Reynolds-stress root modeling based on a statistical theory

Lectures on Statistical Modeling Theory

A New Physically-Based Fully-Realizable Nonequilibrium Reynolds Stress Closure for Turbulence RANS Modeling

Based on the physics underlying turbulence anisotropy in the equilibrium and nonequilibrium limits, a new physically-based, fully-realizable, nonequilibrium k − 2 RANS model has been developed. The model is based on an effective strain rate tensor that accounts for the strain history to which the turbulence has been subjected. This new model is applied to four distinctly different test cases fo...

A Size-dependent Bernoulli-Euler Beam Formulation based on a New Model of Couple Stress Theory

In this paper, a size-dependent formulation for the Bernoulli-Euler beam is developed based on a new model of couple stress theory presented by Hadjesfandiari and Dargush. The constitutive equation obtained in this new model, consists of only one length scale parameter that is capable of capturing the micro-structural size effect in predicting the mechanical behavior of the structure. Having on...

translation of collocations from english into persian, based on ghazalas theory

غزالا همایندها را به صورت ترکیبی از دو یا چند واژه که همواره در متون مختلف زبان ها همراه با هم می آیند تعریف می نماید. از دیدگاه او روند رو به رشد میل به ترجمه ی همایندها در مطالعات ترجمه، به دلیل اهمیت آنها در انسجام ساختار زبان است. این پایان نامه اساسا به ترجمه ی همایندها منحصر شده است. هدف آن بررسی کاربرد راهکارهای غزالا در مورد ترجمه ی همایندها از انگلیسی به فارسی است. هدف دیگر آن یافتن پر...

Statistical Background Modeling Based on Velocity and Orientation of Moving Objects

Background modeling is an important step in moving object detection and tracking. In this paper, we propose a new statistical approach in which, a sequence of frames are selected according to velocity and direction of some moving objects and then an initial background is modeled, based on the detection of gray pixel's value changes. To have used this sequence of frames, no estimator or distribu...