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This paper presents a comprehensive analysis studying the impact of interleaving on harmonic currents and voltages on AC side of parallel three-phase voltage source converters (VSCs). The analysis performed considers the effects o...
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This paper presents a comprehensive analysis studying the impact of interleaving on harmonic currents and voltages on AC side of parallel three-phase voltage source converters (VSCs). The analysis performed considers the effects of modulation index and the interleaving angle. Based on the analysis, the impact of interleaving on the design of AC passive components such as AC line inductor and EMI filter are discussed. The results show that interleaving has the potential benefit to reduce AC passive components. To maximize such benefit, the interleaving angle should be optimized according to the system requirements including THD limit, ripple limit or EMI standards in different operation conditions such as modulation index and PWM strategy. All the analysis is based on an example system containing two two-level VSCs. However the proposed analysis method in frequency domain can be easily extended to multiple VSCs systems with other topologies. Experimental results have verified the analysis results.
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With the gradually increasing applications of fiber-optic distributed temperature sensors(DTS)inunconventional resources exploitation,academic researchers have developed general theoretical modelsfor forward temperature simulation...
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With the gradually increasing applications of fiber-optic distributed temperature sensors(DTS)inunconventional resources exploitation,academic researchers have developed general theoretical modelsfor forward temperature simulation and inverse flow rate profiling during and after stimulation workover.However,there have been no enough field applications for the established theoretical models and somepractical issues still exist such as different completion scenarios are still lack of consideration in currentmodels.This paper presents a DTS flow profiling case for a horizontal multi-stage fractured well in tight gasreservoirs with open-hole packer completion scenarios by applying a newly improved theoretical model.In this paper,for the modeling part,we start with the semi-analytical wellbore-fracture-reservoir coupledflow/thermal model but improve it to consider open-hole packer completion scenario.Compared with theconventional cased,cemented and perforated completion style,the fracture initiation points in open-holestimulated well are more effected by near wellbore in-situ stress field.Therefore,the open-hole packercompletion possibly forms a two-fold flow regime.The formation fluid firstly flows through the fractureinto the open-hole annular space between formation and the packer liner,then flow along the annular spaceuntil meet the frac port on the production pipe.The two-fold flow regime results in double temperature dropsdue to Joule-Thompson cooling effect.The original theoretical model is improved by adding a simulationsub-region representing open-hole annular which helps to understand the flow and heat transfer inside it.With the improved mathematical model,DTS monitoring data during a three-rate production test in ahorizontal multi-stage fractured well in Erdos Basin of China was simulated and analyzed.The improvedmodel with open-hole packer completion was applied and then the gas flow profiling was accomplished.
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The fiber-optic distributed temperatrue sensor (DTS) has been used for flow profiling in horizontal multi-stage fractured wells, and there were some reservoir/wellbore coupled thermal models presented byresearchers. Although curre...
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The fiber-optic distributed temperatrue sensor (DTS) has been used for flow profiling in horizontal multi-stage fractured wells, and there were some reservoir/wellbore coupled thermal models presented byresearchers. Although current theoretical models are developed for some certain application scenarios, theindustry have realized the great potential of DTS for production prediction in unconventional resources. Thispaper presents a DTS flow profiling case for a horizontal multi-stage fractured well in tight gas reservoirswith open-hole packer completion scenarios by applying a newly improved theoretical model. In this paper, we started with the conventional semi-analytical wellbore-fracture-reservoir coupled flow/thermal model which have been developed for cased, perforated, and multi-stage fractured wells, and revisedit to consider the special feature of openhole packer completion scenario. Since the formation fluid firstlyflows through the fracture into the open-hole annular space between formation and the packer liner, thenflow along the annular space until meet the frac port on the production pipe, we add a simulation sub-region representing open-hole annular which helps to understand the flow and heat transfer inside it. Thepresented model successfully simulated the two-fold flow regime caused by the simultaneous flow and heattransmission in the annular space and the production pipe. In each stage, the DTS temperature data possiblyshow double drops due to Joule-Thompson cooling effects at the fracture and frac port locations if theyare not consistent. With the improved mathematical model, DTS monitoring data during a three-rate production test in ahorizontal multi-stage fractured well in Erdos Basin of China was simulated and analyzed. The improvedmodel with open-hole packer completion was applied and then the gas rate prediction was accomplished.
