Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Code and data for "Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1"

Wind energy, together with other renewable energy sources, are expected to grow substantially in the coming decades and play a key role in mitigating climate change and achieving energy sustainability. One of the main challenges in optimizing the design, operation, control, and grid integration of wind farms is the prediction of their performance, owing to the complex multiscale two-way interactions between wind farms and the turbulent atmospheric boundary layer (ABL). From a fluid mechanical perspective, these interactions are complicated by the high Reynolds number of the ABL flow, its inherent unsteadiness due to the diurnal cycle and synoptic-forcing variability, the ubiquitous nature of thermal effects, and the heterogeneity of the terrain. Particularly important is the effect of ABL turbulence on wind-turbine wake flows and their superposition, as they are responsible for considerable turbine power losses and fatigue loads in wind farms. These flow interactions affect, in turn, the structure of the ABL and the turbulent fluxes of momentum and scalars. This review summarizes recent experimental, computational, and theoretical research efforts that have contributed to improving our understanding and ability to predict the interactions of ABL flow with wind turbines and wind farms.

https://link.springer.com/content/pdf/10.1007/s10546-019-00473-0.pdf

Wind-Turbine and Wind-Farm Flows: A Review

Gravity currents: In the environment and the laboratory

Direct numerical simulation of turbulent incompressible plane-channel flow between a smooth wall and one covered with regular three-dimensional roughness elements is performed. While the impact of roughness on the mean-velocity profile of turbulent wall layers is well understood, at least qualitatively, the manner in which other features are affected, especially in the outer layer, has been more controversial. We compare results from the smooth- and rough-wall sides of the channel for three different roughness heights of h + = 5.4, 10.8, and 21.6 for Reτ of 400, to isolate the effects of the roughness on turbulent statistics and the instantaneous turbulence structure at large and small scales. We focus on the interaction between the near-wall and outer-layer regions, in particular the extent to which the near-wall behavior influences the flow further away from the surface. Roughness tends to increase the intensity of the velocity and vorticity fluctuations in the inner layer. In the outer layer, although the roughness alters the velocity fluctuations, the vorticity fluctuations are relatively unaffected. The higher-order moments and the energy budgets demonstrate significant differences between the smooth-wall and rough-wall sides in the processes associated with the wall-normal fluxes of the Reynolds shear stresses and turbulence kinetic energy. The length scales and flow dynamics in the roughness sublayer, the spatially inhomogeneous layer within which the flow is directly influenced by the individual roughness elements, are also examined. Alternative mechanisms involved in producing and maintaining near-wall turbulence in rough-wall boundary layers are also considered. We find that the strength of the inner/outer-layer interactions are greatly affected by the size of the roughness elements.

Effect of Roughness on Wall-Bounded Turbulence

Interactions between the nocturnal atmospheric boundary layer (ABL) and wind turbines (WTs) can be complicated due to the presence of low level jets (LLJ), a region which creates wind speeds higher than geostrophic wind speed. A study has been performed to isolate the effect of mean forcings of the ABL on turbulence energetics and structures in the wake of WT. Large eddy simulation with an actuator line model has been used as a tool to simulate a full-scale 5-MW WT under two different realistic atmospheric states of the stable ABL corresponding to low- and high-stratification. The study clearly demonstrates that the large-scale forcings of thermally stratified atmospheric boundary characterized by shear- and buoyancy-driven turbulence significantly influence the wake structure of a wind turbine. For the WT in low-stratified ABL, the jets occur above the WT resulting in a strong mixed layer behind the WT. High turbulence results in a faster wake recovery. For the WT in high-stratified ABL, the jets occur near the hub-height resulting in an asymmetric wake structure. The jets confine the mixing to hub-height resulting in a slower wake recovery. Vertical shear causes the interaction of the root- and lower-tip vortices resulting in the instability of the root vortex leading to an enhanced shear stress and turbulent kinetic energy. The tip vortices exhibit mutual inductance between adjacent vortex filaments resulting in vortex merging. LLJs are an important metric associated with mean atmospheric forcings that dictate the turbulence generated in WT wake and the wake recovery of a WT in a stable ABL.

The effects of mean atmospheric forcings of the stable atmospheric boundary layer on wind turbine wake

Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction

Snapshot proper orthogonal decomposition (POD) is used to investigate a rough-wall turbulent boundary layer in a channel. One- and two-dimensional POD decompositions have been performed using 3D velocity database from direct numerical simulations (DNS). DNS of a turbulent channel flow with rough walls consisting of 3D roughness elements have been performed at Reτ = 180. 1D analysis has revealed that convergence of the POD for a rough wall is slower compared to the smooth wall, which is attributed to the increase in range of length scales due to roughness. For the egg-carton roughness elements, the depth of a roughness sublayer (ζmode n ) for POD mode n decays with increasing mode number in an exponential manner as ζmode n = 14e −0.86 n h for roughness of height h. The reconstruction of turbulence intensities and shear stress has revealed that the inner layer which includes the roughness sublayer is well captured by first 10 POD modes. 2D POD analysis has revealed that roughness alters the size and spacing...

Kiran Bhaganagar

Papers

Effect of Roughness on Wall-Bounded Turbulence

The effects of mean atmospheric forcings of the stable atmospheric boundary layer on wind turbine wake

Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction

Application of proper orthogonal decomposition (POD) to investigate a turbulent boundary layer in a channel with rough walls

A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables