Investigating Air Handling Requirements of High Load Low Speed Reactivity Controlled Compression Ignition (RCCI) Combustion

Kavuri, Chaitanya; Kokjohn, Sage

doi:10.4271/2016-01-0782

Cited by 11 publications

(11 citation statements)

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“…A number of studies have investigated combustion control strategies to allow for high load dualfuel operation, such as the direct dual fuel stratification [17][18][19] or low temperature combustion [20][21][22][23]. However, these approaches often require relatively complex engine hardware modifications and/or high levels of EGR and boost pressure.…”

Section: Introductionmentioning

confidence: 99%

“…Although dual-fuel research has been carried out at high engine loads, most studies utilised gasoline and NG as a partial substitute for diesel fuel. In addition, the dual-fuel combustion strategies have required high levels of EGR to minimise PRR's, which can lead to fuel economy penalty due to pumping losses [21]. Previous investigations without EGR were limited to low premixed fuel fractions at such conditions.…”

Section: Introductionmentioning

confidence: 99%

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Improvement in high load ethanol-diesel dual-fuel combustion by Miller cycle and charge air cooling

Pedrozo

Zhao

2018

Applied Energy

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement in high load ethanol-diesel dual-fuel combustion by Miller cycle and charge air cooling

Pedrozo

Zhao

2018

Applied Energy

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“…The optimization study which involved 22 input parameters for bowl geometry, injector design, and air-handling resulted in an optimum configuration that improved the net indicated efficiency (NIE) by 6.9% over the baseline system when fueled with DME. Kavuri and colleagues 13–15 also followed a similar approach to identify feasible operating strategies with reactivity controlled compression ignition (RCCI) combustion for low-load-high-speed 13 and high-load-low-speed 14–16 operating conditions. To thoroughly explore the design space, each generation of the GA should be sufficiently populated.…”

Section: Introductionmentioning

confidence: 99%

“…To thoroughly explore the design space, each generation of the GA should be sufficiently populated. Kavuri and Kokjohn 14 found that for a GA with five input parameters, each generation should have a minimum of 96 citizens and run for 50 generations to optimize between computational expense and convergence rate. With this setup, the optimization with five input parameters took ∼50 days to complete.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of machine learning in reducing the computational time/expense and improving the reliability of engine optimization studies

Kavuri

Kokjohn

2018

International Journal of Engine Research

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Past research has shown that multidimensional computational fluid dynamics modeling in combination with a genetic algorithm method is an effective approach for optimizing internal combustion engine design. However, optimization studies performed with a detailed computational fluid dynamics model are time intensive, which limits the practical application of this approach. This study addresses this issue by using a machine learning approach called Gaussian process regression in combination with computational fluid dynamics modeling to reduce the computational optimization time. An approach was proposed where the Gaussian process regression model could be used instead of the computational fluid dynamics model to predict the outputs of the genetic algorithm optimization. In this approach, for every nth generation of the genetic algorithm, the data from the previous n − 1 generations was used to train the Gaussian process regression model. The approach was tested on an engine optimization study with five input parameters. When the genetic algorithm was run solely with computational fluid dynamics, the optimization took 50 days to complete. In comparison with the computational fluid dynamics and Gaussian process regression approach, the computational time was reduced by 62%, and the optimization was completed in 19 days using the same amount of computational resources. Additional parametric studies were performed to investigate the impact of genetic algorithm + Gaussian process regression parameters. Results showed that either reducing the initial dataset size or relaxing the error criterion resulted in increased Gaussian process regression evaluations within the genetic algorithm. However, relaxing the error criterion was found to impact the model predictions negatively. The initial dataset size was found to have a negligible impact on the final optimum design. Finally, the potential of machine learning in further improving the optimization process was explored by using the Gaussian process regression model to check for the robustness of the designs to operating parameter variations during the optimization. The genetic algorithm was repeated with the modified procedure and it was shown that adding the stability check resulted in a different, more reliable and stable optimum solution.

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“…The energy used in driving the supercharger needs to be accounted for. Therefore, based on an assumption that the supercharger is mounted on the engine, with a supercharger efficiency of 0.62 [47], the power consumed by the supercharger is calculated. The Eaton M62 supercharger used for this setup is capable of running at speeds upto 14,000 rpm.…”

Section: Accounting For Supercharger Lossesmentioning

confidence: 99%

An Experimental Investigation of Low Temperature Combustion Regimes in a Light Duty Engine

Kannan¹

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Investigating Air Handling Requirements of High Load Low Speed Reactivity Controlled Compression Ignition (RCCI) Combustion

Cited by 11 publications

References 30 publications

Improvement in high load ethanol-diesel dual-fuel combustion by Miller cycle and charge air cooling

Improvement in high load ethanol-diesel dual-fuel combustion by Miller cycle and charge air cooling

Exploring the potential of machine learning in reducing the computational time/expense and improving the reliability of engine optimization studies

An Experimental Investigation of Low Temperature Combustion Regimes in a Light Duty Engine

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