1. SFT
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  4. Application of Artificial Neural Netw...

Application of Artificial Neural Networks for Mathematical Modelling of Horizontal Jet Fires

Michał Lewak, Ph.D. Eng. , Jarosław Tępiński, Ph.D. Eng.

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DOI: 10.12845/sft.62.2.2023.2, pages: 34 – 48

Abstract

Aim: This article focuses on the use of artificial neural networks to mathematically describe the parameters that determine the size of a jet fire flame. To teach the neural network, the results of a horizontal propane jet fire, carried out experimentally and using CFD mathematical modelling, were used.

Project and methods: The main part of the work consisted of developing an artificial neural network to describe the flame length and propane-air mixing path lengths with good accuracy, depending on the relevant process parameters. Two types of data series were used to meet the stated objective. The first series of data came from field tests carried out by CNBOP-PIB and from research contained in scientific articles. The second type of data was provided by numerical calculations made by the authors. The methods of computational fluid mechanics were used to develop the numerical simulations. The ANSYS Fluent package was used for this purpose. Matlab 2022a was used to develop the artificial neural network and to verify it.

Results: Using the nftool function included in Matlab 2022a, an artificial neural network was developed to determine the flame length Lflame and the length of the Slift-off mixing path as a function of the diameter of the dnozzle and the mass flux of gas leaving the nozzle. Using Pearson’s correlation coefficient, a selection was made of the best number of neurons in the hidden layer to describe the process parameters. The neural network developed allows Lflame and Slift-off values to be calculated with good accuracy.

Conclusions: Artificial neural networks allow a function to be developed to describe the parameters that determine flame sizes in relation to process parameters. For this purpose, the results of the CFD simulations and the results of the jet fire experiments were combined to create a single neural network. The result is a ready-made function that can be used in programmes for the rapid determination of flame sizes. Such a function can support the process of creating scenarios in the event of an emergency. A correctly developed neural network provides opportunities for the mathematical description of jet fires wherever experimental measurements are not possible. Solution proposed by the authors does not require a large investment in ongoing calculations, as the network can be implemented in any programming language.

Keywords: computational fluid mechanics, artificial neural networks, jet fire

Type of article: original scientific article

Bibliography:

  1. Laboureur D.M., Gopalaswami N., Zhang B., Liu Y., Mannan M.S., Experimental study on propane jet fire hazards: Assessment of the main geometrical features of horizontal jet flames, ”Journal of Loss Prevention in the Process Industries” 2016, 41, 355–364, https://doi.org/10.1016/j.jlp.2016.02.013.
  2. Lewak M., Tępiński J., Klapsa W., Wykorzystanie modelu burzliwości -k-ω SST do modelowania matematycznego pożaru strumieniowego, SFT Vol. 59 Issue 1, 2022, pp. 28–40, https://doi.org/10.12845/sft.59.1.2022.1.
  3. Mashhadimoslem H., Ghaemi A., Palacios A., Analysis of deep learning neural network combined with experiments to develop predictive models for a propane vertical jet fire, “Heliyon” 2020, 6, 11, e05511, https://doi.org/10.1016/j.heliyon.2020.e05511.
  4. Levenberg K., A Method for the Solution of Certain Non-Linear 48 SAFETY & FIRE TECHNOLOGY SFT VOL. 62 ISSUE 2, 2023, PP. 34–48 Problems in Least Squares, “Quarterly of Applied Mathematics” 1944, 2 (2), 164–168, https://doi.org/10.1090/qam/10666.
  5. Marquardt D.W., An Algorithm for Least-Squares Estimation of Nonlinear Parameters, “SIAM Journal on Applied Mathematics” 1963, 11(2), 431–441, https://doi.org/10.1137/0111030.
  6. Narkhede M.V., Bartakke P.P., Sutaone M.S., A review on weight initialization strategies for neural networks, “Artificial Intelligence Review” 2022, Vol. 55 (2), 1–32, https://doi.org/10.1007/s10462-021-10033-z.
  7. Tępiński J., Połeć B., Badania na rzecz poprawy bezpieczeństwa w zakładach przemysłowych stwarzających zagrożenie poza swoim terenem, Wydawnictwo CNBOP, Józefów 2020, https://doi.org/10.17381/2020.1.
  8. Lewak M., Tępiński J., Lesiak P., Modelowanie matematyczne pożarów strumieniowych za pomocą obliczeniowej mechaniki płynów CFD, „Przemysł chemiczny” 2022, 101(5), 324–329, https://doi.org/10.15199/62.2022.5.6.
  9. Projekt nr DOB-BIO7/09/03/2015 pod tytułem „Program do oceny ryzyka wystąpienia awarii w obiektach przemysłowych stwarzających zagrożenie poza swoim terenem” finansowany przez Narodowe Centrum Badań i Rozwoju.
  10. Tępiński J., Lewak M., Lesiak P., Badania pożarów strumieniowych propanu przeprowadzone w dużej skali, „Przemysł Chemiczny” 2022, 101 (11), 950–956, https://doi.org/10.15199/62.2022.11.3.