1,721,474 research outputs found
Mass transfer to freely suspended particles at high Péclet number
No full textIn a theoretical analysis, we generalise well-known asymptotic results to obtain expressions for the rate of transfer of material from the surface of an arbitrary, rigid particle suspended in an open pathline flow at large Péclet number,. The flow may be steady or periodic in time. We apply this result to numerically evaluate expressions for the surface flux to a freely suspended, axisymmetric ellipsoid (spheroid) in Stokes flow driven by a steady linear shear. We complement these analytical predictions with numerical simulations conducted over a range of and confirm good agreement at large Péclet number. Our results allow us to examine the influence of particle shape upon the surface flux for a broad class of flows. When the background flow is irrotational, the surface flux is steady and is prescribed by three parameters only: the Péclet number, the particle aspect ratio and the strain topology. We observe that slender prolate spheroids tend to experience a higher surface flux compared to oblate spheroids with equivalent surface area. For rotational flows, particles may begin to spin or tumble, which may suppress or augment the convective transfer due to a realignment of the particle with respect to the strain field.</p
Dataset for "Physics-informed Gaussian process regression for particle-tracking data assimilation"
No full textThis dataset supports the publication:
Lawson, J. M. Physics-informed Gaussian process regression for particle-tracking data assimilation. Phys. Rev. Fluids 2026, 00:004900, 2026. doi:10.1103/zvm4-wtkq
This dataset contains statistics for the validation of Physics-Informed Gaussian Process Regression using synthetic and experimental datasets.
Summary statistics for all three cases (homogeneous isotropic turbulence, turbulent channel flow and square prism wake) are provided.
Additionally, MATLAB code is provided to reproduce figures from the article.</span
Dataset for "Mass transfer to freely suspended particles at high Péclet number"
No full textThis dataset contains all necessary data to reproduce figures 3, 4, 5 and 6 from:
Lawson, J. (2021). Mass transfer to freely suspended particles at high Péclet number. Journal of Fluid Mechanics, 913, A32. doi:10.1017/jfm.2020.1177
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Dataset for "Unsteady forcing of turbulence by a randomly actuated impeller array"
No full textThis is the dataset for:
Lawson, J.M., Ganapathisubramani, B. Unsteady forcing of turbulence by a randomly actuated impeller array. Exp Fluids 63, 13 (2022). https://doi.org/10.1007/s00348-021-03364-8
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Physics-informed gaussian process regression for particle tracking data assimilation
No full textWe introduce a physics-informed Gaussian Process Regression (GPR) method for data assimilation and uncertainty quantification of Particle Tracking Velocimetry (PTV) data. Unlike traditional methods based on regression, our approach transparently incorporates statistical information and physics such as mass conservation, boundary conditions, and statistical symmetries directly into the regression model. Furthermore, GPR quantifies prediction uncertainty and provides physics-constrained estimates of the two-point velocity covariance, a quantity of primary interest in turbulent flows. The methodology is demonstrated using synthetic and experimental data from three canonical turbulent flows: homogeneous isotropic turbulence (HIT), turbulent channel flow (TCF), and the turbulent wake behind a square prism (SPW).In all cases, we make comparisons relative to the performance of the vortex in cell method, VIC+.For HIT, the model leverages isotropy to learn the velocity correlation function from even very noisy and sparse data, achieves a factor of two improvement over VIC+ in velocity prediction error, and accurately quantifies the prediction uncertainty.For TCF, we introduce a novel and scalable approach to train a high-dimensional GP model that respects wall-bounded flow physics.GPR significantly outperforms VIC+ in terms of accuracy, uncertainty estimation, and resolution in this case. In the SPW case, GPR demonstrates improved accuracy in velocity prediction and improved coherence of the vorticity field obtained from independent snapshots of tracers. Our approach lays the groundwork for extensions to time-resolved data, inclusion of acceleration measurements, and reduced-parameter models based on resolvent analysis
Dataset for "Mass transfer from small spheroids suspended in a turbulent fluid"
No full textDataset for:Lawson, J., & Ganapathisubramani, B. (2021). Mass transfer from small spheroids suspended in a turbulent fluid. Journal of Fluid Mechanics, 929, A19. doi:10.1017/jfm.2021.867</span
Mechanisms of mass transfer to small spheres sinking in turbulence
No full textUsing laboratory experiments and numerical simulations, we examine the transfer of soluble material from small, spherical particles sinking in homogeneous turbulence at large Péclet number. A theoretical analysis predicts two distinct mechanisms of convective mass transfer: strain due to turbulence and slip due to gravitational settling. Their relative strength is parametrised by the sinking ratio, Sr = w0 τη/a, where w0 is the quiescent settling velocity, a is the particle radius and τη is the Kolmogorov timescale. This analysis predicts the topology of the concentration wake changes from a symmetric topology at Sr << 1 to an asymmetric topology at Sr >> 1 as the dominant mechanism of mass transfer changes. Particle tracking flow visualisations of small spheres releasing dye in turbulence confirm the existence of this change in mechanism at Sr = O(1). We complement these experiments with numerical simulations of the mass transfer from sinking particles. The transfer rate predicted by the simulation is found to be in good agreement with literature data for mass transfer to turbulent suspensions of solid particles and is consistent with asymptotic expressions for mass transfer in uniform flow when Sr >> 1. A decomposition of the convective fluxes confirms the transition in the transfer mechanism. At Sr = O(1), both mechanisms provide comparable contributions to transfer rate. Cross-correlation analysis reveals that particle-scale knowledge of both the recent strain and velocity history are required to predict the instantaneous transfer rate. Turbulence induced particle rotation has a modest suppression effect upon convective transfer by sinking
Unsteady forcing of turbulence by a randomly actuated impeller array
No full textWe investigate the unsteady forcing of turbulent flow in a well-stirred reactor using opposing arrays of pitched-blade impellers which randomly and independently reverse rotation. We systematically explore the dependence of the large-scale motions and the homogeneity and isotropy of the turbulence upon the forcing. We identify three dimensionless control parameters: the source fraction (the fraction of time spent in clockwise motion), the dimensionless forcing period and an impeller Reynolds number. We find the timescale of unsteady motion corresponds to the forcing period T, the average period of impeller reversal, independently of the impeller angular speed Ω and source fraction. As in jet-stirred tanks, unsteady forcing substantially increases the unsteady kinetic energy, energy dissipation, integral length scale and Taylor microscale Reynolds number (Rλ) and improves the homogeneity and isotropy of the flow, provided the source fraction is chosen optimally and the forcing period is sufficiently large (ΩT > 103); impeller Reynolds number has a relatively small influence.The forcing period must be matched to angular speed: decreasing the forcing period below this threshold results in a less intense, more inhomogeneous turbulent flow. Spectra of two-point velocity increments demonstrate that unsteady energy injection is dominated by axial shear generated across impellers and becomes less prominent at smaller scales.However, even at Rλ ≈ 354, the signature of this unsteady forcing can still be detected in near-dissipation-range statistics. These observations provide insight into optimisation of forcing and the mechanism of energy transfer when using unsteady forcing to generate turbulence in confined vessels
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