Extended computation of the viscous Rayleigh-Taylor instability in a horizontally confined flow

In this article, the classical Rayleigh-Taylor instability is extended to situations where the fluid is completely confined, in both the vertical and horizontal directions. This article starts with the two-dimensional (2D) viscous periodic case with finite height where the effect of adding surface tension to the interface is analyzed. This problem is simulated from a dual perspective: first, the linear stability analysis obtained when the Navier-Stokes equations are linearized and regularized in terms of density and viscosity; and second, looking at the weakly compressible version of a multiphase smoothed particle hydrodynamics (WCSPH) method. The evolution and growth rates of the different fluid variables during the linear regime of the SPH simulation are compared to the computation of the eigenvalues and eigenfunctions of the viscous version of the Rayleigh-Taylor stability (VRTI) analysis with and without surface tension. The most unstable mode, which has the maximal linear growth rate obtained with both approaches, as well as other less unstable modes with more complex structures are reported. The classical horizontally periodic (VRTI) case is now adapted to the case where two additional left and right walls are included in the problem, representing the cases where a two-phase flow is confined in a accelerated tank. This 2D case where no periodic assumptions are allowed is also solved using both techniques with tanks of different sizes and a wide range of Atwood numbers. The agreement with the linear stability analysis obtained by a Lagrangian method such as multiphase WCSPH is remarkable.The research leading to these results was undertaken as part of the SLOWD project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant No. 815044. L.M.G. acknowledges the financial support from the Spanish Ministry for Science, Innovation and Universities (MCIU) under Grant No. RTI2018-096791-B-C21, Hidrodinámica de elementos de amortiguamiento del movimiento de aerogeneradores flotantes

Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations

The main goal of this paper is to provide a Reduced Order Model (ROM) able to predict the liquid induced dissipation of the violent and vertical sloshing problem for a wide range of liquid viscosities, surface tensions and tank filling levels. For that purpose, the Delta Smoothed Particle Hydrodynamics (δ-SPH) formulation is used to build a database of simulation cases where the physical parameters of the liquid are varied. For each simulation case, a bouncing ball-based equivalent mechanical model is identified to emulate sloshing dynamics. Then, an interpolating hypersurface-based ROM is defined to establish a mapping between the considered physical parameters of the liquid and the identified ball models. The resulting hypersurface effectively estimates the bouncing ball design parameters while considering various types of liquids, producing results consistent with SPH test simulations. Additionally, it is observed that the estimated bouncing ball model not only matches the liquid induced dissipation but also follows the liquid center of mass and presents the same sloshing force and phase-shift trends when varying the tank filling level. These findings provide compelling evidence that the identified ROM is a practical tool for accurately predicting critical aspects of the vertical sloshing problem while requiring minimal computational resources

Experimental and Numerical Characterization of Violent Sloshing Flows Using a Single Degree of Freedom Approach

In this work, the most fundamental aspects of an aeronautical sloshing problem have been studied using an alternative and simplified model. This model consists of a single degree of freedom version of the original problem which keeps the essence of the fluid structure interaction and also the most relevant physical aspects of the industrial case. Two independent methodologies have been used: first an experimental rig has been designed to measure and visualize different magnitudes of the problem and also a smoothed particle hydrodynamics formulation has been adapted to obtain a local representation of the flow interaction. Two very different fluids in terms of viscosity have been tested, and the differences in terms of the characteristics of the sloshing regimes, free surface fragmentation and relative kinetic energy have been described and compared. Apart from the comparison of the results obtained by both methodologies in terms of tank acceleration, sloshing forces and free surface evolution, a deep study of the sloshing force has been performed. This study focuses on a deeper understanding of the different aspects that constitute the sloshing force, such as its synchronization with the tank movement, the relation to the movement of the liquid’s center of mass and the physical projection of the force on the pressure and viscous parts. Additionally, a reconstruction of the sloshing force as a sum of the pressure signal recorded by a finite number of pressure sensors has been also performed

Nonlinear reduced-order model for vertical sloshing by employing neural networks

The aim of this work is to provide a reduced-order model to describe the dissipative behavior of nonlinear vertical sloshing involving Rayleigh–Taylor instability by means of a feed forward neural network. A 1-degree-of-freedom system is taken into account as representative of fluid–structure interaction problem. Sloshing has been replaced by an equivalent mechanical model, namely a boxed-in bouncing ball with parameters suitably tuned with performed experiments. A large data set, consisting of a long simulation of the bouncing ball model with pseudo-periodic motion of the boundary condition spanning different values of oscillation amplitude and frequency, is used to train the neural network. The obtained neural network model has been included in a Simulink® environment for closed-loop fluid–structure interaction simulations showing promising performances for perspective integration in complex structural system

Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations

The main goal of this paper is to provide a Reduced Order Model (ROM) able to predict the liquid induced dissipation of the violent and vertical sloshing problem for a wide range of liquid viscosities, surface tensions and tank filling levels. For that purpose, the Delta Smoothed Particle Hydrodynamics (δ-SPH) formulation is used to build a database of simulation cases where the physical parameters of the liquid are varied. For each simulation case, a bouncing ball-based equivalent mechanical model is identified to emulate sloshing dynamics. Then, an interpolating hypersurface-based ROM is defined to establish a mapping between the considered physical parameters of the liquid and the identified ball models. The resulting hypersurface effectively estimates the bouncing ball design parameters while considering various types of liquids, producing results consistent with SPH test simulations. Additionally, it is observed that the estimated bouncing ball model not only matches the liquid induced dissipation but also follows the liquid center of mass and presents the same sloshing force and phase-shift trends when varying the tank filling level. These findings provide compelling evidence that the identified ROM is a practical tool for accurately predicting critical aspects of the vertica

Risk of COVID-19 after natural infection or vaccinationResearch in context

Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health

Risk of COVID-19 after natural infection or vaccinationResearch in context

Summary: Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health