Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation.How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problem...Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation.How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solved to ensure the safe operation of the platform.To address the practical engineering problem that it is difficult to monitor the stress response of the tubular joints of jacket platforms online,a digital twin reduced-order method for real-time prediction of the stress response of tubular joints is proposed.In the offline construction phase,multi-scale modeling and multi-parameter experimental design methods are used to obtain the stress response data set of the jacket structure.Proper orthogonal decomposition is employed to extract the main feature information from the snapshot matrix,resulting in a reduced-order basis.The leave-one-out cross-validation method is used to select the optimal modal order for constructing the reduced-order model(ROM).In the online prediction phase,a digital twin model of the tubular joint is established,and the prediction performance of the ROM is analyzed and verified through using random environmental load and field environmental monitoring data.The results indicate that,compared with traditional numerical simulations of tubular joints,the ROM based on the proposed reduced-order method is more efficient in predicting the stress response of tubular joints while ensuring accuracy and robustness.展开更多
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear ph...A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear physical substructures. The study presented in this paper is focused on further validating the RTHS platform using a nonlinear viscoelastic-plastic damper that has displacement, frequency and temperature-dependent properties. The validation study includes damper component characterization tests, as well as RTHS of a series of single-degree-of-freedom （SDOF） systems equipped with viscoelastic-plastic dampers that represent different structural designs. From the component characterization tests, it was found that for a wide range of excitation frequencies and friction slip loads, the tracking errors are comparable to the errors in RTHS of linear spring systems. The hybrid SDOF results are compared to an independently validated thermal- mechanical viscoelastic model to further validate the ability for the platform to test nonlinear systems. After the validation, as an application study, nonlinear SDOF hybrid tests were used to develop performance spectra to predict the response of structures equipped with damping systems that are more challenging to model analytically. The use of the experimental performance spectra is illustrated by comparing the predicted response to the hybrid test response of 2DOF systems equipped with viscoelastic-plastic dampers.展开更多
Sub-tanks in fuel tank systems of aircrafts transfer fuel to engines in certain order. These sub-tanks and attached tank-accessories affect each other, and make fault diagnosis in such systems rather difficult. Withou...Sub-tanks in fuel tank systems of aircrafts transfer fuel to engines in certain order. These sub-tanks and attached tank-accessories affect each other, and make fault diagnosis in such systems rather difficult. Without real measured data, this paper analyzes fault modes and fault effects of the fuel tank system, including its tankaccessories, of a given aircraft. Fault model of the system is built theoretically, and fault diagnosis criteria are deduced. Such criteria are then quantified to train a back propagation neural network(BPNN) as fault diagnosis model. To realize fault diagnosis of the real fuel tank system, a real-time fault diagnosis platform based on Lab View and Vx Works to perform this diagnosis method is discussed. This platform is a technical groundwork for fault diagnosis in real fuel tank systems.展开更多
In order to satisfy a satellite horizontality requirement in an experiment, it is indispensable to monitor and adjust the horizontality of a large platform loading the satellite under the condition of ultra-low temper...In order to satisfy a satellite horizontality requirement in an experiment, it is indispensable to monitor and adjust the horizontality of a large platform loading the satellite under the condition of ultra-low temperature with real time. So the control system design and control strategy are described in detail to accomplish the horizontality monitoring and adjusting. The system adopts the industry control computer as the upper computer and the SIEMENS S7-300 PLC as the lower computer. The upper computer that bases on industry configuration software IFIX takes charge of monitoring the platform and puts forward the control strategy. PLC takes charge of receiving the adjusting instructions and controlling the legs moving to accomplish the horizontality adjusting. The horizontality adjusting strategy is emphasized and the concept of grads is introduced to establish a mathematics model of the platform inclined state, so the adjusting method is obtained. Accordingly the key question of the automatic horizontality adjusting is solved in this control system.展开更多
The purpose of this study was to find a way to promote the collaboration and interaction of students and bring about the growth of learners through feedback while taking advantage of real-time interactive class via vi...The purpose of this study was to find a way to promote the collaboration and interaction of students and bring about the growth of learners through feedback while taking advantage of real-time interactive class via video conferencing tools.Although real-time interactive class with using video conferencing tools had great advantages,but there were also limitations of active interaction.To this end,real-time interactive tool and cloud-based educational platform were applied to create cases of learner participation classes and analyze the cases.The convergence of real-time interactive class tools and cloud tools has been able to draw students’participation and collaboration in non-face-to-face situations,and it can be seen that it is very helpful in creating learner-centered educational activities based on communication and interaction with students.Through this,the application of the cloud-based educational platform in real-time interactive class could lead students to participate and collaborate even in non-face-to-face situations.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.11472076).
