New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization
Abstract
:1. Introduction
2. Experimental Setup
2.1. Engine and Test Cell Configuration
2.2. Properties of Fuels
3. Methodology
4. Numerical Setup
4.1. Virtual Model of the Engine
4.2. Combustion Modelling
4.2.1. Modelling Laminar Flame Speed
4.2.2. Modelling Turbulent Flame Speed
4.3. Validation of the Virtual Engine Model
5. Results and Discussion
5.1. Combustion of Methane
5.2. Combustion of Methane-Hydrogen Blends
5.3. Combustion of Hydrogen
5.4. Effects on Fuel Consumption
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- McKinsey & Company. Hydrogen Insights Report 2021 Hydrogen Council. 2021. Available online: www.hydrogencouncil.com (accessed on 25 August 2021).
- Höhne, N.; Kuramochi, T.; Warnecke, C.; Röser, F.; Fekete, H.; Hagemann, M.; Day, T.; Tewari, R.; Kurdziel, M.; Sterl, S.; et al. The Paris Agreement: Resolving the inconsistency between global goals and national contributions. Clim. Policy 2017, 17, 16–32. [Google Scholar] [CrossRef]
- Fuel Cells and Hydrogen Joint Undertaking (FCH). Hydrogen Roadmap Europe: A Sustainable Pathway for the European Energy Transition; Publications Office of the European Union: Luxembourg, 2019; p. 70. [Google Scholar] [CrossRef]
- Caglayan, D.G.; Heinrichs, H.U.; Robinius, M.; Stolten, D. Robust design of a future 100% renewable european energy supply system with hydrogen infrastructure. Int. J. Hydrogen Energy 2021, 46, 29376–29390. [Google Scholar] [CrossRef]
- Germane, G.J.; Wood, C.G.; Hess, C.C. Lean Combustion in Spark-Ignited Internal Combustion Engines—A Review; SAE Technical Papers: Warrendale, PA, USA, 1983. [Google Scholar] [CrossRef]
- Dale, J.; Checkel, M.; Smy, P. Application of high energy ignition systems to engines. Prog. Energy Combust. Sci. 1997, 23, 379–398. [Google Scholar] [CrossRef]
- López, J.; Novella, R.; Gomez-Soriano, J.; Martinez-Hernandiz, P.; Rampanarivo, F.; Libert, C.; Dabiri, M. Advantages of the unscavenged pre-chamber ignition system in turbocharged natural gas engines for automotive applications. Energy 2021, 218, 119466. [Google Scholar] [CrossRef]
- Novella, R.; Gomez-Soriano, J.; Martinez-Hernandiz, P.; Libert, C.; Rampanarivo, F. Improving the performance of the passive pre-chamber ignition concept for spark-ignition engines fueled with natural gas. Fuel 2021, 290, 119971. [Google Scholar] [CrossRef]
- Benajes, J.; Novella, R.; Gomez-Soriano, J.; Martinez-Hernandiz, P.; Libert, C.; Dabiri, M. Evaluation of the passive pre-chamber ignition concept for future high compression ratio turbocharged spark-ignition engines. Appl. Energy 2019, 248, 576–588. [Google Scholar] [CrossRef]
- Borges, C.P.; Sobczak, J.C.; Silberg, T.R.; Uriona-Maldonado, M.; Vaz, C.R. A systems modeling approach to estimate biogas potential from biomass sources in Brazil. Renew. Sustain. Energy Rev. 2021, 138, 110518. [Google Scholar] [CrossRef]
- Law, C.K.; Kwon, O.C. Effects of hydrocarbon substitution on atmospheric hydrogen-air flame propagation. Int. J. Hydrogen Energy 2004, 29, 867–879. [Google Scholar] [CrossRef]
- Li, Y.; Bi, M.; Li, B.; Zhou, Y.; Gao, W. Effects of hydrogen and initial pressure on flame characteristics and explosion pressure of methane/hydrogen fuels. Fuel 2018, 233, 269–282. [Google Scholar] [CrossRef]
