A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductiv...A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.展开更多
Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by...Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.展开更多
The thermo-physical properties of nanofluids are highly dependent on the used base fluid.This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG(ethylene glycol)-W...The thermo-physical properties of nanofluids are highly dependent on the used base fluid.This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG(ethylene glycol)-W(water)hybrid nanofluids with mass concentration and temperatures in the ranges 1-5 wt.%and 25-60C,respectively.The characteristics and stability of these mixtures were estimated by TEM(transmission electron microscopy),visual observation,and absorbance tests.The results show that 120 min of sonication and the addition of PVP(polyvinyl pyrrolidone)surfactant can prevent sedimentation for a period reaching up to 20 days.The increase of EG(ethylene glycol)in the base fluid leads to low thermal conductivity and high viscosity.Thermal conductivity enhancement(TCE)decreases from 21.52%to 11.7%when EG:W is changed from 20:80 to 80:20 at 1 wt.%and 60C.A lower viscosity of the base fluid influences more significantly the TCE of the nanofluid.An Artificial Neural Network(ANN)has also been used to describe the effectiveness of these hybrid nanofluids as heat transfer fluids.The optimal number of layers and neurons in these models have been found to be 1 and 5 for viscosity,and 1 and 7 for thermal conductivity.The corresponding coefficient of determination(R^(2))was 0.9979 and 0.9989,respectively.展开更多
In this research work, the thermal conductivity and density of alumina/silica(Al_2O_3/SiO_2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural...In this research work, the thermal conductivity and density of alumina/silica(Al_2O_3/SiO_2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks(ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM–EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations(0.05%, 0.1% and 0.2%) and temperatures(20 to 60 °C). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient o thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure.展开更多
Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and ...Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box com-ponent. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.展开更多
The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundre...The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.展开更多
Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging...Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.展开更多
基金supported by the Fundamental Research Funds for the Central Universities (No.3122020072)the Multi-investment Project of Tianjin Applied Basic Research(No.23JCQNJC00250)。
文摘A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.
文摘Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.
基金This project is supported by Yulingn Zhai and the National Natural Science Foundation of China(No.51806090)the Basic Research Project of Yunnan Province(No.202001AT070081).
文摘The thermo-physical properties of nanofluids are highly dependent on the used base fluid.This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG(ethylene glycol)-W(water)hybrid nanofluids with mass concentration and temperatures in the ranges 1-5 wt.%and 25-60C,respectively.The characteristics and stability of these mixtures were estimated by TEM(transmission electron microscopy),visual observation,and absorbance tests.The results show that 120 min of sonication and the addition of PVP(polyvinyl pyrrolidone)surfactant can prevent sedimentation for a period reaching up to 20 days.The increase of EG(ethylene glycol)in the base fluid leads to low thermal conductivity and high viscosity.Thermal conductivity enhancement(TCE)decreases from 21.52%to 11.7%when EG:W is changed from 20:80 to 80:20 at 1 wt.%and 60C.A lower viscosity of the base fluid influences more significantly the TCE of the nanofluid.An Artificial Neural Network(ANN)has also been used to describe the effectiveness of these hybrid nanofluids as heat transfer fluids.The optimal number of layers and neurons in these models have been found to be 1 and 5 for viscosity,and 1 and 7 for thermal conductivity.The corresponding coefficient of determination(R^(2))was 0.9979 and 0.9989,respectively.
文摘In this research work, the thermal conductivity and density of alumina/silica(Al_2O_3/SiO_2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks(ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM–EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations(0.05%, 0.1% and 0.2%) and temperatures(20 to 60 °C). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient o thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure.
基金Project (No. 2003AA517020) supported by the National Hi-TechResearch and Development Program (863) of China
文摘Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box com-ponent. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.
基金This research was supported by technology innovation fund of the national economy and trade committee , People s Republic of China ,under contract number 02LJ 14 05 01
文摘The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.
文摘Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.
