This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyze...This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS).Pearson's linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compound scould be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized,representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (-0.785), and β-ionone (-0.763). On the basis of these 10 compounds, a model (correlation coefficient of89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjingtea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MStechnique.展开更多
In this paper, we present a comprehensive model for the prediction of the evolution of high-speed train wheel profiles due to wear. The model consists of four modules： a multi-body model implemented with the commerci...In this paper, we present a comprehensive model for the prediction of the evolution of high-speed train wheel profiles due to wear. The model consists of four modules： a multi-body model implemented with the commercial multi-body software SIMPACK to evaluate the dynamic response of the vehicle and track; a local contact model based on Hertzian theory and a novel method, named FaStrip （Sichani et al., 2016）, to calculate the normal and tangential forces, respectively; a wear model proposed by the University of Sheffield （known as the USFD wear function） to estimate the amount of material removed and its distribution along the wheel profile; and a smoothing and updating strategy. A simulation of the wheel wear of the high-speed train CRH3 in service on the Wuhan-Guangzhou railway line was performed. A virtual railway line based on the statistics of the line was used to represent the entire real track. The model was validated using the wheel wear data of the CRH3 operating on the Wuhan- Guangzhou line, monitored by the authors＇ research group. The results of the predictions and measurements were in good agreement.展开更多
Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems.However,tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term o...Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems.However,tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term operation.Both profile shift and tooth surface wear(TSW)can impact the meshing characteristics by altering the involute tooth profile.In this study,a tooth stiffness model of spur gears that incorporates profile shift,TSW,tooth deformation,tooth contact deformation,fillet-foundation deformation,and gear body structure coupling is established.This model efficiently and accurately determines the time-varying mesh stiffness(TVMS).Additionally,an improved wear depth prediction method for spur gears is developed,which takes into consideration the mutually prime teeth numbers and more accurately reflects actual gear meshing conditions.Results show that consideration of the mutual prime of teeth numbers will have a certain impact on the TSW process.Furthermore,the finite element method(FEM)is employed to accurately verify the values of TVMS and load sharing ratio(LSR)of profile-shifted gears and worn gears.This study quantitatively analyzes the effect of profile shift on the surface wear process,which suggests that gear profile shift can partially alleviate the negative effects of TSW.The contribution of this study provides valuable insights into the design and maintenance of spur gear systems.展开更多
文摘This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS).Pearson's linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compound scould be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized,representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (-0.785), and β-ionone (-0.763). On the basis of these 10 compounds, a model (correlation coefficient of89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjingtea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MStechnique.
基金Project supported by the National Natural Science Foundation of China （Nos. U 1434201, 51275427, and 51605394）, and the Scientific Research Foundation of State Key Laboratory of Traction Power （No. 2015TPL_T01 ）, China
文摘In this paper, we present a comprehensive model for the prediction of the evolution of high-speed train wheel profiles due to wear. The model consists of four modules： a multi-body model implemented with the commercial multi-body software SIMPACK to evaluate the dynamic response of the vehicle and track; a local contact model based on Hertzian theory and a novel method, named FaStrip （Sichani et al., 2016）, to calculate the normal and tangential forces, respectively; a wear model proposed by the University of Sheffield （known as the USFD wear function） to estimate the amount of material removed and its distribution along the wheel profile; and a smoothing and updating strategy. A simulation of the wheel wear of the high-speed train CRH3 in service on the Wuhan-Guangzhou railway line was performed. A virtual railway line based on the statistics of the line was used to represent the entire real track. The model was validated using the wheel wear data of the CRH3 operating on the Wuhan- Guangzhou line, monitored by the authors＇ research group. The results of the predictions and measurements were in good agreement.
基金Supported by National Natural Science Foundation of China (Grant No.52275061)。
文摘Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems.However,tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term operation.Both profile shift and tooth surface wear(TSW)can impact the meshing characteristics by altering the involute tooth profile.In this study,a tooth stiffness model of spur gears that incorporates profile shift,TSW,tooth deformation,tooth contact deformation,fillet-foundation deformation,and gear body structure coupling is established.This model efficiently and accurately determines the time-varying mesh stiffness(TVMS).Additionally,an improved wear depth prediction method for spur gears is developed,which takes into consideration the mutually prime teeth numbers and more accurately reflects actual gear meshing conditions.Results show that consideration of the mutual prime of teeth numbers will have a certain impact on the TSW process.Furthermore,the finite element method(FEM)is employed to accurately verify the values of TVMS and load sharing ratio(LSR)of profile-shifted gears and worn gears.This study quantitatively analyzes the effect of profile shift on the surface wear process,which suggests that gear profile shift can partially alleviate the negative effects of TSW.The contribution of this study provides valuable insights into the design and maintenance of spur gear systems.
