Recession of Milam Glacier,Kumaun Himalaya,Observed via Lichenometric Dating of Moraines

Glaciers being very sensitive to climate change have been identified as one of the best indicators of climate change and evidences have proved that most of the Himalayan glaciers have receded with an increased rate during the recent past under the influence of global warming. Lichenometric study was carried out on the moraines of Milam glacier (located in Pithoragarh district of Uttarakhand) with the help of lichen species Dimelaena oreina having an average annual growth rate of 1.31 mm. The study revealed that Milam glacier has receded 1450 m in last 69.37 years with an average recession rate of 20.90 m/year. Since lichenometric studies are cost effective and ecofriendly in comparison to carbon dating, satellite and remote sensing based studies and also reliable, hence, it should be promoted in Himalaya which is an abode of glaciers.     

Structure Inclination Angles in the Convective Atmospheric Surface Layer

Two-point correlations of the fluctuating streamwise velocity are examined in the atmospheric surface layer over the salt flats of Utah’s western desert, and corresponding structure inclination angles are obtained for neutral, stable and unstable conditions. The neutral surface-layer results supplement evidence for the invariance of the inclination angle given in Marusic and Heuer (Phys Rev Lett 99:114504, 2007). In an extension of those results it is found that the inclination angle changes drastically under different stability conditions in the surface layer, varying systematically with the Monin–Obukhov stability parameter in the unstable regime. The variation is parametrized and subsequently can be used to improve existing near-wall models in the large-eddy simulation of the atmospheric surface layer.     

Vulnerability and adaptation to climate variability and water stress in Uttarakhand State, India

This paper presents a participatory approach to investigate vulnerability and adaptive capacity to climate variability and water stress in the Lakhwar watershed in Uttarakhand State, India. Highly water stressed microwatersheds were identified by modelling surface runoff, soil moisture development, lateral runoff, and groundwater recharge. The modelling results were shared with communities in two villages, and timeline exercises were carried out to allow them to trace past developments that have impacted their lives and livelihoods, and stimulate discussion about future changes and possible adaptation interventions.     

A deterministic approach for preparation of seismic hazard maps in North East India

A method of seismic zonation based on deterministic modeling of rupture plane is presented in this work. This method is based on the modeling of finite rupture plane along identified lineaments in the region using the semi-empirical technique, of Midorikawa [(1993) Tectonophysics 218:287–295]. The modeling procedure follows ω² scaling law, directivity effects, and other strong motion parameters. The technique of zonation is applied for technoeconomically important NE part of Brahmaputra valley that falls in the seismic gap region of Himalaya. Zonation map prepared for Brahmaputra valley for earthquakes of magnitude M > 6.0 show that approximately 90,000 km² area fall in the highly hazardous zone IV, which covers region that can have peak ground accelerations of order more than 250 cm/s². The zone IV covers the Tezu, Tinsukia, Dibrugarh, Ziro, North Lakhimpur, Itanagar, Sibsagar, Jorhat, Golaghat, Wokha, Senapati, Imphal, and Kohima regions. The Pasighat, Daring, Basar, and Seppa region belong to zone III with peak ground accelerations of the order 200–250 cm/s². The seismic zonation map obtained from deterministic modeling of the rupture is consistent with the historical seismicity map and it has been found that the epicenter of many moderate and major earthquakes fall in the identified zones.     

Experiments on integral length scale control in atmospheric boundary layer wind tunnel

Accurate predictions of turbulent characteristics in the atmospheric boundary layer (ABL) depends on understanding the effects of surface roughness on the spatial distribution of velocity, turbulence intensity, and turbulence length scales. Simulation of the ABL characteristics have been performed in a short test section length wind tunnel to determine the appropriate length scale factor for modeling, which ensures correct aeroelastic behavior of structural models for non-aerodynamic applications. The ABL characteristics have been simulated by using various configurations of passive devices such as vortex generators, air barriers, and slot in the test section floor which was extended into the contraction cone. Mean velocity and velocity fluctuations have been measured using a hot-wire anemometry system. Mean velocity, turbulence intensity, turbulence scale, and power spectral density of velocity fluctuations have been obtained from the experiments for various configuration of the passive devices. It is shown that the integral length scale factor can be controlled using various combinations of the passive devices.     

Prediction of wind properties in urban environments using artificial neural network

Artificial neural network (ANN) modeling has been performed to predict turbulent boundary layer characteristics for rough terrain based on experimental tests conducted in a boundary-layer wind tunnel to simulate atmospheric boundary layer using passive roughness devices such as spires, barriers, roughness elements on the floor, and slots in the extended test section. Different configurations of passive devices assisted to simulate urban terrains. A part of the wind tunnel test results are used as training sets for the ANN, and the other part of the test results are used to compare the prediction results of the ANN. Two ANN models have been developed in this study. The first one has been used to predict mean velocity, turbulence intensity, and model length scale factor. Results show that ANN is an efficient, accurate, and robust modeling procedure to predict turbulent characteristics of wind. In particular, it was found that the ANN-predicted wind mean velocities are within 4.7%, turbulence intensities are within 6.2%, and model length scale factors are within 3.8% of the actual measured values. In addition, another ANN model has been developed to predict instantaneous velocities that enables calculating the power spectral density of longitudinal velocity fluctuations. Results show that the predicted power spectra are in a good agreement with the power spectra obtained from measured instantaneous velocities.     

Characteristics of helical tip vortices in a wind turbine near wake

Helical tip vortices in the wake of a wind turbine have been investigated in this study. To elucidate the near-wake flow field of the wind turbine, the wake has been explored in the Reynolds number (Re) range 1000 ≤?Re?≤?5000 using qualitative dye flow visualization and quantitative digital particle-image velocimetry techniques. Flow visualization showed the dye getting trapped in the shape of spirals surrounding the helical vortex cores. It was found that the helical vortex core size was increasing with downstream distance. It was also found that the normalized stream-wise component of the wake velocity decreased with increasing tip-speed ratios. The results indicate that vorticity peaks at the center of the core and it decays as the vortex moves downstream, showing that the viscous dissipation is active even at length scales of approximately 5 diameters.