Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the ...Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.展开更多
We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature dis...We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.展开更多
Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are resea...Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.展开更多
In this study of temperature rising in vehicular twin-tube hydraulic gas-precharged shock absorbers,thermodynamic analyses were conducted via simulations.Equations on heat conduction,heat convection as well as radiati...In this study of temperature rising in vehicular twin-tube hydraulic gas-precharged shock absorbers,thermodynamic analyses were conducted via simulations.Equations on heat conduction,heat convection as well as radiation were derived by applying certain laws governing heat transfer;an equivalent thermal resistance network model of a shock absorber undergoing heat transfer was established innovatively;moreover,the shock absorber’s thermodynamic model of control volume system was built by using the first law of thermodynamics;and finally,time required for shock absorber to reach thermal equilibrium and corresponding value of steady temperature were calculated by programming.In this way,a lower thermal equilibrium temperature will be achieved,hence help to improve reliability of shock absorbers in work by offering low ambient temperature,by reducing amplitudes and frequencies of external incentives exerted on them and by increasing flow rate of ambient air passing around them.展开更多
In recent years, Combined electro-thermal system has developed rapidly. In order to provide the initial data for the analysis of the combined electro-thermal system, a practical energy flow calculation method for the ...In recent years, Combined electro-thermal system has developed rapidly. In order to provide the initial data for the analysis of the combined electro-thermal system, a practical energy flow calculation method for the combined electro-thermal system is proposed in this paper. Based on the detailed analysis of the topology structure of the heating network and its hydraulic and thermodynamic model, the forward-backward sweep method for the heat flow of the heating network is established, which is more suitable for the actual radial heating network. The electric and thermal coupling model for heating source, such as thermoelectric unit and electric boiler is established, and the heat flow of heating network and the power flow of power grid are calculated orderly, thus a fast calculation method for the combined electro-thermal system is formed. What’s more, a combined electro-thermal system with two-stage peak-shaving electric boiler is used as the example system. This paper validates the effectiveness and rapidity of this method through the example system, and analyzes the influence for the energy flow of combined electro-thermal system caused by the operating parameters such as the installation location of electric boiler, the outlet water temperature of heat source and the outlet flow rate, etc.展开更多
Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied cat...Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> and Pd/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (<i><span style="white-space:nowrap;"><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">ø</span></span></i>) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners w<span style="white-space:normal;font-family:;" "="">as</span><span style="white-space:normal;font-family:;" "=""> scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (<i>r</i>), the radial distance from the center line of the flame, and (<i>x</i>), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three</span><span style="white-space:normal;font-family:;" "="">-</span><span style="white-space:normal;font-family:;" "="">layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate </span><span style="white-space:normal;font-family:;" "="">the </span><span style="white-space:normal;font-family:;" "="">effects of coded <i>R</i> and <i>X</i> input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9809 & 634.5</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 3528.8 for RSM method compared to 0.9857</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9951 & 7636.4</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 24</span><span style="white-space:normal;font-family:;" "="">,</span><span style="white-space:normal;font-family:;" "="">028.4 for ANN method beside lower values </span><span style="white-space:normal;font-family:;" "="">for error analysis terms.</span>展开更多
In recent years, introduction of alternative energy sources such as solar energy is expected. Solar heat energy utilization systems are rapidly gaining acceptance as one of the best solutions to be an alternative ener...In recent years, introduction of alternative energy sources such as solar energy is expected. Solar heat energy utilization systems are rapidly gaining acceptance as one of the best solutions to be an alternative energy source. However, thermal energy collection is influenced by solar radiation and weather conditions. In order to control a solar heat energy utilization system as accurate as possible, it requires method of solar radiation estimation. This paper proposes the forecast technique of a thermal energy collection of solar heat energy utilization system based on solar radiation forecasting at one-day-ahead 24-hour thermal energy collection by using three different NN models. The proposed technique with application of NN is trained by weather data based on tree-based model, and tested according to forecast day. Since tree-based-model classifies a meteorological data exactly, NN will train a solar radiation with smoothly. The validity of the proposed technique is confirmed by computer simulations by use of actual meteorological data.展开更多
The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabi...The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.展开更多
The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally in...The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.展开更多
基金supported by National Key Basic Research Program of China(973Program,Grant No.2005CB724100,Grant No.2011CB706803)National Natural Science Foundation of China(Grant No.50675076,Grant No.50575087,Grant No.51075161)National Hi-tech Research and Development Program of China(863Program,Grant No.2008AA042802)
文摘Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60672095)the Fundamental Research Funds for the Central Universities,China (Grant No. KYZ201300)the Youth Sci-Tech Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024)
文摘We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.
