arXiv subjects

Riccardo Scarcelli

Publications and source records attributed to Riccardo Scarcelli.

2 recordsLinked to original sources

arXiv2026

Direct numerical simulation of NOx formation in turbulent lean premixed hydrogen-air flames under engine-relevant conditions

In this study, direct numerical simulations (DNS) are employed to investigate NOx formation in turbulent lean premixed hydrogen-air flames under engine-relevant conditions. Various turbulence intensities and molecular transport models are examined to isolate the individual impacts of turbulence intensity, Lewis number, and preferential diffusion on local and global NO production. Results show that global NO production is significantly enhanced in all of the turbulent cases, reaching approximately five times the value of the 1D steady flame at a mixture residence time of 0.1 ms. Increasing turbulence intensity is found to have three competing effects on NO formation: (1) it strengthens turbulence-instability interactions by inducing local super-adiabatic hot spots and elevating the concentrations of key flame radicals within the flame brush, thereby promoting the NO reaction rate locally; (2) it accelerates the turbulent flame speed, reducing the flame-brush residence time and thus suppressing NO production globally; and (3) it reduces post-flame temperature fluctuations, suppressing thermal NO enhancement in the post-flame zone. As a result, the global NO production is slightly lower at higher turbulence intensities among all the turbulent cases considered. Lewis number effects are identified as the primary mechanism driving thermodiffusive NO enhancement, with preferential diffusion playing a secondary role, as evidenced by the nearly identical mean profiles of the NO reaction rate between unity Lewis number turbulent flames and their 1D steady flame counterparts in both the progress variable space and the residence time space. Finally, an excellent correlation between the peak conditional mean NO reaction rate and the stretch factor is identified, and a conceptual model is proposed to improve the predictions of global NO production in practical engine simulations.

physics.flu-dyn↗

arXiv2026

Turbulent mixing of a hydrogen jet in crossflow: direct numerical simulation and model assessment

A numerical study for a hydrogen (H2) jet in an air crossflow (JICF) was performed using direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) approaches, based on a geometry representative of key aspects of port fuel injection (PFI) in a H2-fueled heavy-duty internal combustion engine. The focus was placed on the H2 mixing process and the turbulent species flux model used in the latter two approaches. Based on the DNS data, the performance of LES and RANS on predicting the turbulent flow fields and mixing process was comprehensively evaluated. Results showed that LES performs very well in predicting both the mean velocity and the Reynolds stress. In contrast, RANS significantly under-predicts all Reynolds stress components, while predicting the mean flow field relatively well. Regarding the H2 mixing prediction, LES shows an excellent agreement with DNS, while RANS significantly under-predicts the mixing process. The underlying reasons for the poor performance of RANS were identified by extracting turbulent transport properties used in RANS approach from DNS data. It was found that the turbulent diffusivity used in RANS is much smaller than that derived from DNS, which is attributed to the over-prediction on turbulent Schmidt number (Sct), as well as the under-prediction on turbulent viscosity. By further analyzing the anisotropic components of Sct and the misalignment angle between turbulent species fluxes directly obtained from DNS and those predicted by the RANS mixing model, the commonly used assumption of isotropic turbulent diffusivity in RANS was demonstrated to be invalid for the present configuration. This study provided a unique DNS dataset for H2 jet in a crossflow relevant to H2 PFI engines and generated new insights on improved modeling of turbulent mixing.

physics.flu-dyn↗