Heat and mass transfer analysis in unsteady flow of tangent hyperbolic nanofluid over a moving wedge with buoyancy and dissipation effects.

In this study, a convective heat and mass transfer phenomena in a time-dependent boundary layer flow of tangent hyperbolic nanofluid over a permeable stretching wedge has been examined with respect to some pertinent thermo-physical parameters. Convenient similarity transformation is used to reformulate the dimensional partial differential equations into dimensionless system of ordinary differential equations. The reduced set of equations is solved by the homotopy analysis method implemented in Mathematica environment. The effects of the relevant parameters on velocity, temperature and concentration profiles were examined in detail. The impacts of the parameters on the rates of momentum, heat and mass transfer are also analyzed quantitatively in terms of the wall friction coefficient, local Nusselt number and Sherwood number, respectively. Analysis of the results reveals that the increase in the buoyancy ratio parameter facilitates the flow velocity and the increase in the dissipation parameter maximizes the temperature distribution and nanoparticle concentration near the surface of the wedge. Moreover, the analytic approximations obtained by implementing the homotopy analysis method are found in excellent agreement with some previously published results.

Option pricing of weather derivatives based on a stochastic daily rainfall model with Analogue Year component.

In this study, we analyzed option pricing of rainfall derivatives based on stochastic daily rainfall model. We used Markov Chain Analogue Year model (MCAY) in order to describe occurrence process of daily rainfall. We have included the Analogue Year (AY) component in the Markov Chain (MC), which is a new component incorporated in this study and pricing rainfall derivatives. The inclusion of AY in the MC, provides excellent description of the occurrence process of daily rainfall. The amount of daily rainfall on wet days is obtained using Mixed Exponential distribution because it has the advantage of a better representation of extreme events. Combining the occurrence and amount model, we obtained Markov Chain Analogue Year Mixed Exponential model (MCAYMEM). Daily rainfall data from 2005 to 2017 were taken from Ethiopia National Meteorology Agency (ENMA) in order to assess the model performance. Based on the results of the daily rainfall models, we calculated an option price for different months. The price calculated using MCAYMEM gave an excellent result compared to the price calculated using MCMEM. This accuracy is mainly because of AY component included in the MC in the modeling of occurrence process.