Local Convection and Turbulence in the Amazonia Using Large Eddy Simulation Model

Using a high resolution model of Large Eddies Simulation (LES), named PALM from PArallel LES Model, a set of simulations were performed to understand how turbulence and convection behave in a pasture and forest sites in Amazonia during the dry and rainy seasons. Related to seasonality, dry period presented higher differences of values (40 W m−2) and patterns over the sites, while in the wet period have more similar characteristics (difference of −10 W m−2). The pasture site had more convection than the forest, with effective mixing and a deeper boundary layer (2600 m). The vertical decrease of sensible heat flux with altitude fed convection and also influenced the convective boundary layer (CBL) height. Regarding the components of turbulent kinetic energy equation, the thermal production was the most important component and the dissipation rate responded with higher growth, especially in cases of greatest mechanical production at the forest surface reaching values up to −20.0.

Scaling and modelling of turbulence in variable property channel flows

We derive an alternative formulation of the turbulent kinetic energy equation for flows with strong near-wall density and viscosity gradients. The derivation is based on a scaling transformation of the Navier–Stokes equations using semi-local quantities. A budget analysis of the semi-locally scaled turbulent kinetic energy equation shows that, for several variable property low-Mach-number channel flows, the ‘leading-order effect’ of variable density and viscosity on turbulence in wall bounded flows can effectively be characterized by the semi-local Reynolds number. Moreover, if a turbulence model is solved in its semi-locally scaled form, we show that an excellent agreement with direct numerical simulations is obtained for both low- and high-Mach-number flows, where conventional modelling approaches fail.

Optimal Sensor Placement Based on Simulation of Gas Distribution in Underground Heading Face

The gas sensor is significant for coalmine production safety. In order to carry out optimal gas sensor placement in heading face, the software named Fluent was used to simulate underground gas distribution. Geometry model was established and divided by grids. Gas distribution in heading face was simulated using RNG k-ε model by Fluent according to conversation equation in turbulent state, turbulent kinetic energy equation and turbulent dissipation rate equation. The results show that gas is likely to accumulate in the upper corner, when wind passes through the corner after washing heading face, wind velocity is unstable, the performance of sensor placed in inner side of turning is different from that placed in outer side of turning. Distance of gas sensor to heading face should be no more than 10m which accords with the mine safety regulations well. The result shows that gas can be monitored effectively by applying this method which has an important value and instructive significance.

Sensor Deployment Optimization Based on Simulation of Gas Distribution in Underground Fully Mechanized Coal Face

The gas sensor is very important for coalmine production safety. The Fluent software was used to simulate underground gas distribution. Geometry model was established and divided by grids. Gas distribution in fully mechanized coal face was simulated using RNG k-ε model according to conversation equation in turbulent state, turbulent kinetic energy equation and turbulent dissipation rate equation. The results show that gas is likely to accumulate in the upper corner, when wind passes through the corner after washing fully mechanized coal face, wind velocity is unstable, performance of sensor placed in inner side of turning is different from that placed in outer side of turning. The result shows that gas can be monitored effectively with this method which has an important value and instructive significance.

Scaling and parameterization of stratified homogeneous turbulent shear flow

Homogeneous sheared stratified turbulence was simulated using a DNS code. The initial turbulent Reynolds numbers (Re) were 22, 44, and 89, and the initial dimensionless shear rate (S*) varied from 2 to 16. We found (similarly to Rogers (1986) for unstratified flows) the final value of S* at high Re to be ∼ 11, independent of initial S*. The final S* varies at low Re, in agreement with Jacobitz et al. (1997). At low Re, the stationary Richardson number (Ris) depends on both Re and S*, but at higher Re, it varies only with Re. A scaling based on the turbulent kinetic energy equation which suggests this result employs instantaneous rather than initial values of flow parameters.At high Re the dissipation increases with applied shear, allowing a constant final S*. The increased dissipation occurs primarily at high wavenumbers due to the stretching of eddies by stronger shear. For the high-Re stationary flows, the turbulent Froude number (Frt) is a constant independent of S*. An Frt-based scaling predicts the final value of S* well over a range of Re. Therefore Frt is a more appropriate parameter for describing the state of developed stratified turbulence than the gradient Richardson number.