基金Supported by National Natural Science Foundation of China(Grant No.51305244)Shandong Provincal Natural Science Foundation of China(Grant No.ZR2013EEL015)
文摘Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.
基金Sponsored by the Ministerial Level Advanced Research Foundation (0020707)
文摘In this study of temperature rising in vehicular twin-tube hydraulic gas-precharged shock absorbers,thermodynamic analyses were conducted via simulations.Equations on heat conduction,heat convection as well as radiation were derived by applying certain laws governing heat transfer;an equivalent thermal resistance network model of a shock absorber undergoing heat transfer was established innovatively;moreover,the shock absorber’s thermodynamic model of control volume system was built by using the first law of thermodynamics;and finally,time required for shock absorber to reach thermal equilibrium and corresponding value of steady temperature were calculated by programming.In this way,a lower thermal equilibrium temperature will be achieved,hence help to improve reliability of shock absorbers in work by offering low ambient temperature,by reducing amplitudes and frequencies of external incentives exerted on them and by increasing flow rate of ambient air passing around them.
文摘In recent years, Combined electro-thermal system has developed rapidly. In order to provide the initial data for the analysis of the combined electro-thermal system, a practical energy flow calculation method for the combined electro-thermal system is proposed in this paper. Based on the detailed analysis of the topology structure of the heating network and its hydraulic and thermodynamic model, the forward-backward sweep method for the heat flow of the heating network is established, which is more suitable for the actual radial heating network. The electric and thermal coupling model for heating source, such as thermoelectric unit and electric boiler is established, and the heat flow of heating network and the power flow of power grid are calculated orderly, thus a fast calculation method for the combined electro-thermal system is formed. What’s more, a combined electro-thermal system with two-stage peak-shaving electric boiler is used as the example system. This paper validates the effectiveness and rapidity of this method through the example system, and analyzes the influence for the energy flow of combined electro-thermal system caused by the operating parameters such as the installation location of electric boiler, the outlet water temperature of heat source and the outlet flow rate, etc.
文摘Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> and Pd/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (<i><span style="white-space:nowrap;"><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">ø</span></span></i>) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners w<span style="white-space:normal;font-family:;" "="">as</span><span style="white-space:normal;font-family:;" "=""> scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (<i>r</i>), the radial distance from the center line of the flame, and (<i>x</i>), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three</span><span style="white-space:normal;font-family:;" "="">-</span><span style="white-space:normal;font-family:;" "="">layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate </span><span style="white-space:normal;font-family:;" "="">the </span><span style="white-space:normal;font-family:;" "="">effects of coded <i>R</i> and <i>X</i> input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9809 & 634.5</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 3528.8 for RSM method compared to 0.9857</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9951 & 7636.4</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 24</span><span style="white-space:normal;font-family:;" "="">,</span><span style="white-space:normal;font-family:;" "="">028.4 for ANN method beside lower values </span><span style="white-space:normal;font-family:;" "="">for error analysis terms.</span>
文摘In recent years, introduction of alternative energy sources such as solar energy is expected. Solar heat energy utilization systems are rapidly gaining acceptance as one of the best solutions to be an alternative energy source. However, thermal energy collection is influenced by solar radiation and weather conditions. In order to control a solar heat energy utilization system as accurate as possible, it requires method of solar radiation estimation. This paper proposes the forecast technique of a thermal energy collection of solar heat energy utilization system based on solar radiation forecasting at one-day-ahead 24-hour thermal energy collection by using three different NN models. The proposed technique with application of NN is trained by weather data based on tree-based model, and tested according to forecast day. Since tree-based-model classifies a meteorological data exactly, NN will train a solar radiation with smoothly. The validity of the proposed technique is confirmed by computer simulations by use of actual meteorological data.
文摘The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.
文摘The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.
