Continual advances in the electronics industry and other high heat-flux fields have led to a need for increased heat transfer efficiency. Spray cooling is one of many methods for removing heat from surfaces. Experimental testing conducted at West Virginia University was sponsored by the Air Force Office of Scientific Research in collaboration with the Air Force Research Laboratory to test the effectiveness of using inductive spray charging to enhance the heat transfer rate. Modifications made to the experimental test rig built by Hunnell (2005) enabled the study of electro-hydrodynamics. Experimental testing using working fluids, FC-72 and HFE-7000, was conducted. Electrodes were designed to inductively charge spray droplets (Law, 1978). Research was performed by studying the thermophysics for different spray flow rates ranging from 6 to 10 GPH with a stepwise applied heat load ranging from 0 to 120 W, for an applied electrode voltage between 0 to 6 kV.

Spray Cooling Simulation Implementing TimeScale Analysis and the Monte Carlo Method PAUL JOSEPH KREITZER Spray cooling research is advancing the field of heat transfer and heat rejection in high power electronics. Smaller and more capable electronics packages are producing higher amounts of waste heat, along with smaller external surface areas, and the use of active cooling is becoming a necessity. Spray cooling has shown extremely high levels of heat rejection, of up to 1000 W/cm 2 using water. Simulations of spray cooling are becoming more realistic, but this comes at a price. A previous researcher has used CFD to successfully model a single 3D droplet impact into a liquid film using the level set method. However, the complicated multiphysics occurring during spray impingement and surface interactions increases computation time to more than 30 days. Parallel processing on a 32 processor system has reduced this time tremendously, but still requires more than a day. The present work uses experimental and computational results in addition to numerical correlations representing the physics occurring on a heated impingement surface. The current model represents the spray behavior of a Spraying Systems FullJet 1/8-g spray nozzle. Typical spray characteristics are indicated as follows: flow rate of 1.05x10-5 m 3 /s, normal droplet velocity of 12 m/s, droplet Sauter mean diameter of 48 µm, and heat flux values ranging from approximately 50-100 W/cm 2. This produces non-dimensional numbers of: We 300-1350, Re 750-3500, Oh 0.01-0.025. Numerical and experimental correlations have been identified representing crater formation, splashing, film thickness, droplet size, and spatial flux distributions. A combination of these methods has resulted in a Monte Carlo spray impingement simulation model capable of simulating hundreds of thousands of droplet impingements or approximately one millisecond. A random sequence of droplet impingement locations and diameters is generated, with the proper radial spatial distribution and diameter distribution. Hence the impingement, lifetime and interactions of the droplet impact craters are tracked versus time within the limitations of the current model. A comparison of results from this code to experimental results shows similar trends in surface behavior and heat transfer values. Three methods have been used to directly compare the simulation results with published experimental data, including: contact line length estimates, empirical heat transfer equation calculations, and nondimensional Nusselt numbers. A Nusselt number of 55.5 was calculated for experimental values, while a Nu of 16.0 was calculated from the simulation.

In this work, electrical capacitance tomography (ECT) and neural networks were used to automatically identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments, high-speed images were recorded for human classification of liquid-vapor flow patterns. The corresponding permittivity data obtained from tomograms was then used to train feedforward neural networks to recognize flow patterns. An objective was to determine which subsets of data derived from tomograms could be used as input data by a neural network to classify nine liquidvapor flow patterns. Another objective was to determine which subsets of input data provide high identification success when analyzed by a neural network. Transitional flow patterns associated with common horizontal flow patterns were considered. A unique feature of the current work was the use of the vertical center of mass coordinate in pattern classification. The highest classification success rates occurred using neural network input which included the probability density functions (in time) for both spatially averaged permittivity and center of mass location in addition to the four statistical moments (in time) for spatially averaged permittivity data. The combination of these input data resulted in an average success rate of 98.1% for nine flow patterns. In addition, 99% of the experimental runs were either correctly classified or misclassified by only one flow pattern. NomenclatureA i Individual pixel area [m 2 ] A T Sum of all pixel areas [m 2 ] D Tube diameter [m] D b Bubble diameter [m] ECT Electrical capacitance tomography L Length [m] ____________________

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