Fast CFD Diesel Engine Modelling Using the 1-Dimensional SprayLet Approach Brandenburg University of Technology

Format:

Book

Conference/Event

Author/Creator:

Kurapati, Vinaykumar Reddy, author.

Contributor:

Anders, Borg

Mauss, Fabian

Seidel, Lars

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Spray modelling plays a key role in engine simulations to understand fuel propagation and mixing, combustion, pollutant formation and energy efficiency. The grid dependency, need of calibration of several spray parameters, complexity associated with validation and high computational demand associated with Spray modelling are addressed with 1-dimentional SprayLet model. This work focuses on enhancing the SprayLet model approach with a dual emphasis on computational efficiency and grid independence for advanced engine simulations. Key spray characteristics, such as vapor and liquid penetration lengths, have been systematically evaluated as they play pivotal roles in understanding fuel evaporation, spray-wall interactions, and mixture formation within engines. The SprayLet model has undergone substantial improvements, encompassing the integration of standard sub-models, multiple injector/nozzle handling capabilities, and extensions to the CONVERGE CFD code and Combustion Progress Variable (CPV) combustion model. A rigorous validation process has been implemented using various engine injection conditions as a benchmark. Through detailed comparisons with experimental data and the 3D Stochastic spray model, the validation phase underscores the SprayLet model's exceptional grid independence and computational efficiency. It demonstrates precise predictions of in-cylinder pressure, temperature distributions, and velocity profiles, along with substantial reductions of 95% in computational time. The subsequent verification phase explores the model's adaptability to diverse scenarios, with variations in Start of Injection (SOI) and injection rail pressure. The results confirm its robustness in handling distinct SOI and fuel characteristics while maintaining precise predictions of pressure and the Rate of Heat Release (RoHR). The model's capabilities extend to substantial reductions in computational time while ensuring accuracy. Its grid independence, simple calibration, ease of validation and computational efficiency position it as a promising spray model for optimizing engine performance and reducing emissions

Notes:

Vendor supplied data

Publisher Number:

2024-01-2684

Access Restriction:

Restricted for use by site license