Efficient simulation and auto-calibration of soot particle processes in Diesel engines. (15th March 2020)
- Record Type:
- Journal Article
- Title:
- Efficient simulation and auto-calibration of soot particle processes in Diesel engines. (15th March 2020)
- Main Title:
- Efficient simulation and auto-calibration of soot particle processes in Diesel engines
- Authors:
- Wu, Shaohua
Akroyd, Jethro
Mosbach, Sebastian
Brownbridge, George
Parry, Owen
Page, Vivian
Yang, Wenming
Kraft, Markus - Abstract:
- Graphical abstract: Highlights: The Moment Projection Method is used to simulate soot formation in engines. Seven soot model parameters are calibrated through Hooke-Jeeves optimisation. Data from a heavy-duty Diesel engine at 18 operating points are used for calibration. Significantly improved agreement between model and experiment is obtained. Abstract: Parameters describing soot particle processes are generally derived from a limited number of experimental studies. These parameters then have to be carefully calibrated for different operating conditions in internal combustion engine applications. This paper presents an innovative calibration procedure for soot simulation in Diesel engines. A Diesel engine is simulated using the Stochastic Reactor Model engine code, which is implemented with the Moment Projection Method for handling the soot particle dynamics. The main advantage of the engine-soot model is its low computational cost. The model is then coupled with an advanced statistical toolkit, Model Development Suite, where the Hooke-Jeeves algorithm is adopted to calibrate seven soot model parameters automatically based on the measurement data. The ability of the integrated code for soot model calibration is evaluated by simulating the soot formation and oxidation processes in a heavy-duty Diesel engine which is operated under 18 different conditions. Results suggest that the integrated code is able to calibrate the soot model parameters effectively. A significantGraphical abstract: Highlights: The Moment Projection Method is used to simulate soot formation in engines. Seven soot model parameters are calibrated through Hooke-Jeeves optimisation. Data from a heavy-duty Diesel engine at 18 operating points are used for calibration. Significantly improved agreement between model and experiment is obtained. Abstract: Parameters describing soot particle processes are generally derived from a limited number of experimental studies. These parameters then have to be carefully calibrated for different operating conditions in internal combustion engine applications. This paper presents an innovative calibration procedure for soot simulation in Diesel engines. A Diesel engine is simulated using the Stochastic Reactor Model engine code, which is implemented with the Moment Projection Method for handling the soot particle dynamics. The main advantage of the engine-soot model is its low computational cost. The model is then coupled with an advanced statistical toolkit, Model Development Suite, where the Hooke-Jeeves algorithm is adopted to calibrate seven soot model parameters automatically based on the measurement data. The ability of the integrated code for soot model calibration is evaluated by simulating the soot formation and oxidation processes in a heavy-duty Diesel engine which is operated under 18 different conditions. Results suggest that the integrated code is able to calibrate the soot model parameters effectively. A significant improvement in the match between the simulation results and experimental soot emission is obtained after calibration. … (more)
- Is Part Of:
- Applied energy. Volume 262(2020)
- Journal:
- Applied energy
- Issue:
- Volume 262(2020)
- Issue Display:
- Volume 262, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 262
- Issue:
- 2020
- Issue Sort Value:
- 2020-0262-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-03-15
- Subjects:
- Diesel engine -- Soot -- Calibration
Power (Mechanics) -- Periodicals
Energy conservation -- Periodicals
Energy conversion -- Periodicals
621.042 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03062619 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apenergy.2019.114484 ↗
- Languages:
- English
- ISSNs:
- 0306-2619
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1572.300000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12950.xml