Combustion analysis of a compression-ignition engine fuelled with an algae biofuel blend and diethyl ether as an additive by using an artificial neural network

Biofuels ◽  
2019 ◽  
pp. 1-10 ◽  
Author(s):  
Mahesh Prakash Joshi ◽  
Sukrut Shrikant Thipse
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diethyl Ether ◽  
Compression Ignition ◽  
Combustion Analysis ◽  
Compression Ignition Engine ◽  
Algae Biofuel ◽  
Artificial Neural

The model of compression ignition engine with artificial neural networks

2008 ◽  
Vol 132 (1) ◽  
pp. 44-49
Author(s):  
Krzysztof BRZOZOWSKI ◽  
Jacek NOWAKOWSKI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Compression Ignition ◽  
Control Parameters ◽  
Working Process ◽  
Compression Ignition Engine ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Model Equations

The paper presents an application of artificial neural network in modelling the working process in compression ignition engine. In order to determine the usefulness of proposed method the optimisation task has been formulated. The aim of optimisation process was to find the engine control parameters which enable reduction of the NOx emission. In order to solve the problem, the model equations has to be integrated for values of control parameters whose are given as output from the neural networks implemented.

scholarly journals Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2410 ◽  
Author(s):  
Farzad Jaliliantabar ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Talal Yusaf
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Neural Network Modeling ◽  
Biodiesel Fuel ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Performance And Emissions ◽  
Artificial Neural

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

Computational optimization of reactivity controlled compression ignition combustion to achieve high efficiency and clean combustion

2020 ◽  
pp. 146808742093173 ◽  
Author(s):  
Avilash Jain ◽  
Anand Krishnasamy ◽  
Pradeep V
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Injection ◽  
Direct Injection ◽  
Network Models ◽  
Compression Ignition ◽  
Neural Network Models ◽  
Reactivity Controlled Compression Ignition ◽  
Artificial Neural ◽  
Artificial Neural Network Models

One of the major limitations of reactivity controlled compression ignition is higher unburned hydrocarbon and carbon monoxide emissions and lower thermal efficiency at part load operating conditions. In the present study, a combined numerical approach using a commercial three-dimensional computational fluid dynamics code CONVERGE along with artificial neural network and genetic algorithm is presented to address the above limitation. A production light-duty diesel engine is modified to run in reactivity controlled compression ignition by replacing an existing mechanical fuel injection system with a flexible electronic port fuel injection and common rail direct injection systems. The injection schedules of port fuel injection and direct injection injectors are controlled using National Instruments port and direct injection driver modules. Upon validation of combustion and emission parameters, parametric investigations are carried out to establish the effects of direct-injected diesel fuel timing start of injection (SOI), premixed fuel ratio and intake charge temperature on the engine performance and emissions in reactivity controlled compression ignition. The results obtained show that the start of injection timing and intake charge temperature significantly influence combustion phasing, while the premixed fuel ratio controls mixture reactivity and combustion quality. By utilizing the data generated with the validated computational fluid dynamics models, the artificial neural network models are trained to predict the engine exhaust emissions and efficiency. The artificial neural network models for gross indicated efficiency and oxides of nitrogen (NOx) are then coupled with genetic algorithm to maximize gross indicated efficiency while keeping the NOx and soot emissions within Euro VI emission limits. By optimizing the start of injection timing, premixed fuel ratio and intake charge temperature simultaneously using the artificial neural network models coupled with genetic algorithm, 19% improvement in gross indicated efficiency, 60% and 64% reduction in hydrocarbon and carbon monoxide emissions, respectively, are obtained in reactivity controlled compression ignition compared to the baseline case.

scholarly journals Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

2019 ◽  
Vol 21 (1) ◽  
pp. 151-168 ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Prithwish Kundu ◽  
Muhsin M Ameen ◽  
Tarek Echekki ◽  
Sibendu Som
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Dimensional Manifold ◽  
Neural Network Models ◽  
Compression Ignition Engine ◽  
Memory Footprint ◽  
Lookup Tables ◽  
Artificial Neural

The “curse of dimensionality” has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. This work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

2020 ◽  
pp. 146808742093455
Author(s):  
Dennis Robertson ◽  
Robert Prucka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ignition Delay ◽  
Degrees Of Freedom ◽  
Internal Combustion Engines ◽  
Production Control ◽  
Compression Ignition ◽  
Advanced Combustion ◽  
Artificial Neural ◽  
Polynomial Regressions

The drive to improve performance and efficiency of internal combustion engines has greatly expanded the degrees of freedom of engine systems. As efficiency objectives exceed the capability of traditional combustion strategies, advanced combustion modes are more attractive for production. These advanced combustion strategies typically add sensors, actuators, and degrees of freedom to the combustion process itself. Spark-assisted compression ignition is an efficient production-viable advanced combustion mode characterized by a spark-ignited flame propagation that triggers autoignition in the remaining unburned gas. This research focuses on autoignition modeling for spark-assisted compression ignition combustion phasing control. This work comprehensively evaluates several autoignition model structures and identifies the real-time production control implications of each. The candidate models include four ignition delay correlations, an ignition delay lookup, three polynomial regressions, and an artificial neural network. All are computationally feasible using production controllers, but the artificial neural network model represents autoignition phasing significantly better than the other options evaluated. The polynomial regressions were similar in error and exceeded the accuracy of ignition delay models. The low performance of the induction time integral–based models stems primarily from the exclusion of low-temperature heat release. The regression models are also exercised on an experimental engine dataset to identify the impact of engine phenomenon such as charge stratification on the performance of each model structure. The trends in the model performance as well as the magnitude of the error were similar when evaluated on both spark-assisted compression ignition simulation data and homogeneous charge compression ignition experimental data.

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