This work presents an implementation and evaluation of the Σ-Y atomization model for Diesel spray CFD simulations. The Σ-Y model is based on an Eulerian representation of the spray atomization and dispersion by means of a single-fluid variable density turbulent flow within a RANS framework. The locally homogeneous flow approach has been applied in order to develop a spray vaporization model based on state relationships. A finite-volume solver for model equations has been created using theOpenFOAM CFD open-source C++ library. Model predictions have been compared to experimental data from free Diesel sprays under nonvaporizing and vaporizing conditions. High-speed imaging, PDPA, and Rayleigh-scattering measurements have been used in order to assess the CFD model. Accurate predictions of liquid and vapor spray penetration, as well as axial velocity and mixture fraction profiles, can be simultaneously achieved for a wide range of injection pressure and ambient conditions, despite only having qualitatively correct predictions of droplet size. The success of these predictions supports the mixing-limited vaporization hypothesis. Model accuracy is better for high ambient density and injection pressure conditions. It is proposed that under low ambient density and injection pressure conditions, interfacial dynamics become more important and the single velocity field assumption is less appropriate.
This work evaluates the performance of the Σ-Y Eulerian atomization model at reproducing the internal structure of a diesel spray in the nearfield. In the study, three different computational domains have been used in order to perform 3D and 2D coupled simulations, where the internal nozzle flow and external spray are modeled in one continuous domain, and 2D decoupled simulations, where only the external spray is modeled. While the 3D simulation did the best job of capturing the dense zone of the spray, the 2D simulations also performed well, with the coupled 2D simulation slightly outperforming the decoupled simulation. The similarity in results between the coupled and the decoupled simulation show that internal and external flow calculations can be performed independently. In addition, the use of spatially averaged nozzle outlet conditions, in the case of an axisymmetric (single-hole) convergent nozzle, leads to a slightly worse near-field spray predictions but to an accurate far-field ones. Finally, a novel constraint on turbulent driven mixing multiphase flows is introduced which prevents the slip velocity from exceeding the magnitude of the turbulent fluctuations through a realizable Schmidt number. This constraint increased model stability, allowing for a 4x increase in Courant number.
ElsevierMolina Alcaide, SA.; García Martínez, A.; Pastor Enguídanos, JM.; Belarte Mañes, E.; Balloul, I. (2015). Operating range extension of RCCI combustion concept from low to full load in a heavy-duty engine. Applied Energy. 143:211-227. doi:10.1016/j.apenergy.2015.01.035. overlimit function is used to select the best engine settings for each operating point. 23Finally, engine emissions and performance results from that RCCI operation are 24 compared with conventional Diesel combustion (CDC). 25Results suggest that double injection strategies should be used for RCCI operation from 26 low to mid load. However, from high to full load operation, single injection strategies 27 should be used, mainly to avoid excessive in-cylinder pressure gradients. In addition, it 28 is confirmed the suitability of RCCI combustion to overcome the soot-NOx trade-off 29 characteristic of CDC, from 6 to 24 bar of BMEP, while improving fuel consumption. 30
A comparison between the Σ-Y atomization model and a classical DDM approach has been carried out for diesel spray computational fluid dynamics (CFD) simulations. The Σ-Y model, originally proposed by Vallet and Borghi, is based on a Eulerian representation of the spray atomization and dispersion by means of a single-fluid variable density turbulent flow. The locally homogeneous flow approach has been applied to develop a spray vaporization model based on state relationships. A finite-volume solver for model equations has been created using the OpenFOAM CFD open-source C++ library. In the case of the Lagrangian-discrete droplet method (DDM) approach, the original dieselFoam solver of OpenFOAM is used. Model predictions have been compared to experimental measurements of free diesel sprays under vaporizing conditions from the database of the Engine Combustion Network (ECN). Accurate predictions of liquid and vapor spray penetration, as well as mixture fraction, can be achieved for the nominal condition with both models, although DDM simulations tend to be less accurate. Additionally, the near nozzle flow structure of the Spray A condition of ECN is also studied with both models. The conclusion is a more accurate prediction of the near-field internal structure of the spray in the case of the Eulerian model, due to both a higher mesh resolution and a more adequate modeling approach. Consequently, results shown in this work put in evidence the benefits of using a Eulerian model to predict qualitatively and accurately the diesel spray behavior under different ambient conditions and injection pressures.
Simulating liquid spray first and second atomization is not an easy task. Many models have been developed over the past years, but Eulerian ones have proved their better performance for the dense zone of the spray. In this work a new compressible Eulerian model is used to compute the internal flow together with the spray. Up to five two-equation turbulence models have been tested and its influence is remarkable in terms of spray behavior, but also greatly affects the mass flow rate and the momentum flux. At the end, SST k − ω model proves to be best than the others. Additionally, different types of inlet boundary conditions have been also tested and analyzed. Results when compared with previously obtained experimental data show that the commonly used for external flow time-varying velocity boundary condition gives also good performance for the internal flow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.