A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

Mehdi Jadidi, Luke Di Liddo and Seth B. Dworkin
Additional contact information
Mehdi Jadidi: Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
Luke Di Liddo: Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
Seth B. Dworkin: Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada

Energies, 2021, vol. 14, issue 5, 1-18

Abstract: Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction ( f v ) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is f v . The LSTM is shown to give accurate estimations of f v , achieving an average error (relative to CFD) in the peak f v of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations.

Keywords: soot concentration; estimator; neural network; LSTM; transient diffusion flame; computational fluid dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/5/1394/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/5/1394/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:5:p:1394-:d:509856

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().