Recent experimental and theoretical studies of two-dimensional (2D) **turbulence** reveal that spectrally con- densed **turbulence** which is a system of coupled large-scale coherent flow and broadband **turbulence**, is similar to plasma **turbulence** near the L-H transition threshold. Large condensate vortices fed via the turbulent inverse energy cascade, can control both the level of the broadband **turbulence** by shear decorrelation, and the energy injected into **turbulence** at the forcing scale via sweeping of the forcing-scale vortices. The interaction between these ingredients of spectrally condensed **fluid** **turbulence** is in many aspects similar to the interactions in the zonal flow-GAMs-**turbulence** system in plasma. In this paper we overview recent results on condensed 2D **turbulence** and present evidence of interaction between its three components: condensate structures, **turbulence** and forcing- scale vortices. This is compared with the modifications in the spectra of plasma electrostatic potential during L-H transitions. It is shown that mean zonal flows are spatially and temporally correlated with both the broadband **turbulence** and with the narrow spectral range identified as the spectral range of the underlying instability.

The edge of magnetically confined plasmas, such as tokamaks, is where hot confined plasma encounters neutral gas, material surfaces, and the associated impurities. The transport of heat and particles in this region determines the heat loads and erosion rates of plasma facing components (PFCs). Predicting this transport, and exploring means of reducing heat fluxes to PFCs, has played an important role in designing the ITER divertor [1], and will be critical to the design of a future DEMO device [2, 3]. One of the uncertainties in making these predictions is the transport across the magnetic field, which is not well described by diffusion [4, 5], and is thought to be turbulent [6]. Since the fluctuations can be of similar spatial scales and magnitude to average profiles, significant effort has been devoted to developing and testing 3D flux-driven **fluid** **turbulence** simulation codes, including GBS [7, 8], TOKAM-3D [9] and TOKAM3X [10].

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phenomenon. On the other hand, 3D MHD **turbulence** exhibits forward cascades of energy and an inverse cascade of magnetic helicity. In these processes, the energy cascades toward smaller length-scales, whereas the magnetic helicity in MHD transfer spectral power toward larger length-scales. By contrast, the **fluid** vorticity in 3D hydrodynamics is prohibited from an inverse cascade. The randomly excited 3D Fourier modes nonetheless transfer the spectral energy by conserving the constants of motion in k-space. In freely decaying quantum electron **fluid** **turbulence** reported here, the energy contained in the large-scale eddies is transferred to the smaller scales, leading to a statistically stationary inertial regime associated with the forward cascades of one of the invariants. Decaying **turbulence** often leads to the formation of coherent structures as **turbulence** relaxes, thus making nonlinear interactions rather inefficient when they are saturated. It is to be noted further that the long-scale flow generation in our 3D simulations is observed to be directly proportional to the parameter H . Intermittent flows are thus generated for a small value of H , whereas strong and large scale flows in the ES potential are formed when the magnitude of H is large (see, e.g. figure 1). The physical basis of this observation can be elucidated from the following arguments. The parameter H , which is the ratio between the energy density of the EPOs and the electron kinetic energy density of a warm dense quantum plasma, is associated with a diffraction-like term in equation (1), i.e. H ∇ 2 9 . In this term, the negative imaginary part of the complex evolutionary variable 9 essentially determines the rate of dissipation corresponding to the smaller scales. The smaller H is, the more the dissipation is concentrated at the smaller scales and vice versa. For a moderately higher magnitude of the H parameter, there exists a strong tendency in EPOs to dissipate the smaller and intermittent turbulent eddies. It is therefore this H parameter which essentially characterizes electron flows at nanoscales in our 3D simulations.

