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For complex engineering and scientific applications, Computational Fluid Dynamics (CFD) simulations require a huge amount of computational power. As such, it is of paramount importance to carefully assess the performance of CFD codes and to study them in depth for enabling optimisation and portability. In this paper, we study three complex CFD codes, OpenFOAM, Alya and CHORUS representing two numerical methods, namely the finite volume and finite-element methods, on both structured and unstructured meshes. To all codes, we apply a generic performance analysis method based on a set of metrics helping the code developer in spotting the critical points that can potentially limit the scalability of a parallel application. We show the root cause of the performance bottlenecks studying the three applications on the MareNostrum4 supercomputer. We conclude providing hints for improving the performance and the scalability of each application. [ABSTRACT FROM AUTHOR]

The main computing phases of numerical methods for solving partial differential equations are the algebraic system assembly and the iterative solver. This work focuses on the first task, in the context of a hybrid MPI+X paradigm. The matrix assembly consists of a loop over the elements, faces, edges or nodes of the MPI partitions to compute element matrices and vectors and then of their assemblies. In a MPI+X hybrid parallelism context, X has consisted traditionally of loop parallelism using OpenMP, with different techniques to avoid the race condition, but presenting efficiency or implementation drawbacks. We propose an alternative, based on task parallelism using some extensions to the OpenMP programming model. In addition, dynamic load balance will be applied, especially efficient in the presence of hybrid meshes. This paper presents the proposed methodology, its implementation and its validation through the solution of large computational mechanics problems up to 16k cores. [ABSTRACT FROM AUTHOR]

This work presents a parallel numerical strategy to transport Lagrangian particles in a fluid using a dynamic load balance strategy. Both fluid and particle solvers are parallel, with two levels of parallelism. The first level is based on a substructuring technique and uses message passing interface (MPI) as the communication library; the second level consists of OpenMP pragmas for loop parallelisation at the node level. When dealing with transient flows, there exist two main alternatives to address the coupling of these solvers. On the one hand, a single-code approach consists in solving the particle equations once the fluid solution has been obtained at the end of a time step, using the same instance of the same code. On the other hand, a multi-code approach enables one to overlap the transport of the particles with the next time-step solution of the fluid equations, and thus obtain asynchronism. In this case, different codes or two instances of the same code can be used. Both approaches will be presented. In addition, a dynamic load balancing library is used on the top of OpenMP pragmas in order to continuously exploit all the resources available at the node level, thus increasing the load balance and the efficiency of the parallelisation and uses the MPI. [ABSTRACT FROM AUTHOR]

Abstract: Computational modelling plays an important role in right ventricular (RV) haemodynamic analysis. However, current approaches use smoothed ventricular anatomies. The aim of this study is to characterise RV haemodynamics including detailed endocardial structures like trabeculae, moderator band, and papillary muscles. Four paired detailed and smoothed RV endocardium models (2 male and 2 female) were reconstructed from ex vivo human hearts high‐resolution magnetic resonance images. Detailed models include structures with ≥1 mm2 cross‐sectional area. Haemodynamic characterisation was done by computational fluid dynamics simulations with steady and transient inflows, using high‐performance computing. The differences between the flows in smoothed and detailed models were assessed using Q‐criterion for vorticity quantification, the pressure drop between inlet and outlet, and the wall shear stress. Results demonstrated that detailed endocardial structures increase the degree of intra‐ventricular pressure drop, decrease the wall shear stress, and disrupt the dominant vortex creating secondary small vortices. Increasingly turbulent blood flow was observed in the detailed RVs. Female RVs were less trabeculated and presented lower pressure drops than the males. In conclusion, neglecting endocardial structures in RV haemodynamic models may lead to inaccurate conclusions about the pressures, stresses, and blood flow behaviour in the cavity. [ABSTRACT FROM AUTHOR]

Targeted nasal drug delivery can provide improved efficacy for drug formulations to be delivered at high efficacy rates. Some parameters that influence drug delivery have a dependency on the patient's technique of administration and the spray device itself. When the different parameters, each having a specific range of values are combined, the combinatory permutations for studying its effects on particle deposition become large. In this study, we combine six input spray parameters (the spray half-cone angle, the mean spray exit velocity, the breakup length from the nozzle exit, the diameter of the nozzle spray device, the particle size, and the sagittal angle of the spray) with a range of values to produce 384 combinations of spray characteristics. This was repeated for three inhalation flow rates of 20, 40, and 60 L/min. To reduce the computational costs of a full transient Large Eddy Simulation flow field, we create a time-averaged frozen field and perform the time integration of particle trajectories through the flow field to determine the particle deposition in four anatomical regions of the nasal cavity (anterior, middle, olfactory and posterior) for each of the 384 spray field. A sensitivity analysis determined the significance of each input variable on the deposition. It was found the particle size distribution significantly affected deposition in the olfactory and posterior regions, while the spray device insertion angle was significant for deposition in the anterior and middle regions. Five machine learning models were evaluated based on 384 cases and it was found that despite the small sample dataset the simulation data was sufficient to provide accurate machine-learning predictions. [Display omitted] [ABSTRACT FROM AUTHOR]

