Development of a co-cured composite torque shaft for rudder of high speed aircraft

The Carbon Fibre reinforced Composites are widely used in developing various composite parts of civil and13; military aircraft due to its high specific strength and specific stiffness. Rudder being a primary control surface in an aircraft, it is subjected to various loads and needs high degree of structural integrity. Usually rudders are made of metal with many fasteners. In NAL we have designed and developed a composite rudder. Specialty of this13; rudder is that it has a torque shaft made up of carbon composite and has only few rivets. Conventionally torque13; shaft s are made up of special metals like titanium. The objective of this paper is to highlight the development of13; various tooling techniques used to fabricate the composite torque shaft . All major parts of the torque shaft are13; made by Co-curing technique and the metal attachments are embedded to the composite parts by self locking13; mechanism design. To qualify the torque shaft fatigue tests are done and ageing studies performed to prove13; structural integrity of the torque shaft under extreme environmental conditions. This paper portrays the13; development efforts, tolling and fabrication approaches for successful realization of the CFRP Torque Shaft

Snow cover, snowmelt and runoff in the Himalayan River basins

Not withstanding the seasonal vagaries of both rainfall amount and snowcover extent, the Himalayan rivers retain their basic perennial character. However, it is the component of snowmelt yield that accounts for some 60 to 70 percent of the total annual flow volumes from Hamilayan watersheds. On this large hydropotential predominantly depends the temporal performance of hydropower generation and major irrigation projects. The large scale effects of Himalayan snowcover on the hydrologic responses of a few selected catchments in western Himalayas was studied. The antecedent effects of snowcover area on long and short term meltwater yields can best be analyzed by developing appropriate hydrologic models forecasting the pattern of snowmelt as a function of variations in snowcover area. It is hoped that these models would be of practical value in the management of water resources. The predictability of meltwater for the entire snowmelt season was studied, as was the concurrent flow variation in adjacent watersheds, and their hydrologic significance. And the applicability of the Snowmelt-Runoff Model for real time forecast of daily discharges during the major part of the snowmelt season is examined

Kinetic study of synthesis of bio-fuel additives from glycerol using a hetropolyacid

Concerns about the ever increasing quantities of glycerol produced as a by-product of the process of manufacture of bio-diesel serve as a fuel for research about the alternative uses of glycerol. The esterification of glycerol with acetic acid over Cesium supported heteropolyacid (CsPWA) serving as the catalyst was carried out. The products obtained were mono, di and tri acetins which have wide application as biofuels. A series of experiments were carried out with CsPWA as catalyst and parameters considered for studies were temperature, molar ratio of reactants (acetic acid:glycerol) and the catalyst loading weight percent. Each parameter was varied keeping the other two constant and the results were recorded. Temperature was varied from 80°C to 110°C; molar ratio of glycerol to acetic acid is between 3:1 and 9:1 and catalyst loading of 3%w/w to 7%w/w. The yield and conversion varied for different conditions, but in general, the yield of diacetin and triacetin increased with time. Optimum parameters were adjudged to be 110°C with a molar ratio of 9:1 of the reactants and catalyst loading being 5% weight of reaction mixture where maximum glycerol conversion of 98% was obtained. The results obtained indicate that the esterification of glycerol with acetic acid is a consecutive reaction and the kinetic model was developed based on homogeneous first order reaction series by optimization method using MATLAB, and rate constants (k1, k2 and k3) were determined. From the rate constants at different temperatures, using Arrhenius equation the activation energies (E1, E2 and E3) were also determined

Variabilité des symptômes causés par différents isolats de Cercospora arachidicola sur quelques génotypes d'arachide

L'apparition de tolérance au bénomyl du Cercospora arachidicola, agent causal de la cercosporiose précoce de l'arachide, a déjà été signalée au champ. D'autres rapports indiquent un changement notable de résistance de l'hôte à l'égard de la cercosporiose lors de tests dans des régions géographiques différentes. La connaissance parfaite de la variabilité des agents pathogènes est indispensable pour la réussite d'un programme d'amélioration. Dans le cadre des études de la variabilité du pouvoir pathogène de C. arachidicola, les symptômes causés par huit isolats de ce pathogène ont été comparés sur quatre génotypes cultivés. Des différences significatives entre les génotypes et entre les isolats ont été observées au niveau des symptômes, de la fréquence d'infection et de la taille des lésions. Les génotypes employés dans cette étude peuvent constituer une gamme d'hôte pour séparer différents pathotypes de l'agent pathogène responsable de la cercosporiose hâtiv

Studies on atmospheric gravity wave activity in the troposphere and lower stratosphere over a tropical station at Gadanki

MST radars are powerful tools to study the mesosphere, stratosphere and troposphere and have made considerable contributions to the studies of the dynamics of the upper, middle and lower atmosphere. Atmospheric gravity waves play a significant role in controlling middle and upper atmospheric dynamics. To date, frontal systems, convection, wind shear and topography have been thought to be the sources of gravity waves in the troposphere. All these studies pointed out that it is very essential to understand the generation, propagation and climatology of gravity waves. In this regard, several campaigns using Indian MST Radar observations have been carried out to explore the gravity wave activity over Gadanki in the troposphere and the lower stratosphere. The signatures of the gravity waves in the wind fields have been studied in four seasons viz., summer, monsoon, post-monsoon and winter. The large wind fluctuations were more prominent above 10 km during the summer and monsoon seasons. The wave periods are ranging from 10 min-175 min. The power spectral densities of gravity waves are found to be maximum in the stratospheric region. The vertical wavelength and the propagation direction of gravity waves were determined using hodograph analysis. The results show both down ward and upward propagating waves with a maximum vertical wave length of 3.3 km. The gravity wave associated momentum fluxes show that long period gravity waves carry more momentum flux than the short period waves and this is presented

