A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives,for which the knowledge of the fuels’characteristic...A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives,for which the knowledge of the fuels’characteristic properties forms the backbone.The aim of this study is to predict sooting tendency of fuel molecules for spark-ignition engine applications in terms of their yield sooting indexes(YSI).In conjunction with our previously developed database for gasoline compounds,which includes the physical and chemical properties,such as octane numbers,laminar burning velocity,and heat of vaporization,for more than 600 species,the identification of fuel replacements and additives can thus be performed jointly with respect to both their potential thermal efficiency benefits and emission formation characteristics in spark-ignition engines.For this purpose,a quantitative structure-property relationship(QSPR)model is developed to predict the YSI of fuel species by using artificial neural network(ANN)techniques with 21 well-selected functional group descriptors as input features.The model is trained and cross-validated with the YSI database reported by Yale University.It is then applied to estimate the YSI values of fuels available in the database for gasoline compounds and to explore the sensitivity of fuel’s sooting tendency on molecular groups.In addition,the correlation of YSI values with other properties available in the gasoline fuel database is examined to gain insights into the dependence of these properties.Finally,a selection of potential gasoline blending components is carried out exemplarily,by taking the fuels’potential benefits in thermal engine efficiency and their soot formation characteristics jointly into account in terms of efficiency merit function and YSI,respectively.展开更多
An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquir...An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.展开更多
基金supported by the Fundamental Research Funds for the Central Universities。
文摘A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives,for which the knowledge of the fuels’characteristic properties forms the backbone.The aim of this study is to predict sooting tendency of fuel molecules for spark-ignition engine applications in terms of their yield sooting indexes(YSI).In conjunction with our previously developed database for gasoline compounds,which includes the physical and chemical properties,such as octane numbers,laminar burning velocity,and heat of vaporization,for more than 600 species,the identification of fuel replacements and additives can thus be performed jointly with respect to both their potential thermal efficiency benefits and emission formation characteristics in spark-ignition engines.For this purpose,a quantitative structure-property relationship(QSPR)model is developed to predict the YSI of fuel species by using artificial neural network(ANN)techniques with 21 well-selected functional group descriptors as input features.The model is trained and cross-validated with the YSI database reported by Yale University.It is then applied to estimate the YSI values of fuels available in the database for gasoline compounds and to explore the sensitivity of fuel’s sooting tendency on molecular groups.In addition,the correlation of YSI values with other properties available in the gasoline fuel database is examined to gain insights into the dependence of these properties.Finally,a selection of potential gasoline blending components is carried out exemplarily,by taking the fuels’potential benefits in thermal engine efficiency and their soot formation characteristics jointly into account in terms of efficiency merit function and YSI,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.52071216)the Shanghai Rising-Star Program.
文摘An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.
