Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article ex...Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.展开更多
112 short-period seismographs were set up in the 400 km^2 area of Pingtan Island and its surrounding areas in Fujian.The combined observations of the airgun source and ambient noise source were carried out using a den...112 short-period seismographs were set up in the 400 km^2 area of Pingtan Island and its surrounding areas in Fujian.The combined observations of the airgun source and ambient noise source were carried out using a dense array to receive the 387 airgun signals excited around the island and one month of continuous ambient noise recording.The 1-D P-wave and S-wave shallow velocity model of Pingtan Island is obtained by the inversion of the airgun body wave’s first arrival time data,and the reliability of the velocity model is verified by using the surface wave phase velocity dispersion curve,which can provide initial model for subsequent 3-D imaging.The experimental results show that this experiment is a successful demonstration of local scale green non-destructive detection,which can provide basic data for shallow surface structure research and strong vibration simulation of the Pingtan Island.展开更多
The present study focuses on source rock evaluation of the Sargelu Formation by using core chips of rocks collected from well Atrush-2, Duhok, Kurdistan Region-Iraq. The Rock-Eval pyrolysis and vitrinite reflectance w...The present study focuses on source rock evaluation of the Sargelu Formation by using core chips of rocks collected from well Atrush-2, Duhok, Kurdistan Region-Iraq. The Rock-Eval pyrolysis and vitrinite reflectance were executed. Subsequently, the selected parameters were used for source rock evaluation and 1-D Basin Modelling calibration. The upper part of the formation mainly comprises argillaceous limestone with low content of organic matter (0.64% - 1% TOC). By contrast, the lower part is dominated with shale interval and contains high amounts of TOC values (>4% for 1272 - 1278 m) reveling good to very good quality source rock. Accordingly, good to very good hydrocarbon generation potential is suggested for this formation. Organic matter of the Sargelu Formation contains type II and mixed-type II-III kerogen. The values of Tmax and vitrinite reflectance (Ro%) demonstrate that the formation is thermally mature and in the oil zone. In order to construct a thermal history of the formation and determine the timing of hydrocarbon maturation and generation, the 1-D basin modelling PetroMod 2019.1 was used in this study. Based on the 1-D Basin modelling simulation and its outputs, about 3500 m of overburden have been eroded at the study area. The present-day heat flow was found to be 30 mW/m2. The organic matter of Sargelu Formation entered the early oil zone in 64 Ma and reached the main oil zone ca. 5 Ma. The formation is still in the main oil zone at present-day. In well Atrush-2, the highest rate of oil generation for the Sargelu Formation was in the 8.5 Ma, the onset of oil expulsion from Sargelu Formation was in 50 Ma and the expulsion mass has been reached 0.5 Mtons at present-day.展开更多
Traditional feature-based turbine blade models can match the needs of geometric modeling but could hardly meet the requirement of data extraction in 1-D heat transfer analysis. In this paper, the requirements of data ...Traditional feature-based turbine blade models can match the needs of geometric modeling but could hardly meet the requirement of data extraction in 1-D heat transfer analysis. In this paper, the requirements of data extraction in 1-D heat transfer analysis are taken into consideration as well as geometric representation in parametric design process. An improved turbine blade parametric modeling method is proposed. Based on the modeling method proposed, a system structure of blade modeling process considering 1-D heat transfer analysis is devised. Eventually, a turbine blade parametric modeling system is constructed to test and verify the feasibility of the proposed modeling method and system structure. Experiments show that the blade parametric modeling method proposed can make geometric models better adapt to the specific requirements of 1-D heat transfer analysis and has certain reference value to the creation of high quality digital models.展开更多