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Railways are required to operate with a high reliability and safety level
for transporting passengers. As the wheel is one of the key subsystems of railway
trains, it can contribute significantly to a train’s reliability and sa...
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Railways are required to operate with a high reliability and safety level
for transporting passengers. As the wheel is one of the key subsystems of railway
trains, it can contribute significantly to a train’s reliability and safety. To meet the
requirements, one important way is to implement proper maintenance activities. In
this paper, a modelling approach is developed on the basis of Petri Nets (PN) for the
maintenance analysis of wheels. The detailed maintenance and renewal options to be
explored for wheel maintenance are discussed. The model is solved using Monte
Carlo (MC) simulation to investigate the effectiveness of maintenance actions.
Moreover, the developed model can be flexible, and in this paper different ways in
which the complexity of the model can be extended are discussed. Sensitivity
estimation is performed to assess the effectiveness of maintenance on availability by
applying a cost-benefit analysis. A case study is conducted to demonstrate the
execution of the model. Analysis of the results shows that the model is useful in
predicting life-cycle wheel maintenance activities and costs throughout the train’s life,
and this can be used in maintenance optimization.
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摘要 :
Railways are required to operate with a high reliability and safety level
for transporting passengers. As the wheel is one of the key subsystems of railway
trains, it can contribute significantly to a train’s reliability and safe...
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Railways are required to operate with a high reliability and safety level
for transporting passengers. As the wheel is one of the key subsystems of railway
trains, it can contribute significantly to a train’s reliability and safety. To meet the
requirements, one important way is to implement proper maintenance activities. In
this paper, a modelling approach is developed on the basis of Petri Nets (PN) for the
maintenance analysis of wheels. The detailed maintenance and renewal options to be
explored for wheel maintenance are discussed. The model is solved using Monte
Carlo (MC) simulation to investigate the effectiveness of maintenance actions.
Moreover, the developed model can be flexible, and in this paper different ways in
which the complexity of the model can be extended are discussed. Sensitivity
estimation is performed to assess the effectiveness of maintenance on availability by
applying a cost-benefit analysis. A case study is conducted to demonstrate the
execution of the model. Analysis of the results shows that the model is useful in
predicting life-cycle wheel maintenance activities and costs throughout the train’s life,
and this can be used in maintenance optimization.
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A hybrid RANS/LES turbulence model integrated with transition formulation is developed and tested by a surrogate model problem through joint experimental and computational fluid dynamic approach. The model problem consists of a ci...
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A hybrid RANS/LES turbulence model integrated with transition formulation is developed and tested by a surrogate model problem through joint experimental and computational fluid dynamic approach. The model problem consists of a circular cylinder for generating coherent unsteadiness and a downstream airfoil in the cylinder wake. The cylinder flow is subcritical, with a Reynolds number of 64,000 based upon the cylinder diameter. The quantitative dynamics of vortex shedding and Reynolds stresses in the cylinder near wake were well captured, owing to the turbulence-resolving large eddy simulation method that was invoked in the wake. The hybrid model switched between RANS/LES mode outside boundary layer as expected. According to the experimental and simulation results, the airfoil encountered local flow angle variations up to ±50°. Further processing the data through phase-averaging technique found phase lags along the chordwise locations and both the phase-averaged and mean profiles collapsed into the Law-of-the-wall in the range of 0 < y~+ < 50. The features of high blade loading fluctuations due to unsteadiness and transitional boundary layers are of interest in the aerodynamic studies of full-scale wind turbine blades, making the model problem a comprehensive benchmark case for future model development and validation.
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摘要 :
A hybrid RANS/LES turbulence model integrated with transition formulation is developed and tested by a surrogate model problem through joint experimental and computational fluid dynamic approach. The model problem consists of a ci...
