Open top chambers and infrared lamps : a comparison of heating efficacy and CO₂/CH₄ dynamics in a Lake Superior coastal peatland

Experimental warming provides a method to determine how an ecosystem will respond to increased temperatures. Northern peatland ecosystems, sensitive to changing climates, provide an excellent setting for experimental warming. Storing great quantities of carbon, northern peatlands play a critical role in regulating global temperatures. Two of the most common methods of experimental warming include open top chambers (OTCs) and infrared (IR) lamps. These warming systems have been used in many ecosystems throughout the world, yet their efficacy to create a warmer environment is variable and has not been widely studied. To date, there has not been a direct, experimentally controlled comparison of OTCs and IR lamps. As a result, a factorial study was implemented to compare the warming efficacy of OTCs and IR lamps and to examine the resulting carbon dioxide (CO2) and methane (CH4) flux rates in a Lake Superior peatland. IR lamps warmed the ecosystem on average by 1-2 #°C, with the majority of warming occurring during nighttime hours. OTC's did not provide any long-term warming above control plots, which is contrary to similar OTC studies at high latitudes. By investigating diurnal heating patterns and micrometeorological variables, we were able to conclude that OTCs were not achieving strong daytime heating peaks and were often cooler than control plots during nighttime hours. Temperate day-length, cloudy and humid conditions, and latent heat loss were factors that inhibited OTC warming. There were no changes in CO2 flux between warming treatments in lawn plots. Gross ecosystem production was significantly greater in IR lamp-hummock plots, while ecosystem respiration was not affected. CH4 flux was not significantly affected by warming treatment. Minimal daytime heating differences, high ambient temperatures, decay resistant substrate, as well as other factors suppressed significant gas flux responses from warming treatments.

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.

IMPACT OF ENGINE CALIBRATION ON PM OXIDATION IN A CATALYZED PARTICULATE FILTER OVER A TRANSIENT CYCLE: A MODELLING STUDY

The combustion strategy in a diesel engine has an impact on the emissions, fuel consumption and the exhaust temperatures. The PM mass retained in the CPF is a function of NO2 and PM concentrations in addition to the exhaust temperatures and the flow rates. Thus the engine combustion strategy affects exhaust characteristics which has an impact on the CPF operation and PM mass retained and oxidized. In this report, a process has been developed to simulate the relationship between engine calibration, performance and HC and PM oxidation in the DOC and CPF respectively. Fuel Rail Pressure (FRP) and Start of Injection (SOI) sweeps were carried out at five steady state engine operating conditions. This data, along with data from a previously carried out surrogate HD-FTP cycle [1], was used to create a transfer function model which estimates the engine out emissions, flow rates, temperatures for varied FRP and SOI over a transient cycle. Four different calibrations (test cases) were considered in this study, which were simulated through the transfer function model and the DOC model [1, 2]. The DOC outputs were then input into a model which simulates the NO2 assisted and thermal PM oxidation inside a CPF. Finally, results were analyzed as to how engine calibration impacts the engine fuel consumption, HC oxidation in the DOC and the PM oxidation in the CPF. Also, active regeneration for various test cases was simulated and a comparative analysis of the fuel penalties involved was carried out.

TEST-ANALYSIS MODEL CORRELATION AND CALIBRATION OF A MULTI-COMPONENT PLASTIC PART

The goal of this project is to learn the necessary steps to create a finite element model, which can accurately predict the dynamic response of a Kohler Engines Heavy Duty Air Cleaner (HDAC). This air cleaner is composed of three glass reinforced plastic components and two air filters. Several uncertainties arose in the finite element (FE) model due to the HDAC’s component material properties and assembly conditions. To help understand and mitigate these uncertainties, analytical and experimental modal models were created concurrently to perform a model correlation and calibration. Over the course of the project simple and practical methods were found for future FE model creation. Similarly, an experimental method for the optimal acquisition of experimental modal data was arrived upon. After the model correlation and calibration was performed a validation experiment was used to confirm the FE models predictive capabilities.