对未来R&D经费总量及其与GDP比值进行科学的预测是制定科技发展规划的重要组成部分.提出一种定量分析预测方法———利用多变量灰色MGM(1,n)模型(mu lti-variab le grey model),研究R&D投入与GDP所形成的复杂系统变量之间的相...对未来R&D经费总量及其与GDP比值进行科学的预测是制定科技发展规划的重要组成部分.提出一种定量分析预测方法———利用多变量灰色MGM(1,n)模型(mu lti-variab le grey model),研究R&D投入与GDP所形成的复杂系统变量之间的相互影响关系,对天津市相关数据资料进行实证分析与中长期预测,为政府相关部门制订科学的科技发展规划提供有益的指导作用.展开更多
The main stream of the Yangtze River, Dongting Lake, and the river network in the Jingjiang reach of the Yangtze River constitute a complex water system. This paper develops a one-dimensional （l-D） mathematical mode...The main stream of the Yangtze River, Dongting Lake, and the river network in the Jingjiang reach of the Yangtze River constitute a complex water system. This paper develops a one-dimensional （l-D） mathematical model for flood routing in the river network Of the Jingjiang River and Dongting Lake using the explicit finite volume method. Based on observed data during the flood periods in 1996 and 1998, the model was calibrated and validated, and the results show that the model is effective and has high accuracy. In addition, the one-dimensional mathematical model for the river network and the horizontal two-dimensional （2-D） mathematical model for the Jingjiang flood diversion area were coupled to simulate the flood process in the Jingjiang River, Dongting Lake, and the Jingjiang flood diversion area. The calculated results of the coupled model are consistent with the practical processes. Meanwhile, the results show that the flood diversion has significant effects on the decrease of the peak water level at the Shashi and Chenjiawan hydrological stations near the flood diversion gates, and the effect is more obvious in the downstream than in the upstream.展开更多
In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recentl...In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models’ architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.展开更多
The development of a diesel engine model using one-dimensional （1-D） fluid-dynamic engine simulation codes,and its validation using experimental measurements are described in this paper.The model was calibrated by r...The development of a diesel engine model using one-dimensional （1-D） fluid-dynamic engine simulation codes,and its validation using experimental measurements are described in this paper.The model was calibrated by running the engine on an electric dynamometer at eight steady-state operating conditions.The refined engine model was used to predict the oxides of nitrogen （NOx） less than those measured earlier in the experiments,and hence to recommend changes in the engine for the verification of the results.The refined engine model is greatly influenced by the start of injection angle （ψ）,ignition delay （φ）,premix duration （DP）,and main duration （DM） for the prediction of reduced NOx emissions.It is found that optimum ψ is 6.5° before top dead center （BTDC）.At this angle,the predicted and experimental results are in good agreement,showing only a difference of up to 4%,6.2%,and 7.5% for engine performance,maximum combustion pressure （Pmax）,and NOx,respectively.展开更多
文摘Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.
基金sponsored by the Key Technologies R&D Program of Fujian Earthquake Agency(G201703)the Seismic Science and Technology Spark Program,CEA(XH19023Y)
文摘112 short-period seismographs were set up in the 400 km^2 area of Pingtan Island and its surrounding areas in Fujian.The combined observations of the airgun source and ambient noise source were carried out using a dense array to receive the 387 airgun signals excited around the island and one month of continuous ambient noise recording.The 1-D P-wave and S-wave shallow velocity model of Pingtan Island is obtained by the inversion of the airgun body wave’s first arrival time data,and the reliability of the velocity model is verified by using the surface wave phase velocity dispersion curve,which can provide initial model for subsequent 3-D imaging.The experimental results show that this experiment is a successful demonstration of local scale green non-destructive detection,which can provide basic data for shallow surface structure research and strong vibration simulation of the Pingtan Island.
