The impact of the adaptive cruise control（ ACC）system on improving fuel efficiency is evaluated based on the vehicle-specific power. The intelligent driver model was first modified to simulate the ACC system and it ...The impact of the adaptive cruise control（ ACC）system on improving fuel efficiency is evaluated based on the vehicle-specific power. The intelligent driver model was first modified to simulate the ACC system and it was calibrated by using empirical traffic data. Then, a five-step procedure based on the vehicle-specific power was introduced to calculate fuel efficiency. Five scenarios with different ACC ratios were tested in simulation experiments, and sensitivity analyses of two key ACC factors affecting the perception-reaction time and time headway were also conducted. The simulation results indicate that all the scenarios with ACC vehicles have positive impacts on reducing fuel consumption. Furthermore, from the perspective of fuel efficiency, the extremely small value of the perception-reaction time of the ACC system is not necessary due to the fact that the value of 0.5 and 0.1 s can almost lead to the same reduction in fuel consumption. Finally, the designed time headway of the ACC system is also proposed to be large enough for fuel efficiency, although its small value can increase capacity. The findings of this study provide useful information for connected vehicles and autonomous vehicle manufacturers to improve fuel efficiency on roadways.展开更多
Aircraft conceptual design optimizations that maximize the performance at a design condition （single-point） may result in designs with unsatisfying off-design performance. To further improve aircraft efficiency unde...Aircraft conceptual design optimizations that maximize the performance at a design condition （single-point） may result in designs with unsatisfying off-design performance. To further improve aircraft efficiency under actual flight operations, there is a need to consider multiple flight conditions （multipoint） in aircraft conceptual design and optimization. A new strategy for multipoint optimizations in aircraft conceptual design is proposed in this paper. A wide-body aircraft is taken as an example for both single-point and multipoint optimizations with the objective of maximizing the specific hourly productivity. Boeing 787-8 flight data was used in the multipoint opti- mization to reflect the true objective function. The results show that the optimal design from the multipoint optimization has a 7.72% total specific hourly productivity increase of entire flight missions compared with that of the baseline aircraft, while the increase in the total specific hourly productivity from the single-point optimal design is only 5.73%. The differences between the results of single-point and multipoint optimizations indicate that there is a good option to further improve aircraft efficiency by considering actual flight conditions in aircraft conceptual design and optimization.展开更多
When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS...When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.展开更多
The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimizatio...The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.展开更多
Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea cu...Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.展开更多
Fuel efficiency optimization is of crucial importance in industries.Marine transportation industry is no exception.Multi-disciplinary optimization is a branch of engineering which uses optimization methods for solving...Fuel efficiency optimization is of crucial importance in industries.Marine transportation industry is no exception.Multi-disciplinary optimization is a branch of engineering which uses optimization methods for solving problems in which the objective function is simultaneously affected by several different factors.As one of the tools for this type of optimization,genetic algorithm has a high quality and validity.The objective of the present study is to optimize fuel efficiency in tankers.All presented equations and conditions are valid for tankers.Fuel consumption efficiency of tankers is a function of various influential factors.Given the lack of equations for describing and modeling these factors and unavailability of valid performance database for inferring the equations as well as the lack of literature in this field,the preset study includes five optimizing factors affecting the fuel consumption efficiency of a tanker in genetic algorithm by using the genetic algorithm toolbox of MATLAB software package.展开更多
Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. This effect ...Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. This effect is covered by the notion of string stability. String-stable behavior is thus considered an essential requirement for the design of automatic distance control systems, which are needed to allow for safe driving at time gaps well below 1 s. Using wireless inter-vehicle communications to provide real-time information of the preceding vehicle, in addition to the information obtained by common Adaptive Cruise Control （ACC） sensors, appears to significantly decrease the feasible time gap, which is shown by practical experiments with a test fleet consisting of six passenger vehicles. The large-scale deployment of this system, known as Cooperative ACC （CACC）, however, poses challenges with respect to the reliability of the wireless communication system. A solution for this scalability problem can be found in decreasing the transmission power and/or beaconing rate, or adapting the communications protocol. Although the main CACC objective is to increase road throughput, the first commercial application of CACC is foreseen to be in truck platooning, since short distance following is expected to yield significant fuel savings in this case.展开更多
Automotive manufacturers are currently under pressure to improve fuel efficiency,and at the same time,reduce exhaust gas emission.To meet new emission requirements,modern vehicles are equipped with exhaust gas after-t...Automotive manufacturers are currently under pressure to improve fuel efficiency,and at the same time,reduce exhaust gas emission.To meet new emission requirements,modern vehicles are equipped with exhaust gas after-treatment devices.However,as sulfated ash,phosphorus and sulfur(SAPS) have a detrimental impact on these after-treatment devices,the use of low-or zero-SAPS additives is favored.Irgalube F 10 A is an additive that does not contain any metal,phosphorus or sulfur.It enables formulators to develop ...展开更多
Green manufacturing is a new way out for the auto industry—ZHAO Hang, president of CATARC The green manufacturing means a lot, in terms of the manufacturing process, material and techniques. China spares no efforts t...Green manufacturing is a new way out for the auto industry—ZHAO Hang, president of CATARC The green manufacturing means a lot, in terms of the manufacturing process, material and techniques. China spares no efforts to producing vehicles and it展开更多
Demographics and economics in the next 20 years are being ex amined.They reflect a significant GDP growth and with this a strong demand for commercial aircraft not only in the US and Europe but across Asia and the Mid...Demographics and economics in the next 20 years are being ex amined.They reflect a significant GDP growth and with this a strong demand for commercial aircraft not only in the US and Europe but across Asia and the Middle East The demand will focus on more fuel efficient and more environmentally friendly vehicles.Significant progress is being made with the new regionals,narrow-body,and wide-body aircraft between now and the year 2020.Looking beyond,the world will examine new airplane architectures,new changes in propulsion systems,and higher thermal and propulsion efficiencies.Distributed propulsion options will come into play.With them,higher operating pressure gas generators will be developed and great attention will have to be given to highly integrated propulsion/airplane systems.Energy transfer requirements will lead to bigger gear systems as well as new hybrid systems.The new machines are forecasted to offer improvements in fuel efficiencies of over 40%.There are many technical challenges to make all these things happen.The aerospace engineers and scientists of today and tomorrow face unlimited opportunities to make a difference for what looks like a very exciting future.展开更多
基金The National Natural Science Foundation of China(No.51338003,51478113,51378120)
文摘The impact of the adaptive cruise control（ ACC）system on improving fuel efficiency is evaluated based on the vehicle-specific power. The intelligent driver model was first modified to simulate the ACC system and it was calibrated by using empirical traffic data. Then, a five-step procedure based on the vehicle-specific power was introduced to calculate fuel efficiency. Five scenarios with different ACC ratios were tested in simulation experiments, and sensitivity analyses of two key ACC factors affecting the perception-reaction time and time headway were also conducted. The simulation results indicate that all the scenarios with ACC vehicles have positive impacts on reducing fuel consumption. Furthermore, from the perspective of fuel efficiency, the extremely small value of the perception-reaction time of the ACC system is not necessary due to the fact that the value of 0.5 and 0.1 s can almost lead to the same reduction in fuel consumption. Finally, the designed time headway of the ACC system is also proposed to be large enough for fuel efficiency, although its small value can increase capacity. The findings of this study provide useful information for connected vehicles and autonomous vehicle manufacturers to improve fuel efficiency on roadways.
