Skip to main content
Log in

Bayesian estimate of pre-mixed and diffusive rate of heat release phases in marine diesel engines

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

The rate of heat released during the combustion in Diesel engines is important for many reasons, including performance evaluation, pollutant formation, and control. Combustion in Diesel engines can be generally divided into three phases: pre-mixed, diffusive or mixed-controlled, and late combustion. The objective of this paper is to estimate the rate of heat released by the fuel in a marine Diesel engine, in order to identify the pre-mixed and diffusive phases, using the Sampling Importance Resampling (SIR) Bayesian Particle Filter. Experimental pressure data obtained from a piezoelectric sensor, installed in a research marine diesel engine (MAN Innovator 4c), was used to feed the observation model in such Bayesian approach. The evolution model for the pressure was formulated in terms of a set of ordinary differential equations, coming from the First Law of Thermodynamics, together with a random walk model for the unknown state variable. The proposed approach was able to identify the pre-mixed and diffusive combustion phases, for different engine loads. Results were compared with a simple inversion procedure, showing a good agreement. The combustion ignition delay was also calculated, showing its variation with the engine load.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

A :

Area

A/F:

Air/fuel ratio

B :

Piston bore

CA:

Crankshaft angle

f :

Linear or non-linear function of the state variables

g :

Linear or non-linear function representing the observation model

h :

Heat transfer coefficient

LHV:

Lower heating value

m :

Mass

n :

Engine speed in Hz

n :

Vector of noise associated with the observation model

P :

Pressure

Q :

Heat

t :

Time

T :

Temperature

v :

Average gas velocity within the cylinder

v :

Vector of noise associated with the evolution model

V :

Volume

w :

Weights of particles

W :

Covariance matrix

x :

Mass fraction of burned fuel

y :

Vector of state variables

z :

Vector of observation variables

γ :

Polytropic coefficient

θ :

Crankshaft angle

π :

Probability density function

d:

Displaced

f:

Fuel

gas:

Gas mixture

m:

Mixture

meas:

Measured

mot:

Motored

r:

Reference

w:

Wall

References

  1. Heywood JB (1988) Internal combustion engine fundamentals. McGraw-Hill, New York

    Google Scholar 

  2. Qiu T, Li X, Lei Y, Liu X, Zhang C, Feng X, Xu H (2015) The prediction of fuel injection quality using a NO x sensor for the on-board diagnosis of heavy-duty diesel engines with SCR systems. Fuel 141:192–199

    Article  Google Scholar 

  3. ASTM (2014) Standard test method for cetane number of diesel fuel oil, ASTM D613

  4. Bodisco T, Choy SL, Brown RJ (2013) A Bayesian approach to the determination of ignition delay. Appl Therm Eng 60:79–87

    Article  Google Scholar 

  5. Mosbach S, Braumann A, Man PLW, Kastner CA, Brownbridge GPE, Kraft M (2012) Iterative improvement of Bayesian parameter estimates for an engine model by means of experimental design. Combust Flame 159:1303–1313

    Article  Google Scholar 

  6. Ferguson CR (1986) Internal combustion engines. Wiley, New York

    Google Scholar 

  7. Murayama T, Miyamoto N, Yamada T, Kawashima JI, Itow K (1982) A method to improve the solubility and combustion characteristics of alcohol-Diesel fuel blends. SAE International Congress and Exposition, Detroit, MI, SAE paper 821113

  8. Miyamoto N, Chikahisa T, Murayama T, Sawyer R (1985) Description and analysis of Diesel engine rate of combustion and performance using Wiebe’s functions, SAE International Congress and Exposition, Detroit, MI, SAE paper 850107

  9. Yeliana Y, Cooney C, Worm J, Michalek DJ, Naber JD (2011) Estimation of double-Wiebe function parameters using least square method for burn durations of ethanol-gasoline blends in spark ignition engine over variable compression ratios and EGR levels. Appl Therm Eng 31:2213–2220

    Article  Google Scholar 

  10. Ghojel JI (2010) Review of the development and applications of the Wiebe function: a tribute to the contribution of Ivan Wiebe to engine research. Int J Engine Res 11:297–312

