A T , TMOSPHERIC URBULENCE M M ETEOROLOGICAL ODELING AND AERODYNAMICS No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services. A T , TMOSPHERIC URBULENCE M M ETEOROLOGICAL ODELING AND AERODYNAMICS PETER R. LANG AND FRANK S. LOMBARGO EDITORS Nova Science Publishers, Inc. New York Copyright © 2010 by Nova Science Publishers, Inc. All rights reserved. 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LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Atmospheric turbulence, meteorological modeling, and aerodynamics / [edited by] Peter R. Lang and Frank S. Lombargo. p. cm. Includes index. ISBN 978-1-61728-264-5 (E-Book) 1. Atmospheric turbulence--Mathematical models. 2. Reynolds analogy. 3. Aerodynamics. I. Lang, Peter R. II. Lombargo, Frank S. QC880.4.T8A86 2009 551.5501'5118--dc22 2009016571 Published by Nova Science Publishers, Inc. (cid:212) New York CONTENTS Preface vii Research and Review Studies 1 Chapter 1 Climatology of the Arctic Planetary Boundary Layer 3 Igor Esau and Svetlana Sorokina Chapter 2 Nonequilibrium Thermodynamic Theory of Atmospheric Turbulence 59 Yinqiao Hu and Jinbei Chen Chapter 3 Generalized Scale Invariance of Edge Plasma Turbulence 111 V.P. Budaev Chapter 4 Turbulence, Turbulent Mixing and Diffusion in Shallow-Water Estuaries 167 Hubert Chanson and Mark Trevethan Chapter 5 Turbulent Scalar Transfer Modeling in Reacting Flows 205 Lei-Yong Jiang and Ian Campbell Chapter 6 The Research of the Solution Quality for the k-(cid:31) Turbulence Method with Using Sensitivity Analysis of Flow Properties to Model Coefficients 239 Ewa Błazik-Borowa Chapter 7 Passive Air Sampler for the Determination of Atmospheric Nitrogen Dioxide Using Flat Porous Polyethylene Membrane as Turbulence Limiting Diffuser 277 Yoshika Sekine, Michio Butsugan and Simon F. Watts Chapter 8 Artificial Intelligence Technique for Modelling and Forecasting of Meteorological Data: A Survey 293 Adel Mellit Chapter 9 New Trends on Phenological Modelling 329 Herminia García Mozo vi Contents Chapter 10 Time Dependent Shape Optimization Using Adjoint Variable Method for Reducing Drag 343 Kazunori Shinohara and Hiroshi Okuda Chapter 11 Numerical and Experimental Investigations of Fluid Dynamics of High Speed Flows 429 R. C. Mehta Chapter 12 Aerodynamic Research and Development of Vertical-Axis Wind Turbines with Rotary Blades 469 Victor V. Cheboxarov and Valery V. Cheboxarov Chapter 13 The Determination of Aerodynamic Forces on Sails – Challenges and Status 487 William C. Lasher Chapter 14 Large-Eddy Simulation of Free Shear and Wall-Bounded Turbulent Flows 505 Konstantin N. Volkov Chapter 15 Grand Computational Challenges for Prediction of the Turbulent Wind Flow and Contaminant Transport and Dispersion in the Complex Urban Environment 575 Fue-Sang Lien, Eugene Yee, Bing-Chen Wang and Hua~Ji Chapter 16 A Semi-Analytic Model of Fog Effects on Vision 635 Eric Dumont, Nicholas Hautiere and Romain Gallen Short Commentaries: 671 Short Commentary A On Parameterizing Inclined Stable Boundary Layers 673 Branko Grisogono Short Commentary B Ambient Air Temperature Interpolation in Inhomogeneous Regions 679 Klemen Zakšek and Daniel Joly Short Commentary C Peat Moisture in Relation to Meteorological Factors: Monitoring, Modelling, and Implications for the Application of the Canadian Forest Fire Weather Index System 691 Vladimir Krivtsov, G. Matt Davies, Colin Legg, Teresa Valor and Alan Gray Index 705 PREFACE Turbulence is a type of fluid (gas or liquid) flow in which the fluid undergoes irregular fluctuations, or mixing, in contrast to laminar flow, in which the fluid moves in smooth paths or layers. In turbulent flow the speed of the fluid at a point is continuously undergoing changes in both magnitude and direction. The flow of wind and rivers is generally turbulent in this sense, even if the currents are gentle. The air or water swirls and eddies while its overall bulk moves along a specific direction. This book will give the reader new insights into this natural phenomenon that occurs everyday yet is a puzzle that is not yet fully resolved in classical physics. Among the applications included are: fluid dynamics, aerodynamics, atmospheric and climatology research. Chapter 1 - One of the central problems of the state-of-the-art climatology is to understand the relationship between the observed near-surface processes, first of all the processes dependent on such meteorological quantities as temperature, humidity and wind, and the general atmospheric circulation and its long-term variability. Complications arise from the fact that the near-surface processes are affected by the vertical turbulent exchange. This study provides climatological estimates of the turbulent exchange and quantifies aspects of the turbulent exchange important for the Arctic climate. By definition, the planetary boundary layer (PBL) is the lowermost atmospheric layer where the vertical turbulent exchange (mixing) is non-negligible in description of meteorological processes. The study focuses on the unique Arctic PBL features rarely observed at lower latitudes, namely, turbulence development and mixing under conditions of strong atmospheric static stability and under strongly negative surface