Importance of bioconvection flow on tangent hyperbolic nanofluid with entropy minimization

The amalgamation of microorganisms in the nanofluid is significant in beautifying the thermal conductivity of several systems, such as microfluid devices, chip-shaped microdevices, and enzyme biosensors. The current investigation studies mixed convective flow of the entropy minimization of unsteady MHD tangent hyperbolic nanoliquid because a stretching surface has motile density via convective and slip conditions. For the novelty of this work, the variable transport characteristics caused by dynamic viscosity, thermal conductivity, nanoparticle mass permeability, and microbial organism diffusivity are considered. It is considered that the vertical sheet studying the flow. By using the appropriate alteration, the governing equations for the most recent flow analysis were altered into a non-dimension relation. Through MATLAB Software bvp4c, the PDE model equations have been made for these transformed equations. Engineering-relevant quantities against various physical variables include force friction, Nusselt number, Sherwood number, and microorganism profiles. The results showed good consistency compared to the current literature. Moreover, these outcomes revealed that augmentation in the magnitude of the magnetic field and velocity slip parameter declines the velocity profile. The reverse impact is studied in We. In addition, heat transfer is typically improved by the influence of thermal radiation parameters, Brownian movement, and thermophoretic force. The physical interpretation has existed through graphical and tabular explanations

Bio-convection Eyring-Powell nanofluid through a spinning disk with a heated convective stretching sheet

Recent research has linked the improvement of extrusion features, rotatary heat process and biofuel production to the use of nanoparticles. The prime proposes of the current scrutinization is to inspect the MHD flow of Powell-Eyring fluid induce by the nanofluid and bioconvection flow through a spinning disk. Together with nanofluids, a blend of bioconvective is employed to improve the system's thermal performance which has applications in different technological systems. The flow is considered over a stretable spinning disk. The Buongiorno model has been produced to adequately reflect how the role of nanoliquid affects Brownian motion and thermophoresis characteristics. Arrhenius's activation energy has also been considered. By applying the suitable transformation, the obtained boundary layer expressions are altered into a set of ordinary differential expressions. The three stages Lobatto BVP4c technique is utilized for ordinary differential expressions. The influences of pertinent variables on different profiles are represented graphic form. The study demonstrates tangential and axial velocity reduces with larger magnitude of Ma as the opposite effect is noted for fluid parameters α1. Moreover, the concentration distributions enhance with rising values of κ and Ea . The results are calculated using previously published research, and excellent conformity is discovered

E-Bayesian Estimation of Hierarchical Poisson-Gamma Model on the Basis of Restricted and Unrestricted Parameter Spaces

In this study, we use the idea of the hierarchical model (HM) to estimate an unknown parameter of the hierarchical Poisson-Gamma model using the E-Bayesian (E-B) theory. We propose the idea of hierarchical probability function instead of the traditional hierarchical prior density function. We aim to infer E-B estimates with respect to the conjugate Gamma prior distribution along with the E-posterior risks on the basis of different symmetric and asymmetric loss functions (LFs) under restricted and unrestricted parameter spaces using uniform hyperprior. Whereas, E-B estimators are compared with maximum likelihood estimators (MLEs) using mean squared error (MSE). Monte Carlo simulations are prosecuted to study the efficiency of E-B estimators empirically. It is shown that the LFs under a restricted parameter space dominate to estimate the parameter of the hierarchical Poisson-Gamma model. It is also found that the E-B estimators are more precise than MLEs, and Stein’s LF has the least E-PR. Moreover, the application of outcomes to a real-life example has been made for analysis, comparison, and motivation