This paper analyses the demand of intelligent bookshelf system, designed the overall structure of intelligent Library shelves system based on RFID and create the system use case model. The analysis and design of the i...This paper analyses the demand of intelligent bookshelf system, designed the overall structure of intelligent Library shelves system based on RFID and create the system use case model. The analysis and design of the intelligent bookshelf system structure from two aspects of hardware and software, puts forward the three layer system structure model of intelligent bookshelf system based on RFID technology. The paper provide the analysis method and the design ideas of library intelligent bookshelf system, has the very high application value, contribute to the realization of library intelligent management and improve the level of library services, provide a useful reference to the development of current book positioning system and intelligent goods shelf system solution.展开更多
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul...With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.展开更多
文摘This paper analyses the demand of intelligent bookshelf system, designed the overall structure of intelligent Library shelves system based on RFID and create the system use case model. The analysis and design of the intelligent bookshelf system structure from two aspects of hardware and software, puts forward the three layer system structure model of intelligent bookshelf system based on RFID technology. The paper provide the analysis method and the design ideas of library intelligent bookshelf system, has the very high application value, contribute to the realization of library intelligent management and improve the level of library services, provide a useful reference to the development of current book positioning system and intelligent goods shelf system solution.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152301,and 91852115)the National Numerical Wind tunnel Project(Grand No.NNW2018-ZT1B01).
文摘With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.
