Studies of horizontal two-phase flow using electrical capacitance tomography and R-134a

Date of Award

2017

Degree Name

Ph.D. in Aerospace Engineering

Department

Department of Mechanical and Aerospace Engineering

Advisor/Chair

Advisor: Jamie S. Ervin

Abstract

Future high performance aircraft will need non-conventional thermal management systems to remove challenging heat loads. Historically, aircraft thermal management systems have relied on a combination of air cycle machine and fuel for cooling heat sinks. Next generation tactical aircraft may rely on a vapor compression system as a method to manage thermal loads. Vapor compression systems (VCS) offer higher coefficient of performance (COP) values than aircraft cycle system (ACS). However, the design of a VCS system is more complex since two phases (liquid-vapor) are used to remove heat. In order to incorporate a VCS system in future tactical aircraft design, an understanding of two-phase flow behavior is needed. This experimental dissertation seeks to increase the understanding of two-phase flow behavior and is divided into three segments. In the first segment of this dissertation, electrical capacitance tomography and neural networks were used to identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments, high-speed images were recorded for human visual classification of liquid-vapor flow patterns. The corresponding permittivity data obtained from tomograms was then used to train feed-forward 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 liquid-vapor 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 this research 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. In the second segment, electrical capacitance tomography was used to measure the void fraction and maximum dry angle for refrigerant R-134a flowing in a horizontal tube. These parameters are used for predicting heat transfer coefficients and pressure drop. A new calibration approach was used that accounted for the changes in the dielectric constant of both the liquid and vapor phases due to temperature variations. The capability of the ECT system to measure the void fraction was assessed in static and dynamic experiments. Time-averaged void fractions compared well with correlations found in the literature. Further, a new technique using tomographic images to determine the maximum dry angle was introduced in this dissertation. It was shown that this technique can be used to measure the maximum dry angle for stratified to partially dry out annular flows when compared to existing correlations. In the third segment, electrical capacitance tomography was used to measure the changes in void fraction upstream, downstream, and immediately after a horizontal sudden expansion for refrigerant R-134a. The two-phase flow behavior occurring in the sudden expansion was also analyzed using a high speed camera. An analysis of the effects of selecting a void fraction parameter upstream, downstream, and immediately after a sudden expansion was studied to determine how this parameter affects correlations for pressure losses. Several void fraction correlations found from the literature were applied upstream, downstream, and immediately after a sudden expansion using experimental data. It was found that none of the correlations could model the behavior of the void fraction occurring immediately after a sudden expansion. A modified void fraction correlation was proposed to model the behavior of void fraction occurring right after the expansion. This new void fraction model was able to predict the void fraction immediately after a sudden expansion within 20% or better when compared with experimental data at mass velocities of 150, 200, and 250 kg/m₂s.

Keywords

Two-phase flow Imaging, Vapors Compression testing, Refrigerants Research, Experiments, Engineering, Aerospace Engineering, Horizontal Two Phase Flow, Electrical Capacitance Tomography, Neural Networks Characterization, Two-Phase Flow Dry Angle and Void Fraction Parameters, Sudden Expansion in Two-Phase Flow

Rights Statement

Copyright © 2017, author

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