- Salzano, E.; Cammarota, F.; Di Benedetto, A.; Di Sarli, V. Explosion behavior of hydrogen-methane/air mixtures. J. Loss Prev. Process Ind. 2012, 25, 443–447. [Google Scholar] [CrossRef]
- Di Sarli, V.; Di Benedetto, A.; Long, E.J.; Hargrave, G.K. Time-Resolved Particle Image Velocimetry of dynamic interactions between hydrogen-enriched methane/air premixed flames and toroidal vortex structures. Int. J. Hydrogen Energy 2012, 37, 16201–16213. [Google Scholar] [CrossRef] [Green Version]
- Sjeric, M.; Kozarac, D.; Bogensperger, M. Implementation of a Single Zone k-ϵ Turbulence Model in a Multi Zone Combustion Model; SAE Technical Papers: Warrendale, PA, USA, 2012. [Google Scholar] [CrossRef]
- Borgnakke, C.; Arpaci, V.S.; Tabaczynski, R.J. A Model for the Instantaneous Heat Transfer and Turbulence in a Spark Ignition Engine; SAE Technical Papers: Warrendale, PA, USA, 1980. [Google Scholar] [CrossRef]
- Matthews, R.D.; Chin, Y.W. A Fractal-Based SI Engine Model: Comparisons of Predictions with Experimental Data; SAE Technical Papers: Warrendale, PA, USA, 1991. [Google Scholar] [CrossRef] [Green Version]
- Poulos, S.G.; Heywood, J.B. The Effect of Chamber Geometry on Spark-Ignition Engine Combustion; SAE Technical Papers: Warrendale, PA, USA, 1983. [Google Scholar] [CrossRef]
- Bozza, F.; Gimelli, A.; Senatore, A.; Caraceni, A. A Theoretical Comparison of Various VVA Systems for Performance and Emission Improvements of SI-Engines; SAE Technical Papers: Warrendale, PA, USA, 2001. [Google Scholar] [CrossRef]
- Torregrosa, A.J.; Broatch, A.; Olmeda, P.; Aceros, S. Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine; SAE Technical Papers: Warrendale, PA, USA, 2021; pp. 1–10. [Google Scholar] [CrossRef]
- Giglio, V.; di Gaeta, A. Novel regression models for wiebe parameters aimed at 0D combustion simulation in spark ignition engines. Energy 2020, 210, 118442. [Google Scholar] [CrossRef]
- Lindström, F.; Ångström, H.E.; Kalghatgi, G.; Möller, C.E. An Empirical SI Combustion Model Using Laminar Burning Velocity Correlations; SAE Technical Papers: Warrendale, PA, USA, 2005. [Google Scholar] [CrossRef]
- Liu, J.; Dumitrescu, C.E. Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition. Appl. Energy 2019, 248, 95–103. [Google Scholar] [CrossRef]
- De Bellis, V.; Severi, E.; Fontanesi, S.; Bozza, F. Hierarchical 1D/3D Approach for the Development of a Turbulent Combustion Model Applied to a VVA Turbocharged Engine. Part II: Combustion Model. Energy Procedia 2014, 45, 1027–1036. [Google Scholar] [CrossRef] [Green Version]
- Daniela, T. A quasi-dimensional SI combustion model: A bi-fractal approach. Energy Procedia 2017, 126, 931–938. [Google Scholar] [CrossRef]
- Guijo-Rubio, D.; Durán-Rosal, A.; Gutiérrez, P.; Gómez-Orellana, A.; Casanova-Mateo, C.; Sanz-Justo, J.; Salcedo-Sanz, S.; Hervás-Martínez, C. Evolutionary artificial neural networks for accurate solar radiation prediction. Energy 2020, 210, 118374. [Google Scholar] [CrossRef]
- Reese, K.M. Deep learning artificial neural networks for non-destructive archaeological site dating. J. Archaeol. Sci. 2021, 132, 105413. [Google Scholar] [CrossRef]
