Abstract
We discuss spatio-temporal pattern formation in two separate thermal convective systems. In the first system, hydrothermal waves (HTW) are modeled numerically in an annular channel. A temperature difference is imposed across the channel, which induces a surface tension gradient on the free surface of the fluid, leading to a surface flow towards the cold side. The flow pattern is axially symmetric along the temperature gradient with an internal circulation for a small temperature difference. This axially symmetric flow (ASF) becomes unstable beyond a given temperature difference threshold, and subsequently, symmetry-breaking flow, i. e., rotational oscillating waves or HTW appear. For the second system, Rayleigh–Bénard convection (RBC) is experimentally studied in the non-turbulent regime. When a thin film of liquid is heated, the competing forces of viscosity and buoyancy give rise to convective instabilities. This convective instability creates a spatio-temporal non-uniform temperature distribution on the surface of the fluid film. The surface temperature statistics are studied in both these systems as “order” and “disorder” phase separates. Although the mechanisms that give rise to convective instabilities are different in both cases, we find an agreement on the macroscopic nature of the thermal distributions in these emergent structures.
1 Introduction
Pattern formation during thermal convection is a well-studied phenomenon. The complex structures that emerge as a result of thermally driving the system out-of-equilibrium break spatio-temporal homogeneity. Generally, studies on pattern formation during thermal convection can be broadly grouped into two classes, namely, (i) when the system is close enough to equilibrium and (ii) when the system is driven far from equilibrium. While close-to-equilibrium phenomena offer exciting opportunities to study stable but complex structures and their slow relaxation to steady states, far-from-equilibrium processes open up a plethora of questions regarding turbulence, dissipation, fast time scales, and large-scale order [1], [2], [3], [4], [5], [6], [7].
In this article, we focus on the near-equilibrium dynamics of two thermally driven systems: the Rayleigh–Bénard convection (RBC) system and the hydrothermal wave (HTW) system. We confine ourselves to a pure thermodynamic study of these two systems; thus, the temperature is our key variable of interest. While the RBC system is experimentally studied in the non-turbulent regime (low Rayleigh number), the HTW system is explored over a broader domain, i. e., from axis-symmetric flows to complex rotational oscillating waves [8], [9], [10], [11], [12]. In the RBC system, thermal data of the top layer of the fluid film are obtained through infrared imaging; the HTW system, on the other hand, is simulated numerically based on the Navier–Stokes equations with appropriate boundary conditions. As these systems are driven out-of-equilibrium, the spatial symmetry is broken with the emergence of stable spatio-temporal complex patterns. While these complex patterns are recorded in-plane due to the global thermal driving, a thermodynamic flux orthogonal to the global driving force emerges due to the emergent complex thermal patterns [13]. The thermal patterns and the emergent flux exist as long as the system is being driven. In the near-equilibrium regime, it is shown in this paper that the thermal statistics at the microscopic scale obtained from these two systems show similar behavior. Also, as a numerical model, the HTW system allows us to explore a larger parameter space than the RBC system, thus providing additional insights regarding possible far-from-equilibrium steady states that may behave as attractors for these dissipative systems to asymptotically converge.
2 Methodology
It has been recently shown that RBC and HTW systems satisfy a linear force–flux relationship when close enough to equilibrium [11], [12], [13]. Thus, under non-equilibrium conditions, the entropy production is maximized by the emergent thermodynamic flux, orthogonal to the global driving, in agreement with the maximum entropy production principle [14], [15], [16], [17]. While the linearity in the force–flux relationship concerns the bulk evolution of the system, it is imperative to ask if the underlying distribution of the local variables bears any key role in the nature of the macroscopic evolution of the system. The local variable of interest in the following two systems is temperature and its spatial distribution for our study.
2.1 Rayleigh–Bénard convection
In the RBC system, the temperature of the top fluid film is recorded using an infrared camera. A thin layer of high-viscosity silicone oil is placed between a rigid-free boundary. It is driven from a room temperature equilibrium state to an out-of-equilibrium steady state. As the system reaches a non-equilibrium steady state, a fixed temperature difference is maintained between the free top layer and the rigid bottom layer,
In an RBC system, the convective cells that emerge as a consequence of thermal driving are known to be three-dimensional. Since the image snapshots capture only the top layer of the fluid film, our statistics are strictly restricted to the orthogonal plane that describes the top surface of the convective cells. Thus, the collection of in-plane hot (
The emergent flux denotes the onset of surface thermal gradients. It saturates once the system transitions to a steady state. The growth and saturation of the emergent flux can be empirically described by the following differential equation:
Here,
2.2 Hydrothermal wave system
In contrast, the HTW system is numerically solved in a three-dimensional setting (
The temperature differential across the cell causes a surface tension gradient on the fluid’s free surface, causing a surface flow to the cold side. As a result of the heat convection, numerous flow patterns with internal circulation emerge. The flow pattern in an annular channel is axially symmetric along the temperature gradient and includes internal circulation. Beyond a certain temperature difference threshold, this axially symmetric flow (ASF) becomes unstable, and symmetry-breaking flow, i. e., rotational oscillating waves, occurs (shown in Figure 2). The oscillating waves propagate perpendicular to the temperature gradient applied to the system, i. e., the circumferential direction, and the temperature varies periodically. This circular oscillating flow is referred to as a hydrothermal wave (HTW).
