The Logarithmic Wind Profile in Neutral Stability Conditions

2021 ◽  
pp. 355-367
Author(s):  
Robert V. Rohli ◽  
Chunyan Li
Keyword(s):  
Wind Profile ◽  
Stability Conditions ◽  
Neutral Stability ◽  
Logarithmic Wind Profile

A two-lane lattice model considering taillight effect and man–machine hybrid driving

2020 ◽  
Vol 34 (32) ◽  
pp. 2050365
Author(s):  
Siyuan Chen ◽  
Changxi Ma ◽  
Jinchou Gong
Keyword(s):  
Traffic Flow ◽  
Lattice Model ◽  
Critical Density ◽  
Mkdv Equation ◽  
Flow Stability ◽  
Stability Conditions ◽  
Neutral Stability ◽  
Nonlinear Stability Analysis ◽  
Traffic Flow Stability ◽  
The Stability

At present, drivers can rely on road communication technology to obtain the current traffic status information, and the development of intelligent transportation makes self-driving possible. In this paper, considering the mixed traffic flow with self-driving vehicles and the taillight effect, a new macro-two-lane lattice model is established. Combined with the concept of critical density, the judgment conditions for vehicles to take braking measures are given. Based on the linear analysis, the stability conditions of the new model are obtained, and the mKdV equation describing the evolution mechanism of density waves is derived through the nonlinear stability analysis. Finally, with the help of numerical simulation, the phase diagram and kink–anti-kink waveform of neutral stability conditions are obtained, and the effects of different parameters of the model on traffic flow stability are analyzed. The results show that the braking probability, the proportion of self-driving vehicles and the critical density have significant effects on the traffic flow stability. Considering taillight effect and increasing the mixing ratio of self-driving vehicles can effectively enhance the stability of traffic flow, but a larger critical density will destroy the stability of traffic flow.

Estimation of aerodynamic parameters under non-neutral stability conditions from Alpilles measurement data

Agronomie ◽  
2002 ◽  
Vol 22 (6) ◽  
pp. 619-625
Author(s):  
Wenguang G. Zhao ◽  
Albert Olioso ◽  
Jean-Pierre Lagouarde ◽  
Jean-Marc Bonnefond ◽  
Mark Irvine ◽  
...  
Keyword(s):  
Measurement Data ◽  
Stability Conditions ◽  
Neutral Stability ◽  
Aerodynamic Parameters

Temporal and Spatial Growth of Wind Waves

2007 ◽  
Vol 37 (1) ◽  
pp. 106-114 ◽  
Author(s):  
M. Stiassnie ◽  
Y. Agnon ◽  
P. A. E. M. Janssen
Keyword(s):  
Growth Rates ◽  
Water Waves ◽  
Wind Profile ◽  
Wind Waves ◽  
Shear Velocity ◽  
Spatial Growth ◽  
Wave Celerity ◽  
Temporal And Spatial ◽  
Distinct Maximum ◽  
Logarithmic Wind Profile

Abstract A solution of Rayleigh’s instability equation, which circumvents the apparent critical-layer singularity, is provided. The temporal and spatial growth rates of water waves exposed to a logarithmic wind profile are calculated and discussed. The findings are similar to previously published results, except for shear velocity–to–wave celerity ratios larger than 2, where the newly calculated growth rates start to decrease after having reached a distinct maximum. The ratio of the spatial to temporal growth rates is examined. It is shown to deviate by up to 20% from the leading-order value of 2. The implications of the growth rate to the modal distributions of energy input from wind to waves, for young and mature seas, and in temporal/spatial growth scenarios, are analyzed.

scholarly journals Crosswind integrated concentration for various dispersion parameter systems

MAUSAM ◽  
2021 ◽  
Vol 64 (4) ◽  
pp. 645-654
Author(s):  
KHALED SMESSA ◽  
SOAD METMAN
Keyword(s):  
Wind Profile ◽  
Dispersion Parameter ◽  
Diffusion Experiment ◽  
Gaussian Plume Model ◽  
Dispersion Parameters ◽  
Plume Model ◽  
Statistical Measures ◽  
Gaussian Plume ◽  
Release Height ◽  
Logarithmic Wind Profile

LFkkuh; Lrj izdh.kZu ds fy, xkSlh;u fiPNd ekWMy ¼Gaussian Plume Model½ dk O;kid :i ls iz;ksx fd;k tkrk gSA vuqizLFk iou dh dqy lkanzrk Kkr djus ds fy, xkSlh;u lw= ¼QkWewyk½ dks laxfBr fd;k gSA vuqizLFk iou dh dqy lkanzrk dh x.kuk djus ds fy, izdh.kZu izkpyksa dh fHkUu&fHkUu iz.kkfy;ksa dk mi;ksx fd;k x;k gSA lrg Lrj esa Å¡pkbZ ds vuqlkj iou xfr dh fHkUurk dk o.kZu djus ds fy, ykxfjFehd foaM izksQkby dk mi;ksx fd;k x;k gSA blesa NksM+h tkus okyh izHkkoh Å¡pkbZ dks /;ku  esa j[kk x;k gSA fHkUu fHkUu izdh.kZu izkpy iz.kkfy;ksa ds fy, iwokZuqekfur lkanzrkvksa vkSj dksisugsxu ds folj.k iz;ksx ls izkIr fd, x, izsf{kr vk¡dM+ksa dh rqyuk djus ds fy, lkaf[;dh; ifjekiksa dk mi;ksx fd;k x;k gSA  The Gaussian plume model is the most widely used model for local scale dispersion. The   Gaussian formula has been integrated to obtain the crosswind-integrated concentration. Different systems of dispersion parameters are used to calculate the crosswind integrated concentration. A logarithmic wind profile is used to describe the variation of wind speed with height in the surface layer. The effective release height was taken into consideration. Statistical measures are utilized in the comparison between the predicted concentrations for different dispersion parameter systems and the observed concentrations data obtained from Copenhagen diffusion experiment.

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