Enhanced Finned-Tube Condenser Design and Optimization Susan W. Stewart 173 pages Directed by Dr. Sam V. Shelton Finned-tube heat exchangers are widely used in space conditioning systems, as well as any other application requiring heat exchange between liquids and gases. Their most widespread use is in residential air conditioning systems. Residential systems dictate peak demand on the U.S. national grid, which occurs on the hot summer afternoons, and thereby sets the expensive infrastructure requirement of the nations power plant and electrical distribution system. In addition to peak demand, residential air conditioners are major energy users that dominate residential electrical costs and environmental impact. The only significant opportunity for electrical power use reduction of residential air conditioners is in technology improvement of the finned-tube heat exchangers, i.e., condenser and evaporator coils. With the oncoming redesign of these systems in the next five years to comply with the regulatory elimination of R-22 used in residential air conditioners today, improvement in the design technology of these systems is timely. An air conditioner condenser finned-tube coil design optimization methodology is derived and shown to lead to improved residential air conditioner efficiency at fixed equipment cost. This nonlinear optimization of the 14 required design parameters is impractical by systematic experimental testing and iteration of tens of thousands condenser coils in an air conditioning system. The developed methodology and results can be used in the redesign of residential systems for the new mandated environmentally friendly refrigerants and to meet increasing regulatory minimum system efficiencies. Additionally, plain fins and augmented fins, (louvered), are compared using the developed model and optimization scheme to show the effect of the augmentation on system performance. Furthermore, an isolated condenser model was developed using condenser entropy generation minimization as the figure of merit to minimize the model complexity and computation time. Isolated model optimizations are compared with the system model optimum designs.