A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.

Reconstructing the upper ocean thermal profiles using one-dimensional numerical model

The observation data for 5 d at a station in the South China Sea is presented. After brief anMysis of the wind speed, air temperature from the ship-borne meteorological instruments and temperature and salinity profiles from the CTD （conductivity, temperature, depth recorder） data, the authors find that the CTD casts are too sparse for us to understand the diurnal evolution of the thermal structure in the upper ocean. A one-dimensional （1D） numericM code based on Mellor-Yamada turbulence closure model is used to reconstruct the upper ocean thermal structure, utilizing the atmospheric forcing data from ship-borne weather station. The simulation results show good agreement with the observational data; the significance of breaking waves is also briefly discussed. The evolution of turbulence kinetic energy （TKE） and the contribution from shear production and buoy- ancy production are discussed respectively. Finally, several possible factors which might influence the numerical results are briefly analyzed.

Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.

Wavelet Neural Network Based on NARMA-L2 Model for Prediction of Thermal Characteristics in a Feed System

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.

Bayesian networks modeling for thermal error of numerical control machine tools

The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Ex- periments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy.

Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks

The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)model.The governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless terms.Further,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical illustrations.Additionally,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network combinations.The results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

A thermal flux-diffusing model for complex networks and its applications in community structure detection

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.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

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.

Characteristic analysis of mechanical thermal coupling model for bearing rotor system of high-speed train

Based on Newton’s second law and the thermal network method,a mechanical thermal coupling model of the bearing rotor system of high-speed trains is established to study the interaction between the bearing vibration and temperature.The influence of lubrication on the vibration and temperature characteristics of the system is considered in the model,and the real-time relationship between them is built up by using the transient temperature field model.After considering the lubrication,the bearing outer ring vibration acceleration and node temperature considering grease are lower,which shows the necessity of adding the lubrication model.The corresponding experiments for characteristics of vibration and temperature of the model are respectively conducted.In the envelope spectrum obtained from the simulation signal and the experimental signal,the frequency values corresponding to the peaks are close to the theoretical calculation results,and the error is very small.In the three stages of the temperature characteristic experiment,the node temperature change of the simulation model is consistent with the experiment.The good agreement between simulation and experiments proves the effectiveness of the model.By studying the influence of the bearing angular and fault size on the system node temperature,as well as the change law of bearing lubrication characteristics and temperature,it is found that the worse the working condition is,the higher the temperature is.When the ambient temperature is low,the viscosity of grease increases,and the oil film becomes thicker,which increases the sliding probability of the rolling element,thus affecting the normal operation of the bearing,which explains the phenomenon of frequent bearing faults of high-speed trains in the low-temperature area of Northeast China.Further analysis shows that faults often occur in the early stage of train operation in the low-temperature environment.

Modeling Thermal Protective Performance of Multilayer Fabrics for Firefighters

This paper is to report a prediction model for thermal protective performance of multilayer fabrics based on Matlab neural network toolbox. Then a back propagation (BP) neural network model is developed to predict thermal protective performance of multilayer fabrics for firefighters. The network consists of twelve input nodes, six hidden nodes, and one output node. The inputs are weight, thickness, density of warp and weft, limited oxygen index (LOI), and heat conductivity of each-layer fabric. Thermal protective performance (TPP) rating of multilayer fabrics is the output. In this paper, the data from the experiments are used as learning information for the neural network to develop a reliable prediction model. Finnally the model performance is verified, and the proposed model can be applied to predict the thermal protective performance of multilayer fabrics for firefighters.

Heat Transfer and Flow Analysis in Loop Heat Pipe with Multiple Evaporators Using Network Model

Thermal performance of a loop heat pipe with two evaporators and two condensers was examined using a lumped network model analysis. Thermosyphon-type vertical loop heat pipe and capillary-pump-type horizontal loop heat pipe were calculated by examining the change of heating rate of two evaporators. Calculation results showed that the vapor and liquid flow rates in the loop heat pipe and the thermal conductance of the heat pipe changed significantly depending on the distribution ratio of the heating rate of the multiple evaporators. The thermal performance of the vertical loop heat pipe with two evaporators was also examined and experimental results of flow direction and thermal conductance of the heat pipe agreed with the analytical results. The lumped network model analysis is therefore considered accurate and preferable for the practical design of a loop heat pipe with multiple evaporators.

