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With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. [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]