Modern and highly competitive industry seeks components with high strength and fatigue resistance. Both of these properties may be improved by peening of the component surface and the standard peening processes, such as the shot peening, are widely used in both automotive and aerospace industries. The laser shock peening (LSP), i.e. hardening of the material surface by a laser-induced shock wave, is a modern alternative to the standard peening. Concurrently, the industrial applications of LSP are promoted by recently emerged affordable high power-density lasers. However, the nascent LSP applications are still mostly a trial-and-error processes based on an extensive experimental testing. Consequently, we focused on a highly application-driven development of a framework for LSP modeling, and the internal workings and results of which are the focus of the present contribution.

The immersed boundary (IB) method is an approach in the computational fluid dynamics in which complex geometry conforming meshes are replaced by simple ones and the true simulated geometry is projected onto the simple mesh by a scalar field and adjustment of governing equations. Such an approach is particularly advantageous in topology optimizations (TO) where it allows for substantial speed-up since a single mesh can be used for all the tested topologies. In our previous work, we linked our custom IB variant, the hybrid fictitious domain-immersed boundary method (HFDIB), with a TO framework and successfully carried out an optimization under laminar flow conditions. However, to allow for optimizations of reallife components, the IB approach needs to be coupled with an affordable turbulence modeling. In this contribution, we focus on extending the HFDIB approach by the possibility to perform Reynolds-averaged simulations (RAS). In particular, we implemented the k − ω turbulence model and wall functions for closure variables and velocity.

Particle-laden flow is prevalent both in nature and in industry. Its appearance ranges from the transport of riverbed sediments towards the magma flow; from the deposition of catalytic material inside particulate matter filters in automotive exhaust gas aftertreatment towards the slurry transport in dredging operations. In this contribution, we focus on the particle-resolved direct numerical simulation (PR-DNS) of the particle-laden flow. Such a simulation combines the standard Eulerian approach to computational fluid dynamics (CFD) with inclusion of particles via a variant of the immersed boundary method (IBM) and tracking of the particles movement using a discrete element method (DEM). Provided the used DEM allows for collisions of arbitrarily shaped particles, PR-DNS is based (almost) entirely on first principles, and as such it is a truly high-fidelity model. The downside of PR-DNS is its immense computational cost. In this work, we focus on three possibilities of alleviating the computational cost of PR-DNS: (i) replacing PR-DNS by PR-LES or PR-RANS, while the latter requires combining IBM with wall functions; (ii) improving efficiency of DEM contact solution via adaptively refined virtual mesh; and (iii) developing a method of model order reduction specifically tailored to PR-DNS of particle-laden flows.