Changes in ground surface thermal regimes play a vital role in surface and subsurface hydrology, ecosystem diversity and productivity, and global thermal, water and carbon budgets as well as climate change. Estimating...Changes in ground surface thermal regimes play a vital role in surface and subsurface hydrology, ecosystem diversity and productivity, and global thermal, water and carbon budgets as well as climate change. Estimating spring, summer, autumn and winter air temperatures and mean annual air temperature（MAAT） from 1960 through 2008 over the Heihe River Basin reveals a statistically significant trend of 0.31 °C/decade, 0.28 °C/decade, 0.37 °C/decade, 0.50 °C/decade, and 0.37 °C /decade, respectively. The averaged time series of mean annual ground surface temperature（MAGST） and maximum annual ground surface temperature（MaxAGST） for 1972–2006 over the basin indicates a statistically significant trend of 0.58 °C/decade and 1.27 °C/decade, respectively. The minimum annual ground surface temperature（MinAGST） in the same period remains unchanged as a whole. Estimating surface freezing/thawing index as well as the ratio of freezing index to thawing index（RFT） in the period between 1959 and 2006 over the basin indicates a statistically significant trend of-42.5 °C-day/decade, 85.4 °C-day/decade and-0.018/decade, 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 Chinese Academy of Sciences Key Research Program (No. KZZD-EW-13)the Natural Science Foundation of China (Nos. 91025013, 91325202)+1 种基金the State Key Laboratory of Frozen Soil Engineering (No. SKLFSE-ZY-06), CASthe Major Research Plan of the National Natural Science Foundation of China (No. 2013CBA01802)
文摘Changes in ground surface thermal regimes play a vital role in surface and subsurface hydrology, ecosystem diversity and productivity, and global thermal, water and carbon budgets as well as climate change. Estimating spring, summer, autumn and winter air temperatures and mean annual air temperature（MAAT） from 1960 through 2008 over the Heihe River Basin reveals a statistically significant trend of 0.31 °C/decade, 0.28 °C/decade, 0.37 °C/decade, 0.50 °C/decade, and 0.37 °C /decade, respectively. The averaged time series of mean annual ground surface temperature（MAGST） and maximum annual ground surface temperature（MaxAGST） for 1972–2006 over the basin indicates a statistically significant trend of 0.58 °C/decade and 1.27 °C/decade, respectively. The minimum annual ground surface temperature（MinAGST） in the same period remains unchanged as a whole. Estimating surface freezing/thawing index as well as the ratio of freezing index to thawing index（RFT） in the period between 1959 and 2006 over the basin indicates a statistically significant trend of-42.5 °C-day/decade, 85.4 °C-day/decade and-0.018/decade, 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.
