The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblie...The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.展开更多
Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being purs...Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.展开更多
Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a mod...Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor environment.This study was performed for a stationary flow regime.The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement.Multilayer perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the outputs.Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach.The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model.The average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.展开更多
Irradiated low-enriched uranium as target plates is used to produce,via neutron radiation and from the molybdenum-99 fission product,technetium-99m,which is a radio-element widely used for diagnosis in the field of nu...Irradiated low-enriched uranium as target plates is used to produce,via neutron radiation and from the molybdenum-99 fission product,technetium-99m,which is a radio-element widely used for diagnosis in the field of nuclear medicine.The behavior of this type of target must be known to prevent eventual failures during radiation.The present study aims to assess,via prediction,the thermal–mechanical behavior,physical integrity,and geometric stability of targets under neutron radiation in a nuclear reactor.For this purpose,a numerical simulation using a three-dimensional finite element analysis model was performed to determine the thermal expansion and stress distribution in the target cladding.The neutronic calculation results,target material properties,and cooling parameters of the KAERI research group were used as inputs in our developed model.Thermally induced stress and deflection on the target were calculated using Ansys-Fluent codes,and the temperature profiles,as inputs of this calculation,were obtained from a CFD thermal–hydraulic model.The stress generated,induced by the pressure of fission gas release at the interface of the cladding target,was also estimated using the Redlich–Kwong equation of state.The results obtained using the bonded and unbonded target models considering the effect of the radiation heat combined with a fission gas release rate of approximately 3%show that the predicted thermal stress and deflection values satisfy the structural performance requirement and safety design.It can be presumed that the integrity of the target cladding is maintained under these conditions.展开更多
The phreatic aquifer of Bekalta experienced a progressive degradation of water resources over time: using increasingly important waters for irrigation and drinking water, nitrate pollution, salinization... This aquife...The phreatic aquifer of Bekalta experienced a progressive degradation of water resources over time: using increasingly important waters for irrigation and drinking water, nitrate pollution, salinization... This aquifer is of great economic importance because it is used for irrigation and domestic consumption. Vulnerability map to nitrate pollution is a necessary tool to developing management to preserve the quality of groundwater. This study utilized the Geographic Information System technique and the DRASTIC model to assess the vulnerability of groundwater resources to contamination. The Geographic Information System (GIS) technology represents the best method to solve the main problems in the vulnerability survey. Indeed is allowed for swift organisation, quantification, and interpretation of large volumes of hydrological data with computer accuracy and minimal risk of human errors. The Visio model was exported and loaded into an ESRI Geodatabase in ArcCatalog as defined by the UML model. The purpose of this geodatabase is data harmonization process within modeling groundwater vulnerability to pollution. The resulting map shows evidence for three categories of vulnerability (low, middle and high). The resultant vulnerability map showed the predominant of moderately vulnerability class on the most of the Bekalta region which occupying an area of 68%. The low and high groundwater vulnerability classes occupy respectively an area of 30% and 2% of the total surface of the study area.展开更多
Polycrystalline zinc oxide (ZnO) thin films have been deposited at 450°C onto glass and silicon substrates by pulsed laser deposition technique (PLD). The used source was a KrF excimer laser (248 nm, 25 ns, 5 Hz,...Polycrystalline zinc oxide (ZnO) thin films have been deposited at 450°C onto glass and silicon substrates by pulsed laser deposition technique (PLD). The used source was a KrF excimer laser (248 nm, 25 ns, 5 Hz, 2 J/cm2). The effects of glass and silicon substrates on structural and optical properties of ZnO films have been investigated. X-ray diffraction patterns showed that ZnO films are polycrystalline with a hexagonal wurtzite—type structure with a strong (103) orientation and have a good crystallinity on monocrystalline Si(100) substrate. The thickness and compositional depth profile were studied by Rutherford Backscattering spectrometry (RBS). The average transmittance of ZnO films deposited on glass substrate in the visible range is 70%.展开更多
文摘The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.
文摘Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.
基金supported by the Algerian Atomic Energy Commission
文摘Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor environment.This study was performed for a stationary flow regime.The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement.Multilayer perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the outputs.Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach.The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model.The average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.
文摘Irradiated low-enriched uranium as target plates is used to produce,via neutron radiation and from the molybdenum-99 fission product,technetium-99m,which is a radio-element widely used for diagnosis in the field of nuclear medicine.The behavior of this type of target must be known to prevent eventual failures during radiation.The present study aims to assess,via prediction,the thermal–mechanical behavior,physical integrity,and geometric stability of targets under neutron radiation in a nuclear reactor.For this purpose,a numerical simulation using a three-dimensional finite element analysis model was performed to determine the thermal expansion and stress distribution in the target cladding.The neutronic calculation results,target material properties,and cooling parameters of the KAERI research group were used as inputs in our developed model.Thermally induced stress and deflection on the target were calculated using Ansys-Fluent codes,and the temperature profiles,as inputs of this calculation,were obtained from a CFD thermal–hydraulic model.The stress generated,induced by the pressure of fission gas release at the interface of the cladding target,was also estimated using the Redlich–Kwong equation of state.The results obtained using the bonded and unbonded target models considering the effect of the radiation heat combined with a fission gas release rate of approximately 3%show that the predicted thermal stress and deflection values satisfy the structural performance requirement and safety design.It can be presumed that the integrity of the target cladding is maintained under these conditions.
文摘The phreatic aquifer of Bekalta experienced a progressive degradation of water resources over time: using increasingly important waters for irrigation and drinking water, nitrate pollution, salinization... This aquifer is of great economic importance because it is used for irrigation and domestic consumption. Vulnerability map to nitrate pollution is a necessary tool to developing management to preserve the quality of groundwater. This study utilized the Geographic Information System technique and the DRASTIC model to assess the vulnerability of groundwater resources to contamination. The Geographic Information System (GIS) technology represents the best method to solve the main problems in the vulnerability survey. Indeed is allowed for swift organisation, quantification, and interpretation of large volumes of hydrological data with computer accuracy and minimal risk of human errors. The Visio model was exported and loaded into an ESRI Geodatabase in ArcCatalog as defined by the UML model. The purpose of this geodatabase is data harmonization process within modeling groundwater vulnerability to pollution. The resulting map shows evidence for three categories of vulnerability (low, middle and high). The resultant vulnerability map showed the predominant of moderately vulnerability class on the most of the Bekalta region which occupying an area of 68%. The low and high groundwater vulnerability classes occupy respectively an area of 30% and 2% of the total surface of the study area.
文摘Polycrystalline zinc oxide (ZnO) thin films have been deposited at 450°C onto glass and silicon substrates by pulsed laser deposition technique (PLD). The used source was a KrF excimer laser (248 nm, 25 ns, 5 Hz, 2 J/cm2). The effects of glass and silicon substrates on structural and optical properties of ZnO films have been investigated. X-ray diffraction patterns showed that ZnO films are polycrystalline with a hexagonal wurtzite—type structure with a strong (103) orientation and have a good crystallinity on monocrystalline Si(100) substrate. The thickness and compositional depth profile were studied by Rutherford Backscattering spectrometry (RBS). The average transmittance of ZnO films deposited on glass substrate in the visible range is 70%.