The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant a...The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output parameters.This paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient,Nusselt number,and friction factor of water-based Fe_(3)O_(4)-TiO_(2) magnetic hybrid nanofluids in a mini heat sink under magnetic fields.The data considered for the ANN network is at different Reynolds numbers(239 to 1874),different volume concentrations(0%to 2.0%),and different magnetic fields(250 to 1000 G),respectively.Three types of ANN back-propagation algorithms Levenberg-Marquardt(LM),Broyden-Fletcher-Goldfarb-Shanno Quasi Newton(BFGS),and Variable Learning Rate Gradient Descent(VLGD)were used to train the heat transfer coefficient,Nusselt number,and friction factor data,respectively.The ANOVA t-test analysis was also performed to determine the relative accuracy of the three ANN algorithms.The Nusselt number of 2.0%vol.of Fe_(3)O_(4)-TiO_(2) hybrid nanofluid is enhanced by 38.16%without a magnetic field,and it is further enhanced by 88.93%with the magnetic field of 1000 Gauss at a Reynolds number of 1874,with respect to the base fluid.A total of 126 datasets of heat transfer coefficient,Nusselt number,and friction factor were used as input and output data.The three ANN algorithms of LM,BFGS,and VLGD,have shown good acceptance with the experimental data with root-mean-square errors of 0.34883,0.25341,and 1.0202 with correlation coefficients(R2)of 0.99954,0.9967,and 0.94501,respectively,for the Nusselt number data.Moreover,the three ANN algorithms predict root-mean-square errors of 0.001488,0.005041,and 0.006924 with correlation coefficients(R2)of 0.99982,0.99976,and 0.99486,respectively,for the friction factor data.Compared to BFGS and VLGD algorithms,the LM algorithm predicts high accuracy for Nusselt number,and friction factor data.The proposed Nusselt number and friction factor correlations are also discussed.展开更多
A gas-cooled nuclear reactor combined with a Brayton cycle shows promise as a technology for highpower space nuclear power systems.Generally,a helium-xenon gas mixture with a molecular weight of14.5-40.0 g/mol is adop...A gas-cooled nuclear reactor combined with a Brayton cycle shows promise as a technology for highpower space nuclear power systems.Generally,a helium-xenon gas mixture with a molecular weight of14.5-40.0 g/mol is adopted as the working fluid to reduce the mass and volume of the turbomachinery.The Prandtl number for helium-xenon mixtures with this recommended mixing ratio may be as low as 0.2.As the convective heat transfer is closely related to the Prandtl number,different heat transfer correlations are often needed for fluids with various Prandtl numbers.Previous studies have established heat transfer correlations for fluids with medium-high Prandtl numbers(such as air and water)and extremely lowPrandtl fluids(such as liquid metals);however,these correlations cannot be directly recommended for such helium-xenon mixtures without verification.This study initially assessed the applicability of existing Nusselt number correlations,finding that the selected correlations are unsuitable for helium-xenon mixtures.To establish a more general heat transfer correlation,a theoretical derivation was conducted using the turbulent boundary layer theory.Numerical simulations of turbulent heat transfer for helium-xenon mixtures were carried out using Ansys Fluent.Based on simulated results,the parameters in the derived heat transfer correlation are determined.It is found that calculations using the new correlation were in good agreement with the experimental data,verifying its applicability to the turbulent heat transfer for helium-xenon mixtures.The effect of variable gas properties on turbulent heat transfer was also analyzed,and a modified heat transfer correlation with the temperature ratio was established.Based on the working conditions adopted in this study,the numerical error of the property-variable heat transfer correlation was almost within 10%.展开更多
The thermal behavior of pipes with a twisted tape inside(used to enhance heat transfer through the tube wall)is studied in the laminar flow regime.Oil is used as the work fluid with the corresponding Reynolds Number s...The thermal behavior of pipes with a twisted tape inside(used to enhance heat transfer through the tube wall)is studied in the laminar flow regime.Oil is used as the work fluid with the corresponding Reynolds Number spanning the interval 200–2000.It is found that in such conditions the‘Nusselt Number’(Nu)gradually increases with reducing the tape twist ratio,whereas the friction factor is detrimentally affected by the presence of the tape(as witnessed by the comparison with the companion case where a plain tube is considered).In particular,it is shown that the heat transfer efficiency can be improved by nearly 69%if tape inserts with a relatively low twist ratio are used.On the basis of these findings,it is concluded that loose fit tape inserts are superior to tight fit tapes in terms of heat transfer and ease of replacement.展开更多
Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal c...Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal circuit model is a relatively reliable method of obtaining the internal temperature distribution.However,thermal circuit models without targeted consideration of operating conditions and parameter corrections usually limit the accuracy of the results.This paper proposed a five-node transient thermal circuit model with the introduction of nonlinear thermal resistance,which considered the internal structure and winding layout of the core-type high-frequency transformer.The Nusselt number,a crucial variable in heat convection calculations and directly related to the accuracy of thermal resistance parameters,was calibrated on the basis of the distribution of external cooling air.After parameter calibration,the maximum computational error of the hotspot temperature is reduced by 5.48%compared with that of the uncalibrated model.Finally,an experimental platform for temperature monitoring was established to validate the five-node model and its ability to track the temperature change at each reference point after calibrating the Nusselt number.展开更多
This article explores the optimization of heat transport in a magnetohydrodynamic nanofluid flow with mixedMarangoni convection by using the Response SurfaceMethodology.The convective flow is studied with external mag...This article explores the optimization of heat transport in a magnetohydrodynamic nanofluid flow with mixedMarangoni convection by using the Response SurfaceMethodology.The convective flow is studied with external magnetism,radiative heat flux,and buoyancy.An internal heat absorption through the permeable surface is also taken into account.The governing system includes the continuity equation,Navier-Stokes momentum equation,and the conservation of energy equations,approximated by the Prandtl boundary layer theory.The entropy generation in the thermodynamic system is evaluated.Experimental data(Corcione models)is used to model the single-phase aluminawater nanofluid.The numerical solution for the highly nonlinear differential systemis obtained via Ralston’s algorithm.It is observed that the applied magnetic field leads to a higher entropy generation which is engendered by the Lorentz force within the fluid system.The thermal radiation leads to a higher Bejan number,indicating the importance of the irreversibility of heat transport.Also,the heat absorption process via a permeable surface can be employed to regulate the thermal field.An optimizedNusselt number of 13.4 is obtained at the high levels of radiation,injection,and heat sink parameters.The modeled fluid flow scenario is often seen in drying,coating,and heat exchange processes,especially in microgravity environments.展开更多
This study delves into both experimental and analytical examinations of heat exchange in a straight channel, where Al_(2)O_(3)-water nanofluids are utilized, spanning the Reynolds number spectrum from 100 to 1800. Div...This study delves into both experimental and analytical examinations of heat exchange in a straight channel, where Al_(2)O_(3)-water nanofluids are utilized, spanning the Reynolds number spectrum from 100 to 1800. Diverse volume fractions(1%, 2%, and 3%) of Al_(2)O_(3)-water nanofluids are meticulously prepared and analyzed. The essential physical properties of these nanofluids, critical for evaluating their thermal and flow characteristics, have been comprehensively assessed. From a quantitative perspective, numerical simulations are employed to predict the Nusselt number(Nu) and friction factor(f). The empirical findings reveal intriguing trends: the friction factor experiences an upward trend with diminishing velocity, attributed to heightened molecular cohesion. Conversely, the friction factor demonstrates a decline with diminishing volume fractions, a consequence of reduced particle size. Both the nanofluid's viscosity and heat transfer coefficient exhibit a rise in tandem with augmented volume flow rate and concentration gradient. Notably, the simulation results harmonize remarkably well with experimental data. Rigorous validation against prior studies underscores the robust consistency of these outcomes. In the pursuit of augmenting heat transfer, a volume fraction of 3% emerges as particularly influential, yielding an impressive 53.8% enhancement. Minor increments in the friction factor, while present, prove negligible and can be safely overlooked.展开更多