To resolve these problems, a second category of models has been proposed, the so-called two-**fluid** (or multi-**fluid** models) [18–21] and references therein. These models use one set of equations for each **fluid** in addition to mean flow equations. The models are fairly complex but provide an accurate modeling framework for de-mixing and capture correctly the relative motion of the different **fluid** fragments. Finally, an intermediate class of models, which are significantly simpler than the two-**fluid** models, maintains the individual species fraction but assigns a single velocity for the mixture. The above models are also known as two-equation **turbulence** models because they consist of evolutionary equations for the turbulent kinetic energy per unit mass and its dissipation rate or the equivalent turbulent length scale [22–25]. These models postulate a turbulent viscosity, a Reynolds stress, and dissipation terms, as well as a buoyancy term for modeling RT and RM instabilities. They are able to handle multi-dimensions, multi-fluids, and variable accelerations, however, they too cannot address de-mixing (at least in their present form). A more advanced version of the single **fluid** approach is the BHR (Besnard-Harlow-Rauenzahn) turbulent-mix model [26]. Evolution equations from second-order correlations were developed and gradient-diffusion approxima- tions were applied to close the system of equations [26]. Using a mass weighted averaged decomposition, the original BHR model included full transport equations for Reynolds stress tensor, turbulent mass flux, density fluctuations and the dissipation rate of the turbulent ki- netic energy. A reduced, simpler but still accurate, version of the model has been previously presented [27, 28], showing very promising results.

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As mentioned, there are no efficient algorithms for the detection of strange attractors in cancer ecosystems, whether it be the time lapse imaging of intracellular protein patterning or the attractor Waddington landscape reconstructed from time-series gene expression datasets. The detection of strange attractors even in **fluid** **turbulence** is restricted to experimental mapping techniques such as power/frequency spectra analysis and phase portraits (Lyapunov spectra). However, the attractor reconstruction methods such as Takens’ time-delay coordinate embedding used to achieve these results are not well-suited for large complex datasets. Distinguishing noise from chaos remains a fundamental roadblock in cancer biology. The following are a few algorithmic prospects to overcome this challenge. Takens mentioned entropy measures and multi-fractality as general techniques for strange attractor detection in turbulent systems (Takens, 1980). Entropy and other information-theoretic measures remain amidst the most widely used algorithms used in the network clustering of gene expression datasets. Even simple machine learning algorithms such as decision trees can help distinguish noise from chaos in biological signals (Toker et al., 2020). Artificial Intelligence is emerging as the most powerful tool available in deciphering cancer networks. As such, two additional algorithms are proposed herein as heuristics (approximation tools) to map strange attractors in complex, chaotic datasets: Deep Learning Networks (DLN) and complexity measures from algorithmic information dynamics (AID).

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Yuan and Michaelides (1992) argued that the wake is responsible for the augmentation of **turbulence** and the work done on the particles is responsible for the attenuation of **turbulence**. This approach was developed and extended by Yarin and Hetsroni (1994), who proposed a more detailed expression for the wake. Bolio and Sinclair (1995) adopted the original model of Yuan and Michaelides, and confirmed that the wake enhanced gas **turbulence**. However, it was experimentally observed that turbulent energy may also increase when the particle Reynolds number is small and the wakes are negligible (Hardalupas et al., 1989). Kenning and Crowe (1997) suggested that the work done by the particles on the gas via drag could generate **fluid** **turbulence**. Crowe and Gillandt (1998), Crowe and Wang (2000) and Crowe (2000) derived and improved a detailed **turbulence** modulation model following the work of Kenning and Crowe (1997). These authors argued that the common approach to the derivation of the turbulent kinetic energy balance equation (e.g. Louge et al., 1991), which treats the averaged velocity as if it were a local velocity in the momentum equations of both phases, is not appropriate. In other words, the turbulent kinetic energy equation should be derived from the instantaneous Navier-Stokes equation, which does not include a coupling drag force term. Fessler and Eaton (1999) also pointed out that previous models which used an extra turbulent energy source or sink to represent the influence of the particles did not fully capture the physics of particle-gas interactions.

