Optimization of diesel engine parameters using Taguchi method and design of evolution

Wilson, Vincent H.; Udayakumar,

doi:10.1590/s1678-58782012000400001

Cited by 34 publications

(7 citation statements)

References 3 publications

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“…Other simpler methods may be employed where the effect of output is neglected and only the distribution of inputs is diversified as much as possible. These design of experiments tools are widely used and documented [8,[43][44][45]. In the work of Pei et al [39], the 256 designs used as training data were generated using a Sobol sequence, which is part of CAESES.…”

Section: Training Datamentioning

confidence: 99%

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Badra

Khaled

Tang

et al. 2020

Journal of Energy Resources Technology

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Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

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Section: Training Datamentioning

confidence: 99%

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Badra

Khaled

Tang

et al. 2020

Journal of Energy Resources Technology

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“…Sizing using scaling factors determines components parameters approximately. For more accurate results, different approaches may be used [22] - [26]. Creation of individual genes of parent set p is described as follows:…”

Section: Problem Formulationmentioning

confidence: 99%

Optimization of Hybrid Vehicle Drivetrain with Genetic Algorithm using Matlab and Advisor

Biroš¹,

Karol²

2017

JESTR

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Advantages of hybrid vehicles over pure electric vehicles are obvious and well-known. The paper presents a simulation study of the optimization of hybrid propulsion drivetrain. A vehicle model was optimized in order to scale drivetrain components to achieve better fuel economy and lower greenhouse emissions. A set of ADVISOR data for vehicle was optimized by genetic algorithm in Matlab. Simulation results show significant fuel economy improvement.

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“…In this method, each of the engine input factor level will be combined with other engine input factor levels in a completely randomized manner. 16,17…”

Section: Introductionmentioning

confidence: 99%

Multi-input–multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas/diesel fuel

Ebrahimi

Jazayeri

2019

International Journal of Engine Research

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The aim of this study is to implement the multi-input–multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas and diesel fuel. A single-cylinder heavy-duty diesel engine with a modified bathtub piston bowl profile is set on operation at 9.4 bar indicated mean effective pressure and running at a fixed engine speed of 1300 r/min. A certain amount of diesel fuel mass per cycle is fed into the engine at a fixed equivalence ratio without any exhaust gas recirculation. The optimization targets include reduction in engine emissions as much as possible, avoiding diesel knock occurrence, and achieving low temperature combustion concept with the least or no engine power losses. To implement the optimization, the effects of three control factors on the engine performance are assessed by the design of experiment concept—fractional factorial method. These selected control factors are intake temperature and intake pressure (both at intake valve closing) and the diesel fuel start of injection timing. Some randomized treatment combinations of chosen levels from the three selected control factors are employed to simulate reactivity-controlled compression-ignition combustion. Based on the engine’s responses derived from the simulation, reactivity-controlled compression-ignition combustion’s mathematical model is identified directly using an artificial neural network. Next, an optimization process is conducted using two different optimization algorithms, namely, genetic algorithm and particle swarm optimization algorithm. For assessing and validating the obtained optimal results, the obtained data are used to simulate reactivity-controlled compression-ignition combustion as the engine input factors. The results show that the proposed artificial neural network design is effectively capable of identifying reactivity-controlled compression-ignition combustion’s mathematical model. Also, by optimizing reactivity-controlled compression-ignition combustion through different optimization algorithms, the optimal range of the engine operation at 9.4 bar indicated mean effective pressure is well estimated and extended.

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Optimization of diesel engine parameters using Taguchi method and design of evolution

Cited by 34 publications

References 3 publications

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Optimization of Hybrid Vehicle Drivetrain with Genetic Algorithm using Matlab and Advisor

Multi-input–multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas/diesel fuel

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