Doubly stratified non-Newtonian nanofluid flow featuring nonlinear convection

Purpose The formulation of nonlinear convective non-Newtonian material is reported in this communication. Aspects of thermal radiation and heat source are taken into account for heat transport analysis. The novel stratifications (thermal and solutal) and convective conditions are considered simultaneously. The boundary-layer concept is implemented to simplify the complex mathematical expressions. Design/methodology/approach The well-known optimal homotopy scheme develops the computations. Optimal values regarding nonzero auxiliary variables are calculated and examined. Findings Nonlinear convective flow; Thixotropic non-Newtonian material; Thermal radiation; Heat source; Stratifications and convective conditions; Buongiorno model. Originality/value To the best of authors’ knowledge, no such analysis has yet been reported.

Friction Layer Analysis of a Surface Acoustic Wave Motor

This study applied appropriate assumptions to simplify a surface acoustic wave (SAW) motor model, as well as the elastic friction layer concept to analyze the two-dimensional contact problem. The effect of inertial force was factored into the equation of motion for the friction layer; the relationship between external force and the displacement of the friction layer surface was determined via Fourier transform; and the displacement field under different loads was analyzed using numerical methods. Based on the friction theory proposed by Armstrong, the relative velocity of the friction layer between the slider and stator was considered in the relationship between the normal and tangential forces acting on both contact surfaces. Finally, the deformation and the contact forces acting on the friction layer were evaluated by assuming the displacement of the stator substrate.

Assembly of Soft Electrodes and Ion Exchange Membranes for Capacitive Deionization

The responsible use of water, as well as its reuse and purification, has been a major problem for decades now. In this work, we study a method for adsorbing ions from aqueous solutions on charged interfaces using highly porous electrodes. This water purification process is based on the electric double layer concept, using the method known as capacitive deionization (CDI): If we pump salty solutions through the volume comprised between two porous electrodes while applying a potential difference to them, ions present in the solution are partially removed and trapped on the electrode surfaces. It has been well established that the use of carbon electrodes in combination with ion exchange membranes (membrane-CDI) improves the efficiency of the method above that achieved with bare activated carbon. Another approach that has been tested is based on coating the carbon with polyelectrolyte layers, converting them into “soft electrodes” (SEs). Here we investigate the improvement found when combining SEs with membranes, and it is shown that the amount of ions adsorbed and the ratio between ions removed and electrons transported reach superior values, also associated with a faster kinetics of the process. The method is applied to the partial desalination of up to 100 mM NaCl solutions, something hardly achievable with bare or membrane-covered electrodes. A theoretical model is presented for the ion transport in the presence of both the membrane and the polyelectrolyte coating.

Senet Cnn Based Tomato Leaf Disease Detection

Plants play important roles in the environment. Various plants are fulfilling many demands and basic requirements of the society. Saving such entities in the society is the uttermost necessity in today’s world to deal with plant degradations. Many diseases in plants are common and thus they face degradation. While mainly dealing with common plant such as tomato and potato plants, it is observed that they very often face bacterial and other diseases. A proper precaution can be made to save plant from such diseases. Thus the early prediction of such different diseases can be made, which can be a massive savings to the farmer as well as for the country economy. This paper has adapted a moderate different approach of convolutional neural network called SENet. In this approach, a hybrid process is discussed which uses the advantage of SENet and CNN layer concept for better classification. CNN is performed by using the number of layer and kernel selections. Classification of data is performed using the traditional CNN approach. In this scenario, the quick process occurrence is performed using suppression of less used information. It tries to add weight to each and every feature map in the layer. This approach is used to check, identify and detect the defects in leaf of tomato. The prime motive of the presented approach is; to obtain simple easiest method for the detection of disease in tomato leaf with use of minimal computing resources. Thus an improved, efficient algorithm can be made use in real time implementation of leaf disease prediction with high accuracy and efficiency parameters.