Experiment analysis and computational optimization of the Atkinson cycle gasoline engine through NSGA Ⅱ algorithm using machine learning

Tong, Ji Jun; Li, Yangyang; Liu, Jingping; Cheng, Ran; Guan, Jinhuan; Wang, Shuqian; Liu, Shujing; Hu, Song; Tao, Guo

doi:10.1016/j.enconman.2021.113871

Cited by 32 publications

(12 citation statements)

References 37 publications

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“…According to Figures 8 e,f, the optimal solution has a delayed intake time and an earlier exhaust valve time, which reduces the pumping loss at the operation point of 2800 rpm and 11.42 bar and also reduces the in-cylinder temperature to suppress detonation. 51 , 52 Figure 8 g shows the spark timing for each solution in the Pareto optimal solution set. The spark timing for the optimal solution chosen in the study is −15.9 CAD.…”

Section: Resultsmentioning

confidence: 99%

“…The late closing of the intake valve and early opening of the exhaust valve reduces the pumping loss at the point of operation at 2800 rpm and 11.42 bar, which partially compensates for the adverse effects on fuel economy and power performance because of the retarding spark timing and the use of EGR. Minimizing damage to engine dynamics and economy while suppressing knock is achieved. , …”

Section: Resultsmentioning

confidence: 99%

“…Minimizing damage to engine dynamics and economy while suppressing knock is achieved. 50,51 Figure 7f−i show the changes in the values of the four parameters for all individuals in each generation of the population. From Figure 7f, we can see that the EVT values of all the individuals in the 50th generation are smaller than the initial value of 260 CAD.…”

Section: Results Of the ε-Constrainedmentioning

confidence: 99%

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Optimization of a Spark Ignition Engine Knock and Performance Using the Epsilon-Constrained Differential Evolution Algorithm and Multi-Objective Differential Evolution Algorithm

et al. 2022

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Since the advent of the internal combustion engine, knock has been a vital issue limiting the thermal efficiency of spark ignition engines under heavy load conditions. The occurrence of knock is also directly influenced by several operating parameters simultaneously. In order to investigate the effects of multiple variables on economic performance and power performance under knock limits, this study adopts single-objective optimization and multi-objective optimization methods to optimize the engine operating parameters, including exhaust gas recirculation rate, exhaust valve timing, spark timing, and intake valve timing. The optimization aims to obtain maximum volumetric efficiency, brake mean effective pressure, and minimum brake specific fuel consumption on the knock limit. First, based on the bench test data at the operation point 2800 rpm and 11.42 bar, a one-dimensional simulation engine model is established in GT-power software and verified. Second, four engine operating parameters are input into the GT-power model as controlled parameters. The epsilon-constrained differential evolution algorithm and the multi-objective differential evolution algorithm are employed to optimize the above four parameters to minimize the knock index and the damage to engine performance due to knock suppression, respectively. Finally, the results show that the two optimization algorithms optimize four parameters. The results of the epsilon-constrained differential evolution algorithm indicate that the decreasing extent of the knock index is 73.3%. In addition, the decreasing extent of brake mean effective pressure is 10.2%. What is more, the increased brake specific fuel consumption is only 0.07%. The multi-objective differential evolution algorithm gives a set of nondominated Pareto optimal solution sets. The optimal solution has a 64.4% decrease in the knock index, a 5.78% decrease in brake mean effective pressure, and a 1.45% decrease in brake specific fuel consumption.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Results Of the ε-Constrainedmentioning

confidence: 99%

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Optimization of a Spark Ignition Engine Knock and Performance Using the Epsilon-Constrained Differential Evolution Algorithm and Multi-Objective Differential Evolution Algorithm

et al. 2022

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“…Multiobjective optimization is carried out using the wellknown NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm) based on non-dominant sorting and crowding� this algorithm follows the general scheme of a genetic algorithm with modified mating and survival selection, which greatly improve the optimization speed and the result. 70 The values for the hyperparameters associated with the multiobjective optimization (NSGA-II) are as follows: population size (PS): 100 individuals, maximum number of generation (MNG): 100, crossover fraction (CF): 0.9, and mutation fraction (MF): 0.5, which are adequate values for problems as complex as the addressed one. It should be noticed that NSGA-II with the 100 individuals and 100 generations selected performs a total of 10,000 evaluations during the optimization process.…”

Section: Mlp Modelsmentioning

confidence: 99%

Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs

López-Flores

Lira-Barragán

Rubio-Castro

et al. 2023

Ind. Eng. Chem. Res.

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In this paper, deep learning models are developed based on a multilayer perceptron to forecast 12 month cumulative produced shale gas and 90 day produced flowback water using a study area within the Eagle Ford Formation as a database. These models can help decision-makers have important references when drilling new wells. Latitude, longitude, true vertical depth, lateral longitude, total proppant, and total fracture water are used as input variables. The trained models are evaluated in a study area within the Burgos Basin to analyze the energy benefits, freshwater consumption, and flowback water produced through well drilling for the first time in Mexico. A non-dominated sorting genetic algorithm is used for the minimization of total freshwater consumption and maximization of cumulative produced gas. The multilayer perceptron models showed good results. The coefficient of determination and the mean absolute error of the single-output models remained above 0.93 and below 3.00 for the validation set, respectively.

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“…The results showed that different models have different advantages, among which the GPR model had very good generalization ability in scarce data sets. Many researchers use intelligent algorithms to predict engine performance and emission characteristics under different operating conditions 30,31 and use optimization algorithms to find the optimal fuel economy point or Pato boundary 32 with minimum emissions and maximum fuel economy, providing direction for engine calibration, energy conservation, and emission reduction. It can be seen that accurate prediction of engine characteristics plays an important role.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Verification of Performance and Emission Characteristics of Diesel/Natural Gas Dual-Fuel Engine Based on Intelligent Algorithm

Wan

Huang

et al. 2023

ACS Omega

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With the development of computer application technologies, intelligent algorithm has been widely used in various fields. In this study, a coupled Gaussian process regression and feedback neural network (GPR-FNN) algorithm is proposed, and it is used to predict the performance and emission characteristics of a six-cylinder heavy-duty diesel/natural gas (NG) dual-fuel engine. Using the engine speed, torque, NG substitution rate, diesel injection pressure, and injection timing as inputs, an GPR-FNN model is established to predict the crank angle corresponding to 50% heat release, brake-specific fuel consumption, brake thermal efficiency, and carbon monoxide, carbon dioxide, total unburned hydrocarbon, nitrogen oxides, and soot emissions. Subsequently, its performance is evaluated using experimental results. The results show that the regression correlation coefficients of all output parameters are greater than 0.99, and the mean absolute percentage error is less than 5.9%. In addition, a contour plot is used to compare the experimental results with the GPR-FNN prediction data in detail, and the results show that the prediction model has high accuracy. The results of this study can provide new ideas for the research on diesel/natural gas dual-fuel engines.

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Experiment analysis and computational optimization of the Atkinson cycle gasoline engine through NSGA Ⅱ algorithm using machine learning

Cited by 32 publications

References 37 publications

Optimization of a Spark Ignition Engine Knock and Performance Using the Epsilon-Constrained Differential Evolution Algorithm and Multi-Objective Differential Evolution Algorithm

Optimization of a Spark Ignition Engine Knock and Performance Using the Epsilon-Constrained Differential Evolution Algorithm and Multi-Objective Differential Evolution Algorithm

Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs

Prediction and Verification of Performance and Emission Characteristics of Diesel/Natural Gas Dual-Fuel Engine Based on Intelligent Algorithm

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