Numerical Investigation of Thermal Behavior of CNC Machine Tool and Its Effects on Dimensional Accuracy of Machined Parts

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%.

求解单层织物热湿耦合模型正反问题的物理信息神经网络方法

针对织物热湿耦合模型难以解耦和反问题求解时间长的问题,提出了一种求解单层稳态织物热湿耦合传递模型正反问题的物理信息神经网络(Physics-Informed Neural Networks,PINNs)方法。首先,给出了求解单层织物热湿传递方程正问题的PINNs方法,并采用数值实验验证了方法的有效性。其次,提出了基于热湿舒适性的厚度参数决定反问题,并使用PINNs方法进行求解。数值实验结果显示,PINNs方法在求解参数决定反问题时,仅需5 min即可预测出概率函数,相比于微分方程数值求解和粒子群结合方法,求解效率提高了25倍,展现出显著的优越性和应用潜力。

Thermal Analysis of Vehicular Twin-Tube Hydraulic Gas-Precharged Shock Absorbers

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.

Change of stream network connectivity and its impact on flood control

AbstUrbanization can alter the hydrogeomorphology of streams and rivers,change stream network structures,and reduce stream network connectivity,which leads to a decrease in the storage capacity of stream networks and aggravates flood damage.Therefore,investigation of the ways in which stream network connectivity impacts flood storage capacity and flood control in urbanized watersheds can provide significant benefits.This study developed a framework to assess stream network connectivity and its impact on flood control.First,a few connectivity indices were adopted to assess longitudinal stream network connectivity.Afterward,the static and dynamic storage capacities of stream networks were evaluated using storage capacity indices and a one-dimensional hydrodynamic model.Finally,the impact of stream network connectivity change on flood control was assessed by investigating the changes in stream network connectivity and storage capacity.This framework was applied to the Qinhuai River Basin,China,where intensive urbanization has occurred in the last few decades.The results show that stream network storage capacity is affected by stream network connectivity.Increasing stream network connectivity enhances stream network storage capacity.©2020 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

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/γAl2O3 and Pd/γAl2O3 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 (ø) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners was 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 (r), the radial distance from the center line of the flame, and (x), 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-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 the effects of coded R and X 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 - 0.9809 & 634.5 - 3528.8 for RSM method compared to 0.9857 - 0.9951 & 7636.4 - 24,028.4 for ANN method beside lower values for error analysis terms.

Fast Calculation Method of Energy Flow for Combined Electro-Thermal System and Its Application

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.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

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.

Thermal Energy Collection Forecasting Based on Soft Computing Techniques for Solar Heat Energy Utilization System

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.

用于IGBT模块温度观测的3-D降阶混合型热模型

IGBT模块温度在线监测是提高大容量变流器可靠性的关键技术。在IGBT模块在线温度监测方法中,热阻模型法可对IGBT模块内部的温度分布进行估计,因而受到关注。结合了传感器数据的温度观测器可基于热阻模型,实时修正材料老化、参数温度依赖性和功率损耗计算误差等不确定因素对温度估计造成的影响。然而现有热模型无法同时兼顾系统可观性和建模准确性,且阶数相对较高,难以应用于温度观测器的在线运行。因此,该文提出了一种适用于风冷散热多芯片IGBT模块3-D温度观测器,同时满足可观性、准确性和实时性要求的混合型降阶热模型。首先,分析了热阻模型的拓扑结构及其参数辨识的方法,并探究了模型的状态空间生成规律;然后,基于非线性优化算法对该模型的参数进行修正,以减小因计算流体力学(CFD)仿真模型参数与实际参数不一致所引入的热阻模型与实验物理对象间的误差;随后,使用平衡截断法对热阻模型降阶,相对于现有集总参数热阻模型,进一步提高了实时求解效率;最后,通过仿真和实验验证了所提模型的准确性。