Computational study of the ECN spray C via one-way coupling of internal nozzle flow and ensuing spray

Guo, Hengjie; Torelli, Roberto

doi:10.1016/j.jaerosci.2023.106243

Journal of Aerosol Science

2023

DOI: 10.1016/j.jaerosci.2023.106243

|View full text |Cite

Computational study of the ECN spray C via one-way coupling of internal nozzle flow and ensuing spray

Hengjie Guo¹,

Roberto Torelli²

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A data-driven phase change model for injection flow modeling

Li,

Zhao,

Cheng

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

A deep learning approach is developed to swiftly evaluate phase change in computational fluid dynamics (CFD) simulations of a multi-component, liquid–gas two-phase injection flow. This method significantly improves computational efficiency by using a deep feedforward neural network (DFNN) to replace the complex iterative solution of multi-species vapor–liquid equilibrium (VLE). The DFNN takes instantaneous pressure, temperature, and system composition as input and predicts the corresponding phase equilibrium state. A parametric study was conducted to optimize the neural network's hyperparameters, including the activation function, number of hidden layers, and neurons per hidden layer. The rate of phase change is then calculated as a linear relaxation toward phase equilibrium, guiding subsequent computational steps in the CFD solver. A case study was performed to test the proposed methodology, involving the injection of a superheated liquid ethanol–water mixture into a gaseous nitrogen environment. The simulation results and computational cost were examined. It is found that the DFNN model, while accurately representing the non-ideal non-equilibrium phase change of a multi-component injection flow, speeds up the VLE solution by four orders of magnitude, leading to a 30%–40% reduction in overall flow simulation time. This model shows promise for injection flow simulations, especially for systems with a large number of compositions, such as sustainable aviation fuels.

show abstract

A data-driven phase change model for injection flow modeling

Li,

Zhao,

Cheng

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

show abstract

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.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Computational study of the ECN spray C via one-way coupling of internal nozzle flow and ensuing spray

Cited by 1 publication

References 28 publications

A data-driven phase change model for injection flow modeling

A data-driven phase change model for injection flow modeling

Contact Info

Product

Resources

About