Jin, Xiaoli = 關於脈衝驅動熱湍流的實驗研究 / 金晓莉. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 83-86). / Text in English; abstracts in English and Chinese. / Jin, Xiaoli = Guan yu mai chong qu dong re tuan liu de shi yan yan jiu / Jin Xiaoli. / Abstract --- p.ii / Acknowledge --- p.iv / Table of Contents --- p.vii / List of Figures --- p.xii / List of Tables --- p.xiii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Rayleigh-Benard convection --- p.1 / Chapter 1.2 --- Turbulence driven by time-dependent forcing --- p.5 / Chapter 1.3 --- Motivation --- p.7 / Chapter 1.4 --- Organization of the thesis --- p.8 / Chapter 2 --- Experimental Setup --- p.10 / Chapter 2.1 --- The Convection cell --- p.10 / Chapter 2.2 --- Heating and Cooling --- p.14 / Chapter 2.3 --- Temperature and voltage measurement --- p.15 / Chapter 2.3.1 --- Temperature probes --- p.16 / Chapter 2.3.2 --- Data acquisition: digital multimeter --- p.17 / Chapter 2.3.3 --- Data acquisition: AC Wheatstone bridge and Lock-in amplifier --- p.18 / Chapter 3 --- Steadily driven thermal turbulence: constant heating --- p.21 / Chapter 3.1 --- Local temperature fluctuations --- p.21 / Chapter 3.2 --- Signatures of plume emissions inside conducting plates --- p.26 / Chapter 3.3 --- Nusselt number --- p.33 / Chapter 3.4 --- Correlation functions and Power spectrums --- p.35 / Chapter 4 --- Kicked turbulence: periodically pulsed heating --- p.38 / Chapter 4.1 --- Periodically pulsed heating power --- p.38 / Chapter 4.2 --- In-plate temperature signals --- p.39 / Chapter 4.3 --- Rayleigh number controlling --- p.42 / Chapter 4.3.1 --- Experimental results --- p.42 / Chapter 4.3.2 --- Theoretical explanation: mean-field theory --- p.47 / Chapter 4.4 --- In-plate temperature fluctuation --- p.55 / Chapter 4.5 --- Correlation functions and power spectra --- p.58 / Chapter 4.6 --- Nusselt number enhancement --- p.62 / Chapter 4.6.1 --- Motivation --- p.62 / Chapter 4.6.2 --- Experiment --- p.64 / Chapter 4.6.3 --- Results --- p.66 / Chapter 4.6.4 --- Discussion --- p.70 / Chapter 5 --- Modulated turbulence: sinusoidal heating --- p.75 / Chapter 5.1 --- Motivation --- p.75 / Chapter 5.2 --- Nusselt number measurement --- p.76 / Chapter 6 --- Conclusion --- p.80 / Chapter 6.1 --- Periodically kicked turbulence --- p.80 / Chapter 6.2 --- Sinusoidally modulated turbulence --- p.81 / Chapter 6.3 --- Future works --- p.82 / Bibliography --- p.82

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Numerical simulation of the January and July global climate with the OSU two-level atmospheric general circulation model has generated time series of the global distribution of selected climatic variables. Analyses of these data for the kinetic energy and available potential energy based on 31-day time mean statistics have been made in the form of the zonal mean and the transient and stationary eddies. The generation and dissipation rates of the various forms of energy in this model are also computed, and the energy cycle for January and July is presented in comparison with the corresponding results from observation and from other models. / Graduation date: 1980

Graduation date: 1995

Dans le but d'étudier certains aspects dynamiques de la convection profonde atmosphérique, nous avons développé un modèle bidimensionnel axé sur le développement d'une cellule orageuse simple. Ce modèle considère des éléments de volume, où nous faisons l'hypothèse que les différents champs thermodynamiques sont homogènes. Ces volumes sont fixes dans l'espace et le temps et sont traversés par les flots d'air humide de sorte que leurs contenus varient au cours du temps. Durant ces évolutions l'eau subit des changements de phases. Ces phénomènes, simultanés dans la nature, sont représentés par un mécanisme à étapes successives dans le modèle. Une première étape se déroule en système ouvert: l'air circule pendant un pas de temps d'intégration entre les éléments de volume; l'air conserve ses propriétés pendant le déplacement. Les deux étapes suivantes se produisent dans chaque élément de volume considéré alors en système fermé et isolé: une première étape d'homogénéisation de l'air en pression et ensuite en température et enfin une étape de restauration de l'équilibre des phases de l'eau compte tenu de la nouvelle répartition des constituants et de leur état thermodynamique. Cette discrétisation du mécanisme d'évolution du contenu des éléments de volume nous permet d'utiliser les lois de la thermodynamique classique dans des systèmes ouverts. Ce mécanisme mène à une équation thermodynamique originale. Les autres équations du modèle sont les équations de l'hydrodynamique classique, les équations de la quantité de mouvement et de continuité. Pour l'intégration des équations, nous avons utilisé une méthode de filtrage numérique basée sur les transformations de Laplace, due à P. Lynch (1984) et adaptée à l'intégration par J. Van Isacker (1985). Au niveau du calcul, les champs de masse, de pression et des quantités de mouvement sont adaptés aux échanges de matières entre éléments de volume voisins à l'aide du processus d'intégration. Les équilibrages de phases interviennent comme ajustement du résultat de l'intégration. Ils modifient le défaut de balance hydrostatique qui sera minimisé au cours du pas d'intégration suivant grâce au filtrage de la méthode numérique. Les simulations réalisées à l'aide du modèle restituent de manière raisonnable les caractéristiques essentielles de la convection profonde atmosphérique. Nous avons utilisé le modèle pour étudier le développement d'un orage de masse d'air de manière plus approfondie. Ainsi, le développement initial, la croissance de la cellule convective, la formation de vortex ont été corrélés avec la structure de la flottabilité dans l'étude des mécanismes mis en oeuvre. Nous avons examiné les déplacements horizontaux et les accélérations verticales en termes de mélanges de masses d'air et des changements de phases qu'ils induisent. Dans l'étude de l'évolution des différentes formes d'énergie, cinétique, potentielle et interne et de leurs conversions, nous avons recherché les contributions dominantes à leurs variations et montré les rôles prépondérants joués par les processus de changement de phase et d'homogénéisation locale de la pression dans la variation de l'énergie interne. Dans l'examen de l'effet dynamique de la convection profonde sur le courant moyen, nous avons montré que, dans certains cas, nous avons non seulement transfert vertical d'énergie cinétique mais également création d'énergie cinétique du courant moyen. Le cumulonimbus peut dans certains cas agir comme moteur pour les mouvements atmosphériques à plus grande échelle.

