Papers by Keyword: Temperature Prediction

Abstract: This paper presents a closed-form solution for the temperature prediction in selective laser melting (SLM). This solution is developed for the three-dimensional temperature prediction with consideration of heat input from a moving laser heat source, and heat loss from convection and radiation on the part top boundary. The consideration of heat transfer boundary condition and latent heat in the closed-form solution leads to an improvement on the understanding of thermal development and prediction accuracy in SLM, and thus the usefulness of the analytical model in the temperature prediction in real applications. A moving point heat source solution is used to calculate the temperature rise due to the heat input. A heat sink solution is used to calculate the temperature drop due to heat loss from convection and radiation on the part boundary. The heat sink solution is modified from a heat source solution with equivalent power due to heat loss from convection and radiation, and zero-moving velocity. The temperature solution is then constructed from the superposition of the linear heat source solution and linear heat sink solution. Latent heat is considered using a heat integration method. Ti-6Al-4V is chosen to test the presented model with the assumption of isotropic and homogeneous material. The predicted molten pool dimensions are compared to the documented values from the finite element method and experiments in the literature. The presented model has improved prediction accuracy and significantly higher computational efficiency compared to the finite element model.

Abstract: In wireless sensor network, it is necessary to make effective prediction of sensor node’s data during its sleep period. In this paper a model of rational cubic spline weight function (SWF) neural network with linear denominator was established for sensor node’s temperature prediction. This kind of rational spline function is denoted by 3/1 rational splines. Then we trained and tested the network, the simulation results showed that, compared to the traditional BP neural network, the training speed is higher and the error is smaller. Therefore the prediction model can effectively predict the sensor’s temperature.

Abstract: Based on WNN, an improved model of High Voltage Switchgear temperature prediction is built. The model is optimized with hard threshold de-noising with wavelet package analysis, made an ultra-short term prediction loosely combines Elman neural network. However, the characteristics of the original signal are reserved and the noise interference is mostly eliminated. The experiment results show the processed sample as input one improves the prediction accuracy of Elman Network and reduces the root-mean-square error. And the prediction values show good coincidence to the measured ones that improves the reliability of early warning.

Abstract: The temperature change of the power transmission line and substation equipment can reflect their potential safety hazard caused by their aging and overload. Based on the nonlinear analysis of forecasting substation equipment temperature data can realize effectively early warning of equipment failure and avoid huge losses caused by the accident. This paper puts forward a method for temperature forecasting, based on the chaotic time series and BP neural network. It collects data from wireless temperature sensors to establish a time series of substation equipments’ temperature. Software simulation results showed that the prediction method has higher prediction accuracy than that of the traditional method.

Abstract: How to reduce the energy consumption on airport jobsite is the focus of this paper. We introduce this control technique and its specialist-aided system which can predict material temperature and save energy. After pouring concrete material, the technique can test and predict temperature change trend and concrete changing maturity level, based on key parameter information provided by contractors. Optimal curing method, thermal insulation and construction technology are developed giving the results of the preceding data. Contractors can find optimal solutions to save energy and resourced in airport winter construction using the technique, thus avoiding unnecessary energy consumption.

Abstract: The temperature prediction in blast furnace loses accuracy or Forecasts failure when the temperatures change is at normal levels and obvious. This paper introduces fuzzy membership of samples basing on support vector data description and the fuzzy least squares support vector machine to forecast the blast furnace temperature. Then the simulation was done by using the forecast samples and the model after training by MATLAB. Comparing the simulation results of LS-FSVM with LS-SVM, the model basing on LS-FSVM enhances anti-jamming ability. The accuracy of the temperature prediction in blast furnace promotes significantly when the temperature of blast furnace fluctuates.

Abstract: The explicit statistical model of concrete temperature variation is difficult to reasonably reflect the nonlinear relationship between the historical information and future information. This article is based on neural network intelligence tools and uses the neural network model to describe the concrete temperature variation during the construction. The relationships between the concrete temperature and initial temperature (pouring temperature), environmental temperature, the cement hydration heat temperature increase, water cooling effect and other factors are nonlinear. Establishing the neural network model of concrete temperature variation, exploring the historical temperature information could predict the future temperature information. Applying the intelligent prediction model to a construction project shows that when compared with the traditional explicit temperature statistical model, the temperature neural network prediction model established in this paper has obvious simplicity and superiority.

Abstract: In this paper, support vector regression with ant colony optimization is presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. Ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function ε and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model tool–chip interface temperature with good predictive accuracy.

Abstract: The technique of energy saving in airport winter construction is always the key and difficult point in the field of airport construction. Considering the feature of airport winter construction, this paper develops the material temperature predicting and energy-saving control technique and its specialist-aided system in order to reduce energy consumption on airport jobsite. This technique can predict the changing temperature trend after concrete material pouring and the changing maturity level of concrete, according to the key parameter information that contractors provide. Giving the results the preceding data, it can provide the optimal curing method, thermal insulation and construction technology. And contractors can easily meet the requirement of high strength of structural material while consume energy and resource at a minimum level. This technique helps contractors find optimal solutions to save energy and resources in airport winter construction and avoid the unnecessary energy consumption.

Abstract: Fiber grating sensor network plays an important role in evaluating the bridge health condition and gaining bridge structure characteristic.Bridge structural distortion and variation of stress is associated with temperature.So it is essential to make temperature prediction bases on the length of wave which gained from fiber grating sensor network system.Traditionally, we apply least square method to predict temperature.In this paper,we use method of relevance vector machine (RVM) to make it.