering [29][30][31][32] and is continually being expanded to different design phases. 23 Chen et al 26 have presented two broad categories of robust design, namely Type I and Type II, based on the nature of the uncertain design parameters.…”

Section: Robust Design Optimizationmentioning

confidence: 99%

A new robust design optimization approach for unmanned underwater vehicle design

Alam

Ray

Anavatti

2012

Proceedings of the Institution of Mechanical Engineers, Part M:

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Design of unmanned underwater vehicles is a complex and challenging task. While optimization methods can be applied to identify designs which are faster, more manoeuvrable and flexible to deal with various mission profiles, a practical realization requires the design to be robust, i.e. one with the best average performance under expected parametric variations. In this paper, an optimization framework for the design of underwater vehicles is presented. The framework is subsequently used to identify optimal and robust optimal designs of a small-scale (length nominally less than 500 mm) and light-weight (less than 0.5 kg) toy submarine. The submarine design described in the paper relies heavily on the use of off-the-shelf components in an attempt to contain cost. The differences between the optimal design and the robust optimal design are discussed in detail. The designs identified through the process of optimization are compared with an existing toy submarine to highlight the benefits offered by the present approach. The framework has been used to design two underwater vehicles which are currently being built by the research group.

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Section: Robust Design Optimizationmentioning

confidence: 99%

A new robust design optimization approach for unmanned underwater vehicle design

Alam

Ray

Anavatti

2012

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…The multidisciplinary design optimization (MDO) is widely used in designing complicated coupling system. 1–5 The traditional MDO is performed based on deterministic conditions and ignores the uncertainties in real life. Uncertainties such as manufacturing tolerances, material properties, and boundary conditions can reduce the system reliability.…”

Section: Introductionmentioning

confidence: 99%

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

Yang

Yue

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

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“…In modern practice, engineering optimization problems of complex and/or multi-physics structures are multidisciplinary, multi-objectives, and non-differentiable in nature. For example, the MDO of an aircraft 8 involved many conflicting objectives functions at structure and substructures level, which requires multiple optimizers. However, the optimizer of the substructure’s wing aims to minimize the weight and deflection as design objectives while the optimizer at structure level allow to optimize the total weight and stress as design objectives at the structure level.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

Chagraoui

Soula

2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

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Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

Cited by 11 publications

References 0 publications

A new robust design optimization approach for unmanned underwater vehicle design

A new robust design optimization approach for unmanned underwater vehicle design

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

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