The Mixing Height (MH), Convective Condensation Level (CCL), and Convective Available Potential Energy (CAPE) are computed with different methods and we examined whether these parameters can help to discriminate between weak and strong convection. The observational data set contains soundings released from Stony Plain in Alberta and The Pas in Manitoba for the summers of 2006 and 2007. The major findings were: 1) The Mixing Height values computed with the Heffter method were reliable provided the critical inversion criterion was adjusted for Prairie conditions. 2) The Mixing Height values computed with the Moist Mixed layer method were in good agreement with Mixing Heights computed with the Heffter method. 3) The Mixing Height values computed with the Holzworth parcel method were less useful in that often the potential temperature did not decrease with height above the ground. 4) Observed convective cloud base heights tended to be lower than the CCL computed using the surface parcel method, the 50 mb mixed parcel method, and the moist mixed parcel method. 5) The MH, the sounding-based CCL, and the CAPE did not differentiate between weak and strong convection. 6) We derived a new parameter: the difference between the convective cloud base and the Moist Air Mixing Height. This parameter did discriminate between the likely occurrence of strong and weak convection.

Graduation date: 1990

Intervals of Steady Magnetospheric Convection (SMC) are loosely defined as times when convection in the magnetosphere as a whole is enhanced and there are no substorm signatures. A lack of substorm signatures implies that the large scale structure of the magnetotail is maintained. There have been several quantitative methods developed to detect SMC events. None of these methods are based on observations of convection. The Super Dual Auroral Radar Network (SuperDARN) is a useful tool for studying SMC, because it gives a direct measurement of convection on a global scale. Previous SMC selection methods have made use of ground based magnetometer responses to auroral currents in the atmosphere. These methods resulted in a strong seasonal dependence in SMC occurrence due to seasonal changes in ionospheric conductivity. A new SMC selection criterion was developed to improve upon the previous criteria. This new method identifies all the events found using currently accepted methods plus additional intervals that reduce the seasonal dependence in SMC occurrence. SuperDARN was used to evaluate the old and new selection methods. According to SuperDARN convection observations, the new SMC selection criterion largely eliminated ionospheric conductivity effects. A conceptual model of the conductivity effects on the traditional SMC selection method was developed, and the occurrence of modelled SMC events agrees well with observations. Statistical studies have revealed that the additional SMC intervals have similar properties as events selected using traditional methods. Case studies confirmed the statistical results that SMCs selected by the new criterion have SMC properties. Both SMC events sets have a moderate solar wind driver, enhanced convection, and stable polar cap size. Statistical studies have also shown there was good SuperDARN data coverage during SMC, which is not typical of SuperDARN observations during enhanced and disturbed conditions in the magnetosphere. It is therefore shown to be an excellent tool with which to study SMC.

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

Intervals of Steady Magnetospheric Convection (SMC) are loosely defined as times when convection in the magnetosphere as a whole is enhanced and there are no substorm signatures. A lack of substorm signatures implies that the large scale structure of the magnetotail is maintained. There have been several quantitative methods developed to detect SMC events. None of these methods are based on observations of convection. The Super Dual Auroral Radar Network (SuperDARN) is a useful tool for studying SMC, because it gives a direct measurement of convection on a global scale. Previous SMC selection methods have made use of ground based magnetometer responses to auroral currents in the atmosphere. These methods resulted in a strong seasonal dependence in SMC occurrence due to seasonal changes in ionospheric conductivity. A new SMC selection criterion was developed to improve upon the previous criteria. This new method identifies all the events found using currently accepted methods plus additional intervals that reduce the seasonal dependence in SMC occurrence. SuperDARN was used to evaluate the old and new selection methods. According to SuperDARN convection observations, the new SMC selection criterion largely eliminated ionospheric conductivity effects. A conceptual model of the conductivity effects on the traditional SMC selection method was developed, and the occurrence of modelled SMC events agrees well with observations. Statistical studies have revealed that the additional SMC intervals have similar properties as events selected using traditional methods. Case studies confirmed the statistical results that SMCs selected by the new criterion have SMC properties. Both SMC events sets have a moderate solar wind driver, enhanced convection, and stable polar cap size. Statistical studies have also shown there was good SuperDARN data coverage during SMC, which is not typical of SuperDARN observations during enhanced and disturbed conditions in the magnetosphere. It is therefore shown to be an excellent tool with which to study SMC.