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Stirring of liquid steel in ladles through argon bubbling has various functions like thermal and chemical homogenization, speeding metal-slag mass transfer rates (melt desulfurization), supposedly floatation of inclusions and when necessary steel cool down to cast at the desired temperature. However, this operation is not unfortunately free from serious drawbacks. Among these we have the opening of a slag eye through which oxygen and nitrogen can be absorbed, possible slag entrainment if the stirring intensity of the bath is high, enhancement of melt-refractory and slag-refractory reactions which will degrade steel cleanliness. Another important operational factor, which depends on the steel tapping practice, oxygen and sulfur contents of steel at tapping and amount and type of additions, is the thickness of the slag layer. The thickness of this upper phase definitively influences the stirring conditions of the bath for a given energy input. It is a natural way of thinking that there must be a narrow operating window in the process to get the best from the contact among the various phases involved in this process. Thermally and chemically homogeneous baths, reasonable desulfurization rates lasting a span time from 5 to 8 minutes, low refractory wearing-rates and capture of inclusions that reach the metal-slag interface especially during the rising-time period are the goals of this process. For such a complex-multiphase system reaction-thermodynamics and **fluid** flow phenomena interact intimately in a way difficult to understand even nowadays. The present work is focused on **fluid** flow and specifically, on the **turbulence** of the liquid metal which is close to the metal-slag interface since this region is the critical one for floating inclusions. **Fluid** flow of liquid steel in ladles has been studied from many points of view using physical and mathematical models. The structure of the gas-liquid plume was studied using a mechanistic approach by Krishnapisharhody and Irons [1,2] establishing models to estimate the size of the slag eye opening area (SEO) and the height of the spout region as function of bath height and gas flow rate. The same authors developed correlations linking averaged velocities of the liquid and gas phases and gas volume fraction along the plume height as functions of gas flow rate and bath height [3]. Spout height was defined through a dimensionless variable involving gas flow rate [4] presenting a unified theory for two-phase flows dynamics in the plume [5]. Mixing times are considered as a useful information to estimate the thermal and chemical homogenization speediness of steel in ladles under some given flow rate of gas. Various authors have proposed simple engineering correlations to estimate the mixing time for gas-liquid systems [6]. On this line Mazumdar and Guthrie [7-9] estimate this parameter and the plume velocity

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Heat exchanger is a type of heat transfer equipment which provides the transfer of heat energy between two fluids by varying the temperature of the system. STHE’s are classical heatexchangers which are familiar for its accurate design procedure, reliability, adaptability for high pressure operations and better performance. Hence, STHE plays a prominent role in heat transfer process in most of the chemical, chemical based and oil refining industries. [5-8] The cold **fluid** flows inside the tube which is called tube side **fluid** and the hot **fluid** flows inside the shell which is called shell side **fluid** at varying temperatures. [9].The factors affecting the performance of a STHE are **fluid** **turbulence**, pressure drop in the shell and tube side, individual and overall heat transfer co-efficient, fouling factor, the flow rate of shell side **fluid** and tube side **fluid**, length to diameter ratio of heat exchanger and the baffle configuration. The influence of a baffle has a major part in a STHE in which the baffle holds up the tube bundles and creates the turbulent flow on the shell side. Based on the flow of the **fluid** in the shell, the flow in a

A majority of works on fully developed **turbulence** is concerned with an incompressible **fluid**. The renormalization group approach to such problems has been successful in verifying Kolmogorov scal- ing and provides an eﬃcient tool for a calculation of universal quantities. However, a similar treatment has been only scarcely applied to compressible fluids. In this paper we present an application of the field theoretic renormalization group (RG) onto the scaling regimes of a compressible **fluid**, whose behavior is governed by a proper generalization of stochastic Navier Stokes equation [1]. Similar models of compressible **fluid** were considered in [2–4]. In [2] the phenomenological corrections to the Kolmogorov spectrum were verified in the framework of the skeleton equatios for consistency, while the model, considered in [3], appears to be in fact unrenormalizible. All these papers shows us a necessity of the further investigations of compressibility.

