scholarly journals Individual Cylinder Air-Fuel Ratio Estimation for a Gasoline Engine with Electromagnetic Valve Train

2018 ◽  
Vol 202 ◽  
pp. 02011
Author(s):  
Yaxuan Xu ◽  
Siqin Chang
Keyword(s):  
Steady State ◽  
Gasoline Engine ◽  
Estimation Algorithm ◽  
Valve Train ◽  
Unknown Parameters ◽  
Steady State Condition ◽  
Electromagnetic Valve ◽  
Fuel Ratio ◽  
De Algorithm ◽  
The Individual

For the multi cylinder gasoline engine, the consistency of each cylinder is an important index to affect the emission and the power. In this paper, in order to reduce the air-fuel ratio (A/F) maldistribution of the engine based on the electromagnetic valve train (EMVT), an individual cylinder A/F estimation algorithm is proposed for the individual cylinder A/F control. Based on the analysis of the hybrid and transfer models of the exhaust of each cylinder in steady state, an individual A/F observer is established by using Kalman filter algorithm. Then the unknown parameters in the observer are identified by the differential evolution(DE) algorithm. Only a single wide area exhaust oxygen(UEGO) sensor is needed to identify the unknown parameters and estimate the A/F of each cylinder. The combined simulation of GT-Power and Simulink validates the effectiveness of the proposed estimation approach. The results show that the proposed method can provide good estimation results under steady-state condition.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

scholarly journals Estimation of Individual Cylinder Air-Fuel Ratio in Gasoline Engine with Output Delay

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Changhui Wang ◽  
Zhiyuan Liu
Keyword(s):  
Gasoline Engine ◽  
Delay System ◽  
Exhaust Pipe ◽  
Gas Mixing ◽  
Fuel Ratio ◽  
Torque Generation ◽  
Output Delay ◽  
Reduce Emissions ◽  
The Individual ◽  
Balancing Control

The estimation of the individual cylinder air-fuel ratio (AFR) with a single universal exhaust gas oxygen (UEGO) sensor installed in the exhaust pipe is an important issue for the cylinder-to-cylinder AFR balancing control, which can provide high-quality torque generation and reduce emissions in multicylinder engine. In this paper, the system dynamic for the gas in exhaust pipe including the gas mixing, gas transport, and sensor dynamics is described as an output delay system, and a new method using the output delay system observer is developed to estimate the individual cylinder AFR. With the AFR at confluence point augmented as a system state, an observer for the augmented discrete system with output delay is designed to estimate the AFR at confluence point. Using the gas mixing model, a method with the designed observer to estimate the individual cylinder AFR is presented. The validity of the proposed method is verified by the simulation results from a spark ignition gasoline engine from engine software enDYNA by Tesis.

Electromagnetic valve train for gasoline engine exhaust system

2016 ◽  
Vol 17 (3) ◽  
pp. 361-367 ◽  
Author(s):  
X. Y. Fan ◽  
L. Liu ◽  
S. Q. Chang ◽  
J. T. Xu ◽  
J. G. Dai
Keyword(s):  
Gasoline Engine ◽  
Exhaust System ◽  
Valve Train ◽  
Engine Exhaust ◽  
Electromagnetic Valve

scholarly journals Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1884 ◽  
Author(s):  
Ahmed M. Agwa ◽  
Attia A. El-Fergany ◽  
Gamal M. Sarhan
Keyword(s):  
Fuel Cells ◽  
Steady State ◽  
Fuel Cell ◽  
Cell Model ◽  
Optimization Methods ◽  
Pem Fuel Cells ◽  
Vital Role ◽  
Unknown Parameters ◽  
Steady State Condition ◽  
Search Optimization

In simulation studies, the precision of fuel cell models has a vital role in the quality of results. Unfortunately, due to the shortage of manufacturer data given in the datasheets, several unknown parameters should be defined to establish the fuel cell model for further precise analysis. This research addresses a novel application of the atom search optimization (ASO) algorithm to generate these unknown parameters of the fuel cell model and in particular of the polymer exchange membrane (PEM) type. The objective of this study is to establish an accurate model of the PEM fuel cells, which will provide accurate results of modeling and simulation in a steady-state condition. Simulations and further demonstrations were performed under MATLAB/SIMULINK. The viability of the proposed models was appraised by comparing its simulation results with the experimental results of number of commercial PEM fuel cells. In the same context, the obtained numerical results by the proposed ASO-based method were compared to other challenging optimization methods-based results. Finally, parametric tests were made which indicated the robustness of the ASO results as well. It can be stated here that ASO performs well and has a good capability to extract the unknown parameters with lesser errors.

Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

2020 ◽  
Vol 234 (14) ◽  
pp. 3347-3361
Author(s):  
Seunghyup Shin ◽  
Sangyul Lee ◽  
Minjae Kim ◽  
Jihwan Park ◽  
Kyoungdoug Min
Keyword(s):  
Deep Learning ◽  
Steady State ◽  
Gasoline Engine ◽  
Pressure Rise ◽  
Learning Model ◽  
Operating Conditions ◽  
Cylinder Pressure ◽  
Steady State Condition ◽  
Brake Mean Effective Pressure ◽  
Deep Learning Model

Recently, deep learning has played an important role in the rise of artificial intelligence, and its accuracy has gained recognition in various research fields. Although engine phenomena are very complicated, they can be predicted with high accuracy using deep learning because they are based on the fundamentals of physics and chemistry. In this research, models were built with deep neural networks for gasoline engine prediction. The model consists of two sub-models. The first predicts the knock occurrence, and the second predicts performance, combustion, and emissions. This includes maximum cylinder pressure, crank angle at maximum cylinder pressure, maximum pressure rise rate, and brake mean effective pressure, brake-specific fuel consumption, brake-specific nitrogen oxides, and brake-specific carbon oxide, which are representative results of the engine (for normal combustion cases without knock). Model input parameters were selected considering engine operating conditions, and physically measurable sensor values. For test cases, the accuracy of the first model for knock classification is 99.0%, and the coefficient of determination (R2) values for the second model are all above 0.99. Test times of both models were approximately 2 ms. The robustness of all the models was verified using K-fold cross-validation. A sensitivity study of accuracy, according to the amount of training utilized, was also conducted to determine how many data points are required to effectively train the deep learning model. Accordingly, a deep learning approach was applied to predict the steady-state conditions of a gasoline engine. Achieved model accuracies and robustness proved deep learning to be an effective modeling approach, and test time was recognized to be able to apply for the real-time prediction. The sensitivity analysis can be applied for the preliminary study to define the number of experimental points for the deep learning model.

Modeling Analysis on Improving Cylinder Balance in A Gasoline Engine Using Electromagnetic Valve Train

2019 ◽  
Vol 20 (6) ◽  
pp. 1195-1203
Author(s):  
Yaxuan Xu ◽  
Siqin Chang ◽  
Liang Liu ◽  
Tianbo Wang ◽  
Maoyang Hu
Keyword(s):  
Gasoline Engine ◽  
Valve Train ◽  
Electromagnetic Valve ◽  
Modeling Analysis

scholarly journals Modified tension infiltrometer and its use to determine the unsaturated hydraulic conductivity of upper vadose zone

1998 ◽  
Vol 18 ◽  
Author(s):  
Golam Shabbir Sattar
Keyword(s):  
Steady State ◽  
Hydraulic Conductivity ◽  
Vadose Zone ◽  
Water Pressure ◽  
Fundamental Property ◽  
Pressure Head ◽  
Unknown Parameters ◽  
Steady State Condition ◽  
Tension Infiltrometer ◽  
Unsaturated Hydraulic Conductivity

A modification of the CSIRO type tension infiltrometer was designed (designated UNCEL type) and tested. This infiltrometer is considerably cheaper, but more versatile, than any other infiltrometer reported so far. The modified infiltrometer was used in a reconstructed vadose zone to measure the unsaturated hydraulic conductivity and effect of compaction associated with the upper vadose zone. Hydraulic conductivity, a function of matric potential, is a fundamental property for water entry into the soil as well as in the upper vadose zone. Tension infiltrometer technique for determining unsaturated hydraulic conductivity, which is also a function of soil water pressure head, was compared with the existing techniques. The analysis of data from tension infiltrometer is based mainly on flow rate at steady-state condition. The steady-state measurement also enables to determine unknown parameters on the basis of at least two negative heads at the same location. This modified infiltrometer imposes pressure potentials at soil surface from 0.01 to 0.15 m of water, as a result macropores with an air entry value of less than applied tension are excluded. This is practically useful when investigating the influence of structure and compaction of upper vadose zone. Measurement of infiltration at various negative pressure head, with a single disc diameter allowed sensitive measurement of hydraulic properties of upper vadose zone with minimal surface disturbance. The newly designed tension infiltrometer and adopted schemes of calculation enable to determine unsaturated hydraulic conductivity of upper vadose zone with relevance to structural effects more accurately.

scholarly journals The impact of running-in on the friction of an automotive Gasoline-engine and in particular on its piston assembly and valve-train