基金supported by the Fundamental Research Funds for Central Universities(NUAA NS2016010)
文摘Aircraft conceptual design optimizations that maximize the performance at a design condition （single-point） may result in designs with unsatisfying off-design performance. To further improve aircraft efficiency under actual flight operations, there is a need to consider multiple flight conditions （multipoint） in aircraft conceptual design and optimization. A new strategy for multipoint optimizations in aircraft conceptual design is proposed in this paper. A wide-body aircraft is taken as an example for both single-point and multipoint optimizations with the objective of maximizing the specific hourly productivity. Boeing 787-8 flight data was used in the multipoint opti- mization to reflect the true objective function. The results show that the optimal design from the multipoint optimization has a 7.72% total specific hourly productivity increase of entire flight missions compared with that of the baseline aircraft, while the increase in the total specific hourly productivity from the single-point optimal design is only 5.73%. The differences between the results of single-point and multipoint optimizations indicate that there is a good option to further improve aircraft efficiency by considering actual flight conditions in aircraft conceptual design and optimization.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.
文摘Fuel efficiency optimization is of crucial importance in industries.Marine transportation industry is no exception.Multi-disciplinary optimization is a branch of engineering which uses optimization methods for solving problems in which the objective function is simultaneously affected by several different factors.As one of the tools for this type of optimization,genetic algorithm has a high quality and validity.The objective of the present study is to optimize fuel efficiency in tankers.All presented equations and conditions are valid for tankers.Fuel consumption efficiency of tankers is a function of various influential factors.Given the lack of equations for describing and modeling these factors and unavailability of valid performance database for inferring the equations as well as the lack of literature in this field,the preset study includes five optimizing factors affecting the fuel consumption efficiency of a tanker in genetic algorithm by using the genetic algorithm toolbox of MATLAB software package.
文摘Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. This effect is covered by the notion of string stability. String-stable behavior is thus considered an essential requirement for the design of automatic distance control systems, which are needed to allow for safe driving at time gaps well below 1 s. Using wireless inter-vehicle communications to provide real-time information of the preceding vehicle, in addition to the information obtained by common Adaptive Cruise Control （ACC） sensors, appears to significantly decrease the feasible time gap, which is shown by practical experiments with a test fleet consisting of six passenger vehicles. The large-scale deployment of this system, known as Cooperative ACC （CACC）, however, poses challenges with respect to the reliability of the wireless communication system. A solution for this scalability problem can be found in decreasing the transmission power and/or beaconing rate, or adapting the communications protocol. Although the main CACC objective is to increase road throughput, the first commercial application of CACC is foreseen to be in truck platooning, since short distance following is expected to yield significant fuel savings in this case.
文摘Automotive manufacturers are currently under pressure to improve fuel efficiency,and at the same time,reduce exhaust gas emission.To meet new emission requirements,modern vehicles are equipped with exhaust gas after-treatment devices.However,as sulfated ash,phosphorus and sulfur(SAPS) have a detrimental impact on these after-treatment devices,the use of low-or zero-SAPS additives is favored.Irgalube F 10 A is an additive that does not contain any metal,phosphorus or sulfur.It enables formulators to develop ...
文摘Green manufacturing is a new way out for the auto industry—ZHAO Hang, president of CATARC The green manufacturing means a lot, in terms of the manufacturing process, material and techniques. China spares no efforts to producing vehicles and it
文摘Demographics and economics in the next 20 years are being ex amined.They reflect a significant GDP growth and with this a strong demand for commercial aircraft not only in the US and Europe but across Asia and the Middle East The demand will focus on more fuel efficient and more environmentally friendly vehicles.Significant progress is being made with the new regionals,narrow-body,and wide-body aircraft between now and the year 2020.Looking beyond,the world will examine new airplane architectures,new changes in propulsion systems,and higher thermal and propulsion efficiencies.Distributed propulsion options will come into play.With them,higher operating pressure gas generators will be developed and great attention will have to be given to highly integrated propulsion/airplane systems.Energy transfer requirements will lead to bigger gear systems as well as new hybrid systems.The new machines are forecasted to offer improvements in fuel efficiencies of over 40%.There are many technical challenges to make all these things happen.The aerospace engineers and scientists of today and tomorrow face unlimited opportunities to make a difference for what looks like a very exciting future.
