LARGE MOTION VISUALIZATION AND ESTIMATION FOR FLAPPING WING SYSTEMS

Studies of fluid-structure interactions associated with flexible structures such as flapping wings require the capture and quantification of large motions of bodies that may be opaque. Motion capture of a free flying insect is considered by using three synchronized high-speed cameras. A solid finite element representation is used as a reference body and successive snapshots in time of the displacement fields are reconstructed via an optimization procedure. An objective function is formulated, and various shape difference definitions are considered. The proposed methodology is first studied for a synthetic case of a flexible cantilever structure undergoing large deformations, and then applied to a Manduca Sexta (hawkmoth) in free flight. The three-dimensional motions of this flapping system are reconstructed from image date collected by using three cameras. The complete deformation geometry of this system is analyzed. Finally, a computational investigation is carried out to understand the flow physics and aerodynamic performance by prescribing the body and wing motions in a fluid-body code. This thesis work contains one of the first set of such motion visualization and deformation analyses carried out for a hawkmoth in free flight. The tools and procedures used in this work are widely applicable to the studies of other flying animals with flexible wings as well as synthetic systems with flexible body elements.

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.

Thermal Tracking and Estimation for Microprocessors

Due to increasing integration density and operating frequency of today's high performance processors, the temperature of a typical chip can easily exceed 100 degrees Celsius. However, the runtime thermal state of a chip is very hard to predict and manage due to the random nature in computing workloads, as well as the process, voltage and ambient temperature variability (together called PVT variability). The uneven nature (both in time and space) of the heat dissipation of the chip could lead to severe reliability issues and error-prone chip behavior (e.g. timing errors). Many dynamic power/thermal management techniques have been proposed to address this issue such as dynamic voltage and frequency scaling (DVFS), clock gating and etc. However, most of such techniques require accurate knowledge of the runtime thermal state of the chip to make efficient and effective control decisions. In this work we address the problem of tracking and managing the temperature of microprocessors which include the following sub-problems: (1) how to design an efficient sensor-based thermal tracking system on a given design that could provide accurate real-time temperature feedback; (2) what statistical techniques could be used to estimate the full-chip thermal profile based on very limited (and possibly noise-corrupted) sensor observations; (3) how do we adapt to changes in the underlying system's behavior, since such changes could impact the accuracy of our thermal estimation. The thermal tracking methodology proposed in this work is enabled by on-chip sensors which are already implemented in many modern processors. We first investigate the underlying relationship between heat distribution and power consumption, then we introduce an accurate thermal model for the chip system. Based on this model, we characterize the temperature correlation that exists among different chip modules and explore statistical approaches (such as those based on Kalman filter) that could utilize such correlation to estimate the accurate chip-level thermal profiles in real time. Such estimation is performed based on limited sensor information because sensors are usually resource constrained and noise-corrupted. We also took a further step to extend the standard Kalman filter approach to account for (1) nonlinear effects such as leakage-temperature interdependency and (2) varying statistical characteristics in the underlying system model. The proposed thermal tracking infrastructure and estimation algorithms could consistently generate accurate thermal estimates even when the system is switching among workloads that have very distinct characteristics. Through experiments, our approaches have demonstrated promising results with much higher accuracy compared to existing approaches. Such results can be used to ensure thermal reliability and improve the effectiveness of dynamic thermal management techniques.