China
Africa
Explore chapters and articles related to this topic
Afterword
Published in Robert G. Deissler, Turbulent Fluid Motion, 2020
Advances in solving the turbulence problem continue to be made along theoretical, computational, and experimental lines. Considerable progress in understanding turbulence mechanisms, particularly spectral energy transfer and the interaction between energy transfer and dissipation, has been made by using theoretical and computational methods. Much of the recent activity in turbulence research is related to advances in high-speed computation. For example, deductive–computational solutions of the unaveraged Navier-Stokes equations are now available, at least for turbulent flows at low and moderate Reynolds numbers. A Reynolds number higher than those that can be handled by available computers and computational schemes, of course, can always be picked. However, at least from a research standpoint, high–Reynolds-number turbulence does not differ qualitatively from that at lower Reynolds numbers; the turbulent energy is just spread out over a wider range of wavenumbers, there being no bifurcations in going from low to high Reynolds numbers except possibly in the transition region between laminar and turbulent flow. The mathematical and computational methods appropriate for low and for high Reynolds numbers of course may differ.
Wave Turbulence in Vibrating Plates
Published in Christos H. Skiadas, Charilaos Skiadas, Handbook of Applications of Chaos Theory, 2017
Cadot Olivier, Ducceschi Michele, Humbert Thomas, Miquel Benjamin, Mordant Nicolas, Josserand Christophe, Touzé Cyril
Turbulence is a general term used for describing the erratic motions displayed by nonlinear systems that are driven far from their equilibrium position and thus display complicated motions involving different time and length scales. Without other precision, the term generally refers to hydrodynamic turbulence, as the main field of research has been directed toward irregular motions of fluids and the solutions of Navier–Stokes equations. During the twentieth century, theoretical developments showed important breakthroughs thanks to the qualitative ideas of Richardson and the quantitative arguments of Kolmogorov that culminated in the so-called K41 theory [25,30,31]. This statistical approach, although giving successful predictions, still faces an irreducible obstacle due to the lack of closure in the infinite hierarchy of moment equations.
Atmospheric Turbulence in the Anisotropic Boundary Layer
Published in N. Blaunstein, N. Kopeika, Optical Waves and Laser Beams in the Irregular Atmosphere, 2017
Coherent structures have been actively studied during the recent decades (see, e.g., References 43–79). Near-wall small-scale turbulence, turbulent convection in the near-surface atmospheric layer in the presence of wind shear, “cloud streets” in the atmosphere, and “Langmuir circulation” in seas and lakes are objects of intense studies. In addition, periodic large vortices in jet engine wakes have been investigated. It was shown that the main energy carriers in turbulent flows are large-scale ordered vortices, which influence significantly the formation of all flow characteristics. It was found that large-scale turbulent motions are deterministic, that is, are not random. The studies apply different methods of turbulence visualization (usually, coloring of flow). However, the resolution of the used visualization methods is low. That is why, only large-scale components of turbulent flows can be clearly seen, as a rule. Small-scale inhomogeneities usually remain invisible.
Bejan’s numerical heat and mass flow visualization in turbulent boundary layer regime
Published in Waves in Random and Complex Media, 2023
S. P. Suresha, G. Janardhana Reddy, Hussain Basha
The unsteady turbulent boundary layer flow along a vertical plate found significant applications in the field of engineering and science due to its abundant applications in cooling electronic equipment, modeling of heat exchangers, chemical processing units, cooling of nuclear reactors, etc. Turbulence phenomena has been investigated in the field of engineering for its vital role in momentum transport, dispersion, and mixing, heat and mass transport, surface drag, etc. Especially in chemical engineering, turbulent flow determines the heat and mass transfer, affecting chemical reactions and the performance of chemical processes. From the literature review, it is confirmed that, many researchers have worked on unsteady natural convective laminar flows over a vertical cylinder/plate by applying diverse physical and chemical effects [1–6]. Actually, in nature, flow is turbulent and research on turbulence has several uses, but compared to laminar flow, there has been very little research on turbulent flow. Because the turbulence flow process is a tedious physical phenomenon to understand accurately even though scientists/engineers can comprehend the differences between laminar and turbulent processes intuitively. Moreover, turbulent flow is irregular, rotational, intermittent, highly disordered, diffusive, and dissipative. Thus, the visualization techniques are the adequate and required tools to understand the chaotic behavior of the turbulent flow phenomena under transient buoyancy-motivated conditions.
Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow
Published in Journal of Turbulence, 2020
Rui Fang, David Sondak, Pavlos Protopapas, Sauro Succi
Development of practical, high fidelity turbulence models is a key challenge facing scientists and engineers. Most fluid systems of interest are in a state of turbulence, which is a disordered fluid flow characterised by many interacting and collectively organised length and time scales. Predictive models must perform well and be computationally tractable in spite of the challenges posed by the turbulence phenomenon. The gold standard would be direct numerical simulations (DNS) of turbulent fluid systems, but such simulations will remain largely out of reach at the Reynolds numbers found in nature for the foreseeable future. A variety of turbulence models have been developed over the decades and have been applied to various engineering fields with differing success rates. Two dominant approaches in the engineering sciences are Reynolds-averaged Navier-Stokes (RANS) models and Large Eddy Simulation (LES) models, the former being much more computationally tractable than the latter but less accurate. New hybrid RANS-LES models are under active development to take advantage of the strengths of both approaches [1]. In recent years, researchers have started to leverage existing DNS databases of turbulent flows to build machine learning models that learn to represent closures in the RANS equations [2].
Development of a CFD model for steam cracker radiant coil using molecular kinetics
Published in Indian Chemical Engineer, 2020
Deepak Pal, Ananth Sharma, Abduljelil Iliyas
The flow inside radiant coils is highly turbulent where Reynolds number can go even higher than 100,000. Turbulent flows are inherently more difficult to describe than laminar flows, and for practical applications modelling is required. Turbulence flows can be modelled using various approaches, which can be broadly categorised as; direct numerical simulation (DNS), large eddy simulation (LES) and the Reynolds-averaged Navier–Stokes equation (RANS). The RANS approach is the most studied and widely used of the three approaches used and is highly suitable for tackling practical industrial flow situations. It is based on the solution of time-averaged conservation equations in which the conservation of mass, momentum and energy are sought to be expressed in terms of time-averaged, smoothened variable quantities which are usually of engineering interest. Several RANS based turbulence models are available in commercial CFD software’s. However, none of the models are universal and for a given case a simple model might yield better results than a complicated one.