Marine predators play a fundamental role in mantaining the function of marine ecosys- tems and they are sentinels of the their ecosystem health. Their study however presents a challenge due to complexity of their behaviour and of their turbulent pelagic environ- ment. Recently, progress in bio-logging technology, remote sensing and modelling have opened novel possibility of addressing these inter-disciplinary questions. My work has explored how (sub-)mesoscale **turbulence** structures marine predators habitat by affect- ing their movement, their foraging behaviour, and the trophic chain they depend on. The (sub)mesoscale is indeed a regime which is expected to have a twofold structuring role on the ecology of top predators: Firstly, through bottom-up effects (because of its impact on lower trophic levels); and secondly, by direct means, because the (sub)mesoscale oc- curs on temporal scales of days to weeks, which are the same of the behavioral switches of predators. I focused on the case of the Kerguelen region, which is an ideal end-to-end case study because (i) several marine predators species have large colonies on the island, (ii) the area is located in a highly dynamical ocean regime dominated by the Antarctic Circumpolar Current, with strong (sub)mesoscale activity, (iii) the trophic web in the area is relatively simple and (iv) production is dominated by iron limitation, making it possible to disentangle physical and ecological effects. I combined bio-logging, remote sensing, in-situ (samples from ships and autonomous platforms like ”bio-argo” profilers) with ecological and Lagrangian modelling to study how (sub)mesoscale features affects movements and foraging behaviours of marine predators from a mechanical and ecolog- ical point of view. My work had two main axes: a first one that relates marine predator to the mechanical effect of (sub-)mesoscale **turbulence** (part II) and a second one where I combined ecology and stirring to describe the “biological” mesoscale landscape explored by Kerguelen’s marine predators (part III).

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The requirement to meet the challenge of producing cleaner and more efficient power plants will intensify further over the next few years. This challenge requires an increased commitment to research by the transporta- tion industry. The internal combustion engine represents one of the more challenging **fluid** mechanics problems to model because the flow is compressible with large den- sity variations, relatively high Mach number, turbulent, unsteady, cyclic, and non-stationary, both spatially and temporally. Much progress has been made in CFD model development for engines in recent years.

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The combined use of Computational **Fluid** Dynamics, (CFD) and FE (Finite Element) in CAD- centred system can significantly accelerate the design process. However, this process may have a drawback of increasing design complexity. This fact unavoidably increases the dependence on external “specialized” development partners for simulation and optimization. The continuous improvement of CAD-embedded FSI (**Fluid** System Interaction) software packages has progressively reduced the necessity for these external partners. These software packages are designed to keep pace with the unavoidable design development. The geometry changes are followed by engineering simulations, interpretations and optimizations. To make FSI usable for mechanical designers and design engineers from other engineering disciplines, CFD software package have been largely automated to minimize the specialist expertise required to operate traditional CFD software. The capabilities of CAD-embedded CFD to handle fairly complex geometries and also to simulate complex industrial turbulent flows with heat and mass transfer is then extremely important, together with simulation’s **turbulence** capabilities.

**Turbulence** is a longstanding problem in **fluid** mechanics and many publications have already been devoted to its study [1–4]. But, there is no analytical or numerical solution, apart from a small number of cases which are always in need of models and simulations. Consequently, the experimental methods are inevitable and many scientists are making increasing use of optical techniques. The most effective optical methods are ones for which no measuring probe is introduced into the flow. In these optical techniques called diagnostic techniques, an electromagnetic wave usually characterized by a laser beam is sent into the turbulent flow studied, where fluctuations in temperature, pressure or density create random variations of the refractive index called optical **turbulence**. Under these circumstances, any attempt to solve the problem requires an understanding of the physics of optical wave propagation phenomenon through a turbulent medium.

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Many physical systems possess a multiplicity of stable states so that more than one solution or system configuration can be found at long times. In such situations, a key issue is usually maintaining the system in a desired state against ambient noise or switching the system from one (undesirable) state to another (preferred) state in an efficient, robust way: e.g. in liquid crystal displays [61], power grids [71], arrays of coupled lasers [37], turbulent **fluid** flows [46] and even in the human brain [3]. Either objective involves detailed knowledge of a state’s basin of attraction, defined as the set of all initial conditions of the system whose long time behaviour is to converge to that state. Initial conditions located just outside the basin boundary determine how the system can be efficiently disturbed to trigger a new stable state. Knowledge of how the basin boundary of a state moves (in phase space) when the system is manipulated (e.g. by modifying the boundary conditions) opens up the possibility of enhancing the nonlinear stability of that state. However, locating a basin boundary is a fully nonlinear (nonlocal) problem so that the traditional tools of linearising the system around the state or even weakly nonlinear analysis provide no traction. Existing fully nonlinear approaches - solving the governing equations while searching for the finite-amplitude disturbances to just knock the system out of one state into another, or mapping out the stable and unstable manifolds of nearby solutions in phase space to identify the basin boundary - are impractical for all but the smallest systems.

