Comparison and implementation of machine learning models for predicting the combustion phases of hydrogen-enriched Wankel rotary engines

Wang, Huaiyu; Ji, Changwei; Su, Teng; Cheng, Shen; Ge, Yunshan; Yang, Jinxin; Wang, Shuofeng

doi:10.1016/j.fuel.2021.122371

Cited by 32 publications

(7 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The material cutting pressure verification is necessary to know if the cutting phenomenon is achieved or not. The normal pressure generated at the minimum cutting force is described by equation (5). is approximately 1600 N/mm 2 , which is comparable to the yield strength required to cut the material from the workpiece and is thus perfectly acceptable.…”

Section: B Cutting Force and Temperature Minimizationmentioning

confidence: 99%

“…Prior knowledge of cutting forces and temperature is very useful in determining the cutting power of a machining operation, tool life, and other effects on the workpiece as well as on machine capacity, to achieve this, several research studies are being carried out to model the influence of cutting variables on the quality on the quality of machining in order to increase productivity and the capability of the tool-machine. These studies aim to understand the process of cutting phenomena in such a way as to choose the optimal, efficient, and cost-effective cutting parameters, such as studies to predict cutting force either by statistical [1], analytical or numerical modelling [2][3][4], others have used artificial intelligence model (SVM and GPR) focusing on cutting force only [5] or on the study of vibrations (tool dynamics) [6]. This paper uses an artificial intelligence model specifically an artificial neural network based on Oxley's prediction theory in combination with the JC plasticity equation to predict several cutting effects, including cutting force Fc, the shear temperature in the cutting zone T and chip thickness e c , while considering the speed of cut Vc, the advance f, the deep cut a p and the angle of the cut as input variables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cutting Forces and Temperature Optimization in Turning using a Predictive Machining Theory, ANN, and MOGA

Outemsaa¹,

Farissi²,

Hamouti³

et al. 2022

IJETAE

View full text Add to dashboard Cite

To minimise stresses on the tool and workpiece, such as wear, thermal effect, workpiece stresses, cutting power, etc., the cutting force and the heat in the cutting area should be minimised. This work aims to introduce an artificial intelligence tool, more precisely the neural network, to achieve optimized cutting conditions. Oxley cutting modelling in conjunction with Johnson-Cook of an AISI 1045 material is converted to an artificial neural network model which will be used to determine a fitness function to be optimized. The Artificial Neural Network is constructed based on the training data collected from the predictive model of Oxley and JC, the choice of the most accurate ANN of minimal MSE= 0.001108 is based on a specific method of tuning the hyperparameters which result in an architecture; two hidden layers, 25 neurons for each hidden layer, a sigmoid activation function, a trainlm learning algorithm, and a learning rate of 0.01. A multi-objective optimization is performed using the MATLAB tool to obtain the optimum values for cutting velocity Vc, advance f, penetration depth ap, and cutting angle of the tool. It is found that the neural network is a more rapid calculation of cutting conditions such as shear zone forces, shear zone temperatures, and others. contrary to the Oxley and JC mathematical model which will require a lot of calculations. The optimum values for cutting conditions are 208 mm/min for cutting speed, 0.06mm/rev for f, 0.38 for ap, and 10° for clearance angle.

show abstract

Section: B Cutting Force and Temperature Minimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cutting Forces and Temperature Optimization in Turning using a Predictive Machining Theory, ANN, and MOGA

Outemsaa¹,

Farissi²,

Hamouti³

et al. 2022

IJETAE

View full text Add to dashboard Cite

show abstract

“…When applied to model prediction in biogas-powered diesel engines, machine learning improves accuracy, flexibility, non-linear relationship management, optimisation capabilities, scalability, and generalisation. These benefits lead to improved comprehension and optimisation [25,28].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Metamodel Development for Waste-Derived Biogas-Powered Dual-Fuel Engines Using Modern Machine Learning with K-Cross Fold Validation

Alruqi

Hanafi

Sharma³

2023

Fermentation

View full text Add to dashboard Cite

Attention over greenhouse gas emissions has driven interest in cleaner energy sources including alternative fuels. Waste-derived biogas, which is produced by the anaerobic digestion of organic waste such as municipal solid waste, agricultural residues, and wastewater sludge, is an intriguing biofuel source due to its abundant availability and promise of lowering emissions. We investigate the potential of waste-derived biogas as an alternative fuel for a dual-fuel engine that also uses diesel as a secondary fuel in this study. We suggest using a modern machine learning XGBoost model to forecast engine performance. Data acquired with thorough lab-based text will be used to create prognostic models for each output in this effort. Control factors impacting engine performance, including pilot fuel injection pressure, engine load, and pilot fuel injection time, will be employed. The effects of these control elements on engine reaction variables such as brake thermal efficiency (BTE), peak pressure (Pmax), nitrogen oxides (NOx), carbon monoxide (CO), and unburned hydrocarbons (UHC) were simulated. The created models were tested using a variety of statistical approaches, including the coefficient of determination (0.9628–0.9892), Pearson’s coefficient (0.9812–0.9945), mean absolute error (0.4412–5.89), and mean squared error (0.2845–101.7), all of which indicated a robust prognostic model. The use of the increased compression ratio helped in the improvement of BTE with a peak BTE of 26.12%, which could be achieved at an 18.5 compression ratio 220 bar fuel injection pressure peak engine load. Furthermore, our findings give light regarding how to improve the performance of dual-fuel engines that run on waste-derived biogas, with potential implications for cutting emissions in the transportation sector.

show abstract

“…The optimization process can be accelerated by using the surrogate model approach, and has achieved excellent results in practical applications, especially in the optimization of experimental parameters. Statistical methods are very effective in the application of constructing surrogate models and are very effective in the engineering field [16][17][18]. Ji et al employed the genetic algorithm to optimize the engine performance based on support vector machine intelligent regression.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of the Flue Gas Heat Recovery of a Marine Dual-Fuel Engine Based on RSM and GA

2022

Self Cite

View full text Add to dashboard Cite

Implementation of flue gas waste heat recovery is an effective way to improve the energy utilization of marine engines. This paper aims to model and optimize a marine four-stroke dual-fuel (DF) engine coupled with a flue gas waste heat recovery system. Firstly, the DF engine and waste heat recovery system were respectively modeled in GT-Power and Simulink environments and verified with experimental data. Then, a regression model was built using the response surface method, with the intake temperature, compression ratio, and pilot fuel injection timing as input parameters and parametric analysis was performed. Finally, multi-objective optimization of the waste heat recovery system was performed using a genetic algorithm. The result showed that the optimal solution is obtained when the intake temperature is 306.18 K, the geometric compression ratio is 14.4, and the pilot fuel injection timing is −16.68 °CA after the top dead center. The corresponding brake-specific fuel consumption was 155.18 g/kWh, reduced by 3.24%, and the power was 8025.62 kW, increased by 0.32%. At the same time, 280.98 kW of flue gas waste heat generation was obtained.

show abstract

Comparison and implementation of machine learning models for predicting the combustion phases of hydrogen-enriched Wankel rotary engines

Cited by 32 publications

References 62 publications

Cutting Forces and Temperature Optimization in Turning using a Predictive Machining Theory, ANN, and MOGA

Cutting Forces and Temperature Optimization in Turning using a Predictive Machining Theory, ANN, and MOGA

Prognostic Metamodel Development for Waste-Derived Biogas-Powered Dual-Fuel Engines Using Modern Machine Learning with K-Cross Fold Validation

Modeling and Optimization of the Flue Gas Heat Recovery of a Marine Dual-Fuel Engine Based on RSM and GA

Contact Info

Product

Resources

About