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Communities around the world are increasingly interested in nature-based solutions to the mitigation of coastal risks by coastal forests, but it remains unclear how much protective benefits vegetation provides, particularly in the limit of highly energetic flows after tsunami impact. The current study, using a three-dimensional incompressible computational fluid dynamics model with a fluid–structure interaction approach, aims to quantify how energy reflection and dissipation vary with different degrees of rigidity and vegetation density of a coastal forest. We represent tree trunks as cylinders and use the elastic modulus of hardwood trees such as pine or oak to characterize the rigidity of these cylinders. The numerical results show that energy reflection increases with rigidity only for a single cylinder. In the presence of multiple cylinders, the difference in energy reflection created by varying rigidity diminishes as the number of cylinders increases. Instead of rigidity, we find that the blockage area created by the presence of multiple tree trunks dominates energy reflection. As tree trunks are deformed by the hydrodynamic forces, they alter the flow field around them, causing turbulent kinetic energy generation in the wake region. As a consequence, trees dissipate flow energy, highlighting coastal forests reducing the onshore energy flux of tsunamis by means of both reflection and dissipation. • The number of tree trunks and the spacing between them are more important for tsunami-risk mitigation than the species-specific properties. • Deflection of tree trunks alters the flow field around them, causing turbulent kinetic energy generation in the wake region. • Coastal forests reduces the onshore energy flux of tsunamis by means of both reflection and dissipation. [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 this work we present a robust interface coupling algorithm called Compact Interface quasi-Newton (CIQN). It is designed for computationally intensive applications using an MPI multi-code partitioned scheme. The algorithm allows to reuse information from previous time steps, feature that has been previously proposed to accelerate convergence. Through algebraic manipulation, an efficient usage of the computational resources is achieved by: avoiding construction of dense matrices and reduce every multiplication to a matrix–vector product and reusing the computationally expensive loops. This leads to a compact version of the original quasi-Newton algorithm. Altogether with an efficient communication, in this paper we show an efficient scalability up to 4800 cores. Three examples with qualitatively different dynamics are shown to prove that the algorithm can efficiently deal with added mass instability and two-field coupled problems. We also show how reusing histories and filtering does not necessarily makes a more robust scheme and, finally, we prove the necessity of this HPC version of the algorithm. The novelty of this article lies in the HPC focused implementation of the algorithm, detailing how to fuse and combine the composing blocks to obtain an scalable MPI implementation. Such an implementation is mandatory in large scale cases, for which the contact surface cannot be stored in a single computational node, or the number of contact nodes is not negligible compared with the size of the domain. [ABSTRACT FROM AUTHOR]