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In dilute turbulent particle-laden flows, such as atmospheric dispersion of pollutants or virus particles, the dynamics of tracer-like to low inertial particles are significantly altered by the fluctuating motion of the carrier fluid phase. Neglecting the effects of fluid velocity fluctuations on particle dynamics causes poor prediction of particle transport and dispersion. To account for the effects of fluid phase fluctuating velocity on the particle transport, stochastic differential equations coupled with large eddy simulation are proposed to model the fluid velocity seen by the particle. The drift and diffusion terms in the stochastic differential equation are modelled using neural networks ('neural stochastic differential equations'). The neural networks are trained with direct numerical simulations (DNS) of decaying homogeneous isotropic turbulence at low and moderate Reynolds numbers. The predictability of the proposed models are assessed through a priori analyses and a posteriori simulations of decaying homogeneous isotropic turbulence at low-to-high Reynolds numbers. Total particle fluctuating kinetic energy are under-predicted by 40% with no model, compared to the DNS data. In contrast, the proposed model predictions match total particle fluctuating kinetic energy to within 5% of the DNS data for low to high-inertia particles. For inertial particles, the model matches the variance of uncorrelated particle velocity to within 10% of DNS results, compared to 60-70% under-prediction with no model. It is concluded that the proposed mathematical and computational framework is applicable for flow configurations involving tracer and inertial particles, such as transport and dispersion of pollutants or virus particles.

For many of the one billion sufferers of respiratory diseases worldwide, managing their disease with inhalers improves their ability to breathe. Poor disease management and rising pollution can trigger exacerbations which require urgent relief. Higher drug deposition in the throat instead of the lungs limits the impact on patient symptoms. To optimise delivery to the lung, patient-specific computational studies of aerosol inhalation can be used. However in many studies, inhalation modelling does not represent an exacerbation, where the patient's breath is much faster and shorter. Here we compare differences in deposition of inhaler particles in the airways of a healthy male, female lung cancer and child cystic fibrosis patient. We aimed to evaluate deposition differences during an exacerbation with image-based healthy and diseased patient models. We found that during an exacerbation, particles progressing to the lower airways were distributed similarly to those inhaled during healthy breathing, but fewer in quantity. Throat deposits were halved in the healthy patient compared to the diseased patients under extreme inhalation, due to changes in the detailed shape of the throat. Our results identify that the modelled upper airway must be patient-specific, and an exacerbating profile tested for optimal measurement of reliever inhaler deposition.

The structural complexity of cold-water corals is threatened by ocean acidification. Increased porosity and weakening of structurally critical parts of the reef framework may lead to rapid physical collapse on an ecosystem scale, reducing their potential for biodiversity support. We can use computational models to describe the mechanisms leading to reef-crumbling. However, the implementation of such models into an efficient predictive tool that allows us to determine risk and timescales of reef collapse is missing. Here, we identified possible surrogate models to represent the branching architecture of the cold-water coral species Lophelia pertusa. For length scales greater than 13 cm, a continuum finite element mechanical approach can be used to analyse mechanical competence whereas at smaller length scales, mechanical surrogate models need to explicitly account for the statistical differences in the structure. We showed large morphological variations between L. pertusa colonies and branches, as well as dead and live skeletal structures, which need to be considered for the development of rapid monitoring tools for predicting risk of cold-water coral reefs crumbling. This will allow us to investigate timescales of changes, including the impact of exposure times to acidified waters on reef-crumbling.

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