Multilayer Structured Rectangular Microstrip Antenna for ISM Band Applications

In this paper efforts have been made to design and simulate the Multilayer Structured Rectangular Microstrip antenna for ISM Band applications. The shape will provide the bandwidth which is required in various wireless applications like Bluetooth (2.4 GHz-2.484 GHz), RFID (2.4 GHz - 2.5 GHz), and WLAN (3.6 GHz) etc. Coaxial feed technique is used for its simplicity. The performance of the designed antenna is analyzed in terms of Bandwidth, Return loss, Gain, VSWR, Directivity and Radiation Pattern. FR – 4 epoxy substrate has been used, which has dielectric constant of 4.4. DOI: 10.17762/ijritcc2321-8169.150311

Experimental studies in antisolvent crystallization: Effect of antisolvent ratio and mixing patterns

18-25The crystals size and distribution play an important role in drug properties which has a major impact on the performance e.g., stability, solubility and bioavailability. The crystal size distribution (CSD) depends on the hydrodynamics and local degree of supersaturation in the crystallizer. In this study, we have investigated the effects of various operating conditions (antisolvent ratio, power, agitator design) using different mixing techniques such as impellers and ultrasound on CSD and average crystal size (ACS). It is found that mixing plays a dominant role in CSD and ACS. The hydrofoil (axial flow impeller) provides a wide range of ACS (406 to 240 μm) at lower power as compared to Rushton turbine (radial flow impeller) (395 to 375 μm). The mixed flow impeller produces the intermediate crystal size (365 to 345 μm). The increase in the antisolvent ratio results in a decrease in ACS. The same results observed for the power input

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

418-423Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C

SPECTRUM SENSING USING HARD DECISION LOGIC FOR COGNITIVE RADIO NETWORKS (140443

cognitive ratio tecnology has been proposed to improve spectrum efficiency by having the cognitive ratios act as secondary user to opportunistically  access under utilized frequancy bands

Insight into theories of heat and mass transfer at the solid-fluid interface using direct numerical simulation and large eddy simulation

Solid-fluid heat-transfer coefficients have an important role in the design of chemical processing equipment. The major resistance to heat transfer lies in a region very close to the wall, where experimental measurements are very difficult. The validity and accuracy of the models developed for the estimation of the heat- and mass-transfer coefficient still do not have general applicability for the entire range of Reynolds and Prandtl numbers, because of the limited knowledge of near-wall turbulence. There have been two approaches for such model development: one is an analytical approach, which considers the momentum, mass, and heat transfer to be analogous in nature and the understanding of one of these processes can be used to predict the other two; the other approach is heuristic, based on the visualization of the behavior of the coherent structures in the near-wall region. The continuous movement of fluid elements to and away from the wall (coherent structures) affects the transport phenomena. The models for the quantification of this behavior have been developed for the estimation of heat- and mass-transfer rates in the literature. However, both these approaches contain parameters fitted empirically to obtain good agreement with the experimental heat- and mass-transfer data. These models must be tested for their formulation and empirical constants on the basis of accurate solutions of governing equations of heat, mass, and momentum transfer. This is possible using direct numerical simulation (DNS) and large eddy simulation (LES), which can accurately predict the near-wall flow pattern. An attempt has been made to exploit the ability of DNS and LES to develop insight into hitherto used models, based on analogies and/or heuristic arguments

Multipurpose Agriculture Machine

Now a days,all other sector including agriculture sector will changes which will be needed because to meet the future food and other need .farmers will need to know new technologies ,whichwill not have much effect on crop yield if the future rains are reduced ,so that you will respond properly to the increased demand. Using newtechnology, farmers can increase production capacity. This technique is related to the entire process of sowing .traditional sowing method are time consuming & costly and labour intensive. For increase productivity need to replace this process to multipurpose seed sowing machine to reduce overall seed sowing cost , labour cost, efforts

Scheduling of Energy-Integrated Batch Process Systems Using a Pattern-Based Framework

In this paper, a novel pattern-based method is developed for the generation of optimal schedules for energy-integrated batch process systems. The proposed methodology is based on the analysis of available schedules for the identification of repetitive patterns. It is shown that optimal schedules of energy-integrated batch processes are composed of several repeating sections (or building blocks), and their sizes and relative positions are dependent on the scheduling horizon and constraints. Based on such a decomposition, the proposed pattern-based algorithm generates an optimal schedule by computing the number and sequence of these blocks. The framework is then integrated with rigorous optimization-based approach wherein it is shown that the learning from the pattern-based solution significantly improves the performance of rigorous optimization. The main advantage of the pattern-based method is the significant reduction in computational time required to solve large scheduling problems, thus enabling the possibility of on-line rescheduling. Three literature examples were considered to demonstrate the presence of repeating patterns in optimal schedules of energy-integrated batch systems. The effectiveness of the proposed methodology was illustrated using an integrated reactor-separator system