During the last several years,the increase in cooling power requirements for heat exchangers have led to an escalation in heat transfer studies being performed on the use of nanofluids as heat transfer fluids.However,...During the last several years,the increase in cooling power requirements for heat exchangers have led to an escalation in heat transfer studies being performed on the use of nanofluids as heat transfer fluids.However,limited effort has been attempted to relate and interpret these findings or the anomalies associated with them.The paper compiles test data from several studies conducted on different types of heat exchangers.In this review,a concentrated effort is spent to clarify the ambiguities regarding the effect of nanoparticle size on the nanofluid thermal conductivity and Nusselt number.Results show that the nanofluid thermal conductivity is not influenced by the nanoparticle size,but by the clustering of the particles themselves.The less compact the structure of the nanoparticle clustering is,the greater the enhancement in the nanofluid thermal conductivity is.Data were also compiled to interpret the relation between the nanofluid flow pattern,nanoparticles volume fraction in the base fluid,and the convective heat transfer.The results from the majority of the heat exchanger studies show an increase in the heat transfer coefficient with the increase in nanoparticle volume fraction.However,studies conducted on plate heat exchanges display some inconsistencies.In the majority of the heat exchanger studies with the exception of few,the decrease in the nanoparticle size is shown to result in an enhancement of the bulk fluid Nusselt number.Compiled test data also reveal that the effectiveness of the alumina nanoparticles is dependent on the flow pattern.The increase in the nanoparticles concentration is shown to result in an increase in the nanofluid heat transfer enhancement as the fluid is transitioning from laminar to turbulent flow.In general,the smaller the nanoparticle size is,the greater the enhancement in the fluid Nusselt number is.展开更多
This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network(ANN)analysis on the basis of the back ...This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network(ANN)analysis on the basis of the back propagation algorithm.The derivation of the proposed model relies on a large number of experimental data obtained from the tests performed with the platform of supercritical flow and heat transfer.Different topology structures,training algo-rithms and transfer functions are employed in model optimization.The performance of the optimal ANN model is evaluated with the mean relative error,the determination coefficient,the number of iterations and the convergence time.It is demonstrated that the model has high prediction accuracy when the tansig transfer function,the Levenberg-Marquardt training algo-rithm and the three-layer topology of 4-9-1 are selected.In addition,the accuracy of the ANN model is observed to be the highest compared with other classic empirical correlations.Mean relative error values of 4.4%and 3.4%have been achieved for modeling of the Nusselt number and friction factor respectively over the whole experimental data set.The ANN model estab-lished in this paper is shown to have an excellent performance in learning ability and general-ization for characterizing the flow and heat transfer law of hydrocarbon fuel,which can provide an alternative approach for the future study of supercritical fluid characteristics and the associ-ated engineering applications.展开更多
基金supported by the Recovery and Resilience Plan(PRR)and by European Funds Next Generation EU under the Project“AET—Alliance for Energy Transition,”no.C644914747-00000023,investment project no.56 of the Incentive System“Agendas for Business Innovation”.
文摘The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output parameters.This paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient,Nusselt number,and friction factor of water-based Fe_(3)O_(4)-TiO_(2) magnetic hybrid nanofluids in a mini heat sink under magnetic fields.The data considered for the ANN network is at different Reynolds numbers(239 to 1874),different volume concentrations(0%to 2.0%),and different magnetic fields(250 to 1000 G),respectively.Three types of ANN back-propagation algorithms Levenberg-Marquardt(LM),Broyden-Fletcher-Goldfarb-Shanno Quasi Newton(BFGS),and Variable Learning Rate Gradient Descent(VLGD)were used to train the heat transfer coefficient,Nusselt number,and friction factor data,respectively.The ANOVA t-test analysis was also performed to determine the relative accuracy of the three ANN algorithms.The Nusselt number of 2.0%vol.of Fe_(3)O_(4)-TiO_(2) hybrid nanofluid is enhanced by 38.16%without a magnetic field,and it is further enhanced by 88.93%with the magnetic field of 1000 Gauss at a Reynolds number of 1874,with respect to the base fluid.A total of 126 datasets of heat transfer coefficient,Nusselt number,and friction factor were used as input and output data.The three ANN algorithms of LM,BFGS,and VLGD,have shown good acceptance with the experimental data with root-mean-square errors of 0.34883,0.25341,and 1.0202 with correlation coefficients(R2)of 0.99954,0.9967,and 0.94501,respectively,for the Nusselt number data.Moreover,the three ANN algorithms predict root-mean-square errors of 0.001488,0.005041,and 0.006924 with correlation coefficients(R2)of 0.99982,0.99976,and 0.99486,respectively,for the friction factor data.Compared to BFGS and VLGD algorithms,the LM algorithm predicts high accuracy for Nusselt number,and friction factor data.The proposed Nusselt number and friction factor correlations are also discussed.
