![](http://www.damtp.cam.ac.uk/research/fluidshr/files/styles/portrait/public/portraits/rrk26.jpg?itok=0_tLfQ9q)
Career
- 2017- date: DAMTP, Cambridge University
- 1996-2017: Mathematics, Bristol University
- 1992-1996: Mathematics, Newcastle University
- 1992 : PhD, MIT
Research
General fluid mechanics: the Navier-Stokes equations (nonlinear dynamics, transition and turbulence); geophysical and astrophysical fluid mechanics (e.g. convection; stably stratified flows; tidal, precessional and librational motion of planetary interiors and subsurface oceans; accretion disks);
Selected Publications
- see homepage (www.damtp.cam.ac.uk/user/rrk26)
Publications
Heat transport in Rayleigh-Bénard convection with linear marginality
– Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
(2022)
380,
20210039
(doi: 10.1098/rsta.2021.0039)
Weakly nonlinear analysis of the viscoelastic instability in channel flow for finite and vanishing Reynolds numbers
– Journal of Fluid Mechanics
(2022)
940,
a11
(doi: 10.1017/jfm.2022.222)
Sensitivity and downstream influence of the impinging leading-edge vortex instability in a bileaflet mechanical heart valve
– Journal of Fluid Mechanics
(2022)
936,
a41
(doi: 10.1017/jfm.2022.49)
Finite-amplitude elastic waves in viscoelastic channel flow from large
to zero Reynolds number
(2022)
Non-equivalence of quasilinear dynamical systems and their statistical
closures
(2022)
Improved assessment of the statistical stability of turbulent flows
using extended Orr-Sommerfeld stability analysis
(2022)
Data‐Driven Identification of Turbulent Oceanic Mixing From Observational Microstructure Data
– Geophysical Research Letters
(2021)
48,
ARTN e2021GL094978
(doi: 10.1029/2021GL094978)
Optimal perturbation growth on a breaking internal gravity wave
– Journal of Fluid Mechanics
(2021)
925,
A16
(doi: 10.1017/jfm.2021.603)
Degeneracy of turbulent states in two-dimensional channel flow
– Journal of Fluid Mechanics
(2021)
917,
a57
(doi: 10.1017/jfm.2021.336)
Revealing the state space of turbulence using machine learning
– Physical Review Fluids
(2021)
6,
034402
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