Measuring energy spectra of TeV gamma-ray emission from the Cygnus region of our galaxy with Milagro

High energy gamma rays can provide fundamental clues to the origins of cosmic rays. In this thesis, TeV gamma-ray emission from the Cygnus region is studied. Previously the Milagro experiment detected five TeV gamma-ray sources in this region and a significant excess of TeV gamma rays whose origin is still unclear. To better understand the diffuse excess the separation of sources and diffuse emission is studied using the latest and most sensitive data set of the Milagro experiment. In addition, a newly developed technique is applied that allows the energy spectrum of the TeV gamma rays to be reconstructed using Milagro data. No conclusive statement can be made about the spectrum of the diffuse emission from the Cygnus region because of its low significance of 2.2 σ above the background in the studied data sample. The entire Cygnus region emission is best fit with a power law with a spectral index of α=2.40 (68% confidence interval: 1.35-2.92) and a exponential cutoff energy of 31.6 TeV (10.0-251.2 TeV). In the case of a simple power law assumption without a cutoff energy the best fit yields a spectral index of α=2.97 (68% confidence interval: 2.83-3.10). Neither of these best fits are in good agreement with the data. The best spectral fit to the TeV emission from MGRO J2019+37, the brightest source in the Cygnus region, yields a spectral index of α=2.30 (68% confidence interval: 1.40-2.70) with a cutoff energy of 50.1 TeV (68% confidence interval: 17.8-251.2 TeV) and a spectral index of α=2.75 (68% confidence interval: 2.65-2.85) when no exponential cutoff energy is assumed. According to the present analysis, MGRO J2019+37 contributes 25% to the differential flux from the entire Cygnus at 15 TeV.

Calibration of the HAWC Gamma-Ray Observatory

The High-Altitude Water Cherenkov (HAWC) Experiment is a gamma-ray observatory that utilizes water silos as Cherenkov detectors to measure the electromagnetic air showers created by gamma rays. The experiment consists of an array of closely packed water Cherenkov detectors (WCDs), each with four photomultiplier tubes (PMTs). The direction of the gamma ray will be reconstructed using the times when the electromagnetic shower front triggers PMTs in each WCD. To achieve an angular resolution as low as 0.1 degrees, a laser calibration system will be used to measure relative PMT response times. The system will direct 300ps laser pulses into two fiber-optic networks. Each network will use optical fan-outs and switches to direct light to specific WCDs. The first network is used to measure the light transit time out to each pair of detectors, and the second network sends light to each detector, calibrating the response times of the four PMTs within each detector. As the relative PMT response times are dependent on the number of photons in the light pulse, neutral density filters will be used to control the light intensity across five orders of magnitude. This system will run both continuously in a low-rate mode, and in a high-rate mode with many intensity levels. In this thesis, the design of the calibration system and systematic studies verifying its performance are presented.

Computational fluids domain reduction to a simplified fluid network

The primary goal of this project is to demonstrate the practical use of data mining algorithms to cluster a solved steady-state computational fluids simulation (CFD) flow domain into a simplified lumped-parameter network. A commercial-quality code, “cfdMine” was created using a volume-weighted k-means clustering that that can accomplish the clustering of a 20 million cell CFD domain on a single CPU in several hours or less. Additionally agglomeration and k-means Mahalanobis were added as optional post-processing steps to further enhance the separation of the clusters. The resultant nodal network is considered a reduced-order model and can be solved transiently at a very minimal computational cost. The reduced order network is then instantiated in the commercial thermal solver MuSES to perform transient conjugate heat transfer using convection predicted using a lumped network (based on steady-state CFD). When inserting the lumped nodal network into a MuSES model, the potential for developing a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track temperatures near specific objects (such as equipment in vehicles).

Experimental investigation of regular fluids and nanofluids during flow boiling in a single microchannel at different heat fluxes and mass fluxes

The dissipation of high heat flux from integrated circuit chips and the maintenance of acceptable junction temperatures in high powered electronics require advanced cooling technologies. One such technology is two-phase cooling in microchannels under confined flow boiling conditions. In macroscale flow boiling bubbles will nucleate on the channel walls, grow, and depart from the surface. In microscale flow boiling bubbles can fill the channel diameter before the liquid drag force has a chance to sweep them off the channel wall. As a confined bubble elongates in a microchannel, it traps thin liquid films between the heated wall and the vapor core that are subject to large temperature gradients. The thin films evaporate rapidly, sometimes faster than the incoming mass flux can replenish bulk fluid in the microchannel. When the local vapor pressure spike exceeds the inlet pressure, it forces the upstream interface to travel back into the inlet plenum and create flow boiling instabilities. Flow boiling instabilities reduce the temperature at which critical heat flux occurs and create channel dryout. Dryout causes high surface temperatures that can destroy the electronic circuits that use two-phase micro heat exchangers for cooling. Flow boiling instability is characterized by periodic oscillation of flow regimes which induce oscillations in fluid temperature, wall temperatures, pressure drop, and mass flux. When nanofluids are used in flow boiling, the nanoparticles become deposited on the heated surface and change its thermal conductivity, roughness, capillarity, wettability, and nucleation site density. It also affects heat transfer by changing bubble departure diameter, bubble departure frequency, and the evaporation of the micro and macrolayer beneath the growing bubbles. Flow boiling was investigated in this study using degassed, deionized water, and 0.001 vol% aluminum oxide nanofluids in a single rectangular brass microchannel with a hydraulic diameter of 229 µm for one inlet fluid temperature of 63°C and two constant flow rates of 0.41 ml/min and 0.82 ml/min. The power input was adjusted for two average surface temperatures of 103°C and 119°C at each flow rate. High speed images were taken periodically for water and nanofluid flow boiling after durations of 25, 75, and 125 minutes from the start of flow. The change in regime timing revealed the effect of nanoparticle suspension and deposition on the Onset of Nucelate Boiling (ONB) and the Onset of Bubble Elongation (OBE). Cycle duration and bubble frequencies are reported for different nanofluid flow boiling durations. The addition of nanoparticles was found to stabilize bubble nucleation and growth and limit the recession rate of the upstream and downstream interfaces, mitigating the spreading of dry spots and elongating the thin film regions to increase thin film evaporation.