34 resultados para digital terrain analysis


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Adhesives are widely used to execute the assembly of aerospace and automotive structures due to their ability to join dissimilar materials, reduced stress concentration, and improved fatigue resistance. The mechanical behavior of adhesive joints can be studied either using analytical models or by conducting mechanical tests. However, the complexity owing to multiple interfaces, layers with different properties, material and geometric nonlinearity and its three-dimensional nature combine to increase the difficulty in obtaining an overall system of governing equations to predict the joint behavior. On the other hand, experiments are often time consuming and expensive due to a number of parameters involved. Finite element analysis (FEA) is profoundly used in recent years to overcome these limitations. The work presented in this paper involves the finite element modeling and analysis of a composite single lap joint where the adhesive-adherend interface region was modeled using connector elements. The computed stresses were compared with the experimental stresses obtained using digital image correlation technique. The results showed an agreement. Further, the failure load predicted using FEA was found to be closer to the actual failure load obtained by mechanical tests.

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The study follows an approach to estimate phytomass using recent techniques of remote sensing and digital photogrammetry. It involved tree inventory of forest plantations in Bhakra forest range of Nainital district. Panchromatic stereo dataset of Cartosat-1 was evaluated for mean stand height retrieval. Texture analysis and tree-tops detection analyses were done on Quick-Bird PAN data. The composite texture image of mean, variance and contrast with a 5x5 pixel window was found best to separate tree crowns for assessment of crown areas. Tree tops count obtained by local maxima filtering was found to be 83.4 % efficient with an RMSE+/-13 for 35 sample plots. The predicted phytomass ranged from 27.01 to 35.08 t/ha in the case of Eucalyptus sp. while in the case of Tectona grandis from 26.52 to 156 t/ha. The correlation between observed and predicted phytomass in Eucalyptus sp. was 0.468 with an RMSE of 5.12. However, the phytomass predicted in Tectona grandis was fairly strong with R-2=0.65 and RMSE of 9.89 as there was no undergrowth and the crowns were clearly visible. Results of the study show the potential of Cartosat-1 derived DSM and Quick-Bird texture image for the estimation of stand height, stem diameter, tree count and phytomass of important timber species.

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Using remotely sensed Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall and topographic data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM), the impact of oroghraphical aspects such as topography, spatial variability of elevation and altitude of apexes are examined to investigate capacious summer monsoon rainfall over the Western Ghats (WG) of India. TRMM 3B42 v7 rainfall data is validated with Indian Meteorological Department (IMD) gridded rainfall data at 0.5 degrees resolution over the WG. The analysis of spatial pattern of monsoon rainfall with orography of the WG ascertains that the grade of orographic precipitation depends mainly on topography of the mountain barrier followed by steepness of windward side slope and altitude of the mountain. Longer and broader, i.e. cascaded topography, elevated summits and gradually increasing slopes impel the enhancement in precipitation. Comparing topography of various states of the WG, it has been observed that windward side of Karnataka receives intense rainfall in the WG during summer monsoon. It has been observed that the rainfall is enhanced before the peak of the mountain and confined up to the height about 800m over the WG. In addition to this, the spatial distribution of heavy and very heavy rainfall events in the last 14 years has also been explored. Heavy and very heavy rain events on this hilly terrain are categorized with a threshold of precipitation (R) in the range 150>R>120mmday(-1) and exceeding 150mmday(-1) using probability distribution of TRMM 3B42 v7 rainfall. The areas which are prone to heavy precipitation are identified. The study would help policy makers to manage the hazard scenario and, to improve weather predictions on mountainous terrain of the WG.

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The standard procedure of groundwater resource estimation in India till date is based on the specific yield parameters of each rock type (lithology) derived through pumping test analysis. Using the change in groundwater level, specific yield, and area of influence, groundwater storage change could be estimated. However, terrain conditions in the form of geomorphological variations have an important bearing on the net groundwater recharge. In this study, an attempt was made to use both lithology and geomorphology as input variables to estimate the recharge from different sources in each lithology unit influenced by the geomorphic conditions (lith-geom), season wise separately. The study provided a methodological approach for an evaluation of groundwater in a semi-arid hard rock terrain in Tirunelveli, Tamil Nadu, India. While characterizing the gneissic rock, it was found that the geomorphologic variations in the gneissic rock due to weathering and deposition behaved differently with respect to aquifer recharge. The three different geomorphic units identified in gneissic rock (pediplain shallow weathered (PPS), pediplain moderate weathered (PPM), and buried pediplain moderate (BPM)) showed a significant variation in recharge conditions among themselves. It was found from the study that Peninsular gneiss gives a net recharge value of 0.13 m/year/unit area when considered as a single unit w.r.t. lithology, whereas the same area considered with lith-geom classes gives recharge values between 0.1 and 0.41 m/year presenting a different assessment. It is also found from this study that the stage of development (SOD) for each lith-geom unit in Peninsular gneiss varies from 168 to 230 %, whereas the SOD is 223 % for the lithology as a single unit.