Applied Mechanics and Materials Vols. 29-32

Abstract: This paper describes a theoretical and experimental analysis on full-filed stress distribution from thermoelastic measurements and its application to determination of stress concentration. The sum of the principal stress can be measured by Thermal Stress Analysis (TSA). Lock-in Thermography has been applied to measure the sum of principal stress distribution of component structure by its high thermal resolving. In this study, Finite element method is used to calculate the sum of principal stress distribution, and the thermoelastic effect model is developed to study the relationship between the temperature deviation and the applied stress in an elastic material. Experiments were carried out with ANSI 7071 high strength aluminum alloys ply and ones with a crack under cyclic load. The thermoelastic constant is obtained for ANSI 7071 high strength aluminum alloys materials. The stress concentration factor is calculated for a ply with modeling crack under the condition of different loads. The experiment was carried out with high strength aluminum alloys component structure with rivet joints. The experimental results show the stress distribution can be measured and analyzed the contact stress distribution between ply and rivet by using Lock-in thermography. It was found that the structure stress can be evaluated with good accuracies by the lock in thermography.

Abstract: In this paper we propose a Monte-Carlo method for the simulation of the angle-dependent light transmittance of thermotropic material. The results show that the scattering light increased as temperature rose, and most of light transmitted went through the sample of thermotropic material at the angles between 10~40 deg. The results also indicate that the light transmittance measurement of thermotropic material by spectrophotometer without an integrating sphere is not accurate. As a conclusion, Monte Carlo simulation is an effective method for the determination of angle-dependent light transmittance of thermotropic material, and results of these simulations can be used to calculate the shading coefficient of window for building energy efficiency.

Abstract: To compare the indoor thermal environment under different building envelope constructions, a Matlab-based tool was presented for building envelope performance simulation. An application study of two cases illustrates energy efficient buildings can provide more suitable indoor environment than non-energy efficient buildings in cold winter and hot summer. In conclusion, this paper provides a new and fast way for the prediction of indoor thermal environment.

Abstract: In the present study a GUI tool for the prediction of building energy performance based on a three-layered BP neural network and MATLAB was developed. The inputs for this tool are the 18 building envelope parameters. The outputs are building heating, cooling and total energy consumptions and the energy saving rate. Compared with the complicated mathematical equations, this tool provides a very easy and effective method for students to learn the effects of building envelope performance parameters on the building energy performance. Thus, this tool can be used in building physics and building energy efficiency courses for the design of energy efficient building.

Abstract: In this paper, prediction system is developed based on a fuzzy neural network(FNN) by using the past groundwater level data to discover fuzzy rules and make future predictions. The learning algorithm is implemented to the past historical data. Compared to other predictors, our results show that the FNN predictor can reduce significantly both relative mean errors and root mean squared errors of predicted groundwater level. It is demonstrated that FNN performs well for groundwater data analysis and its feasibility of applying FNN to groundwater level prediction.

Abstract: A daily discharge prediction system is developed by the Artificial Neural Networks (ANN) using real daily discharge data, ANN have great generalization ability and guarantee global minima for given training data. In the paper, with 8 years long-term daily information, the ANN can construct a very high precision daily discharge forecasting system. The experiment shows that the predicted curve well regresses the observed curve. It can be concluded that this technique can be seen as a very promising option to solve nonlinear regression.

Abstract: In view of the problem that it is difficult to calculate the Fanger’s PMV equation due to its complicated iterative process, a backpropagation neural network (BPNN) model was built to predict PMV. Air temperature, relative humidity, mean radiant temperature, air velocity, metabolic rate and clothing index were used as the input of neural network and PMV output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy absolute error below 5%. As a conclusion, this study has a real significance, because it gives a new method with reliability and accuracy in the prediction of PMV.

Abstract: This paper analyzes the ecological environment problems from the deeper causes of concept, system, industrial structure, city planning layout, preventive measure, etc. And it proposes the implementation countermeasure of ecological environment sustainable development from the perspective of structural models of coal resource-based cities’ ecological environment sustainable development.