We develop a hybrid model for large-eddy simulation of particle-laden turbulent flow, which is a combination of the approximate deconvolution model for the resolved scales and a stochastic model for the sub-grid scales. The stochastic model is based on a priori results of direct numerical simulation of turbulent channel flow. In order to correctly predict the flux of particles towards the walls...

The paper provides a discussion of the most relevant aspects of yield curve modeling. Two classes of models are considered: stochastic and parsimonious function based, through the approaches developed by Vasicek (1977) and Nelson and Siegel (1987). Yield curve estimates for Croatia are presented and their dynamics analyzed and finally, a comparative analysis of models is conducted. Keywords—the...

We introduce and analyze stochastic optimization methods where the input to each gradient updateis perturbed by bounded noise. We show that this framework forms the basis of a unified approachto analyze asynchronous implementations of stochastic optimization algorithms. In this framework,asynchronous stochastic optimization algorithms can be thought of as serial methods operatin...

The daily weather series simulated with observed weather data and weather data generated with conventional WGEN model taking dry and wet day as stochastic parameter and DWS model were statistically compared and analyzed. These three weather data were respectively used to drive maize growth simulation model. The corresponding yield results were compared. The results showed there was no significa...

A novel multigrid algorithm for computing the principal eigenvector of column stochastic matrices is developed. The method is based on the Exact Interpolation Scheme multigrid approach of Brandt and Ron, whereby the prolongation is adapted to yield a better and better coarse representation of the sought eigenvector. A special feature of the present approach is the squaring of the stochastic mat...

We model a retailer whose supplier is subject to complete supply disruptions. We combine discrete-event uncertainty (disruptions) and continuous sources of uncertainty (stochastic demand or supply yield), which have different impacts on optimal inventory settings. This prevents optimal solutions from being found in closed form. We develop a closed-form approximate solution by focusing on a sing...

In this paper we develop a new model for the dynamics of forward curves of commodities exhibiting seasonalities, such as natural gas, electricity or agricultural commodities. In the existing literature on the subject, the first state variable in multi-factor models is the commodity price, which combines seasonal and stochastic features and may be unobservable. We propose to use instead the aver...

The formulations of the user-optimal dynamic traffic assignment (UODTA)-based network design problem (NDP), signal setting design problem (SSD), and the integrated NDP and SSD problem (NDP-SSD) for the deterministic (DET), stochastic (SP) and robust (RO) optimizations are reviewed. The modifications of the simulated annealing (SA), genetic algorithm (GA) and reactive tabu search (RTS) for the n...