Nanofluids holding three distinct sorts of nanosized particles suspended in base fluid possess excellent thermal performance. In light of this novel use in coolant applications, the current work ...dealt with the optimal design and performance estimation of a ternary hybrid nanofluid, based on a modern machine learning prediction technique. The synthesis of (Cu), (TiO2), and (SiO2) ternary hybrid nanoparticles suspended in water over a symmetrically stretching sheet was scrutinized. The flow over a stretching sheet is the most noteworthy symmetry analysis for momentum and thermal boundary layers, due to the implications of heat transfer, and is applied in various industries and technological fields. The governing equations were transformed to a dimension-free series of ODEs, by handling similarity transformable with symmetry variables, after which, the series of ODEs were treated scientifically, with the help of the Wolfram Language tool. The precision of the current estimates was assessed by comparison to existing research. Moreover, the natures of the physical phenomena were forecast by designing a support vector machine algorithm with an emphasis on machine learning, which delivers a robust and efficient structure for every fluid application that infers physical influences. To validate the proposed research, some of the statistical metrics were taken for error assessment between true and anticipated values. It was revealed that the presented approach is the best strategy for predicting physical quantities. This investigation established that ternary hybrid nanofluid possesses excellent thermal performance, greater than that of hybrid nanofluid. The current optimization process delivers a new beneficial viewpoint on the production of polymer sheets, glass fiber, petroleum, plastic films, heat exchangers, and electronic devices. Hence, the obtained results are recommended for the development of industrial devices setups.
An incompressible MHD nanofluid boundary layer flow over a vertical stretching permeable surface employing Buongiorno’s design investigated by considering the convective states. The Brownian motion ...and thermophoresis effects are used to implement the nanofluid model. Operating the similarity transmutations, to transform the governing partial differential equations into ordinary differential equations consisting of the momentum, energy, and concentration fields and later worked by using a program written together with the stiffness shifting in Wolfram Language. The consequences of various physical parameters on the velocity, temperature, and concentration fields are analyzed, such as magnetic parameter M, Brownian motion parameter Nb, thermophoresis parameter Nt, Lewis number Le, temperature Biot number Biθ, concentration Biot number Biϕ, and suction parameter fw. Furthermore, the Skin friction coefficient, local Nusselt, and local Sherwood numbers concerning magnetic parameter for various values of physical parameters (i.e. fw, Nb) are obtained graphically, then the outcome is validated with other recent works. Finally, introduced a new environment to employ machine learning by performing the sensitivity analysis based on the iterative method for predicting the Skin friction coefficient, reduced Nusselt number, and Sherwood number with respect to magnetic parameter for suction parameter and Brownian motion parameter. Machine learning algorithms provide a strong and quick data processing structure to enhance the actual research procedures and industrial application of fluid mechanics. These techniques have been upgraded and organized for fluid flow characteristics. The present optimization process has the potential for a new perspective on the metallurgical process, heat exchangers in electronics, and some medicinal applications.
Machine learning techniques have received a lot of interest in the exploration to minimize the computational cost of computational fluid dynamics simulation. The present article investigates ...application of heat and mass transfer in magnetohydrodynamic flow over a stretching sheet in metallurgy process by employing the learning methodology based on gradient descent. It is anticipated that the consequences of the current work will show the benefits of future research to enhance the development in the domains of science and engineering. A tabular and graphical evaluation greatly demonstrates the similarity between current and previous outcomes in the prescribed fluid flow model.
Secured IoT based Smart Vehicle Tracking System Vanitha, M.; Joice, C. Sheeba; Selvi, M. ...
2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC),
2022-Nov.-10
Conference Proceeding
This paper proposes an Android mobile application that gives information about the real time location of the buses under the organization. ESP8266 Node MCU and GPS Module is used to get geographic ...coordinates and the vehicle location is updated to the application through the internet which would give the exact location of buses may help the users to plan their way to reach their destination on time. The RFID (Radio Frequency Identification)-based access control system can only be unlocked by those who have been authenticated. The service will then activate and authenticate the person as a result of this action. The RFID reads an ID number from an RFID tag and transfers the information to a database that can be accessed via an Android app. The Android platform necessitates open-source development, making it the most practical and user-friendly option. Human evolution has included the development of transportation systems. It is impossible to imagine life without automobiles. To accommodate the large population, the number of automobiles has been significantly increasing. This resulted in a rise in the number of accidents. The accident-prevention methods in use today are all static and outdated. Furthermore, no adequate accident detection mechanism exists.
