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LES Investigation of ECN Spray G2 with an Eulerian Stochastic Field Cavitation Model
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Due to an ongoing trend of high injection pressures in the realm of internal combustion engines, the role of cavitation that typically happens inside the injector nozzle has become increasingly important. In this work, a large Eddy Simulation (LES) with cavitation modeled on the basis of an Eulerian Stochastic Field (ESF) method and a homogeneous mixture model is performed to investigate the role of cavitation on the Engine Combustion Network (ECN) spray G2. The Eulerian stochastic field cavitation model is coupled to a pressure based solver for the flow, which lowers the computational cost, thereby making the methodology highly applicable to realistic injector geometries. Moreover, the nature of the Eulerian stochastic field method makes it more convenient to achieve a high scalability when applied to parallel cases, which gives the method the edge over cavitation models that are based on Lagrangian tracking.
The result of the Eulerian stochastic field simulation is compared against that from a typical single volume fraction solver for validation. Vortex structures and its correspondence to cavitation are shown, and the behavior of the size Probability Density Function (PDF) at different probe locations at different times are acquired to demonstrate the capability of the Eulerian stochastic field model to capture cavitation and potentially providing more statistical information in each cell as compared to the typical single volume fraction solver. Major cavitation zones that are observed in the result indicates the important role of cavitation in ECN spray G2, therefore inferring a need to take cavitation into consideration in future spray studies.
CitationChen, B. and Oevermann, M., "LES Investigation of ECN Spray G2 with an Eulerian Stochastic Field Cavitation Model," SAE Technical Paper 2018-01-0291, 2018, https://doi.org/10.4271/2018-01-0291.
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