In this article, a time dependent partial differential equation is used to model the nonlinear boundary value problem describing heat transfer through a radial porous moving fin with rectangular profile. The study is performed by applying a numerical solver in MATLAB (pdepe), which is a centered finite difference scheme. The thermal conductivity and fin surface emissivity are linearly ...

in this paper, wavelet collocation method has been used to solve nonlinear fin problem with temperature dependent thermal conductivity and heat transfer coefficient. thermal conductivity of fin materials varies any type so that we consider thermal conductivity as the general function of temperature.  here we consider three particular cases, where we assume that thermal conductivity is constant,...

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...

The nonlinear dynamical system modeling the immobilized enzyme kinetics with Michaelis-Menten mechanism for an irreversible reaction without external mass transfer resistance is considered. Laplace transform homotopy perturbation method is used to obtain the approximate solution of the governing nonlinear differential equation, which consists in determining the series solution convergent to the...

Chaos in a fractional-order jerk model using hyperbolic tangent nonlinearity is presented. A fractional integrator in the model is approximated by linear transfer function approximation in the frequency domain. Resulting chaotic attractors are demonstrated with the system orders as low as 2.1.

Model transfer refers to the process of transferring information from a model that was previously identified for one task (source) to a new task (target). For decision tasks in unknown Markov environments, we profit through model transfer by using information from related tasks, e.g. transition knowledge and solution (policy) knowledge, to quickly determine an appropriate model of the new task ...