Significance of the inclined magnetic field on the water-based hybrid nanofluid flow over a nonlinear stretching sheet.

This work addresses a theoretical exploration of the water-based hybrid nanofluid flow over a nonlinear elongating surface. The flow is taken under the effects of Brownian motion and thermophoresis factors. Additionally, the inclined magnetic field is imposed in the present study to investigate the flow behavior at different angle of inclination. Homotopy analysis approach is used for the solution of modeled equations. Various physical factors, which are encountered during process of transformation, have been discussed physically. It is found that the magnetic factor and angle of inclination have reducing impacts on the velocity profiles of the nanofluid and hybrid nanofluid. The nonlinear index factor has direction relation with the velocity and temperature of the nanofluid and hybrid nanofluid flows. The thermal profiles of the nanofluid and hybrid nanofluid are augmented with the increasing thermophoretic and Brownian motion factors.CuO-H2Onanofluid flow has enhanced heat transfer rate thanAg-H2Onanofluid flow. On the other hand, theCuO-Ag/H2Ohybrid nanofluid has better thermal flow rate thanCuO-H2OandAg-H2Onanofluids. From this table it has noticed that, Nusselt number has increased by 4% for silver nanoparticles whereas for hybrid nanofluid this incrimination is about 15%, which depicts that Nusselt number is higher for hybrid nanoparticles.

Estimation of Parameters on Probability Density Function Using Enhanced GLUE Approach.

The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelligence and machine learning. The study examines challenges related to estimating density functions from random variables. Based on minimal predictions regarding densities, the study discusses a framework for evaluating probability density functions. During the Bayesian approach, which is to generate correct samplings that reflect the probability aspect of the variables, sampling is widely used to estimate as well as define the probabilistic model of unknown variables. Because of its effectiveness and extensive application, the generalized likelihood uncertainty estimation (GLUE) method has earned the most popularity among the various methodologies. The Bayesian technique allows parameters of the model to be estimated using prior expertise in the parameter results and experimental observations. The study uses a number of engineering issues that were lately looked into to illustrate the effectiveness of the upgraded GLUE. As the focus is on the examination of sampling effectiveness in view of engineering components, only a brief summary is provided to describe every challenge. The suggested GLUE method's outcomes are contrasted with those obtained using MCMC. Nevertheless, using the GLUE approach, the model's mean squared error of prediction is substantially higher than that of the previous algorithms. The methods' results are affected by the assumptions being made on parameter values in advance. The concepts of prediction accuracy, as well as the utility of geometric testing, are presented. Such notions are valuable in demonstrating that the GLUE approach defines an inconsistent and incoherent statistical inference process.

Estimation of Parameters and Pooling in Nonlinear Flooding Event Scenarios with Bayesian Model.

Water-related tragedies are the highest common of all proven natural calamities and pose severe attacks on people and on socioeconomic status development. Due to the obvious controversy surrounding, their volume, location and time of incidence, geological swaths, and geophysical interrelations, flood events are difficult to completely control. Hence, complete flood prevention is always considered to be a viable choice. The specialized flood occurrences are investigated by developing a structural measure. In this paper, nonlinear flood event circumstance is determined by using a statistical Bayesian parametric approach for parameter estimation. A popular tool for estimating a flood design is model of nonlinear flood event. Nonlinear flood event models are subjected to a Bayesian technique for estimating parameter. The approach is based on the minimization function of square for models with nonlinear calculated peak discharges in terms of parameters. The observed and calculated peak discharges for numerous storms in the watershed, data on the pattern of error observed, and previous information on values of parameter all influence this objective function. The subsequent matrix for covariance is a measure of the calculated parameters' accuracy. Rainfall and runoff data from a Harvey River sample are used in this study to show the process.

Numerical simulation of 3D Darcy-Forchheimer fluid flow with the energy and mass transfer over an irregular permeable surface.

The Jeffrey fluid model is capable of accurately characterizing the stress relaxation behavior of non-Newtonian fluids, which a normal viscous fluid model is unable to perform. The primary objective of this paper is to provide a comprehensive investigation into the effects of MHD and thermal radiation on the 3D Jeffery fluid flow over a permeable irregular stretching surface. The consequences of the Darcy effect, variable thickness and chemical reaction are also considered. The phenomena have been modeled as a nonlinear system of PDEs. Using similarity substitution, the modeled equations are reduced to a dimensionless system of ODEs. The parametric continuation method (PCM) is used to determine the numerical solution to the obtained sets of nonlinear differential equations. The impact of physical parameters on temperature, velocity and mass profiles are presented through Figures and Tables. It has been noticed that the energy profile magnifies with the increment of porosity term, thermal radiation and heat source term, while diminishing with the flourishing upshot of power index and Deborah number. Furthermore, the porosity term and wall thickness parameter enhance the skin friction.