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A hybrid RANS/LES turbulence model integrated with transition formulation is developed and tested by a surrogate model problem through joint experimental and computational fluid dynamic approach. The model problem consists of a circular cylinder for generating coherent unsteadiness and a downstream airfoil in the cylinder wake. The cylinder flow is subcritical, with a Reynolds number of 64,000 based upon the cylinder diameter. The quantitative dynamics of vortex shedding and Reynolds stresses in the cylinder near wake were well captured, owing to the turbulence-resolving large eddy simulation method that was invoked in the wake. The hybrid model switched between RANS/LES mode outside boundary layer as expected. According to the experimental and simulation results, the airfoil encountered local flow angle variations up to ±50°. Further processing the data through phase-averaging technique found phase lags along the chordwise locations and both the phase-averaged and mean profiles collapsed into the Law-of-the-wall in the range of 0 < y~+ < 50. The features of high blade loading fluctuations due to unsteadiness and transitional boundary layers are of interest in the aerodynamic studies of full-scale wind turbine blades, making the model problem a comprehensive benchmark case for future model development and validation.
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Mobile stations supporting the 802.1 lu standard can access WLAN automatically when they are within the coverage of the network service provided by this WLAN. To achieve this goal, the stations need to keep "on" states including i...
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Mobile stations supporting the 802.1 lu standard can access WLAN automatically when they are within the coverage of the network service provided by this WLAN. To achieve this goal, the stations need to keep "on" states including idle and active all the time. However, studies have noted that the idleness of stations often lead to considerable power consumption. Although the conventional power saving mode (PSM) can provide energy saving effect to some extent, its own disadvantage leads to lower energy efficiency when the number of stations accessing the target WLAN. In this paper, we propose a Schedule-Aware PSM (S-PSM), which can improve the energy efficiency in 802.1 lu WLAN. Particularly, we use the Generic advertisement service (GAS) defined in 802.11u standard to broadcast the transmission schedule information and all stations switch off their radios based on this information accordingly. We introduce the Respond Contention Window to reduce the collision probability of competition channel. When there is no packet in the access point (AP), AP broadcasts the GAS frame and actives the Idle Timer. All stations will turn into sleep and AP will not send GAS frame until Idle Timer expires. Simulations have shown that our proposed scheme can significantly reduce power consumption compared with the conventional PSM.
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Knowledge Distillation (KD) as a model compression technique has been widely used in Graph Neural Networks (GNNs). Recently research has demonstrated that GNNs with shallow or deep hidden layers have different representational abi...
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Knowledge Distillation (KD) as a model compression technique has been widely used in Graph Neural Networks (GNNs). Recently research has demonstrated that GNNs with shallow or deep hidden layers have different representational abilities on nodes with low or high degrees. The use of multi-teacher models can provide diverse knowledge sources for the student model. However, a significant amount of information is missing when the knowledge is transferred through the deep hidden layers in the multi-teacher models. It is difficult for multi-teacher models to transfer the information of each hidden layer to the student model while preserving the local semantics of their respective models. To solve this problem, we propose a novel multi-teacher knowledge distillation method. Multi-teacher Local Semantic distillation from Graph Neural Networks (MLS-GNN). MLS-GNN is able to agglomerate the representation capabilities of shallow and deep GNN teacher models on nodes with different degrees. Not only that, the local semantics of each hidden layer in multi-teacher models is preserved through nodes and edges. Finally, the adaptive weights are assigned to different teacher models to stabilize the knowledge acquisition process and summarize the local semantics. We conducted experiments on five public datasets for node classification task, and the results show that MLS-GNN has excellent performance and better than other baseline methods.
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Today’s high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage h...
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Today’s high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic priority functions to prioritize and schedule jobs. But, once configured and deployed by the experts, such priority functions can hardly adapt to the changes of job loads, optimization goals, or system settings, potentially leading to degraded system efficiency when changes occur. To address this fundamental issue, we present RLScheduler, an automated HPC batch job scheduler built on reinforcement learning. RLScheduler relies on minimal manual interventions or expert knowledge, but can learn high-quality scheduling policies via its own continuous ‘trial and error’. We introduce a new kernel-based neural network structure and trajectory filtering mechanism in RLScheduler to improve and stabilize the learning process. Through extensive evaluations, we confirm that RLScheduler can learn high-quality scheduling policies towards various workloads and various optimization goals with relatively low computation cost. Moreover, we show that the learned models perform stably even when applied to unseen workloads, making them practical for production use.
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