Basic statistical analyses of candidate nickel-hydrogen cells for the Space Station Freedom

Nickel-Hydrogen (Ni/H2) secondary batteries will be implemented as a power source for the Space Station Freedom as well as for other NASA missions. Consequently, characterization tests of Ni/H2 cells from Eagle-Picher, Whittaker-Yardney, and Hughes were completed at the NASA Lewis Research Center. Watt-hour efficiencies of each Ni/H2 cell were measured for regulated charge and discharge cycles as a function of temperature, charge rate, discharge rate, and state of charge. Temperatures ranged from -5 C to 30 C, charge rates ranged from C/10 to 1C, discharge rates ranged from C/10 to 2C, and states of charge ranged from 20 percent to 100 percent. Results from regression analyses and analyses of mean watt-hour efficiencies demonstrated that overall performance was best at temperatures between 10 C and 20 C while the discharge rate correlated most strongly with watt-hour efficiency. In general, the cell with back-to-back electrode arrangement, single stack, 26 percent KOH, and serrated zircar separator and the cell with a recirculating electrode arrangement, unit stack, 31 percent KOH, zircar separators performed best

ARES I Upper Stage Subsystems Design and Development

From 2005 through early 2011, NASA conducted concept definition, design, and development of the Ares I launch vehicle. The Ares I was conceived to serve as a crew launch vehicle for beyond-low-Earth-orbit human space exploration missions as part of the Constellation Program Architecture. The vehicle was configured with a single shuttle-derived solid rocket booster first stage and a new liquid oxygen/liquid hydrogen upper stage, propelled by a single, newly developed J-2X engine. The Orion Crew Exploration Vehicle was to be mated to the forward end of the Ares I upper stage through an interface with fairings and a payload adapter. The vehicle design passed a Preliminary Design Review in August 2008, and was nearing the Critical Design Review when efforts were concluded as a result of the Constellation Program s cancellation. At NASA Glenn Research Center, four subsystems were developed for the Ares I upper stage. These were thrust vector control (TVC) for the J-2X, electrical power system (EPS), purge and hazardous gas (P&HG), and development flight instrumentation (DFI). The teams working each of these subsystems achieved 80 percent or greater design completion and extensive development testing. These efforts were extremely successful representing state-of-the-art technology and hardware advances necessary to achieve Ares I reliability, safety, availability, and performance requirements. This paper documents the designs, development test activity, and results

Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities

Limitations in the Use of the Equivalent Diameter

This paper deals with the inaccuracy assessment of the friction pressure loss estimation based on Darcy formula combined with an equivalent hydraulic diameter and a friction factor valid for circular pipes when applied to a square rod bundle. The assessment has been done by comparing the analytical and semi-empirical predictions with two different CFD codes results: CFX and NEPTUNE_CFD. Two different analytical approaches have been considered: the whole-bundle and sub-channel approaches, both for laminar and turbulent flow conditions. Looking at results, it is reasonable to assume that an error in the range of 11% - 23% is likely when using equivalent diameter in the laminar regime. In the case of turbulent regime, the equivalent diameter works better and the error is in the range between a few percent and ~12%

Volcanic ash detection and retrievals using MODIS data by means of neural networks

Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis. <br><br> In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases. <br><br> The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part. <br><br> The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km<sup>−2</sup> has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement

Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjallajökull event, and equal to 74% for the Grimsvötn event

COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy

Mediterranean maquis is a complex and widespread ecosystem in the region, intrinsically prone to fire. Many species have developed specific adaptation traits to cope with fire, ensuring resistance and resilience. Due to the recent changes in socio-economy and land uses, fires are more and more frequent in the urban-rural fringe and in the coastlines, both now densely populated. The detection of fires and the monitoring of vegetation regrowth is thus of primary interest for local management and for understanding the ecosystem dynamics and processes, also in the light of the recurrent droughts induced by climate change. Among the main objectives of the COSMO-SkyMed radar constellation mission there is the monitoring of environmental hazards; the very high revisiting time of this mission is optimal for post-hazard response activities. However, very few studies exploited such data for fire and vegetation monitoring. In this research, Cosmo-SkyMed is used in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth. The positive results obtained in this research highlight the importance of the very high-resolution continuous acquisitions and the multi-polarization information provided by COSMO-SkyMed for monitoring fire impacts on vegetation

Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks

The lesson learned from the recent Icelandic Eyjafjallajokull volcanic eruption is the need to obtain accurate near real time retrievals in order to sample the phenomenon evolution. In particular, because of the harming effects of fine volcanic ash particles on aircrafts, the real time tracking of volcanic clouds is a key issue for aviation safety. The current mostly utilized procedure for the ash retrievals is based on the Brightness Temperature Difference (BTD) algorithm, using the 11 and 12 micron channels measurements and radiative transfer model computation. This latter requires many input parameters and is time consuming, preventing the utilization during the crisis phases. In this work a fast and accurate Neural Network (NN) approach has been developed to detect and retrieve volcanic ash cloud properties using multispectral IR measurements. The exploited data come from the Moderate Resolution Imaging Spectroradiometer (MODIS) acquired over Mt. Etna volcano during the 2001, 2002 and 2006 eruptive events. The procedure consists in two separate steps that uses the three MODIS channels 28, 31 and 32: the detection and the ash retrievals. The detection is reduced to a classification problem. In this context several classes can be individuated, such as free sea surface, meteorological clouds, and ash plume. To maintain the solution of the problem as easy as possible we have simplified the scenario identifying only two classes on the MODIS images: 'ash' and 'no ash' pixels. This approach is coherent with the philosophy of this work in which the time passed to obtain the result is a stringent factor. For the ash mass retrieval, the trained network replicates the model. In fact, in order to have a network able to learn a behavior and to represent it through a functional approximation, it is necessary to provide appropriate information by an ensemble of examples. These latter can be obtained from a model if a direct measure is not available. In this work the results obtained with the BTD procedure have been considered. The results obtained from the entire procedure are encouraging, indeed the confusion matrix for the test set has an accuracy greater than 90%. Moreover the ash mass retrieval shows a good agreement with that achieved by BTD procedure