Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Numerical analysis of a modified evacuated tubes solar collector

The need for renewable energy sources, facing the consequences of Climate Change, results in growing investment for solar collectors’ use. Research in this field has accompanied this expansion and evacuated tube solar collector stands as an important study focus. Thus, several works have been published for representing the stratification of the fluid inside the tubes and the reservoir, as well as analytical modeling for the heat flow problem. Based on recent publications, this paper proposes the study of solar water heating with evacuated tubes, their operation characteristics and operating parameters. To develop this work, a computational tool will be used - in this case, the application of computational fluid dynamics (CFD) software. In possession of the implemented model, a numerical simulation will be performed to evaluate the behavior of the fluid within this solar collector and possible improvements to be applied in the model.

Experimental and numerical study of heat transfer in hot machined workpiece using infrared radiation

One of the greatest problems found in machining is related to the cutting tool wear. A way for increasing the tool life points out to the development of materials more resistant to wear, such as PCBN inserts. However, the unit cost of these tools is considerable high, around 10 to 20 times compared to coated carbide insert, besides its better performance occurs in high speeds requiring modern machine tools. Another way, less studied is the workpiece heating in order to diminish the shear stress material and thus reduce the machining forces allowing an increase of tool life. For understanding the heat transfer influences by conduction in this machining process, a mathematical model was developed to allow a simplified numerical simulation, using the finite element method, in order to determine the temperature profiles inside the workpiece.