Combustion engine optimization: a multiobjective approach

Jakobsson, Stefan; Saif-Ul-Hasnain, Muhammad; Rundqvist, Robert; Edelvik, Fredrik; Andersson, Barbro; Patriksson, Michael; Ljungqvist, Mattias; Lortet, Dimitri; Wallesten, Johan

doi:10.1007/s11081-009-9090-6

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…It, however, failed to represent the convex Pareto frontier of OKA1, since OKA1 has a very strong nonlinear behavior close to the minimum solution to one of the objective functions. They note that qualSolve was developed in a project on simulation-based multiobjective optimization of the Volvo D5 diesel engine with three black-box objective functions and five decision variables [77] involving CFD simulation using Star-CD [78]. The authors consider the execution time, as a main downside of the qualSolve method for MOPs.…”

Section: Summary Of Methods In the Adaptive Framework: Typementioning

confidence: 99%

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Tabatabaei

Hakanen

Hartikainen

et al. 2015

Struct Multidisc Optim

102

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A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods Tabatabaei, Mohammad; Hakanen, Jussi; Hartikainen, Markus; Miettinen, Kaisa; Sindhya, Karthik Tabatabaei, M., Hakanen, J., Hartikainen, M., Miettinen, K., & Sindhya, K. (2015). A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods. Structural and Multidisciplinary Optimization, 52 (1) AbstractComputationally expensive multiobjective optimization problems arise, e.g. in many engineering applications, where several conflicting objectives are to be optimized simultaneously while satisfying constraints. In many cases, the lack of explicit mathematical formulas of the objectives and constraints may necessitate conducting computationally expensive and time-consuming experiments and/or simulations. As another challenge, these problems may have either convex or nonconvex or even disconnected Pareto frontier consisting of Pareto optimal solutions. Because of the existence of many such solutions, typically, a decision maker is required to select the most preferred one. In order to deal with the high computational cost, surrogate-based methods are commonly used in the literature. This paper surveys surrogate-based methods proposed in the literature, where the methods are independent of the underlying optimization algorithm and mitigate the computational burden to capture different types of Pareto frontiers. The methods considered are classified, discussed and then compared. These methods are divided into two frameworks: the sequential and the adaptive frameworks. Based on the comparison, we recommend the adaptive framework to tackle the aforementioned challenges.

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Section: Summary Of Methods In the Adaptive Framework: Typementioning

confidence: 99%

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Tabatabaei

Hakanen

Hartikainen

et al. 2015

Struct Multidisc Optim

102

View full text Add to dashboard Cite

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“…Radial Basis Functions (RBFs) is becoming an established approach in recent years to simulate different problems in many areas of engineering [1][2][3][4][5][6][7][8][9][10]. This technique was first proposed by Kansa [11], after the work of Hardy [12] on multivariate approximation.…”

Section: Radial Basis Functionsmentioning

confidence: 99%

Prediction of Internal Combustion Engines Performance Related to Fuel Properties Using Radial Basis Functions

Carvalho¹,

Machado²,

Colaço³

2014

Anais Do XXII Simpósio Internacional De Engenharia Automotiva

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Accurate simulation of fuel properties influence in internal combustion engines performance is a very complex approach and combines many physical and chemical concepts such as combustion phenomena, chemical kinetics, fluid dynamics, turbulence and thermodynamics. The right modelling of that is still a challenge and currently available software packages for engines simulation usually consider standard or surrogate fuels. Besides that, new engine technologies, including those for sport motor application, can achieve better performances if custom fuels are formulated and applied. This development usually requires a large number of tests with different fuels, which leads to very expensive researches. Therefore, simulation is an option to reduce the number of tests and the associated costs. This paper presents the use of a response surface approach based on Radial Basis Functions to simulate a flexible fuel engine running with distinct blends of iso-octane, n-heptane, toluene and ethanol. Performance, energetic efficiency and pollutant emissions are predicted in different operating conditions.

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“…The method presented in this article was developed in a project on simulation based multiobjective optimization of the Volvo D5 diesel engine (see Jakobsson et al 2008;Saif-Ul-Hasnain 2008). The number of design parameters was five and the number of objectives three, including emissions of soot, NO x , and Indicated Mean Effective Pressure (IMEP), which is a measure of the work output from the engine.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

A method for simulation based optimization using radial basis functions

et al. 2009

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We propose an algorithm for the global optimization of expensive and noisy black box functions using a surrogate model based on radial basis functions (RBFs). A method for RBF-based approximation is introduced in order to handle noise. New points are selected to minimize the total model uncertainty weighted against the surrogate function value. The algorithm is extended to multiple objective functions by instead weighting against the distance to the surrogate Pareto front; it therefore constitutes the first algorithm for expensive, noisy and multiobjective problems in the literature. Numerical results on analytical test functions show promise in comparison to other (commercial) algorithms, as well as results from a simulation based optimization problem

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Combustion engine optimization: a multiobjective approach

Cited by 7 publications

References 22 publications

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Prediction of Internal Combustion Engines Performance Related to Fuel Properties Using Radial Basis Functions

A method for simulation based optimization using radial basis functions

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