Effect of COD: SO42- Ratio, HRT and Linoleic Acid Concentration on Mesophilic Sulfate Reduction: Reactor Performance and Microbial Population Dynamics

Biological sulfate (SO42-) reduction was examined in anaerobic sequential batch reactors (ASBRs) operated under different hydraulic retention times (HRTs) ranging from 12 to 36 h and COD (Chemical Oxygen Demand)/SO42- ratios of 2.4, 1.6 and 0.8. Competition between SO42- reducing bacteria (SRBs), methane producing archaea (MPAs) and homoacetogens (HACs) was examined in controls and cultures treated with linoleic acid (LA). The ASBR performance was influenced by the COD/SO42- ratio in control cultures with a SO42- reduction of 87% at a COD/SO42- ratio of 0.8. At a 12 h HRT, in both control and LA treated cultures, greater than 75% SO42- removal was observed under all the conditions examined. In control reactors operating at a 36 h HRT, high levels of MPAs belonging to Methanobacteriales and Methanosarcinales were detected; however, in comparison, under low COD/SO42- ratio and with decreasing HRT conditions, a relative increase in SRBs belonging to Desulfovibrio and Desulfatibacillum was observed. Adding 0.5 gL(-1) LA suppressed Methanobacteriales, while increasing the LA concentration to 1 gL(-1) completely suppressed MPAs with a relative increase in SRBs. HACs belonging to Bacteroidetes were observed in the control and in cultures operated at 12 h HRT with a COD/SO42- ratio of 1.6 and fed 0.5 gL(-1) LA; however, with all other LA levels (0.5 and 1.0 gL(-1)) and HRTs (12, 24 and 36 h), HACs were not detected

PCO-IB: Churn Analysis P2P Networks Using A Peer Co-Operative Intensive Based Schema

The Peer-to-Peer networks used technology of distributed computing.  The P2P network is essential for network communication. P2P networks are utilized in many applications due to these benefits. For example, record sharing, broadcast communications, and media streaming. There are a lot of nodes connected to the P2P network. Peers of network frequently join and leave the network at the same time. In the P2P network, this kind of paradigm is called churn. Numerous new examination works uncovered that stir is the primary issue looked by the present P2P organization. Content availability, data accuracy, and overhead were significantly reduced by the churn process. An Incentive-Based (IB) schema was proposed in this paper in order to circumvent the limitations of the P2P network for multimedia transmission. The IB schema that has been proposed encourages fair communication and cooperation among the nodes. Multimedia transmission efficiency in real-time P2P networks is maximized by the IB schema. In this paper, IB outline for the most part centered around the upgrade of the P2P organizations. The proposed construction is carried out utilizing Organization Test system. In P2P networks, the proposed IB schema improved multimedia transmission performance

Enhancing COVID-19 Diagnosis: A Multi-Modal Approach Utilizing the CNN Algorithm in Automated Applications

Rapidly identifying COVID-19 patients is essential for effective disease control and management. To address this need, we have developed an automated application that utilizes multi-modal data, including Chest X-ray, Electrocardiogram (ECG), and CT scan images, for precise COVID-19 patient identification. This application comprises a two-stage process, starting with a web-based questionnaire and then the submission of medical images for verification. Leveraging various ML and DL techniques, including CNN, KNN, Logistic Regression, Decision Tree, and NaiveBayes, We conducted extensive model training and validation for LSTM, InceptionV3, SVM, Resnet, and MobileNet. The CNN algorithm emerged as the top-performing method across all modalities, demonstrating exceptional accuracy, precision, recall, F-score, and a minimal false prediction rate. Confusion matrices were employed for comprehensive result evaluation. This study highlights the potential of multi-modal data analysis, particularly the CNN algorithm, for efficiently and accurately identifying COVID-19 patients

Study of the Topology Mismatch Problem in Peer-to-Peer Networks

The advantages of peer-to-peer (P2P) technology are innumerable when compared to other systems like Distributed Messaging System, Client-Server model, Cloud based systems. The vital advantages are not limited to high scalability and low cost. On the other hand the p2p system suffers from a bottle-neck problem caused by topology mismatch. Topology mismatch occurs in an unstructured peer-to-peer (P2P) network when the peers participating in the communication choose their neighbors in random fashion, such that the resultant P2P network mismatches its underlying physical network, resulting in a lengthy communication between the peers and redundant network traffics generated in the underlying network[1] However, most P2P system performance suffers from the mismatch between the overlays topology and the underlying physical network topology, causing a large volume of redundant traffic in the Internet slowing the performance. This paper surveys the P2P topology mismatch problems and the solutions adapted for different applications

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree's classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method