Currently,vehicle-related particulate matter is the main determinant air pollution in the urban environment.This study was designed to investigate the level of fine(PM_(2.5))and coarse particle(PM_(10))concentration o...Currently,vehicle-related particulate matter is the main determinant air pollution in the urban environment.This study was designed to investigate the level of fine(PM_(2.5))and coarse particle(PM_(10))concentration of roadside vehicles in Addis Ababa,the capital city of Ethiopia using artificial neural network model.To train,test and validate the model,the traffic volume,weather data and particulate matter concentrations were collected from 15 different sites in the city.The experimental results showed that the city average 24-hr PM_(2.5)concentration is 13%-144%and 58%-241%higher than air quality index(AQI)and world health organization(WHO)standards,respectively.The PM_(10)results also exceeded the AQI(54%-65%)and WHO(8%-395%)standards.The model runs using the Levenberg-Marquardt(Trainlm)and the Scaled Conjugate Gradient(Trainscg)and comparison were performed,to identify the minimum fractional error between the observed and the predicted value.The two models were determined using the correlation coefficient and other statistical parameters.The Trainscg model,the average concentration of PM_(2.5)and PM_(10)exhaust emission correlation coefficient were predicted to be(R^(2)=0.775)and(R^(2)=0.92),respectively.The Trainlm model has also well predicted the exhaust emission of PM_(2.5)(R~2=0.943)and PM_(10)(R^(2)=0.959).The overall results showed that a better correlation coefficient obtained in the Trainlm model,could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.展开更多
The modeling of inlet and exhaust systems of internal combustion engine is very important in order to evaluate the engine performance.This paper presents new pressure losses models which can be included in a one dimen...The modeling of inlet and exhaust systems of internal combustion engine is very important in order to evaluate the engine performance.This paper presents new pressure losses models which can be included in a one dimensional engine simulation code.In a first part,a CFD analysis is made in order to show the importance of the density in the modeling approach.Then,the CFD code is used,as a numerical test bench,for the pressure losses models development.These coefficients depend on the geometrical characteristics of the junction and an experimental validation is made with the use of a shock tube test bench.All the models are then included in the engine simulation code of the laboratory.The numerical calculation of unsteady compressible flow,in each pipe of the inlet and exhaust systems,is made and the calculated engine torque is compared with experimental measurements.展开更多
文摘Currently,vehicle-related particulate matter is the main determinant air pollution in the urban environment.This study was designed to investigate the level of fine(PM_(2.5))and coarse particle(PM_(10))concentration of roadside vehicles in Addis Ababa,the capital city of Ethiopia using artificial neural network model.To train,test and validate the model,the traffic volume,weather data and particulate matter concentrations were collected from 15 different sites in the city.The experimental results showed that the city average 24-hr PM_(2.5)concentration is 13%-144%and 58%-241%higher than air quality index(AQI)and world health organization(WHO)standards,respectively.The PM_(10)results also exceeded the AQI(54%-65%)and WHO(8%-395%)standards.The model runs using the Levenberg-Marquardt(Trainlm)and the Scaled Conjugate Gradient(Trainscg)and comparison were performed,to identify the minimum fractional error between the observed and the predicted value.The two models were determined using the correlation coefficient and other statistical parameters.The Trainscg model,the average concentration of PM_(2.5)and PM_(10)exhaust emission correlation coefficient were predicted to be(R^(2)=0.775)and(R^(2)=0.92),respectively.The Trainlm model has also well predicted the exhaust emission of PM_(2.5)(R~2=0.943)and PM_(10)(R^(2)=0.959).The overall results showed that a better correlation coefficient obtained in the Trainlm model,could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.
文摘The modeling of inlet and exhaust systems of internal combustion engine is very important in order to evaluate the engine performance.This paper presents new pressure losses models which can be included in a one dimensional engine simulation code.In a first part,a CFD analysis is made in order to show the importance of the density in the modeling approach.Then,the CFD code is used,as a numerical test bench,for the pressure losses models development.These coefficients depend on the geometrical characteristics of the junction and an experimental validation is made with the use of a shock tube test bench.All the models are then included in the engine simulation code of the laboratory.The numerical calculation of unsteady compressible flow,in each pipe of the inlet and exhaust systems,is made and the calculated engine torque is compared with experimental measurements.
