TALLY HORN! ECHOES OF HOOFBEATS AND HAUNTS: RECORDED SELECTIONS FOR HORN AND PIANO

In the 17th and 18th centuries, the European hunting horn was an instrument associated with the thrill of the chase, rustic scenes, and the pageantry of royal hunts. The sound of this horn and the music that evolved from its heritage would inspire composers writing for the modern horn. TALLY HOrn! Echoes of Hoofbeats and Haunts: Recorded Selections for Horn and Piano are two compact discs of works for horn and piano as well as solo horn reflecting this association with the hunt and include additional works of expressive contrast – lyrical pieces and nocturnes. Nine of the pieces reflect the modern horn’s beginnings and they are Abbot’s Alla Caccia, Berge’s Horn-Lokk, Boutry’s Chassacor, Bozza’s En Fôret, Büsser’s La Chasse de St. Hubert, Pessard’s In the Forest, Piantoni’s Air de Chasse, Schmid’s Im Tiefsten Walde, and Vinter’s Hunter’s Moon. By contrast, three of the selections, Gliére’s Nocturne, Strauss’ Nocturno, and Van Ecchaute’s Nachtpoëma are nocturnes that showcase the expressive quality of the horn. In addition, Bush’s Autumn Poem, Bacon’s Song After the Rain, and Webber’s Summer Pastures are included for their lyric qualities and evocations of nature. The largest work of the project is John Williams’s Concerto for Horn. Its five movements feature both hunting horn traits and expressive qualities. Alejandro Hernandez-Valdez and Grace Cho were the two pianists engaged for the recording. The recording engineer was Edward Kelly. The producer was Carlos Rodriquez. The pieces were recorded at the Spencerville Seventh-day Adventist Church sanctuary in Spencerville, Maryland between November 2010 and February 2011.

Event-Code Interaction Directed Test Cases

The Graphical User Interface (GUI) is an integral component of contemporary computer software. A stable and reliable GUI is necessary for correct functioning of software applications. Comprehensive verification of the GUI is a routine part of most software development life-cycles. The input space of a GUI is typically large, making exhaustive verification difficult. GUI defects are often revealed by exercising parts of the GUI that interact with each other. It is challenging for a verification method to drive the GUI into states that might contain defects. In recent years, model-based methods, that target specific GUI interactions, have been developed. These methods create a formal model of the GUI’s input space from specification of the GUI, visible GUI behaviors and static analysis of the GUI’s program-code. GUIs are typically dynamic in nature, whose user-visible state is guided by underlying program-code and dynamic program-state. This research extends existing model-based GUI testing techniques by modelling interactions between the visible GUI of a GUI-based software and its underlying program-code. The new model is able to, efficiently and effectively, test the GUI in ways that were not possible using existing methods. The thesis is this: Long, useful GUI testcases can be created by examining the interactions between the GUI, of a GUI-based application, and its program-code. To explore this thesis, a model-based GUI testing approach is formulated and evaluated. In this approach, program-code level interactions between GUI event handlers will be examined, modelled and deployed for constructing long GUI testcases. These testcases are able to drive the GUI into states that were not possible using existing models. Implementation and evaluation has been conducted using GUITAR, a fully-automated, open-source GUI testing framework.

FLOW REGIME DRIVEN THERMAL ENHANCEMENT IN INTERNALLY-GROOVED TUBES

Internally-grooved refrigeration tubes maximize tube-side evaporative heat transfer rates and have been identified as a most promising technology for integration into compact cold plates. Unfortunately, the absence of phenomenological insights and physical models hinders the extrapolation of grooved-tube performance to new applications. The success of regime-based heat transfer correlations for smooth tubes has motivated the current effort to explore the relationship between flow regimes and enhanced heat transfer in internally-grooved tubes. In this thesis, a detailed analysis of smooth and internally-grooved tube data reveals that performance improvement in internally-grooved tubes at low-to-intermediate mass flux is a result of early flow regime transition. Based on this analysis, a new flow regime map and corresponding heat transfer coefficient correlation, which account for the increased wetted angle, turbulence, and Gregorig effects unique to internally-grooved tubes, were developed. A two-phase test facility was designed and fabricated to validate the newly-developed flow regime map and regime-based heat transfer coefficient correlation. As part of this setup, a non-intrusive optical technique was developed to study the dynamic nature of two-phase flows. It was found that different flow regimes result in unique temporally varying film thickness profiles. Using these profiles, quantitative flow regime identification measures were developed, including the ability to explain and quantify the more subtle transitions that exist between dominant flow regimes. Flow regime data, based on the newly-developed method, and heat transfer coefficient data, using infrared thermography, were collected for two-phase HFE-7100 flow in horizontal 2.62mm - 8.84mm diameter smooth and internally-grooved tubes with mass fluxes from 25-300 kg/m²s, heat fluxes from 4-56 kW/m², and vapor qualities approaching 1. In total, over 6500 combined data points for the adiabatic and diabatic smooth and internally-grooved tubes were acquired. Based on results from the experiments and a reinterpretation of data from independent researchers, it was established that heat transfer enhancement in internally-grooved tubes at low-to-intermediate mass flux is primarily due to early flow regime transition to Annular flow. The regime-based heat transfer coefficient outperformed empirical correlations from the literature, with mean and absolute deviations of 4.0% and 32% for the full range of data collected.