The nasal valve is the narrowest part of the nasal airway which is responsible for the largest part of the nasal resistance. Even little changes in the aperture can affect the flow downstream through the nose significantly. Its principal function is to limit airflow for example during a rapid and short inhalation, also called a sniff. Coupling Computational Fluid Dynamics (CFD) with Fluid–Structure Interaction (FSI) allows solving and exchanging force and displacement between the solid and fluid domains and offers a more accurate representation of the physical system in confined flow cases. Furthermore, particle transport and deposition are performed in this study to reveal the effect of the complex coupling on the nasal cavity deposition of inhaled aerosols. Two different configurations are used to model the nasal valve and differences in magnitudes in deformations are observed during the sniff. A comparison between FSI results and the in-vivo evaluation of the deformation shows an acceptable agreement as to the first step of validation. In addition, the results demonstrated that FSI increases significantly the particle deposition in the nasal cavity and the micro-particle diameter is the critical range parameter to enhance deposition with nasal valve deformation during a sniff. • The results demonstrated that FSI increases significantly the particle deposition in the nasal cavity. • The micro-particle diameter is the critical range parameter to enhance deposition with nasal valve deformation during a sniff. • The Comparison between FSI results and the in-vivo evaluation of the deformation shows an acceptable agreement as first step of validation. [ABSTRACT FROM AUTHOR]

In view of the ongoing COVID-19 pandemic and its effects on global health, understanding and accurately modelling the propagation of human biological aerosols has become crucial. Worldwide, health professionals have been one of the most affected demographics, representing approximately 20% of all cases in Spain, 10% in Italy and 4% in China and US. Methods to contain and remove potentially infected aerosols during Aerosol Generating Procedures (AGPs) near source offer advantages in reducing the contamination of protective clothing and the surrounding theatre equipment and space. In this work we describe the application of computational fluid dynamics in assessing the performance of a prototype extraction hood as a means to contain a high speed aerosol jet. Whilst the particular prototype device is intended to be used during tracheotomies, which are increasingly common in the wake of COVID-19, the underlying physics can be adapted to design similar machines for other AGPs. Computational modelling aspect of this study was largely carried out by Barcelona Supercomputing Center using the high performance computational mechanics code Alya. Based on the high fidelity LES coupled with Lagrangian frameworks the results demonstrate high containment efficiency of generated particles is feasible with achievable air extraction rates. • Evaluation of a new device to reduce the risk of infection during Aerosol Generating Procedures for use in COVID-19 surgical tracheotomy. • High fidelity LES coupled with Lagrangian frameworks is used as results to demonstrate the efficiency. • Accurate numerical modelling of the propagation of human biological aerosols. [ABSTRACT FROM AUTHOR]

Accurate prediction of deformation and collapse of the upper airway during breathing is required for effective and personalised treatment of obstructive sleep apnoea (OSA). While numerical modelling techniques such as fluid–structure interaction (FSI) are promising, an outstanding challenge is to accurately predict the deformation of the airway during breathing and thus the occurrence of OSA. These difficulties arise because the effective stiffness of the soft tissue in the human upper airway varies due to neuromuscular effects on the stiffness of the underlying muscles. In addition, both the elasticity and anisotropy of the soft tissues along the upper airway are poorly characterised. Finally, gravitational effects on anatomic features are yet to be considered. In this study, a validated FSI technique is introduced that allows prediction of the extent and position of the major deformation in the upper airway. This technique is used to analyse the behaviour of the upper airway in the two most common sleeping positions and for a range of effective tissue stiffnesses. The results demonstrate that sleeping position, gravity and soft tissue stiffness (used here as a proxy for neuromuscular effects) are the main factors that affect upper airway collapse. Therefore, this study provides new insights into the mechanisms of OSA and a new methodology that significantly advances the patient-specific treatment of OSA. [ABSTRACT FROM AUTHOR]