    Article  Google Scholar 

  11. Borman G, Nishiwaki K (1987) Internal combustion engine heat transfer. Prog Energy Combust Sci 13:1–46

    Article  Google Scholar 

  12. Estumano DC, Hamilton FC, Colaço MJ, Leiroz AJK, Orlande HRB, Carvalho RN, Dulikravich GS (2015) Bayesian estimate of mass fraction of burned fuel in internal combustion engines using pressure measurements. In: Rodrigues et al. (eds) Engineering optimization IV. CRC Press, Eh Leiden, The Netherlands, pp 997–1003

    Google Scholar 

  13. K.Z. Mendera, A. Spyra, M. Smereka, Mass fraction burned analysis,Journal of KONES Internal Combustion Engines 3-4 (2002) 193:201

  14. Mittal M, Zhu G, Schock HJ, Stuecken T, Hung DLS (2008) Burn rate analysis of an ethanol-gasoline, dual fueled, spark ignition engine. In: ASME 2008 International Mechanical Engineering Congress and Exposition, Paper No. IMECE2008-66139. doi:10.1115/IMECE2008-66139

  15. Yeliana Y (2011) Parametric combustion modeling for ethanol- gasoline fuelled spark ignition engines. PhD Thesis, Dept. of Mech. Engineering, Michigan Technological University

  16. Yeliana Y, Cooney C, Worm J, Michalek D, Naber J (2008) Wiebe function parameter determination for mass fraction burn calculation in an ethanol-gasoline fuelled SI engine. J KONES Powertrain Transp 15:3

    Google Scholar 

  17. Mittal M, Zhu G, Schock H (2009) Fast mass-fraction-burned calculation using the net pressure method for real-time applications. Proc Inst Mech Eng Part D J Automob Eng. doi:10.1243/09544070JAUTO1006

    Google Scholar 

  18. Yeliana Y, Cooney C, Worm J, Naber J (2008) The calculation of mass fraction burn of ethanol-gasoline blended fuels using single and two-zone models. SAE Technical Paper 2008-01-0320. doi:10.4271/2008-01-0320

  19. Catania AE, Finesso R, Spessa E (2011) Predictive zero-dimensional combustion model for DI Diesel engine feed-forward control. Energy Convers Manag 52:3159–3175

    Article  Google Scholar 

  20. Dogahe MA (2012) Estimation of mass fraction of residual gases from cylinder pressure data and its application to modeling for SI engine. J Appl Math Islam Azad Univ Lahijan Iran 8(4):15–28

    Google Scholar 

  21. Chung J, Oh S, Min K, Sunwoo M (2013) Real-time combustion parameter estimation algorithm for light-duty Diesel engines using in-cylinder pressure measurement. Appl Therm Eng 60:33–43

    Article  Google Scholar 

  22. Finesso R, Spessa E (2014) A real time zero-dimensional diagnostic model for the calculation of in-cylinder temperatures, HRR and nitrogen oxides in Diesel engines. Energy Convers Manag 79:498–510

    Article  Google Scholar 

  23. Colaço MJ, Teixeira CV, Dutra LM (2010) Thermal analysis of a Diesel engine operating with Diesel-bioDiesel blends. Fuel 89:3742–37520

    Article  MATH  Google Scholar 

  24. Colaço MJ, Teixeira CV, Dutra LM (2010) Thermodynamic simulation and optimization of Diesel engines operating with Diesel and BioDiesel blends using experimental data. Inverse Probl Sci Eng 18:787–812

    Article  MATH  Google Scholar 

  25. Hamilton FC, Colaço MJ, Carvalho RN, Leiroz AJK (2014) Heat transfer coefficient estimation of an internal combustion engine using particle filters. Inverse Probl Sci Eng 22:483–506

    Article  Google Scholar 

  26. Maybeck P (1979) Stochastic models, estimation and control. Academic, New York

    MATH  Google Scholar 

  27. Kaipio J, Somersalo E (2004) Statistical and computational inverse problems. In: Applied mathematical sciences, vol 160. Springer, New York

  28. Kalman R (1960) A new approach to linear filtering and prediction problems. ASME J Basic Eng 82:35–45

    Article  Google Scholar 

  29. Winkler R (2003) An introduction to bayesian inference and decision. Probabilistic, Gainsville

    Google Scholar 

  30. Ristic B, Arulampalam S, Gordon N (2004) Beyond the kalman filter. Artech House, Boston

    MATH  Google Scholar 

  31. Anderson BDO, Moore JB (1979) Optimal filtering. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  32. Wan EA, Van der Merwe R (2000) The unscented Kalman Filter for nonlinear estimation. In: Proc. IEEE Symposium of Adaptive System for Signal Processing, Comm. and Control (AS-SPCC), Lake Louise, Alberta, Canada, pp 153–158

  33. Pitt MK, Shephard N (1999) Filtering via simulation: auxiliary particle filters. J Am Stat Assoc 94(446):590–599

    Article  MathSciNet  MATH  Google Scholar 

  34. Arulampalam S, Maskell S, Gordon N, Clapp T (2001) A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50:174–188

    Article  Google Scholar 

  35. Doucet A, Freitas N, Gordon N (2001) Sequential Monte Carlo methods in practice. Springer, New York

    Book  MATH  Google Scholar 

  36. Del Moral P, Doucet A, Jasra A (2006) Sequential Monte Carlo methods for Bayesian computation, In: Bayesian Statistics 8. Oxford University Press, New York

  37. Kantas N, Doucet A, Singh SS, Maciejowski JM (2009) An overview of sequential Monte Carlo methods for parameter estimation in general state-space models. In: Proceedings of 15th IFAC Symposium on Systems Identification

  38. Andrieu C, Doucet A, Holenstein R (2010) Particle Markov chain Monte Carlo. J R Stat Soc B 72(3):269–342

    Article  MathSciNet  MATH  Google Scholar 

  39. Chopin N, Jacob P, Papaspiliopoulos O (2011) SMC2, ArXiv Mathematics e-prints, 1101.1528, January

  40. Colaço MJ, Orlande HRB, Silva WB, Dulikravich GS (2012) Application of two Bayesian filters to estimate unknown heat fluxes in a natural convection problem. J Heat Transfer 134:092501–092511

    Article  Google Scholar 

  41. Pasqualette MA, Antunes JJM, Vieira DSN, Colaço MJ, Leiroz AJK (2014) Methods for the determination of the ignition delay in a marine Diesel engine operating with marine Diesel oil. In: VIII Brazilian Conference on Mechanical Engineering, August 10–15, Uberlândia, Brazil

  42. Assanis DN, Filipi ZS, Fiveland SB, Syrimis MA (2003) Predictive ignition delay correlation under steady-state and transient operation of a direct injection diesel engine. J Eng Gas Turbines Power 125:450–457

    Article  Google Scholar 

  43. Katrašnik T, Trenc F, Oprešnik SR (2006) A new criterion to determine the start of combustion in Diesel engines. J Eng Gas Turbines Power 128:928–933

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Brazilian agencies for the fostering of science, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) and Brazilan National Agency of Oil, Gas and Biofuels (ANP) for the financial support for this work. The support provided by Petrobras Research and Development Center (CENPES) and PEUGEOT (grant FAPERJ/PEUGEOT 111.161/2014) is also greatly appreciated. Mr. Marcelo Pasqualette and Ms. Fabiana Hamilton are also grateful to the PIBIC/UFRJ/CNPq and PRH37/ANP (http://prh.mecanica.ufrj.br) scholarships.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcelo J. Colaço.

Additional information

Technical Editor: Francis HR Franca.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pasqualette, M.A., Estumano, D.C., Hamilton, F.C. et al. Bayesian estimate of pre-mixed and diffusive rate of heat release phases in marine diesel engines. J Braz. Soc. Mech. Sci. Eng. 39, 1835–1844 (2017). https://doi.org/10.1007/s40430-016-0649-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40430-016-0649-9

Keywords

Navigation