Squeezing Flow of Carbon Nanotubes-Based Nanofluid in Channel Considering Temperature-Dependent Viscosity: A Numerical Approach

Controllability of Impulsive Neutral Fractional Stochastic Systems

The study of dynamic systems appears in various aspects of dynamical structures such as decomposition, decoupling, observability, and controllability. In the present research, we study the controllability of fractional stochastic systems (FSF) and examine the Poisson jumps in finite dimensional space where the fractional impulsive neutral stochastic system is controllable. Sufficient conditions are demonstrated with the aid of fixed point theory. The Mittag-Leffler (ML) matrix function defines the controllability of the Grammian matrix (GM). The relation to symmetry is clear since the controllability Grammian is a hermitian matrix (since the integrand in its definition is hermitian) and this is the complex version of a symmetric matrix. In fact, such a Grammian becomes a symmetric matrix in the specific scenario where the controllability Grammian is a real matrix. Some examples are provided to demonstrate the feasibility of the present theory.

Irreversibility analysis of an unsteady micropolar CNT-blood nanofluid flow through a squeezing channel with activation energy-Application in drug delivery

BACKGROUND AND OBJECTIVE

Due to the low toxicity, unique physiochemical properties, and appropriate surface modifications, Carbon Nanotubes (CNTs) are used as target carriers in drug delivery systems. In the present problem, we have considered both single-walled and multi-walled CNTs to study the impact of irreversibility on the micropolar nanofluid flow through a squeezing channel with the base fluid blood. The blood is considered a micropolar fluid in the presence of different blood cells and their rotational nature. Further, blood is influenced by the external magnetic field parallel to the microrotation along with viscous and Joule dissipations.

METHOD

Highly coupled and nonlinear partial differential equations are solved with Homotopy Analysis Method (HAM) after simplified equations using similarity transformation. Further, we have concluded the minimum squared residual errors to show the method's accuracy. A comparison made with the existing literature and shows a good agreement.

RESULTS

The angular velocity of the fluid particles is enhanced by increasing the squeezing number. In the case of the squeezing, volume fraction has improved the viscous drag and is found high for MWCNT embedded nanofluid. The heat transfer rate is higher for the MWCNT embedded nanofluid than the SWCNT embedded nanofluid. A descent found in entropy generation boosts up with the Brinkman parameter while opposite phenomena appear for radiation and Hartman number and vortex viscosity. Both Bejan number and entropy generation profiles are restricted with an increase in vortex viscosity.

CONCLUSION

SWCNTs are showed to be more effective and efficient than the MWCNTs in elevating velocity, temperature and irreversibility of the system. Outcomes of this problem will help to understand the implementation of the drug carrier and irreversibility phenomena during drug delivery.

Galerkin finite element analysis for the augmentation in thermal transport of ternary-hybrid nanoparticles by engaging non-Fourier's law

Boosting of thermal transportation is the demand of current era. Several techniques have been used to do so. One of an important way is the mixing of nanoparticles to boost thermal performance. Current investigation has been prepared to study the inclusion of tri hybrid nanoparticles in Prandtl fluid model past over a stretched heated sheet. Modelling of consider problem has been done due to consideration of movement in flow in Cartesian coordinates which results coupled partial differential equation system thermal transport in presented by considering generalized heat flux model and heat generation/absorption. The derived coupled complex partial differential equations (PDEs) system is simplified by engaging boundary layer theory. Such developed model is used in coolants regarding automobiles, dynamics in fuel and production of solar energy, fuel cells, optical chemical sensors, automotive parts, dental products, cancer therapy, electrical insulators and dental products. Handling of complex PDEs for the solution is a challenging task. Due to complexity in computational work these PDEs have been transformed into ordinary differential equations (ODEs) after applying similarity transformation afterwards converted ODEs have been approximated via finite element algorithm coded in MAPLE 18.0 symbolic computational package. Comparative study has been presented for the validity of code and authenticity of obtained result. It is observed that fluid velocity for tri-hybrid nanoparticles is higher than fluidic motion for pure fluid, nanofluid and hybrid nanomaterial.

Intelligent computing technique based supervised learning for squeezing flow model

In this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations into nonlinear ordinary differential equations. A dataset is generated based on squeezing fluid flow system USF-CPP for the LMBNN through the Runge–Kutta method by the suitable variations of Reynolds number and volume flow rate. To attain approximation solutions for USF-CPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model’s accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.