文摘Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation.How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solved to ensure the safe operation of the platform.To address the practical engineering problem that it is difficult to monitor the stress response of the tubular joints of jacket platforms online,a digital twin reduced-order method for real-time prediction of the stress response of tubular joints is proposed.In the offline construction phase,multi-scale modeling and multi-parameter experimental design methods are used to obtain the stress response data set of the jacket structure.Proper orthogonal decomposition is employed to extract the main feature information from the snapshot matrix,resulting in a reduced-order basis.The leave-one-out cross-validation method is used to select the optimal modal order for constructing the reduced-order model(ROM).In the online prediction phase,a digital twin model of the tubular joint is established,and the prediction performance of the ROM is analyzed and verified through using random environmental load and field environmental monitoring data.The results indicate that,compared with traditional numerical simulations of tubular joints,the ROM based on the proposed reduced-order method is more efficient in predicting the stress response of tubular joints while ensuring accuracy and robustness.
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
基金NSERC Discovery under Grant 371627-2009 and NSERC RTI under Grant 374707-2009 EQPEQ programs
文摘A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear physical substructures. The study presented in this paper is focused on further validating the RTHS platform using a nonlinear viscoelastic-plastic damper that has displacement, frequency and temperature-dependent properties. The validation study includes damper component characterization tests, as well as RTHS of a series of single-degree-of-freedom （SDOF） systems equipped with viscoelastic-plastic dampers that represent different structural designs. From the component characterization tests, it was found that for a wide range of excitation frequencies and friction slip loads, the tracking errors are comparable to the errors in RTHS of linear spring systems. The hybrid SDOF results are compared to an independently validated thermal- mechanical viscoelastic model to further validate the ability for the platform to test nonlinear systems. After the validation, as an application study, nonlinear SDOF hybrid tests were used to develop performance spectra to predict the response of structures equipped with damping systems that are more challenging to model analytically. The use of the experimental performance spectra is illustrated by comparing the predicted response to the hybrid test response of 2DOF systems equipped with viscoelastic-plastic dampers.
文摘Sub-tanks in fuel tank systems of aircrafts transfer fuel to engines in certain order. These sub-tanks and attached tank-accessories affect each other, and make fault diagnosis in such systems rather difficult. Without real measured data, this paper analyzes fault modes and fault effects of the fuel tank system, including its tankaccessories, of a given aircraft. Fault model of the system is built theoretically, and fault diagnosis criteria are deduced. Such criteria are then quantified to train a back propagation neural network(BPNN) as fault diagnosis model. To realize fault diagnosis of the real fuel tank system, a real-time fault diagnosis platform based on Lab View and Vx Works to perform this diagnosis method is discussed. This platform is a technical groundwork for fault diagnosis in real fuel tank systems.
文摘In order to satisfy a satellite horizontality requirement in an experiment, it is indispensable to monitor and adjust the horizontality of a large platform loading the satellite under the condition of ultra-low temperature with real time. So the control system design and control strategy are described in detail to accomplish the horizontality monitoring and adjusting. The system adopts the industry control computer as the upper computer and the SIEMENS S7-300 PLC as the lower computer. The upper computer that bases on industry configuration software IFIX takes charge of monitoring the platform and puts forward the control strategy. PLC takes charge of receiving the adjusting instructions and controlling the legs moving to accomplish the horizontality adjusting. The horizontality adjusting strategy is emphasized and the concept of grads is introduced to establish a mathematics model of the platform inclined state, so the adjusting method is obtained. Accordingly the key question of the automatic horizontality adjusting is solved in this control system.
文摘The purpose of this study was to find a way to promote the collaboration and interaction of students and bring about the growth of learners through feedback while taking advantage of real-time interactive class via video conferencing tools.Although real-time interactive class with using video conferencing tools had great advantages,but there were also limitations of active interaction.To this end,real-time interactive tool and cloud-based educational platform were applied to create cases of learner participation classes and analyze the cases.The convergence of real-time interactive class tools and cloud tools has been able to draw students’participation and collaboration in non-face-to-face situations,and it can be seen that it is very helpful in creating learner-centered educational activities based on communication and interaction with students.Through this,the application of the cloud-based educational platform in real-time interactive class could lead students to participate and collaborate even in non-face-to-face situations.
