minitube controller for automated disp pump

MINITUBE CONTROLLER FOR AUTOMATED DISP PUMP SERIES

One of the modern technologies that have been successful as an analysis tool is the technique of artificial neural networks (ANNs). The inability of power-law correlations to catch up with all these phenomena occurring during two phase convection lies as the reason for the inaccuracy of the predictions. All these phenomena occur over a short pipe length.

MINITUBE CONTROLLER FOR AUTOMATED DISP PUMP SERIES

The process of phase change in pipes undergoes a series of flow regimes, which go from single phase flow, onset of bubble formation, annular flow boiling, film boiling, mist flow and superheated vapor flow, depending on mass flow rate, degree of superheating, pressure, tube diameter, orientation and vapor quality. Years later, it was shown that when experimental data from other authors are used, the errors from this and other correlations can be as large as 50%. For instance, using their correlation for forced convective boiling, Gungor and Winterton (1986) obtained errors of 21.4% for saturated boiling and 25% for sub-cooled boiling with respect to their own measurements. Traditional power-law correlations have proved to be inaccurate for this kind of processes, even though there is a need of good predictions in these applications. Heat transfer prediction of condensation and evaporation processes in refrigeration and air conditioning units is a complex task when compared to the prediction of single phase heat transfer. This is particularly true in the case of complex flow situations occurring in novel applications, such as the cooling of electronic devices with miniature size evaporators. While the thermal sciences must continue to gradually increase knowledge and insight of fundamental phenomena, there are some new technologies, such as artificial neural networks (ANNs) that can be used as application tools to supplement such understanding. Unfortunately, there are critical applications related to energy efficiency, environmental impact, optimal system design and control, where traditional techniques fail to provide an adequate prediction and more advanced prediction methods are required. Much of the progress of heat transfer has been driven by the necessity to predict the performance of a thermal system, which results from applications of fundamental laws (mass, momentum, and energy conservation) for basic problems, supplemented with em- pirical correlations for more complex cases (complexities stemming from system geometry, flow conditions and appearance of simultaneous heat transfer mechanisms). The problem, however, is that the heat transfer and pressure drop in this kind of systems may be significantly different from what has been reported in conventional size evaporators, and there is a lack of reliable information about the thermal performance of these devices. The reduction of channel size is now a reality and mini-tubes with hydraulic diameters from 200 μm to 3 mm are commonly used.

In response to this demand, miniature-size compact heat exchangers with capacity to operate as efficient heat sinks have been recently developed. This fact has created a need for efficient heat dissipation. Recent developments of high performance electronic equipment have led to a general reduction of spacing and increase in power. Los resultados obtenidos en esta investigación demuestran la conveniencia de usar redes neuronales artificiales para la determinación correcta de la transferencia de calor evaporadores de minitubos. Un 75% de las mediciones se usan para entrenar varias configuraciones de red neuronal y 25% de los datos se emplean para determinar el error de predicción de cada configuración. Con este banco de pruebas experimental fue posible obtener una cantidad considerable de datos que permiten caracterizar el desempeño térmico del proceso de evaporación en consideración. Se desarrolló un sistema experimental, incluye un ciclo de refrige- ración basado en el ciclo de Rankine inverso, instrumentado con equipo de medición y un sistema de adquisición de datos para obtener información del desempeño térmico bajo diferentes condiciones de operación.

En esta investigación se utilizan redes neuronales para determinar la tasa de transferencia de calor convectiva durante la evaporación de un refrigerante en el interior de un minitubo.

Minitube controller for automated disp pump