RESUMO
In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg-Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.
RESUMO
This work presents a fault-tolerant (FT) scheme based on the application of non-integer order observers also called fractional observers, the case of study is a double pipe countercurrent heat exchanger (HE). The aim of the FT is to detect sensors faults as soon as possible, and to provide a healthy signal in order to replace the faulty sensor signal by the fractional observer estimation. To develop the FT scheme a bank of high gain fractional order observers (HGFOO) is proposed. The Riemann-Liouville (RL) fractional derivative definition is used to solve each fractional observer. Experimental measures from a HE were used to test the performance of the fractional observers and the control scheme. The results show the robustness of the proposed observers.
RESUMO
Miniaturization encourages the development of new manufacturing processes capable of fabricating features, like micro-channels, in order to use them for different applications, such as in fuel cells, heat exchangers, microfluidic devices and micro-electromechanical systems (MEMS). Many studies have been conducted on heat and fluid transfer in micro-channels, and they appeared significantly deviated from conventional theory, due to measurement errors and fabrication methods. The present research, in order to deal with this opportunity, is focused on a set of experiments in the micro-milling of channels made of aluminum, titanium alloys and stainless steel, varying parameters, such as spindle speed, depth of cut per pass (ap), channel depth (d), feed per tooth (fz) and coolant application. The experimental results were analyzed in terms of dimensional error, channel profile shape deviation from rectangular and surface quality (burr and roughness). The micro-milling process was capable of offering quality features required on the micro-channeled devices. Critical phenomena, like run-out, ploughing, minimum chip thickness and tool wear, were encountered as an explanation for the deviations in shape and for the surface quality of the micro-channels. The application of coolant and a low depth of cut per pass were significant to obtain better superficial quality features and a smaller dimensional error. In conclusion, the integration of superficial and geometrical features on the study of the quality of micro-channeled devices made of different metallic materials contributes to the understanding of the impact of calibrated cutting conditions in MEMS applications.