基金supported by the National Key Research and Development Program of China(No.2018YFB1900501)the CNSA program(No.D010501)。
文摘A gas-cooled nuclear reactor combined with a Brayton cycle shows promise as a technology for highpower space nuclear power systems.Generally,a helium-xenon gas mixture with a molecular weight of14.5-40.0 g/mol is adopted as the working fluid to reduce the mass and volume of the turbomachinery.The Prandtl number for helium-xenon mixtures with this recommended mixing ratio may be as low as 0.2.As the convective heat transfer is closely related to the Prandtl number,different heat transfer correlations are often needed for fluids with various Prandtl numbers.Previous studies have established heat transfer correlations for fluids with medium-high Prandtl numbers(such as air and water)and extremely lowPrandtl fluids(such as liquid metals);however,these correlations cannot be directly recommended for such helium-xenon mixtures without verification.This study initially assessed the applicability of existing Nusselt number correlations,finding that the selected correlations are unsuitable for helium-xenon mixtures.To establish a more general heat transfer correlation,a theoretical derivation was conducted using the turbulent boundary layer theory.Numerical simulations of turbulent heat transfer for helium-xenon mixtures were carried out using Ansys Fluent.Based on simulated results,the parameters in the derived heat transfer correlation are determined.It is found that calculations using the new correlation were in good agreement with the experimental data,verifying its applicability to the turbulent heat transfer for helium-xenon mixtures.The effect of variable gas properties on turbulent heat transfer was also analyzed,and a modified heat transfer correlation with the temperature ratio was established.Based on the working conditions adopted in this study,the numerical error of the property-variable heat transfer correlation was almost within 10%.
文摘The thermal behavior of pipes with a twisted tape inside(used to enhance heat transfer through the tube wall)is studied in the laminar flow regime.Oil is used as the work fluid with the corresponding Reynolds Number spanning the interval 200–2000.It is found that in such conditions the‘Nusselt Number’(Nu)gradually increases with reducing the tape twist ratio,whereas the friction factor is detrimentally affected by the presence of the tape(as witnessed by the comparison with the companion case where a plain tube is considered).In particular,it is shown that the heat transfer efficiency can be improved by nearly 69%if tape inserts with a relatively low twist ratio are used.On the basis of these findings,it is concluded that loose fit tape inserts are superior to tight fit tapes in terms of heat transfer and ease of replacement.
基金supported by the National Natural Science Foundation of China(Grant 52207180)Xi'an High Voltage Apparatus Research Institute Co.Ltd.(Grant K222301-01)the Anhui Provincial Natural Science Foundation(Grant 2208085UD18).
文摘Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal circuit model is a relatively reliable method of obtaining the internal temperature distribution.However,thermal circuit models without targeted consideration of operating conditions and parameter corrections usually limit the accuracy of the results.This paper proposed a five-node transient thermal circuit model with the introduction of nonlinear thermal resistance,which considered the internal structure and winding layout of the core-type high-frequency transformer.The Nusselt number,a crucial variable in heat convection calculations and directly related to the accuracy of thermal resistance parameters,was calibrated on the basis of the distribution of external cooling air.After parameter calibration,the maximum computational error of the hotspot temperature is reduced by 5.48%compared with that of the uncalibrated model.Finally,an experimental platform for temperature monitoring was established to validate the five-node model and its ability to track the temperature change at each reference point after calibrating the Nusselt number.
文摘This article explores the optimization of heat transport in a magnetohydrodynamic nanofluid flow with mixedMarangoni convection by using the Response SurfaceMethodology.The convective flow is studied with external magnetism,radiative heat flux,and buoyancy.An internal heat absorption through the permeable surface is also taken into account.The governing system includes the continuity equation,Navier-Stokes momentum equation,and the conservation of energy equations,approximated by the Prandtl boundary layer theory.The entropy generation in the thermodynamic system is evaluated.Experimental data(Corcione models)is used to model the single-phase aluminawater nanofluid.The numerical solution for the highly nonlinear differential systemis obtained via Ralston’s algorithm.It is observed that the applied magnetic field leads to a higher entropy generation which is engendered by the Lorentz force within the fluid system.The thermal radiation leads to a higher Bejan number,indicating the importance of the irreversibility of heat transport.Also,the heat absorption process via a permeable surface can be employed to regulate the thermal field.An optimizedNusselt number of 13.4 is obtained at the high levels of radiation,injection,and heat sink parameters.The modeled fluid flow scenario is often seen in drying,coating,and heat exchange processes,especially in microgravity environments.