radiation balance. The proposed reconstruction of the Arctic PBL climatology is based on episodic turbulence field campaigns as well as on regular radiosounding, reanalysis and satellite data. The authors found that the main difficulty of the data analysis appears not from data scarcity as it has been traditionally claimed but from data inconsistency and low quality. The reconstruction reveals that the Arctic PBL is stably stratified in 70% to 100% of observations. But the continues periods of stably stratified PBL (SBL) development are typically less than 100 hours (4 days). The Arctic PBL is shallow. Its depth is typically less than 200 m and frequently less than 50 m. But over the Arctic Ocean, this shallow PBL is often well mixed due to the conductive heat transport through ice and the convection over polynyas. The Arctic PBL can often dynamically decouple from the rest of the atmosphere. During the periods of decoupling the temperature variability within the PBL is much larger (in wintertime) or smaller (in summertime) than in the air aloft. Stability of the PBL and developing of the temperature inversions maintain high relative humidity (often viii Peter R. Lang and Frank S. Lombargo above 90%) and the Arctic haze and fog. Models are sensitive to the Arctic PBL dynamics. However the model resolution is usually inadequate to the requirements of the employed turbulence parameterization schemes. Chapter 2 - Turbulence is one of the ubiquitous natural phenomena in everyday experience, and a puzzle that is not yet fully resolved in classical physics. A nonequilibrium thermodynamic theory of atmospheric turbulence is developed. The entropy equilibrium equation of atmospheric system with dynamic processes is advanced, and then Fourier’s and Flick’s laws, Newton’s Law, and both the Dufour and the Soret effects—the cross coupling effect between the dynamic and turbulent transport processes in the atmosphere—and the turbulent intensity theorem are uniformly deducted by atmospheric nonequilibrium thermodynamics. These laws and theorem are partially validated by using observed data and, further, their phenomenological coefficients are determined. The turbulent intensity theorem reveals that the macroscopic cause of the development of fluid turbulence is a result of the shearing effects of velocity together with temperature and proves that both Reynolds turbulence and Rayleigh-Bénard turbulence coexist in the atmosphere. The discovery of the coupling effect phenomenon between the thermodynamic and dynamic processes breaks through the viewpoint of the theories of traditional turbulent transport, Fourier’s and Flick’s laws, and Newton’s Law—i.e., the turbulent transport flux of one kind of macroscopic quantity is equivalent to the gradient transport flux of this macroscopic quantity. Moreover, the coupling principle between the thermodynamic and dynamic processes deems that the turbulent transport flux of one kind of macroscopic quantity should include the velocity coupling transport in addition to the gradient transport flux of this macroscopic quantity. Consequently, the vertical turbulent transport flux of energy and matter should count in the cross coupling effect of the vertical velocity, i.e., convergence and divergence motion, in addition to the turbulent transport flux caused by their vertical gradient. The earth’s surface is characterized by spatial heterogeneity over a wide range of scales. The heterogeneity of underlying surface brings on the advection to lead the convergence and divergence motion. It may be an important cause of the imbalance of energy budget of the ground surface. The cross coupling principle of the vertical velocity and vertical turbulent transport may offer a possible clue to the development of the theory of atmospheric boundary layer with heterogeneous underlying surface and to overcome the difficulties encountered in the imbalance problem of ground energy budget and in the parameterization of the boundary layer with the heterogeneous underlying surface. Chapter 3 - Experimental study of edge plasma turbulence in fusion devices such as tokamaks, stellarators and linear machines are presented. The fluctuations observed in the edge plasma of fusion devices are intermittent and self-similar. Non-Gaussian statistics, long- range correlation, superdiffusion and multifractality are observed in the edge plasma turbulence. The turbulence scalings display universal properties. Edge plasma turbulence exhibits a generalized (extended) self-similarity in an extended scale range. Such properties are predicted by the log-Poisson model of intermittent turbulence that considers a stochastic multiplicative cascade of energy and a strong effect of a dissipation range on an inertial range. Hidden statistical symmetries, hierarchy of moments and generalized scale invariance are behind the log-Poisson model. Experimental structure functions scalings have a nonlinear functional dependence on the order index. Such dependence is described by the log-Poisson model captured a topology of singular dissipative structures in case of strong anisotropy from a strong magnetic field. The experimental scalings are rather well fitted by the log-Poisson