- Flores-Fernández, J.M.; Herrera-López, E.J.; Sánchez-Llamas, F.; Rojas-Calvillo, A.; Cabrera-Galeana, P.A.; Leal-Pacheco, G.; González-Palomar, M.G.; Femat, R.; Martínez-Velázquez, M. Development of an optimized multi-biomarker panel for the detection of lung cancer based on principal component analysis and artificial neural network modeling. Expert Syst. Appl. 2012, 39, 10851–10856. [Google Scholar] [CrossRef]
- Lapuerta, M.; Armas, O.; Hernández, J. Diagnosis of DI Diesel combustion from in-cylinder pressure signal by estimation of mean thermodynamic properties of the gas. Appl. Therm. Eng. 1999, 19, 513–529. [Google Scholar] [CrossRef]
- Payri, F.; Molina, S.; Martín, J.; Armas, O. Influence of measurement errors and estimated parameters on combustion diagnosis. Appl. Therm. Eng. 2006, 26, 226–236. [Google Scholar] [CrossRef]
- Benajes, J.; Novella, R.; Gomez-Soriano, J.; Barbery, I.; Libert, C.; Rampanarivo, F.; Dabiri, M. Computational assessment towards understanding the energy conversion and combustion process of lean mixtures in passive pre-chamber ignited engines. Appl. Therm. Eng. 2020, 178, 115501. [Google Scholar] [CrossRef]
- Benajes, J.; Novella, R.; Gomez-Soriano, J.; Barbery, I.; Libert, C. Advantages of hydrogen addition in a passive pre-chamber ignited SI engine for passenger car applications. Int. J. Energy Res. 2021, 45, 13219–13237. [Google Scholar] [CrossRef]
- Wahiduzzaman, S.; Morel, T.; Sheard, S. Comparison of Measured and Predicted Combustion Characteristics of a Four-Valve SI Engine; SAE Transactions: Warrendale, PA, USA, 1993; pp. 810–819. [Google Scholar]
- Mirzaeian, M.; Millo, F.; Rolando, L. Assessment of the Predictive Capabilities of a Combustion Model for a Modern Downsized Turbocharged SI Engine; SAE Technical Paper: Warrendale, PA, USA, 2016. [Google Scholar]
- Huang, Z.; Zhang, Y.; Zeng, K.; Liu, B.; Wang, Q.; Jiang, D. Measurements of laminar burning velocities for natural gas-hydrogen-air mixtures. Combust. Flame 2006, 146, 302–311. [Google Scholar] [CrossRef]
- Ömer, G. Correlations of Laminar Combustion Data for Alternative S.I. Engine Fuels; SAE Technical Paper 841000: Warrendale, PA, USA, 1984; p. 26. [Google Scholar] [CrossRef]
- Di Sarli, V.; Di Benedetto, A. Laminar burning velocity of hydrogen-methane/air premixed flames. Int. J. Hydrogen Energy 2007, 32, 637–646. [Google Scholar] [CrossRef]
- Ma, F.; Wang, Y.; Liu, H.; Li, Y.; Wang, J.; Ding, S. Effects of hydrogen addition on cycle-by-cycle variations in a lean burn natural gas spark-ignition engine. Int. J. Hydrogen Energy 2008, 33, 823–831. [Google Scholar] [CrossRef]
- Perini, F.; Paltrinieri, F.; Mattarelli, E. A quasi-dimensional combustion model for performance and emissions of SI engines running on hydrogen-methane blends. Int. J. Hydrogen Energy 2010, 35, 4687–4701. [Google Scholar] [CrossRef]
- Ji, C.; Wang, D.; Yang, J.; Wang, S. A comprehensive study of light hydrocarbon mechanisms performance in predicting methane/hydrogen/air laminar burning velocities. Int. J. Hydrogen Energy 2017, 42, 17260–17274. [Google Scholar] [CrossRef]
- Smith, G.P.; Golden, D.M.; Frenklach, M.; Moriarty, N.W.; Eiteneer, B.; Goldenberg, M.; Bowman, C.T.; Hanson, R.K.; Song, S.; Gardiner, W.C., Jr.; et al. Current and future releases of GRI-Mech. GRI-Mech 3.0. Available online: http://www.me.berkeley.edu/gri_mech/ (accessed on 25 August 2021).
