Computational fluid dynamics–guided engine combustion system design optimization using design of experiments

Pei, Yuanjiang; Zhang, Anqi; Pal, Pinaki; Zhao, Longmao; Som, Sibendu

doi:10.1016/b978-0-323-88457-0.00008-4

Cited by 2 publications

(5 citation statements)

References 22 publications

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“…The model can be optimised once the CFD model is validated. Here, the ANSYS Design Exploration 16.0 software was used to solve the selected DOE and surrogate models [ 13 ]. Fig.…”

Section: Surrogate Model Methodologymentioning

confidence: 99%

“…The selection of a proper DOE is significant [ 13 , 14 ] while performing numerical experiments. In this study, the distribution of design points was determined by using four types of DOE, namely BBD, CCD, LHDs and OSFD.…”

Section: Surrogate Model Methodologymentioning

confidence: 99%

“…In addition to using surrogate models, researchers have explored the combination of machine learning (ML) [ 23 , [38] , [39] , [40] , [41] , [42] , [43] ] genetic algorithms (GA) [ 23 , 38 , [43] , [44] , [45] , [46] , [47] ] design of experiments (DoE) [ 13 , 48 ] and computational fluid dynamics (CFD) [ 4 , 5 , 8 , 12 , 27 , 28 , 49 ] in engineering optimisation. Moiz et al [ 43 ] presented an integrated ML and GA approach to optimising internal combustion engines, achieving comparable results to traditional CFD-GA approaches with significant time and cost savings.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the number of studies focusing on applying different surrogate models (e.g., PR and Kriging-based) in optimisation has increased. However, it seems that the choice of surrogate model is very problem-dependent, and even for the same problem, small variations in the DOE can change the ranking of the methods [ 13 , 14 ]. Some comparative studies on PR and Kriging-based models in design optimisation have been reported [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

Mukhtar¹,

Yasir²,

Nasir³

2023

Heliyon

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“…The model can be optimised once the CFD model is validated. Here, the ANSYS Design Exploration 16.0 software was used to solve the selected DOE and surrogate models [ 13 ]. Fig.…”

Section: Surrogate Model Methodologymentioning

confidence: 99%

Section: Surrogate Model Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

Mukhtar¹,

Yasir²,

Nasir³

2023

Heliyon

View full text Add to dashboard Cite

“…Finally, the top-ranked design candidates were found via the RSM-based optimization approach and validated in CFD analysis. The details of each step of CFD-DoE guided optimization are reported in a recent publication [39]. The specific space-filling method and RSM approach used in this work to achieve the best design will be discussed in the next section.…”

Section: Optimization Workflowmentioning

confidence: 99%

Numerical Optimization of Spray-Guided Spark Assistance for Cold Idle Operation in a Heavy-Duty Gasoline Compression Ignition Engine

et al. 2023

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This article describes the results of a response surface model (RSM)-based numerical optimization campaign for spray-guided spark assistance at cold operations in a heavy-duty gasoline compression ignition (GCI) engine. On the basis of an earlier work on spark-assisted GCI cold combustion, a space-filling design of experiments (DoE) method was first undertaken to investigate a multitude of hardware design variables and engine operating parameters. The main design variables included the number of injector nozzles, fuel split quantities and injection timings, and spark timing. The objective variables were engine combustion efficiency (ŋc), maximum pressure rise rate (MPRR), and engine-out nitrogen oxide (NOx) emissions. A total of 150 design candidates were automatically generated using the Sobol sequence method provided by the commercial software package, CAESES. Then, closed-cycle computational fluid dynamic (CFD) spark-assisted GCI simulations under cold idling operations were performed. The outcomes from the CFD-DoE design campaign were utilized to construct high-fidelity RSMs that allowed for further design optimization of the spark plug- and fuel injector-related design variables, along with fuel injection strategy parameters. A merit function with respect to objective variables was formulated with an appropriate weight assignment on each objective variable. Finally, the best design candidate was identified from the RSM-based optimization process and further validated in the CFD analysis. The best design candidate showed the potential to significantly improve combustion efficiency (ŋc > 90%) over the baseline at cold idle while satisfying MPRR and NOx emissions constraints (MPRR < 5 bar/CAD and NOx < 4.5 g/kWh).

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Computational fluid dynamics–guided engine combustion system design optimization using design of experiments

Cited by 2 publications

References 22 publications

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

Numerical Optimization of Spray-Guided Spark Assistance for Cold Idle Operation in a Heavy-Duty Gasoline Compression Ignition Engine

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