Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the mai...Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption.It is difficult to analyze the influence of fuel consumption with multiple and complex factors.The Adaptive Neuro-Fuzzy Inference System(ANFIS)approach was employed to develop a vehicle fuel consumption model based on multivariate input.The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting.The performance of the ANFIS network was validated using Root Mean Square Error(RMSE)and Mean Average Error(MAE)which related to the setting of ANFIS parameters.The experimental results indicated that the training data sam-ple,number,and type of membership functions are the most important factor affecting the performance of the ANFIS network.However,the number of epochs does not necessarily significantly improve the system performance,too many the number of epochs setting may not provide the best results and lead to excessive responding time.The results also demonstrate that three factors,consisted of the engine size,driving speed,and the number of passengers,are important factors that influence the change of vehicle fuel consumption.The selected ANFIS mod-els with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.展开更多
文摘Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption.It is difficult to analyze the influence of fuel consumption with multiple and complex factors.The Adaptive Neuro-Fuzzy Inference System(ANFIS)approach was employed to develop a vehicle fuel consumption model based on multivariate input.The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting.The performance of the ANFIS network was validated using Root Mean Square Error(RMSE)and Mean Average Error(MAE)which related to the setting of ANFIS parameters.The experimental results indicated that the training data sam-ple,number,and type of membership functions are the most important factor affecting the performance of the ANFIS network.However,the number of epochs does not necessarily significantly improve the system performance,too many the number of epochs setting may not provide the best results and lead to excessive responding time.The results also demonstrate that three factors,consisted of the engine size,driving speed,and the number of passengers,are important factors that influence the change of vehicle fuel consumption.The selected ANFIS mod-els with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.
文摘为了改善柴油机燃烧室内燃油喷雾撞壁和混合气形成情况,提出一种直喷式柴油机多点分布式导向台燃烧室。将这种新燃烧室结构参数化,对7个设计变量进行多参数协同优化。以一台230 mm缸径的中速船用柴油机为基础模拟缸内工作过程,采用拉丁超立方取样的方法从设计空间得到600个样本点,根据模拟结果分析各设计参数对发动机性能的影响,根据不同的优化目标得到3种新燃烧室结构。仿真结果表明:在75%负荷工况下与原机ω型燃烧室相比,Ⅰ型燃烧室的指示油耗率(indicated specific fuel consumption,ISFC)降低1.83%,烟粒(soot)排放量降低86.83%;Ⅱ型燃烧室的ISFC降低0.97%,NO_(x)排放量降低7.44%,soot排放量降低68.26%;Ⅲ型燃烧室的INO_(x)和soot排放量分别降低10.52%和58.08%,ISFC基本不变。
