To date, a full-scale solar sail has never flown in space. Furthermore, solar sail technology development represents a field that only recently has enjoyed significant support. The goal of this work is to contribute to the development of a low-mass ODS for solar sails that would include research and development in the areas of photogrammetry and thermography. The focus of this work was on the development of the thermography system. A measurement protocol was designed for obtaining accurate temperature measurements using thermal imaging when heat was applied to the membrane surface. Two main limitations were considered during the experimental process. The first is that conventional infrared detector arrays must be kept cool. To minimize the effect that an imager’s operating temperature would have on the ODS, a miniature, un-cooled microbolometer was used to acquire temperature measurements from the membrane surface. A second limitation is that a detector array cannot distinguish between emitted and reflected photons, thus presenting a significant problem if one cannot predict the reflected component or if the reflected component is significantly greater than the emitted. To address this limitation, spectral properties of the membrane, including reflectance and transmission, were analyzed using a Hemispherical Directional Reflectometer (HDR) to predict the effects that optical properties would have on sail membrane temperatures. A thermal modeling strategy was also developed. The results of this investigation are presented.
Thermal signature may be one of the defining factors in determining the applicability of fuel cell auxiliary power unit (APU) technology in military applications. Thermal characterization is important for military applications given that identification and detection may be accomplished through observation of its thermal signature. The operating modes and power takeoff operations of a vehicle will likely determine the thermal profile. The objective of our study was to develop and implement a protocol for quantifying the thermal characteristics of a methanol fuel cell and an idling tractor engine under representative characteristic operations. APU thermal characteristics are a special case for which standardized testing procedures do not presently exist. A customized testing protocol was developed and applied that is specific to an APU-equipped vehicle. Initial testing was conducted on the methanol APU-equipped Freightliner tractor using a high-performance radiometric infrared system. The APU profile calls for a series of infrared images to be collected at three different viewing angles and two different elevations under various loads. The diesel engine was studied in a similar fashion using seven different viewing angles and two different elevations. Raw data collected according to the newly developed methodology provided the opportunity for computer analysis and thermal profiling of both the fuel cell and the diesel engine.
The purpose of this continued study is to model correctly the concentration of carbon monoxide (CO) in the troposphere of Harrisonburg, VA using an atmospheric modeling software program coupled with an experimental technique. In previous years, multiple raw data sets were collected using a technique known as gas filter correlation radiometry (GFCR) developed at NASA Langley Research Center. This technique utilizes the infrared (IR) radiance of the full moon and combines a ground-based IR data collection system with a blackbody calibration to yield a power value of the radiant stream. The raw data are processed by differencing a radiance stream obtained from the moon as passed through an evacuated cell against a cell containing a fixed concentration of CO. This power value is then compared with those simulated by the atmospheric modeling software HITRAN-PC. HITRAN-PC can simulate the atmosphere of Harrisonburg with a few key changes of input. It can then model the transmittance of the atmosphere, and by applying an algorithm developed in-house, we can correlate this transmission to a corresponding power value. The modeling is performed multiple times with various estimated values of CO, simulating clean and polluted conditions. Once the power value from the data and the power value from the modeling converge, the CO concentration is determined.