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Most of the tubular structures in heat exchangers and steam generators are subjected to cross flow of **fluid** and hence they are checked for vortex shedding, **fluid** elastic instability and **turbulence**. In pool type fast breeder reactors a new type of **fluid** elastic instability of shell structures due to weir flow has been observed during commissioning of fast reactor Super Phenix. A similar type of instability has been checked for PFBR. The fretting wear of tubular structures at loose supports is very common phenomenon. The excitation forces of various mechanisms like vortex shedding, **fluid** elastic instability and **turbulence** are discussed.

It is well-known that fully developed turbulent **fluid** flows are intermittent and multifractal (Frisch, 1995 and references therein). The intermittent **turbulence** and associated multi- fractal characteristics are also evident in the analyses of space plasmas observations (e.g., Sorriso-Valvo et al., 1999; Con- solini and Chang, 2001; Bruno et al., 2001, 2003; Forman and Burlaga, 2003; Tam et al., 2005; Weygand et al, 2005; and Chang, 2009). Through the analysis of probability distri- bution functions (PDFs) for field fluctuations, intermittency in **turbulence** is characterized by a strong non-Gaussian be- havior of PDF at small scales. The multifractal characteris- tics have generally been analyzed with structure functions or singular spectra based on the partition functions of the prob- ability measures (Halsey et al., 1986).

Depletion of fossil fuels has driven efforts to find other sources of renewable energy. Located in the Ring of Fire, Indonesia has approximately 28.91 GW of geothermal energy potential, but less than 5% of these resources have been utilized thus far (Pambudi 2018). The Indonesian government plans to improve the utilization of geothermal power plants; unfortunately, as demonstrated in Fig. 1, it can be seen that electricity generation from geothermal power plants in Indonesia has thus far increased only incrementally. The utilization of energy generation is dependent upon the quality of the geothermal **fluid**; such **fluid** is not always suitable for existing

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separated. This did not happen for turbulent flow because of turbulent mixing, which brings higher velocity **fluid** closer to the wall. Results of runs to a shorter distance are presented in Figure 4.17. This shorter distance is less than the distance to separation, so the laminar case could be run. The results further demonstrate the self-limiting nature of ablation, since increasing the amount of ablation reduces velocity and temperature near the wall. The cases presented in Figure 4.17 were not run far enough downstream to be considered converged solutions, so values of the heat flux to the wall are not presented. It should be noted that while the laminar case is not physically reasonable, it provides a base case from which the effects of radiation, **turbulence** and ablation can be analyzed.

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The cause of the **turbulence** which is identified by the proportion of the air volumetric (β) is due to the changes of the flow velocity of both fluids, in which the velocity of **fluid** air increase while the **fluid** water decrease so when the fluids pass through the 900 curve, there is a **turbulence** as the result of bug centrifugal force from horizontal to vertical and the influence of gravitation on the **fluid** water is getting bigger. For the flow pattern phenomena, water has significant role because the density of water has bigger mass compared with the air so the influence of the gravitation occurs on the water [3] and [6]. The direction of the centrifugal and gravitation forces has significant role in the formation of the flow pattern phenomena in 90 0 curve, which is in the form of bubble in the bottom of horizontal pipe when it is about to pass through the 90 0 curve; it is because the gravitation has influence on the return force which interacts with the velocity of the fluids from horizontal direction [12].

Through the multiphase flow evaluated with Fluent of ANSYS, in this study, the effect of velocity, pressure and **turbulence** in commercial steel tubes was analyzed using the κ-ε model with a velocity of 5m/s. All this will be detailed later with various graphs in the results section. The **fluid** that we worked with was liquid water at a temperature of 20°C and the simulations were carried out with Fluent in 2D due to the high computational resource that simulations in 3D provoke. Figure 2 shows the mesh of the elbow.

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