As the proportion of natural gas consumption in the energy market gradually increases,optimizing the design of gas storage surface system(GSSS)has become a current research focus.Existing studies on the two independen...As the proportion of natural gas consumption in the energy market gradually increases,optimizing the design of gas storage surface system(GSSS)has become a current research focus.Existing studies on the two independent injection pipeline network(InNET)and production pipeline network(ProNET)for underground natural gas storage(UNGS)are scarce,and no optimization methods have been proposed yet.Therefore,this paper focuses on the flow and pressure boundary characteristics of the GSSS.It constructs systematic models,including the injection multi-condition coupled model(INM model),production multi-condition coupled model(PRM model),injection single condition model(INS model)and production single condition model(PRS model)to optimize the design parameters.Additionally,this paper proposes a hybrid genetic algorithm based on generalized reduced gradient(HGA-GRG)for solving the models.The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network.For the GSSS,nine different condition scenarios are considered,and iterative process analysis and sensitivity analysis of these scenarios are conducted.Moreover,simulation scenarios are set up to verify the applicability of different scenarios to the boundaries.The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890×10^(4) CNY,and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655×10^(4) CNY,demonstrating greater applicability and economy than those considering only one or two types of conditions.The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS.展开更多
Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition...Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.展开更多
The experimental investigation of axial-flow pump has been rapidly developed to meet the needs of South-to-North Water Diversion Project of China. Owing to the boundary conditions of hub, blade tip clearance, much of ...The experimental investigation of axial-flow pump has been rapidly developed to meet the needs of South-to-North Water Diversion Project of China. Owing to the boundary conditions of hub, blade tip clearance, much of the physical phenomena and laws involved in this complex flow field can＇t be fully determined. The flow characteristics of the high efficiency axial-flow pump have been simulated by RNG k-e turbulence model and SIMPLEC arithmetic based on FLUENT software. Numerical results indicate that the data from the prediction show agreement with the experimental results, static pressure on pressure side of blades increases slightly at circumferential direction with radius increasing, and keep almost constant at the same radial while increasing gradually from inlet to exit on the suction side along flow direction at design conditions. The static pressure, total pressure and velocity at inlet, impeller outlet and vane outlet were measured by a five-hole probe, and a contrastive experiment was done to investigate the influence of hub leakage. The experimental results show that inlet flow is almost axial and the prerotation is very small at various conditions. The meridional velocity and circulation distribution are almost identical at impeller outlet at design conditions due to steady flow and high efficiency. The residual circulation exits at downstream of the guide vane, and the circumferential velocity component increases linearly from hub to tip at small flow rate conditions. Hub leakage in adjustable blades results in the decrease of the meridional velocity and circulation at blade exit near hub. The results of numerical simulation and experiments supply important flow structure information for the high-efficiency axial-flow pump.展开更多
基金funded by the National Natural Science Foun-dation of China,grant number 51704253 and 52474084。
文摘As the proportion of natural gas consumption in the energy market gradually increases,optimizing the design of gas storage surface system(GSSS)has become a current research focus.Existing studies on the two independent injection pipeline network(InNET)and production pipeline network(ProNET)for underground natural gas storage(UNGS)are scarce,and no optimization methods have been proposed yet.Therefore,this paper focuses on the flow and pressure boundary characteristics of the GSSS.It constructs systematic models,including the injection multi-condition coupled model(INM model),production multi-condition coupled model(PRM model),injection single condition model(INS model)and production single condition model(PRS model)to optimize the design parameters.Additionally,this paper proposes a hybrid genetic algorithm based on generalized reduced gradient(HGA-GRG)for solving the models.The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network.For the GSSS,nine different condition scenarios are considered,and iterative process analysis and sensitivity analysis of these scenarios are conducted.Moreover,simulation scenarios are set up to verify the applicability of different scenarios to the boundaries.The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890×10^(4) CNY,and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655×10^(4) CNY,demonstrating greater applicability and economy than those considering only one or two types of conditions.The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS.
基金supported by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045).
文摘Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA05Z207)National Science and Technology Support Scheme of China (Grant No. 2008BAF34B10)Jiangsu Provincial Graduate Student Innovation Foundation of China (Grant No. CX08B_064Z)
文摘The experimental investigation of axial-flow pump has been rapidly developed to meet the needs of South-to-North Water Diversion Project of China. Owing to the boundary conditions of hub, blade tip clearance, much of the physical phenomena and laws involved in this complex flow field can＇t be fully determined. The flow characteristics of the high efficiency axial-flow pump have been simulated by RNG k-e turbulence model and SIMPLEC arithmetic based on FLUENT software. Numerical results indicate that the data from the prediction show agreement with the experimental results, static pressure on pressure side of blades increases slightly at circumferential direction with radius increasing, and keep almost constant at the same radial while increasing gradually from inlet to exit on the suction side along flow direction at design conditions. The static pressure, total pressure and velocity at inlet, impeller outlet and vane outlet were measured by a five-hole probe, and a contrastive experiment was done to investigate the influence of hub leakage. The experimental results show that inlet flow is almost axial and the prerotation is very small at various conditions. The meridional velocity and circulation distribution are almost identical at impeller outlet at design conditions due to steady flow and high efficiency. The residual circulation exits at downstream of the guide vane, and the circumferential velocity component increases linearly from hub to tip at small flow rate conditions. Hub leakage in adjustable blades results in the decrease of the meridional velocity and circulation at blade exit near hub. The results of numerical simulation and experiments supply important flow structure information for the high-efficiency axial-flow pump.
