Therapeutic Nanodevices

Lee, Stephen C.; Ruegsegger, Mark; Barnes, Philip D.; Smith, Bryan; Ferrari, Mauro

doi:10.1007/3-540-29838-x_10

Cited by 4 publications

(1 citation statement)

References 103 publications

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“…The interpolation increases the continuity required for the finite-element method (FEM)-H 1 -regularisation-SDE parametrisation framework developed by Boyko et al (2022) and used for the subsequent model fitting. The velocity vector is double-rotated (Lee et al, 2004) in the mean wind direction using a moving window approach. The direction of the mean wind for the rotation transform is calculated from the 30 m sonic anemometer using a moving average on a scale of 1 hr.…”

Section: Datasetmentioning

confidence: 99%

A stochastic stability equation for unsteady turbulence in the stable boundary layer

Boyko

Vercauteren

2023

Quart J Royal Meteoro Soc

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The atmospheric boundary layer is particularly challenging to model in conditions of stable stratification, which can be associated with intermittent or unsteady turbulence. We develop a modelling approach to represent unsteady mixing possibly associated with turbulence intermittency and with unresolved fluid motions, called sub‐mesoscale motions. This approach introduces a stochastic parametrisation by randomising the stability correction used in the classical Monin–Obhukov similarity theory. This randomisation alters the turbulent momentum diffusion and accounts for sporadic events that cause unsteady mixing. A data‐driven stability correction equation is developed, parametrised, and validated with the goal to be modular and easily combined with existing Reynolds‐averaged Navier–Stokes models. Field measurements are processed using a statistical model‐based clustering technique, which simultaneously models and classifies the non‐stationary stable boundary layer. The stochastic stability correction obtained includes the effect of the static stability of the flow on the resolved flow variables, and additionally includes random perturbations that account for localised intermittent bursts of turbulence. The approach is general and effectively accounts for the stochastic mixing effects of unresolved processes of possibly unknown origin.

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Section: Datasetmentioning

confidence: 99%

A stochastic stability equation for unsteady turbulence in the stable boundary layer

Boyko

Vercauteren

2023

Quart J Royal Meteoro Soc

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Nanodevice design through the functional abstraction of biological macromolecules

Bhalerao

Eteshola

Keener

et al. 2005

Applied Physics Letters

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Biologic macromolecules are prefabricated, functional nanocomponents readily incorporated into nanodevices. Semibiologic nanodevice design typically depends on knowledge of specific biomolecules by individual biologist designers, so individual devices seldom take full advantage of available biodiversity and are poorly optimized. Available informational resources (proteomic and genomic databases) were built to reflect evolutionary relationships between organisms, molecules, and biologic systems, and are lacking in their explicit functional properties. This limits their direct utility in nanodevice design. We discuss the need, and possible structure for an information framework that captures the function of biological macromolecules to enable rational nanodevice design and optimization.

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The Preparation and Evaluation of Sodium and Alkylammonium Montmorillonite and Polysaccharide Nanocomposites as Sustained Release Excipients

Rocha

Gomes²,

Dornelas

et al. 2008

Polymer-Plastics Technology and Engineering

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Therapeutic Nanodevices

Cited by 4 publications

References 103 publications

A stochastic stability equation for unsteady turbulence in the stable boundary layer

A stochastic stability equation for unsteady turbulence in the stable boundary layer

Nanodevice design through the functional abstraction of biological macromolecules

The Preparation and Evaluation of Sodium and Alkylammonium Montmorillonite and Polysaccharide Nanocomposites as Sustained Release Excipients

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