In the HTW system, surface temperature statistics are obtained over a well-defined region of interest, as shown in Figure 2. To account for the oscillatory waves, thermal statistics are also obtained along the angular coordinates such that
Similarly, the emergent fluxes are obtained along both circumferential and radial directions. The emergent flux along the θ-direction is obtained by taking the angular gradient of the temperature scalar,
The angular heat flux represents the magnitude of the interference effect between the heat conduction and viscous dissipation and is an energy source that produces emergent convection. The resulting emergent convection widens the angular temperature distribution. Thus, the angular standard deviation increases as the emergent convection grows. Therefore, the relationship between the normal standard deviation and the angular standard deviation may give the relationship between the applied energy and the energy required to form emergent convection. The HTW system is different from the RBC system because the emergent order in RBC quenches at a non-equilibrium steady state. In the HTW system, the emergent order has a periodic behavior. Being a numerical simulation also allows us a wider parameter space to explore the various aspects of the stability of the oscillatory phenomena as a function of the input parameters and time.
3 Results
In Figures 3a and 3c, we plot the temperature standard deviation and the emergent flux as a function of time for the RBC system. The different regions of the standard deviation time-series plot, growth, decline, and subsequent rise, have been discussed in great detail in previous works. Especially, plots discussing the stages of the emergent order as a function of the standard deviation or emergent flux for the RBC are not considered here as they have been previously reported [10]. The solid lines in Figure 3c denote the fits described in equation (3). In Figures 3b and 3d, the linear relationship between emergent flux and surface temperature standard deviation are discussed for the two samples. The slopes of the linear fits (
In this paper, we want to draw attention to the fact that in the HTW system as well, this trend in the surface temperature standard deviation time series is preserved. In Figure 4, we present standard deviation and emergent flux plots calculated for the HTW system when the sample is subjected to a temperature difference,
In Figure 5, standard deviation and emergent flux along the θ-direction are shown for the HTW simulations at
Finally, in Figure 6, we plot the two surface temperature standard deviation variables with respect to each other as the HTW system exhibits steady-state oscillations. We already discussed in Figure 4a how these two variables oscillate out-of-phase with respect to each other. Therefore, in the
4 Discussion
In this paper, we discuss pattern formation in two thermally driven convective fluid systems. We consider temperature as our thermodynamic variable of choice and use it to quantify spatio-temporal pattern formation and, in general, the macroscopic dynamics of the two systems under consideration. In the RBC system, the temperature is explicitly measured through infrared thermography. In the HTW system, the temperature at every lattice grid point is obtained by solving the Navier–Stokes equation. From the obtained temperature time-series data, first-order statistics are computed. While the time evolution of the mean temperature captures the macroscopic dynamics of the system as it relaxes to a non-equilibrium steady state, the time evolution of the standard deviation is of particular interest as it allows us to identify the regimes in time when complex spatio-temporal patterns emerge.
The peculiarity in the standard deviation time series – steady rise followed by a steep drop and then a gradual rise – as patterns emerge has been discussed before, both in the context of the RBC system and during pattern formation in other dissipative systems [20], [21], [22]. In this paper, we consider the HTW system numerically and observe similar peculiarity in the surface temperature standard deviation time-series plots, shown in Figure 4. This further strengthens the argument against the observed unusual trend as an experimental artifact. Also, one can observe the similarity between the time evolution of the emergent flux and the surface temperature standard deviation, in both RBC and HTW systems, from Figures 3 and 5. Further, from Figures 3b, 3d, and 5 it can be established that in both systems, a linear relationship exists between emergent flux and surface temperature standard deviation after the onset of emergent order. In contrast, no correlation exists before order emerges.
This may not be just a coincidence but may instead have deeper implications. For a low energy flux applied to the system (small temperature difference), the systems’ response to the energy flux is symmetric, producing a uniform temperature distribution. In this case, ASF occurs in the (HTW) system, and the angular heat flux remains almost zero. The width of the temperature distribution increases with increasing energy flux. Therefore, the standard deviation of the temperature distribution corresponds to the applied energy flux. Beyond the temperature difference threshold (or critical Rayleigh number,
Recently, it has also been shown in the context of the local equilibrium hypothesis in RBC that the emergent flux and the thermodynamic force, calculated as the gradient of the inverse temperature, i. e.,
Funding statement: The research received no external funding.
Acknowledgment
We thank the organizing committee of the Joint European Thermodynamics Conference 2021 (JETC 2021) for inviting us to submit our work for the special issue.
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