Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Kerswell, Rich"'
Viscoelastic shear flows support additional chaotic states beyond simple Newtonian turbulence. In vanishing Reynolds number flows, the nonlinearity in the polymer evolution equation alone can sustain inertialess 'elastic' turbulence (ET) while 'elast
Externí odkaz:
http://arxiv.org/abs/2408.11508
Autor:
Lin, Yufeng, Kerswell, Rich
In this note, we discuss a poorly known alternative boundary condition to the usual Neumann or `stress-free' boundary condition typically used to weaken boundary layers when diffusion is present but very small. These `diffusion-free' boundary conditi
Externí odkaz:
http://arxiv.org/abs/2405.02874
Predicting and perhaps mitigating against rare, extreme events in fluid flows is an important challenge. Due to the time-localized nature of these events, Fourier-based methods prove inefficient in capturing them. Instead this paper uses wavelet-base
Externí odkaz:
http://arxiv.org/abs/2403.05263
Autor:
Kerswell, Rich, Page, Jacob
Motivated by the recent numerical results of Khalid et al., Phys. Rev. Lett., 127, 134502 (2021), we consider the large-Weissenberg-number ($W$) asymptotics of the centre mode instability in inertialess viscoelastic channel flow. The instability is o
Externí odkaz:
http://arxiv.org/abs/2312.09340
Autor:
Lewy, Theo, Kerswell, Rich
The extrusion of polymer melts is known to be susceptible to `melt fracture' instabilities, which can deform the extrudate, or cause it to break entirely. Motivated by this, we consider the impact that the recently discovered polymer diffusive instab
Externí odkaz:
http://arxiv.org/abs/2311.05251
We present a novel probabilistic deep learning approach, the 'Stochastic Latent Transformer' (SLT), designed for the efficient reduced-order modelling of stochastic partial differential equations. Stochastically driven flow models are pertinent to a
Externí odkaz:
http://arxiv.org/abs/2310.16741
We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data and apply it to stratified flows. The PINN is a fully-connected deep neural network fed with time-resolved, three-component velocity field
Externí odkaz:
http://arxiv.org/abs/2309.14722
Convolutional autoencoders are used to deconstruct the changing dynamics of two-dimensional Kolmogorov flow as $Re$ is increased from weakly chaotic flow at $Re=40$ to a chaotic state dominated by a domain-filling vortex pair at $Re=400$. The highly
Externí odkaz:
http://arxiv.org/abs/2309.12754
We investigate the linear instability of two-layer stratified shear flows in a sloping two-dimensional channel, subject to non-zero longitudinal gravitational forces. We reveal three previously unknown instabilities, distinct from the well-known Kelv
Externí odkaz:
http://arxiv.org/abs/2309.10056
Publikováno v:
Journal of Fluid Mechanics, 981, A2 (2024)
Beneitez et al. (Phys. Rev. Fluids, 8, L101901, 2023) have recently discovered a new linear "polymer diffusive instability" (PDI) in inertialess rectilinear viscoelastic shear flow using the FENE-P model when polymer stress diffusion is present. Here
Externí odkaz:
http://arxiv.org/abs/2308.14879