- Dong, C.; Zhou, Q.; Zhang, X.; Zhao, Q.; Xu, T.; Hui, S. Experimental study on the laminar flame speed of hydrogen/natural gas/air mixtures. Front. Chem. Eng. China 2010, 4, 417–422. [Google Scholar] [CrossRef]
- Ravi, S.; Petersen, E.L. Laminar flame speed correlations for pure-hydrogen and high-hydrogen content syngas blends with various diluents. Int. J. Hydrogen Energy 2012, 37, 19177–19189. [Google Scholar] [CrossRef]
- Bozza, F.; Gimelli, A.; Strazzullo, L.; Torella, E.; Cascone, C. Steady-State and Transient Operation Simulation of a “Downsized” Turbocharged SI Engine; SAE Technical Paper: Warrendale, PA, USA, 2007. [Google Scholar]
- Broatch, A.; Novella, R.; García-Tíscar, J.; Gomez-Soriano, J.; Pal, P. Analysis of combustion acoustic phenomena in compression–ignition engines using large eddy simulation. Phys. Fluids 2020, 32, 085101. [Google Scholar] [CrossRef]
- Broatch, A.; Novella, R.; García-Tíscar, J.; Gomez-Soriano, J.; Pal, P. Investigation of the effects of turbulence modeling on the prediction of compression-ignition combustion unsteadiness. Int. J. Engine Res. 2021, 1468087421990478. [Google Scholar] [CrossRef]
- Benajes, J.; Novella, R.; Gomez-Soriano, J.; Martinez-Hernandiz, P.; Libert, C.; Dabiri, M. Performance of the passive pre-chamber ignition concept in a spark-ignition engine for passenger car applications. In Proceedings of the SIA Powertrain Electronics, Paris, France, 12–13 June 2019. [Google Scholar]
- Experimental study on thermal efficiency and emission characteristics of a lean burn hydrogen enriched natural gas engine. Int. J. Hydrogen Energy 2007, 32, 5067–5075. [CrossRef]
- Performance study using natural gas, hydrogen-supplemented natural gas and hydrogen in AVL research engine. Int. J. Hydrogen Energy 1983, 8, 715–720. [CrossRef]
- Lim, G.; Lee, S.; Park, C.; Choi, Y.; Kim, C. Knock and Emission Characteristics of Heavy-Duty HCNG Engine with Modified Compression Ratios; SAE Technical Papers: Warrendale, PA, USA, 2013; Volume 2. [Google Scholar] [CrossRef]
- Singh, S.; Mishra, S.; Mathai, R.; Sehgal, A.K.; Suresh, R. Comparative Study of Unregulated Emissions on a Heavy Duty CNG Engine using CNG & Hydrogen Blended CNG as Fuels. SAE Int. J. Engines 2016, 9, 2292–2300. [Google Scholar] [CrossRef]
Engine | 4-Stroke SI |
---|---|
Number of cylinders [-] | 1 |
Displacement [cm] | 404 |
Bore-Stroke [mm] | 80.0–80.5 |
Compression ratio [-] | 13.4:1 |
Valvetrain [-] | DOHC |
Number of valves [-] | 2 intake and 2 exhaust |
Fuel injection system [-] | PFI (Pmax = 6 bar) |
Property | Unit | Methane CH4 | Hydrogen H2 |
---|---|---|---|
Molecular Weight | [u] | 16.04 | 2.02 |
A/Fst | [-] | 17.2 | 34.3 |
Lower Heating Value | [MJ/kg] | 50 | 120 |
Density | [kg/m] | 0.657 | 0.0823 |
Carbon Atoms per Molecule | [-] | 1 | 0 |
Hydrogen Atoms per Molecule | [-] | 4 | 2 |
RON | [-] | >120 | >130 |
Auto-ignition temperature | [K] | 600 | 645 |
Sweeping Variable | Unit | Range |
---|---|---|
Temperature | [K] | 800–300 |
Pressure | [bar] | 30–10 |
[-] | 2–1 | |
EGR | [%] | 40–0 |
H2 mass | [%] | 100–0 |
Parameter | Experiment | Simulation |
---|---|---|
CA10 [CAD] | −8.8 | −10.8 |
CA50 [CAD] | 4.8 | 1.7 |
CA90-10 [CAD] | 35.4 | 30.2 |
HRRmax [J/CAD] | 43.5 | 40.1 |
IMEP [bar] | 13.58 | 12.71 |
ISFC [g/kWh] | 171.9 | 167.0 |
Ind. eff. gross [%] | 42.78 | 43.06 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Molina, S.; Novella, R.; Gomez-Soriano, J.; Olcina-Girona, M. New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization. Energies 2021, 14, 6732. https://doi.org/10.3390/en14206732
Molina S, Novella R, Gomez-Soriano J, Olcina-Girona M. New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization. Energies. 2021; 14(20):6732. https://doi.org/10.3390/en14206732
Chicago/Turabian StyleMolina, Santiago, Ricardo Novella, Josep Gomez-Soriano, and Miguel Olcina-Girona. 2021. "New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization" Energies 14, no. 20: 6732. https://doi.org/10.3390/en14206732