Development of a PN Surrogate Model based on Mixture Quality in a Modern GDI Engine Oxford Brookes University

Format:

Book

Conference/Event

Author/Creator:

Sciortino, Davide Domenico, author.

Contributor:

Biagiotti, Federico

Bonatesta, Fabrizio

Cary, Mark

Hopkins, Edward

Jiang, Changzhao

Verma, Sunny

Witt, Dennis

Conference Name:

15th International Conference on Engines & Vehicles (2021-09-12 : Capri, Italy)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines. The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomisation and secondary droplets break-up. A validated approach, based on the Bai-Onera model of droplet-wall interaction, is used to capture the details of liquid film formation. A multi-component surrogate fuel blend model reproduces the relevant characteristics of the E5 95RON gasoline used in parallel experiments. A fixed, but region-specific, wall temperature scheme is used for the in-cylinder simulations, based on available experimental data. Regression techniques were used to construct the PN surrogate model, through the identification of relevant relationships between experimental engine-out PN emission levels and modelled air-fuel mixture quality indicators. To maximise model usefulness and applicability, these indicators are then correlated to engine control parameters and easily-accessible measurements. The results show that engine-out PN depends largely on the phasing of the injection process, the strength of fuel atomisation and the in-cylinder fuel distribution before the combustion event. Within limits, engine sooting tendencies can be reliably predicted without reliance on combustion characteristics, which are complex to measure in real time

Notes:

Vendor supplied data

Publisher Number:

2021-24-0013

Access Restriction:

Restricted for use by site license