FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313479338> ?p ?o ?g. }

• W4313479338 endingPage "104372" @default.
• W4313479338 startingPage "104372" @default.
• W4313479338 abstract "Numerical modeling of high-pressure liquid fuel injection remains a challenge in various applications. Indeed, experimental observations have shown that injected liquid fuel jet undergoes a continuous change of state from classical two-phase atomization and spray droplets evaporation to a dense-fluid mixing phenomenon depending on the ambient pressure, temperature, and fuel properties. Accordingly, a predictive and efficient computational fluid dynamics (CFD) model that can represent the possible coexistence of subcritical and supercritical regimes during the fuel injection event is required. The widely used Lagrangian Discrete Droplet Method (DDM) requires parameter tuning of model constants and cannot model the dense near-nozzle region. Meanwhile, the high computational cost of Interface Capturing Methods (ICM) has prohibited their application to industrial cases. Thus, another alternative is an Eulerian Diffuse Interface Model (DIM), where the unresolved interface features are modeled instead of being tracked. Accordingly, the current work proposes a fully compressible multi-component two-phase real-fluid model (RFM) with a diffused interface and closed by a thermodynamic equilibrium tabulation method based on a real-fluid equation of state. The RFM model is complemented with a postulated surface density equation for fuel atomization modeling within the Large Eddy Simulation (LES) framework. The Engine Combustion Network (ECN) Spray A injector non-evaporating and nominal evaporating conditions are used as a reference for the proposed model validation. Simulations are performed using the proposed RFM model that has been implemented in the CONVERGE CFD solver. Under the non-evaporating condition, the RFM model can capture well the fuel mass distribution in the near-nozzle field, but also the interfacial surface area. Besides, the predicted drop size from simulations falls within the experimental data range. On the other hand, under the evaporating condition, spray liquid and vapor penetrations and fuel mixture fraction distribution are also accurately predicted. The vaporization effect on the surface area density is revealed to enhance surface generation in the dense spray region while reducing the surface density in the dilute spray region. The mean droplet size is also relatively reduced under the evaporating condition in the diluted spray region. Overall, the accuracy and computationally efficiency of the proposed RFM model coupled with the surface density equation for high-pressure fuel injection modeling are confirmed, allowing its use for high pressure industrial configurations in future studies." @default.
• W4313479338 created "2023-01-06" @default.
• W4313479338 creator A5002426781 @default.
• W4313479338 creator A5009478366 @default.
• W4313479338 creator A5029511911 @default.
• W4313479338 date "2023-03-01" @default.
• W4313479338 modified "2023-10-06" @default.
• W4313479338 title "Modeling and LES of high-pressure liquid injection under evaporating and non-evaporating conditions by a real fluid model and surface density approach" @default.
• W4313479338 cites W1523286183 @default.
• W4313479338 cites W1579300970 @default.
• W4313479338 cites W1970881453 @default.
• W4313479338 cites W1982733100 @default.
• W4313479338 cites W1986599790 @default.
• W4313479338 cites W1988075634 @default.
• W4313479338 cites W1992681681 @default.
• W4313479338 cites W1996006035 @default.
• W4313479338 cites W2002974847 @default.
• W4313479338 cites W2003753431 @default.
• W4313479338 cites W2010289433 @default.
• W4313479338 cites W2015437704 @default.
• W4313479338 cites W2022973729 @default.
• W4313479338 cites W2023248330 @default.
• W4313479338 cites W2027571497 @default.
• W4313479338 cites W2029310239 @default.
• W4313479338 cites W2034133890 @default.
• W4313479338 cites W2039419358 @default.
• W4313479338 cites W2045545335 @default.
• W4313479338 cites W2045843521 @default.
• W4313479338 cites W2049683124 @default.
• W4313479338 cites W2052297596 @default.
• W4313479338 cites W2058435561 @default.
• W4313479338 cites W2060786385 @default.
• W4313479338 cites W2061614228 @default.
• W4313479338 cites W2069166573 @default.
• W4313479338 cites W2071876135 @default.
• W4313479338 cites W2082335044 @default.
• W4313479338 cites W2089342178 @default.
• W4313479338 cites W2089972923 @default.
• W4313479338 cites W2090613357 @default.
• W4313479338 cites W2129288307 @default.
• W4313479338 cites W2135643278 @default.
• W4313479338 cites W2152394886 @default.
• W4313479338 cites W2168575889 @default.
• W4313479338 cites W2200256347 @default.
• W4313479338 cites W2303405018 @default.
• W4313479338 cites W2317389141 @default.
• W4313479338 cites W2483164779 @default.
• W4313479338 cites W2593441606 @default.
• W4313479338 cites W2596702664 @default.
• W4313479338 cites W2620801090 @default.
• W4313479338 cites W2742817315 @default.
• W4313479338 cites W2767325365 @default.
• W4313479338 cites W2773673993 @default.
• W4313479338 cites W2795969800 @default.
• W4313479338 cites W2897069287 @default.
• W4313479338 cites W2899418145 @default.
• W4313479338 cites W2904310738 @default.
• W4313479338 cites W2913832710 @default.
• W4313479338 cites W2980093910 @default.
• W4313479338 cites W2982306457 @default.
• W4313479338 cites W3012267662 @default.
• W4313479338 cites W3019156468 @default.
• W4313479338 cites W3047379523 @default.
• W4313479338 cites W3159653916 @default.
• W4313479338 cites W3163375809 @default.
• W4313479338 cites W3196875410 @default.
• W4313479338 cites W3197325642 @default.
• W4313479338 cites W4210497355 @default.
• W4313479338 cites W4214584177 @default.
• W4313479338 cites W4220703750 @default.
• W4313479338 cites W4236242143 @default.
• W4313479338 cites W4280588927 @default.
• W4313479338 cites W4281801964 @default.
• W4313479338 doi "https://doi.org/10.1016/j.ijmultiphaseflow.2022.104372" @default.
• W4313479338 hasPublicationYear "2023" @default.
• W4313479338 type Work @default.
• W4313479338 citedByCount "2" @default.
• W4313479338 countsByYear W43134793382023 @default.
• W4313479338 crossrefType "journal-article" @default.
• W4313479338 hasAuthorship W4313479338A5002426781 @default.
• W4313479338 hasAuthorship W4313479338A5009478366 @default.
• W4313479338 hasAuthorship W4313479338A5029511911 @default.
• W4313479338 hasBestOaLocation W43134793382 @default.
• W4313479338 hasConcept C105923489 @default.
• W4313479338 hasConcept C121332964 @default.
• W4313479338 hasConcept C121448008 @default.
• W4313479338 hasConcept C127413603 @default.
• W4313479338 hasConcept C135186712 @default.
• W4313479338 hasConcept C1633027 @default.
• W4313479338 hasConcept C178790620 @default.
• W4313479338 hasConcept C185592680 @default.
• W4313479338 hasConcept C18762648 @default.
• W4313479338 hasConcept C192562407 @default.
• W4313479338 hasConcept C196558001 @default.
• W4313479338 hasConcept C199360897 @default.
• W4313479338 hasConcept C2777871205 @default.
• W4313479338 hasConcept C2778770139 @default.
• W4313479338 hasConcept C2779985886 @default.

• next