文摘This study delves into both experimental and analytical examinations of heat exchange in a straight channel, where Al_(2)O_(3)-water nanofluids are utilized, spanning the Reynolds number spectrum from 100 to 1800. Diverse volume fractions(1%, 2%, and 3%) of Al_(2)O_(3)-water nanofluids are meticulously prepared and analyzed. The essential physical properties of these nanofluids, critical for evaluating their thermal and flow characteristics, have been comprehensively assessed. From a quantitative perspective, numerical simulations are employed to predict the Nusselt number(Nu) and friction factor(f). The empirical findings reveal intriguing trends: the friction factor experiences an upward trend with diminishing velocity, attributed to heightened molecular cohesion. Conversely, the friction factor demonstrates a decline with diminishing volume fractions, a consequence of reduced particle size. Both the nanofluid's viscosity and heat transfer coefficient exhibit a rise in tandem with augmented volume flow rate and concentration gradient. Notably, the simulation results harmonize remarkably well with experimental data. Rigorous validation against prior studies underscores the robust consistency of these outcomes. In the pursuit of augmenting heat transfer, a volume fraction of 3% emerges as particularly influential, yielding an impressive 53.8% enhancement. Minor increments in the friction factor, while present, prove negligible and can be safely overlooked.
文摘During the last several years,the increase in cooling power requirements for heat exchangers have led to an escalation in heat transfer studies being performed on the use of nanofluids as heat transfer fluids.However,limited effort has been attempted to relate and interpret these findings or the anomalies associated with them.The paper compiles test data from several studies conducted on different types of heat exchangers.In this review,a concentrated effort is spent to clarify the ambiguities regarding the effect of nanoparticle size on the nanofluid thermal conductivity and Nusselt number.Results show that the nanofluid thermal conductivity is not influenced by the nanoparticle size,but by the clustering of the particles themselves.The less compact the structure of the nanoparticle clustering is,the greater the enhancement in the nanofluid thermal conductivity is.Data were also compiled to interpret the relation between the nanofluid flow pattern,nanoparticles volume fraction in the base fluid,and the convective heat transfer.The results from the majority of the heat exchanger studies show an increase in the heat transfer coefficient with the increase in nanoparticle volume fraction.However,studies conducted on plate heat exchanges display some inconsistencies.In the majority of the heat exchanger studies with the exception of few,the decrease in the nanoparticle size is shown to result in an enhancement of the bulk fluid Nusselt number.Compiled test data also reveal that the effectiveness of the alumina nanoparticles is dependent on the flow pattern.The increase in the nanoparticles concentration is shown to result in an increase in the nanofluid heat transfer enhancement as the fluid is transitioning from laminar to turbulent flow.In general,the smaller the nanoparticle size is,the greater the enhancement in the fluid Nusselt number is.
基金The authors gratefully acknowledge funding support from the Program for National Natural Science Foundation of China(51876005 and 52122604).
文摘This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network(ANN)analysis on the basis of the back propagation algorithm.The derivation of the proposed model relies on a large number of experimental data obtained from the tests performed with the platform of supercritical flow and heat transfer.Different topology structures,training algo-rithms and transfer functions are employed in model optimization.The performance of the optimal ANN model is evaluated with the mean relative error,the determination coefficient,the number of iterations and the convergence time.It is demonstrated that the model has high prediction accuracy when the tansig transfer function,the Levenberg-Marquardt training algo-rithm and the three-layer topology of 4-9-1 are selected.In addition,the accuracy of the ANN model is observed to be the highest compared with other classic empirical correlations.Mean relative error values of 4.4%and 3.4%have been achieved for modeling of the Nusselt number and friction factor respectively over the whole experimental data set.The ANN model estab-lished in this paper is shown to have an excellent performance in learning ability and general-ization for characterizing the flow and heat transfer law of hydrocarbon fuel,which can provide an alternative approach for the future study of supercritical fluid characteristics and the associ-ated engineering applications.