文摘The present study focuses on source rock evaluation of the Sargelu Formation by using core chips of rocks collected from well Atrush-2, Duhok, Kurdistan Region-Iraq. The Rock-Eval pyrolysis and vitrinite reflectance were executed. Subsequently, the selected parameters were used for source rock evaluation and 1-D Basin Modelling calibration. The upper part of the formation mainly comprises argillaceous limestone with low content of organic matter (0.64% - 1% TOC). By contrast, the lower part is dominated with shale interval and contains high amounts of TOC values (>4% for 1272 - 1278 m) reveling good to very good quality source rock. Accordingly, good to very good hydrocarbon generation potential is suggested for this formation. Organic matter of the Sargelu Formation contains type II and mixed-type II-III kerogen. The values of Tmax and vitrinite reflectance (Ro%) demonstrate that the formation is thermally mature and in the oil zone. In order to construct a thermal history of the formation and determine the timing of hydrocarbon maturation and generation, the 1-D basin modelling PetroMod 2019.1 was used in this study. Based on the 1-D Basin modelling simulation and its outputs, about 3500 m of overburden have been eroded at the study area. The present-day heat flow was found to be 30 mW/m2. The organic matter of Sargelu Formation entered the early oil zone in 64 Ma and reached the main oil zone ca. 5 Ma. The formation is still in the main oil zone at present-day. In well Atrush-2, the highest rate of oil generation for the Sargelu Formation was in the 8.5 Ma, the onset of oil expulsion from Sargelu Formation was in 50 Ma and the expulsion mass has been reached 0.5 Mtons at present-day.
文摘Traditional feature-based turbine blade models can match the needs of geometric modeling but could hardly meet the requirement of data extraction in 1-D heat transfer analysis. In this paper, the requirements of data extraction in 1-D heat transfer analysis are taken into consideration as well as geometric representation in parametric design process. An improved turbine blade parametric modeling method is proposed. Based on the modeling method proposed, a system structure of blade modeling process considering 1-D heat transfer analysis is devised. Eventually, a turbine blade parametric modeling system is constructed to test and verify the feasibility of the proposed modeling method and system structure. Experiments show that the blade parametric modeling method proposed can make geometric models better adapt to the specific requirements of 1-D heat transfer analysis and has certain reference value to the creation of high quality digital models.
文摘对未来R&D经费总量及其与GDP比值进行科学的预测是制定科技发展规划的重要组成部分.提出一种定量分析预测方法———利用多变量灰色MGM(1,n)模型(mu lti-variab le grey model),研究R&D投入与GDP所形成的复杂系统变量之间的相互影响关系,对天津市相关数据资料进行实证分析与中长期预测,为政府相关部门制订科学的科技发展规划提供有益的指导作用.
基金supported by the National Key Technologies Research and Development Program (Grant No. 2006BAB05B02)
文摘The main stream of the Yangtze River, Dongting Lake, and the river network in the Jingjiang reach of the Yangtze River constitute a complex water system. This paper develops a one-dimensional （l-D） mathematical model for flood routing in the river network Of the Jingjiang River and Dongting Lake using the explicit finite volume method. Based on observed data during the flood periods in 1996 and 1998, the model was calibrated and validated, and the results show that the model is effective and has high accuracy. In addition, the one-dimensional mathematical model for the river network and the horizontal two-dimensional （2-D） mathematical model for the Jingjiang flood diversion area were coupled to simulate the flood process in the Jingjiang River, Dongting Lake, and the Jingjiang flood diversion area. The calculated results of the coupled model are consistent with the practical processes. Meanwhile, the results show that the flood diversion has significant effects on the decrease of the peak water level at the Shashi and Chenjiawan hydrological stations near the flood diversion gates, and the effect is more obvious in the downstream than in the upstream.
文摘In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models’ architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.
基金Sponsored by the National Natural Science Foundation of China (50576063)
文摘The development of a diesel engine model using one-dimensional （1-D） fluid-dynamic engine simulation codes,and its validation using experimental measurements are described in this paper.The model was calibrated by running the engine on an electric dynamometer at eight steady-state operating conditions.The refined engine model was used to predict the oxides of nitrogen （NOx） less than those measured earlier in the experiments,and hence to recommend changes in the engine for the verification of the results.The refined engine model is greatly influenced by the start of injection angle （ψ）,ignition delay （φ）,premix duration （DP）,and main duration （DM） for the prediction of reduced NOx emissions.It is found that optimum ψ is 6.5° before top dead center （BTDC）.At this angle,the predicted and experimental results are in good agreement,showing only a difference of up to 4%,6.2%,and 7.5% for engine performance,maximum combustion pressure （Pmax）,and NOx,respectively.
