P. Rajendra Prasad

The effect of hydrogeology on variations in the electrical conductivity of groundwater fluctuations

Abstract A regular monthly inventory of water-level height and water quality of about 100 wells along the coast from Visakhapatnam to Bhimilipatnam was made for a 13-month period during 1974–1975. The electrical conductivity of the waters is found to vary with seasonal water-level fluctuations. It is, however, observed that this variation has two different patterns. The first pattern is that when the water level lowers the electrical conductivity decreases, and the second pattern is that when the water level lowers the electrical conductivity increases. The geological environment is the same for both cases, but the two patterns result from water-table fluctuations in two different hydrogeological zones.

Use of geophysical and hydrochemical tools to investigate seawater intrusion in coastal alluvial aquifer, Andhra Pradesh, India

India has a very long coastline and 25 % of the country’s population live in the coastal zone. Urban centers are located along the coast and three out of four metro cities are located on the coast. The high population density along the banks of major rives and coast Increasing population and demand for water putting the coastal aquifers under stress and causing sea water inrush and salinity upcoming in the coastal aquifers. Apart from sea water contamination, urban waste releases and agriculture inputs threatening the coastal groundwater aquifer systems. Generally coastal areas receive more pollutant loads from different sources including geogenic and anthropogenic sources. Central Godavari delta is located adjacent to the Bay of Bengal Coast, Andhra Pradesh, India and is drained by Pikaleru, Kunavaram and Vasalatippa drains. The area is occupied by recent Quaternary alluvium and gone through a series of marine transgression and regression. The entire study area comes under Godavari central canal command area, water is available throughout year except first week of June and last week of April in the canals. Water requirements for irrigation met from surface water in the delta. There is no groundwater pumping for agriculture as wells as for domestic purpose due to brackish nature of the groundwater at shallow depths. The groundwater depths varying from 0.8 to 3.4 m dug wells and in bore wells located near the coast 4.5–13.3 m. The established groundwater flow direction is to be towards Bay of Bengal from Amalapuram. Geophysical and hydrochemical tools were applied to identify the source of the salinity and to assess the saline water intrusion in the Godavari delta. Electrical Resistivity Tomography (ERT) surveys were carried out at several locations in the deltaic region to delineate the aquifer geometry and to identify saline water aquifer zones. The results inferred from ERT indicate 12–15 m thick loamy sands were existed from surface to subsurface and it is followed by 18–25 m thick clay layers. The thickness of clay is being increased toward Sea from inland. The low resistivity values in the delta are attributed to existence of the thick marine clays in the subsurface and relative high resistivities are attributed to existence of fresh water. The resistivity values similar to saline water 0.86) and SO4 −2/Cl− ( 0.05) indicated marine palaeo salinity, dilution of marine clays and dissolution of evaporites. The high SO4 −2/Cl in the post monsoon is attributed to dilution groundwater salinity due to rainfall infiltration and irrigation return flows in the delta. The low Na+2/Cl− ratios in upstream of the delta are due to sand exposures and isolated fresh water lances in the perched aquifers.

Electrical resistivity imaging over natural (in situ) geological samples using physical model studies

To understand the characteristic responses of natural geological samples, viz., black granite, green marble, and graphite sheet, and to have “an a priori” knowledge of their physical property through electrical resistivity imaging, the physical model laboratory setup has been established to conduct scale model studies over targets of finite dimensions and resistivities. The present experiment involves IRIS make SYSCAL Pro-96 measuring system using 48 electrodes with 2-cm interelectrode separation in the laboratory model tank. In the present communication, we have presented the 2D cross section images using Wenner, Wenner–Schlumberger, and dipole–dipole array configurations over the resistive (granite, marble) and conductive (graphite) sheets. In the case of resistive target (black granite sheet, green marble), the combined usage of dipole–dipole and Wenner–Schlumberger arrays provided more accurate measures on target parameters, i.e., the combined usage of both the arrays is preferable in searching high-resistive targets beneath the low-resistive ones over burden. The shape of the resistive target (green marble sheet) is inappropriate when the thickness of the target is greater than one half of the minimum array separation. As the thickness of the target increases, the signatures of the target become feeble, and hence, the shape of the resistive target is not properly reflected in the corresponding tomogram. The response over graphite sheet indicates that the true parameters of the target are not reflected in the cross section, and the existence of the low-resistive (high-conductive) target in the host medium (water) deviates the resistivity of the medium. The target parameters from the cross section using dipole–dipole array are somewhat correlated with true parameters in the case of thin targets at shallow depths. In the case of the sequence of layers of gravel–marble gravel–sand gravel simulated in a small model tank in the physical model laboratory, the thickness of the high-resistive marble layer beneath the low-resistive gravel layer is enhanced conspicuously because of the significant resistivity contrast between gravel and marble.

Process Intensification with Coaxially Placed Entry Region Vanes Assembly Turbulence Promoter in Homogeneous Flow

Abstract The entry region vane turbulence promoters are inserted at the downstream of a circular electrolytic cell and limiting current data are measured at the copper micro electrodes fixed on an electrode support. The effect of flow rate of the electrolyte, effect of velocity on mass transfer at the wall, effects of geometric parameters diameter of the vane (dv) from 0.02 m to 0.04 m, the angle of the vane (γ) from 15° to 60°, sectorial angle (α)/number of the vanes (N) from 4 to 8, diameter of the annular rod of mass transfer is investigated. The electrolyte was equimolal potassium ferricyanide, potassium ferrocyanide and excess sodium hydroxide. Limiting current data had been obtained for the reduction of potassium ferricyanide ion. The mass transfer correlation is based on the law of the wall similarity. The effect of each parameter was studied in terms of friction factor. A model was developed for mass transfer. The correlation may be extended to a wider range of parameters by virtue of the law of the wall. The experimental data on mass transfer was modelled in terms of mass transfer function and Reynolds number geometric parameters.

Artificial Neural Networks Model to Improve the Performance Index of the Coil-Disc Assembly in Tube Flow

An artificial neural networks model to enhance the performance index of mass transfer function in tube flow by means of entry region coil-disc assembly promoter was inserted coaxially is presented in this paper. Popular Backpropagation algorithm was utilized to test, train and normalize the network data to envisage the performance of mass transfer function. The experimental data of the study is separated into two sets one is training sets and second one is validation sets. The 248 sets of the experimental data were used in training and 106 sets for the validation of the artificial neural networks using MATLAB 7.7.0, particularly tool boxes to predict the performance index of the mass transfer in tube for faster convergence and accuracy. The weights were initialized within the range of [–1, 1]. The network limitations in all attempts taken learning rate as 0.10 and momentum term as 0.30. The finest model was selected based on the MSE, STD and R2. In this, network with 5_8_1 configuration is recommended for mass transfer training. This research work reveals that artificial neural networks with adding more number of layers and nodes in hidden layer may not increase the performance of mass transfer function.