2019 ◽  
Author(s):  
Christoph Knauder ◽  
Hannes Allmaier ◽  
Stefan Salhofer ◽  
Theodor Sams
Keyword(s):  
Gasoline Engine ◽  
Combustion Engine ◽  
Valve Train ◽  
Term Operation ◽  
Machine Elements ◽  
The Individual ◽  
Direct Acting ◽  
The Impact ◽  
Piston Assembly

Generally, mating surfaces that are in tribological contact undergo a running-in process at the beginning of theiroperational lifetime. During this running-in phase the tribological operating condition change significantly leading ideallyto long term operation with a minimum of continuous wear. While this process and its duration is rather well understoodfor single machine elements like journal bearings, it is the aim of this work to investigate the running-in behaviour ofmore complex systems like an internal combustion engine and its sub-assemblies. To gain insight into the influenceand duration of this running-in phase a series of tests have been performed under realistic engine operating conditions.To be able to separate the running-in processes for the individual subsystems piston assembly, valve-train and journalbearings of the crank train, a large series of tests have been conducted for a conventional gasoline passenger carengine. The results show a strong influence of the running-in process on total engine friction, which can be attributedmostly to the direct acting valve-train and to a considerably lesser extent to the piston assembly.

scholarly journals The impact of running-in on the friction of an automotive gasoline engine and in particular on its piston assembly and valve train

2017 ◽  
Vol 232 (6) ◽  
pp. 749-756 ◽  
Author(s):  
Christoph Knauder ◽  
Hannes Allmaier ◽  
Stefan Salhofer ◽  
Theodor Sams
Keyword(s):  
Gasoline Engine ◽  
Combustion Engine ◽  
Journal Bearings ◽  
Operating Conditions ◽  
Valve Train ◽  
Term Operation ◽  
The Individual ◽  
Direct Acting ◽  
The Impact ◽  
Piston Assembly

Generally, mating surfaces that are in tribological contact undergo a running-in process at the beginning of their operational lifetime. During this running-in phase, the tribological operating condition changes significantly leading ideally to long-term operation with a minimum of continuous wear. While this process and its duration are rather well understood for single machine elements like journal bearings, it is the aim of this work to investigate the running-in behaviour of more complex systems like an internal combustion engine and its sub-assemblies. To gain insight into the influence and duration of this running-in phase, a series of tests have been performed under realistic engine operating conditions. To be able to separate the running-in processes for the individual subsystems’ piston assembly, valve train and journal bearings of the crank train, a large series of tests have been conducted for a conventional gasoline passenger car engine. The results show a strong influence of the running-in process on total engine friction, which can be attributed mostly to the direct acting valve train and to a considerably lesser extent to the piston assembly.

scholarly journals Computational Performance of Disparate Lattice Boltzmann Scenarios under Unsteady Thermal Convection Flow and Heat Transfer Simulation

Computation ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 65
Author(s):  
Aditya Dewanto Hartono ◽  
Kyuro Sasaki ◽  
Yuichi Sugai ◽  
Ronald Nguele
Keyword(s):  
Heat Transfer ◽  
Steady State ◽  
Natural Convection ◽  
Lattice Boltzmann ◽  
Numerical Results ◽  
Convection And Heat Transfer ◽  
Steady State Condition ◽  
Physical Systems ◽  
Flow And Heat Transfer ◽  
Computational Performance

The present work highlights the capacity of disparate lattice Boltzmann strategies in simulating natural convection and heat transfer phenomena during the unsteady period of the flow. Within the framework of Bhatnagar-Gross-Krook collision operator, diverse lattice Boltzmann schemes emerged from two different embodiments of discrete Boltzmann expression and three distinct forcing models. Subsequently, computational performance of disparate lattice Boltzmann strategies was tested upon two different thermo-hydrodynamics configurations, namely the natural convection in a differentially-heated cavity and the Rayleigh-Bènard convection. For the purposes of exhibition and validation, the steady-state conditions of both physical systems were compared with the established numerical results from the classical computational techniques. Excellent agreements were observed for both thermo-hydrodynamics cases. Numerical results of both physical systems demonstrate the existence of considerable discrepancy in the computational characteristics of different lattice Boltzmann strategies during the unsteady period of the simulation. The corresponding disparity diminished gradually as the simulation proceeded towards a steady-state condition, where the computational profiles became almost equivalent. Variation in the discrete lattice Boltzmann expressions was identified as the primary factor that engenders the prevailed heterogeneity in the computational behaviour. Meanwhile, the contribution of distinct forcing models to the emergence of such diversity was found to be inconsequential. The findings of the present study contribute to the ventures to alleviate contemporary issues regarding proper selection of lattice Boltzmann schemes in modelling fluid flow and heat transfer phenomena.

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