Multi-zone model for diesel engine simulation based on chemical kinetics mechanism

Neshat, Elaheh; Honnery, Damon; Saray, Rahim Khoshbakhti

doi:10.1016/j.applthermaleng.2017.04.090

Cited by 26 publications

(10 citation statements)

References 31 publications

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“…The white-box identification technique derives the engine models by resorting to physical laws. Examples of white-box identification on a diesel engine include CFD models (Reitz, Rutland 1995;Payri et al 2004), chemical kinetic models (Westbrook et al 2006;Ra, Reitz 2008) and analytical multi-zone models (Benajes et al 2016;Neshat et al 2017). These mathematical models are difficult to exploit due to numerous physical parameters and complex assumptions, which are demanding for estimation, particularly for engine operators.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Methods in Monitoring Operating Behaviour of Marine Two-Stroke Diesel Engine

et al. 2020

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The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Methods in Monitoring Operating Behaviour of Marine Two-Stroke Diesel Engine

et al. 2020

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“…The MRE of the model is less than 10% through experiments. E. Neshat et al [6] proposed a new multi-region model based on chemical dynamics for simulating the diesel engine closed loop. 14 reactions were used to simulate NO X formation in their model.…”

Section: Introductionmentioning

confidence: 99%

Prediction NOX Emissions for High Speed DI Diesel Engine Based on PSOBP

Tan¹,

Yang²,

Yang³

et al. 2020

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In this paper, BP neural network (BPNN) is studied to model and predict NO X emission of direct injection diesel engine. The model selects four parameters as input, namely rotation speed, load, exhaust temperature and fuel-air ratio. Through testing, it is concluded that the prediction performance of BPNN model will be greatly affected by the initial weight and threshold, so that the prediction accuracy of the model is not high. In order to reduce the influence of initial weight and threshold on the prediction performance of BPNN model, this paper adopts Particle Swarm Optimization (PSO) algorithm to optimize the initial weight and threshold of BPNN, and establishes the corresponding prediction model of NO X emission of diesel engine. The results show that the prediction model of BPNN optimized by PSO algorithm can effectively reduce the influence of initial weight and threshold on BPNN and make the prediction results of the model more reliable. In particular, when PSO adopts non-linear dynamic weight strategy and synchronous learning factor strategy, the prediction performance of BPNN model is more provided with reliability.

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“…developed a new multi zone model for diesel engine simulation. The model was coupled to a semi detailed chemical kinetics mechanism and could predict engine performance and exhaust NO x and PM accurately .…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic modeling and validation of in‐cylinder flow in diesel engines

Neshat

Nazemian

Honnery

2019

Env Prog and Sustain Energy

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The formation of a suitable air and fuel mixture in diesel engines is one of the most important factors in quality of combustion and engine exhaust emissions. The mixture formation depends on mass transfer phenomenon inside the combustion chamber. This study presents a new thermodynamic model to simulate the mass transfer phenomenon in diesel engines. Diesel engine is simulated utilizing a thermodynamic multi zone model coupled to a semi‐detailed chemical kinetics mechanism. Heat and mass transfer submodels are linked to the multi zone model. Bulk flow and diffusion mass transfer between zones are considered in the mass transfer submodel. Bulk mass transfer simulates fluid motion between different zones caused by piston speed and difference in zonal temperature and pressure. Diffusion mass transfer occurs due only to difference in composition of different zones. The results indicate diesel engine performance and exhaust emissions can be predicted accurately applying mass transfer submodels. Bulk mass transfer mechanism has more significant effects on engine performance and emission compared to diffusion mechanism. Neglecting mass transfer submodels, calculated in‐cylinder peak pressure is under predicted and both of exhaust NOx and soot are over predicted. The rates of soot oxidation reactions decrease and rates of NOx important reactions increase when mass transfer submodels are ignored. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

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Multi-zone model for diesel engine simulation based on chemical kinetics mechanism

Cited by 26 publications

References 31 publications

Machine Learning Methods in Monitoring Operating Behaviour of Marine Two-Stroke Diesel Engine

Machine Learning Methods in Monitoring Operating Behaviour of Marine Two-Stroke Diesel Engine

Prediction NOX Emissions for High Speed DI Diesel Engine Based on PSOBP

Thermodynamic modeling and validation of in‐cylinder flow in diesel engines

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