Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns

A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric turbulence theoretical models and to evaluate them against the experimentally measured data

Modeling Lubricating System of Reducer of the Excavator

Представлены результаты моделирования системы смазки редуктора хода экскаватора. Использовалось сетевое гидравлическое моделирование системы и 3-мерные гидродинамические модели некоторых ее узлов. Решена проблема балансировки расходов масла.This paper presents modeling lubricating system of reducer of the excavator. Network modeling of fluid Dynamics and 3d Computational Fluid Dynamics was used. The problem of fluxes balancing was solved. This paper presents numerical analysis of oil main of planetary reducer. Numerical analysis was made to save equal oil flow rates through outlet ports. Net modeling was made to solve this problem. Oil flow was laminar. The model involves hydrodynamic resistances. Certain resistances were determined by empirical formulae. Other resistances were determine by 3d modeling

Modeling Lubricating System of Reducer of the Excavator

Представлены результаты моделирования системы смазки редуктора хода экскаватора. Использовалось сетевое гидравлическое моделирование системы и 3-мерные гидродинамические модели некоторых ее узлов. Решена проблема балансировки расходов масла.This paper presents modeling lubricating system of reducer of the excavator. Network modeling of fluid Dynamics and 3d Computational Fluid Dynamics was used. The problem of fluxes balancing was solved. This paper presents numerical analysis of oil main of planetary reducer. Numerical analysis was made to save equal oil flow rates through outlet ports. Net modeling was made to solve this problem. Oil flow was laminar. The model involves hydrodynamic resistances. Certain resistances were determined by empirical formulae. Other resistances were determine by 3d modeling