Most widely used Reynolds-averaged Navier-Stokes (RANS) models employ the Boussinesq approximation, which assumes a linear relationship between the turbulent Reynolds stresses and the mean-velocity gradient tensor. This assumption, which can be very stringent, is more suited for simple shear-flows and is regarded as an important shortcoming for the improvement of the representation of turbulence in complex geometries. Correction of the local turbulence length scales, as achieved, for example, by the introduction of a correction term in the eddy-viscosity equation of a Spalart-Allmaras model, then indeed only allows relatively small corrections to the base-line model and may not be sufficiently flexible to adapt to common situations where the Reynolds stresses are not aligned with the velocity gradient tensor. For the variational data-assimilation step, we consider a vectorial source correction term in the momentum equations (output quantity) together with the Spalart-Allmaras model, which allows full flexibility to adapt to any mean-flow topology. We show how the machine-learning framework should be adapted, in particular with respect to the vectorial nature of the correction term and given invariance properties. As for the input quantities, we discuss the impact of considering either local quantities for the nondimensionalization or global ones that characterize the configuration. We showcase the procedure on the periodic hill configuration, for which a rich direct numerical simulation database is available in the literature: in particular, the availability of the full mean-flow solution for a range of Reynolds numbers and geometries ensures identification of “physical” correction terms and allows learning of turbulence models that accurately extrapolate to new Reynolds numbers and geometries.

Copper particles embedded into polymer surfaces by cold spray (CS) have been confirmed as a method for antifouling (AF) protection. CS is a low-heat coating process which can be used or employed over large areas. The key parameters that indicate AF potency are particle penetration depth and copper surface coverage. Adaptation of this process towards industrial level requires the process inputs leading to the best outputs to be identified. In this paper, copper particles were deposited onto two polymers used in marine applications with CS, however, without the use of gas-heating hence deposition was free of any additional heat input. Analysis was carried out to determine which process conditions and material properties can lead to most effective AF coating results.

Cold Spray is a novel technology for the application of coatings onto a variety of substrate materials. In this method, melting temperatures are not crossed and the bonding is realized by the acceleration of powder particles through a carrier gas in a converging-diverging nozzle and their high energy impact over a substrate material. The critical aspect of this technology is the acceleration process and the multiphase nature of it. Three different nozzle designs were experimented under constant conditions and their performance simulated using Computational Fluid Dynamics tools. The Deposition Efficiency was measured using titanium as feedstock material and it was shown that it decreases with the cross-sectional throat area of the nozzle. Computational results based on a one-way coupled multiphase approach did not agree with this observation, while more sophisticated modelling techniques with two-way couplings can partially provide high-quality outcomes, in agreement with experimental data.

Cold spray (CS) is attracting interest of research and industry due to its rapid, solid-state particle deposition process and respective advantages over conventional deposition technologies. The acceleration of the particles is critical to the efficiency of CS, and previous investigations rarely consider the particle feed rate. However, because higher particle loadings are typically used in the process, the effect of this cannot be assumed negligible. This study therefore investigates the particle velocities in the supersonic jet of an advanced CS system at low- and high pressure levels and varying particle feed rates using particle image velocimetry. The particle dispersion and velocity evolution along the jet axis were investigated for several feedstock materials. It was found that the average particle velocity noticeably decreases with increasing particulate loading in all cases. The velocity distribution and particle dispersion were also observed to be influenced by the feed rate. Effects are driven by both mass loading and volume fraction, depending on the feedstock’s particle velocity parameter. Increased particle feed rates hence affect the magnitude and distribution of impact velocity and consequently the efficiency of CS. In particular, numerical models neglecting this interconnection are required to be further improved, based on these experimental studies.