Analytical Target Cascading Framework for Diesel Engine Calibration Optimisation

Kianifar, Mohammed Reza; Campean, Felician

doi:10.4271/2014-01-2583

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…The choice of these level of noise was based on the comprehensive experimental studies such as those reported in Berger (2012) and Kianifar et al (2014), which systematically considered the experimental variability associated with modelling different engine responses.…”

Section: Test Functions and Test Casesmentioning

confidence: 99%

“…The aim of this case study was to predict the fuel consumption response of a Diesel engine on an engine calibration test. The test data was collected using 127 test points scheduled based on an adaptive D-Optimal DoE, covering the range of 6 design parameters, with 15 additional space-filling test points for validation (for more details see (Kianifar et al 2014)). This case study can be categorised as a problem of:…”

Section: Diesel Engine Fuel Consumptionmentioning

confidence: 99%

“…-Medium scale: 6 design parameters; -Medium-to high-order nonlinearity: due to the expected physical behaviour of the fuel consumption response (Kianifar et al 2014); -Medium DoE sample: 127 Model Building points for 6 parameters, which is bigger than a small DoE of 60 (i.e. 10 × 6) points, but smaller that the "large" DoE (i.e.…”

Section: Diesel Engine Fuel Consumptionmentioning

confidence: 99%

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Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Kianifar

Campean

2019

Struct Multidisc Optim

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Metamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes, and on this basis presents a systematic evaluation of the performance of frequently used metamodeling techniques. A set of metamodeling techniques, selected based on their reported use for engineering problems (i.e. Polynomial, Radial Basis Function, and Kriging), were systematically evaluated in terms of accuracy and robustness against a carefully assembled set of 18 test functions covering different types of problems, sampling conditions and noise conditions. A set of four real-world engineering case studies covering both computer simulation and physical experiments were also analysed as validation tests for the proposed guidelines. The main conclusions drawn from the study are that Kriging model with Matérn 5/2 correlation function performs consistently well across different problem types with smooth (i.e. not noisy) data, while Kriging model with Matérn 3/2 correlation function provides robust performance under noisy conditions, except for the very high noise conditions, where the Kriging model with nugget appears to provide better models. These results provide engineering practitioners with a guide for the choice of a metamodeling technique for problem types and modelling conditions represented in the study, whereas the evaluation framework and benchmarking problems set will be useful for researchers conducting similar studies. Structural and Multidisciplinary Optimization (2020) 61:159-186 Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide 161

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Section: Test Functions and Test Casesmentioning

confidence: 99%

Section: Diesel Engine Fuel Consumptionmentioning

confidence: 99%

Section: Diesel Engine Fuel Consumptionmentioning

confidence: 99%

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Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Kianifar

Campean

2019

Struct Multidisc Optim

Self Cite

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Effect of the engine calibration parameters on gasoline partially premixed combustion performance and emissions compared to conventional diesel combustion in a light-duty Euro 6 engine

et al. 2018

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On the Use of Multi-Disciplinary Optimisation Methods for Road Vehicle Passive Suspension Design

Yang

Ballo

Previati

et al. 2019

Volume 3: 21st International Conference on Advanced Vehicle Technologies; 16th International Conference on Design Education

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Two widely used decomposition-based multi-disciplinary optimisation (MDO) methods, namely analytical target cascading (ATC) and collaborative optimisation (CO), are applied to the design of the suspension system of a road vehicle. Instead of directly optimising the spring stiffness and the damping coefficient, three parameters of the spring and three parameters of the damper are selected as design variables. Discomfort, road holding, and the total mass of the spring-damper system, are considered as objective functions. An investigation is completed to analyse the performance of the two decomposition methods compared with the conventional all-in-one (AiO) formulation in terms of efficiency and applicability.

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Analytical Target Cascading Framework for Diesel Engine Calibration Optimisation

Cited by 6 publications

References 25 publications

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Effect of the engine calibration parameters on gasoline partially premixed combustion performance and emissions compared to conventional diesel combustion in a light-duty Euro 6 engine

On the Use of Multi-Disciplinary Optimisation Methods for Road Vehicle Passive Suspension Design

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