Search arXiv⌕ Search

arXiv · physics/0506034

Absense of slow transients, and the effect of imperfect vertical alignment, in turbulent Rayleigh-Benard convection

Abstract

We report experimental results for the influence of a tilt angle beta relative to gravity on turbulent Rayleigh-Benard convection of cylindrical samples. The measurements were made at Rayleigh numbers R up to 10^11 with two samples of height L equal to the diameter D (aspect ratio Gamma = D/L = 1). The fluid was water with a Prandtl number sigma = 4.38. In contrast to the experiences reported by Chilla et. al. (2004) for a similar sample but with Gamma = 0.5 (D = 0.5 and L = 1.0 m), we found no long relaxation times. For R = 9.4 times 10^10 we measured the Nusselt number N as a function of tilt angle beta and obtained a small beta dependence about a factor of 50 smaller than the result found by Chilla et. al. (2004) for their Gamma = 0.5 sample. We measured side-wall temperatures at eight equally spaced azimuthal locations on the horizontal mid-plane of the sample and used their cross-correlation functions to find the turn-over time of the large-scale circulation (LSC). The resulting Reynolds numbers R_e^cc were found to increase with beta. An important conclusion is that the increase of R_e^cc with beta of the LSC does not significantly influence the heat transport. Over the range 10^9 < R < 10^11 the enhancement of R_e^cc at constant beta due to the tilt could be described by a power law of R with an exponent of -1/6, consistent with a simple model that balances the additional buoyancy due to the tilt angle by the shear stress across the boundary layers. Even a small tilt angle dramatically suppressed the azimuthal meandering and the sudden reorientations characteristic of the LSC in a sample with beta = 0. The azimuthal mean of the temperature at the horizontal mid-plane within our resolution was independent of beta.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guenter Ahlers, Eric Brown, Alexei Nikolaenko. 2005-06-03. Absense of slow transients, and the effect of imperfect vertical alignment, in turbulent Rayleigh-Benard convection. https://doi.org/10.1017/s0022112006009888

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Geometry-induced self-excited dynamo in a regular tetrahedron

We present a rotation-free magnetohydrodynamic dynamo driven by laminar thermal convection in a regular tetrahedral cavity. The tetrahedral boundaries organize the convective flow into a robust pattern of helical convection cells without global rotation or turbulence. Direct numerical simulations demonstrate exponential amplification of a weak seed magnetic field followed by nonlinear saturation. In the reference calculation, the saturated magnetic energy exceeds the kinetic energy. The velocity field develops $D_4$ dihedral symmetry, while the self-generated magnetic field exhibits a corresponding signed $D_4$ symmetry, including antisymmetry under $π$ rotations about the two horizontal axes. Analysis of the velocity and magnetic-field structures reveals a closed induction cycle sustained by geometry-induced helical convection. This system provides a conceptually simple setting for isolating and understanding the fundamental physical processes underlying magnetohydrodynamic dynamo action.

physics.flu-dyn↗

Projection-based segregated, iterative, and multi-stage physics-informed neural network for incompressible Navier-stokes equations

Physics-informed neural networks(PINNs) offer a mesh-free and continuously differentiable approach to fluid simulation. However, standard monolithic PINNs for incompressible Navier-Stokes (NS) equations often suffer from severe constraint coupling and spectral biases, limiting both convergence and accuracy. To address this challenge, we propose a segregated, iterative, and multi-stage PINN (SIM-PINN) inspired by the classical projection method in computational fluid dynamics. In SIM-PINN, the nonlinear NS equations are decoupled into the linearized momentum and pressure Poisson equations. Each is addressed separately using dedicated multi-stage neural networks and coupled through projection-based inner iterations. Each stage of the multi-stage neural networks for the momentum equations corresponds to one iteration of the projection method, in which the velocity fields are progressively refined at targeted frequencies. Upon iteration convergence, solutions to the fully coupled and nonlinear NS equations are recovered. By integrating the segregated and iterative framework with the multi-stage training, SIM-PINN alleviates the optimization difficulties of the incompressible NS equations arising from nonlinearity, constraint coupling, and spectral biases. The improved convergence and accuracy are demonstrated by simulations of steady lid-driven cavity and unsteady Kolmogorov flows.

physics.flu-dyn↗

Bayesian Kolmogorov-Arnold networks for trustworthy Reynolds-stress closures

Data-driven turbulence closures can improve Reynolds-averaged Navier-Stokes (RANS) predictions, but their use is limited by two issues: learned constitutive relations are often difficult to inspect, and predictions typically lack reliable uncertainty estimates. We develop a Bayesian tensor-basis Kolmogorov-Arnold network (BKAN) for Reynolds-stress anisotropy that addresses both within a single closure. Learnable univariate spline functions on network edges provide an inspectable representation that can, where sufficiently simple, be approximated symbolically. Variational posteriors over spline coefficients yield a predictive distribution separating epistemic uncertainty, due to limited training data, from aleatoric uncertainty, the irreducible scatter of high-fidelity targets at given inputs. Galilean and rotational invariance, symmetry, and tracelessness are enforced through the tensor basis, while physical realisability is treated separately through soft regularisation. A controlled regression benchmark assesses predictive accuracy, calibration, and Bayesian parameterisation. Placing the posterior on spline coefficients gives the best overall balance of accuracy, calibration, parameter cost, and interpretability. Applied to Reynolds-stress anisotropy, the BKAN is accurate in distribution, with calibrated, informative uncertainty that tends to identify larger prediction errors. Under geometric extrapolation, however, the model becomes overconfident: epistemic uncertainty increases but does not fully account for growing prediction errors. Finally, the framework is extended to three-dimensional wind-farm wakes, where the learned correction transfers to unseen layouts and posterior samples are propagated through the RANS solver, with the aleatoric component injected as a spatially correlated field, to obtain uncertainty bounds on the predicted wake flow.

physics.flu-dyn↗