Comparison of Physics-Based, Semi-empirical and Neural Network-based Models for Model-based Combustion Control in a 3.0 L Diesel Engine

Hu, Song; D’Ambrosio, Stefano; Finesso, Roberto; Manelli, Andrea; Marzano, Mario Rocco; Mittica, Antonio; Ventura, Loris; Wang, Hechun; Wang, Yinyan

doi:10.3390/en12183423

Cited by 13 publications

(6 citation statements)

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“…The classification of nanofluids and the analysis of theoretical advances to modeling the transfer coefficients are given in [19]. In particular, it is noted that a rigorous theory of transfer processes in nanofluids has not yet been developed, and the application of thermal conductivity modeling by molecular dynamics methods still gives predictions different from the classical theory [33][34][35][36][37][38][39][40][41]. However, none of the works mention how to avoid sediment on the bottom of the engine cooling channel of "light" nanoparticles under the action of inertial and gravitational forces.…”

Section: Literature Surveymentioning

confidence: 99%

Hydrogen Containing Nanofluids in the Spark Engine’s Cylinder Head Cooling System

et al. 2021

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The article is devoted to the following issues: boiling of fluid in the cooling jacket of the engine cylinder head; agents that influenced the thermal conductivity coefficient of nanofluids; behavior of nanoparticles and devices with nanoparticles in the engine’s cylinder head cooling system. The permissible temperature level of internal combustion engines is ensured by intensification of heat transfer in cooling systems due to the change of coolants with “light” and “heavy” nanoparticles. It was established that the introduction of “light” nanoparticles of aluminum oxide into the water in a mass concentration of 0.75% led to an increase in its thermal conductivity coefficient by 60% compared to the base fluid at a coolant temperature of 90 °C, which corresponds to the operating temperature of the engine cooling systems. At the indicated temperature, the base fluid has a thermal conductivity coefficient of 0.545 W/(m °С), for nanofluid with particles its value was 0.872 . At the same time, a positive change in the parameters of the nanofluid in the engine cooling system was noted: the average movement speed increased from 0.2 to 2.0 m/s; the average temperature is in the range of 60–90 °C; heat flux density 2 × 102–2 × 106 ; heat transfer coefficient 150–1000 . Growth of the thermal conductivity coefficient of the cooling nanofluid was achieved. This increase is determined by the change in the mass concentration of aluminum oxide nanoparticles in the base fluid. This will make it possible to create coolants with such thermophysical characteristics that are required to ensure intensive heat transfer in cooling systems of engines with various capacities.

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Section: Literature Surveymentioning

confidence: 99%

Hydrogen Containing Nanofluids in the Spark Engine’s Cylinder Head Cooling System

et al. 2021

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“…Recently developed technologies to reduce CO 2 and pollutant emissions from diesel engines include downsizing [2], alternative fuels [3], advanced combustion [4,5] and injection systems [6], control algorithms [7][8][9][10], innovative ATS (after-treatment systems) [11], and recovery of thermal energy [12].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping

D’Ambrosio

Finesso

Hardy³

et al. 2021

Energies

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In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NOx model has been implemented to improve robustness in terms of NOx prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NOx emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NOx emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.

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“…Artificial neural networks are already used in vehicle simulation and control applications as well. In [19], authors are using artificial neural networks for modeling the main combustion metrics of diesel engines as an alternative for parameter tuning of dynamical models. Authors in [20] are simulating vertical tire and suspension dynamics while traversing road irregularities with recurrent neural networks.…”

Section: Introduction 1literature Outlookmentioning

confidence: 99%

Fast Motion Model of Road Vehicles with Artificial Neural Networks

2021

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Nonlinear optimization-based motion planning algorithms have been successfully used for dynamically feasible trajectory planning of road vehicles. However, the main drawback of these methods is their significant computational effort and thus high runtime, which makes real-time application a complex problem. Addressing this field, this paper proposes an algorithm for fast simulation of road vehicle motion based on artificial neural networks that can be used in optimization-based trajectory planners. The neural networks are trained with supervised learning techniques to predict the future state of the vehicle based on its current state and driving inputs. Learning data is provided for a wide variety of randomly generated driving scenarios by simulation of a dynamic vehicle model. The realistic random driving maneuvers are created on the basis of piecewise linear travel velocity and road curvature profiles that are used for the planning of public roads. The trained neural networks are then used in a feedback loop with several variables being calculated by additional numerical integration to provide all the outputs of the original dynamic model. The presented model can be capable of short-term vehicle motion simulation with sufficient precision while having a considerably faster runtime than the original dynamic model.

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Comparison of Physics-Based, Semi-empirical and Neural Network-based Models for Model-based Combustion Control in a 3.0 L Diesel Engine

Cited by 13 publications

References 50 publications

Hydrogen Containing Nanofluids in the Spark Engine’s Cylinder Head Cooling System

Hydrogen Containing Nanofluids in the Spark Engine’s Cylinder Head Cooling System

Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping

Fast Motion Model of Road Vehicles with Artificial Neural Networks

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