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Języki publikacji
Abstrakty
One of the main problems of today’s automotive manufacturers are emission norms that are getting much stricter. According to these high demands, car manufacturers are developing new systems to keep exhaust emissions at the lowest possible level. The resonance expansion system for emissions reduction of internal combustion engines could decrease emissions production not only in modern vehicles but also in older vehicles by additional mounting on the exhaust system. This article shows the resonance expansion system technical solution and simulations of fluid flow done in flow simulation software. The resonance expansion system is also patented, and further experiments for design improvement are planned in the near future.
Słowa kluczowe
Rocznik
Tom
Strony
279--289
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
- Faculty of Mechanical Engineering, Technical University of Košice, Letná 9,04001 Košice, Slovakia
autor
- Faculty of Mechanical Engineering, Technical University of Košice, Letná 9,04001 Košice, Slovakia
Bibliografia
- 1. Pachiannan Tamilselvan, et al. 2019. “A Literature Review of Fuel Effects on Performance and Emission Characteristics of Low-Temperature Combustion Strategies.” Applied Energy 251: 113380. DOI: https://doi.org/10.1016/j.apenergy.2019.113380.
- 2. Elsayed Ahmed, et al. 2017. “Investigation of Baffle Configuration Effect on the Performance of Exhaust Mufflers.” Case Studies in Thermal Engineering 10: 86-94. DOI: https://doi.org/10.1016/j.csite.2017.03.006.
- 3. Mohamad Barhm, et al. 2020. “A Hybrid Method Technique for Design and Optimization of Formula Race Car Exhaust Muffler.” International Review of Applied Sciences and Engineering 11(2): 174-180. DOI: https://doi.org/10.1556/1848.2020.20048.
- 4. Chríbik Andrej, Marián Polóni, Matej Minárik, Radivoje Mitrovic, Zarko Miskovic. 2019 “The Effect of Inert Gas in the Mixture with Natural Gas on the Parameters of the Combustion Engine.” Computational and Experimental Approaches in Materials Science and Engineering 410: 26. DOI: https://doi.org/10.1007/978-3-030-30853-7_24.
- 5. Czech Piotr. 2011. „Diagnosing of disturbances in the ignition system by vibroacoustic signals and radial basis function - preliminary research”. Communications in Computer and Information Science 239: 110-117. DOI: https://doi.org/10.1007/978-3-642-24660-9_13. Springer, Berlin, Heidelberg. ISBN: 978-3-642-24659-3. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Modern transport telematics, 11th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 19-22, 2011.
- 6. Czech Piotr. 2013. „Diagnosing a car engine fuel injectors' damage”. Communications in Computer and Information Science 395: 243-250. DOI: https://doi.org/10.1007/978-3-642-41647-7_30. Springer, Berlin, Heidelberg. ISBN: 978-3-642-41646-0; 978-3-642-41647-7. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Activities of transport telematics, 13th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 23-26, 2013.
- 7. Czech Piotr. 2012. „Identification of Leakages in the Inlet System of an Internal Combustion Engine with the Use of Wigner-Ville Transform and RBF Neural Networks”. Communications in Computer and Information Science 329: 175-182. DOI: https://doi.org/10.1007/978-3-642-34050-5_47. Springer, Berlin, Heidelberg. ISBN: 978-3-642-34049-9; 978-3-642-34050-5. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Telematics in the transport environment, 12th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 10-13, 2012.
- 8. Chen Jun, Xiong Shi. 2011. “CFD Numerical Simulation of Exhaust Muffler.” Seventh International Conference on Computational Intelligence and Security. DOI: https://doi.org/10.1109/cis.2011.321.
- 9. Shao Ying-li. 2011. “A Study on Exhaust Muffler Using a Mixture of Counter-Phase Counteract and Split-Gas Rushing.” Procedia Engineering 15: 4409-4413. DOI: https://doi.org/10.1016/j.proeng.2011.08.828.
- 10. Om Ariara Guhan C.P., et al. 2018. “Exhaust System Muffler Volume Optimization of Light Commercial Vehicle Using CFD Simulation.” Materials Today: Proceedings 5(2): 8471-8479. DOI: https://doi.org/10.1016/j.matpr.2017.11.543.
- 11. Fu Jun, et al. 2015. “Modification of Exhaust Muffler of a Diesel Engine Based on Finite Element Method Acoustic Analysis.” Advances in Mechanical Engineering 7(4): 168781401557595. DOI: https://doi.org/10.1177/1687814015575954.
- 12. Kuric Ivan, et al. 2021. “Analysis of Diagnostic Methods and Energy of Production Systems Drives.” Processes 9(5): 843. DOI: https://doi.org/10.3390/pr9050843.
- 13. Lu Q., T. Tettamanti. 2021. “Impacts of Connected and Automated Vehicles on Freeway with Increased Speed Limit.” International Journal of Simulation Modelling 20(3): 453-464. DOI: https://doi.org/10.2507/ijsimm20-3-556.
- 14. Ojstersek Robert, et al. 2020. “Simulation Study of a Flexible Manufacturing System Regarding Sustainability.” International Journal of Simulation Modelling 19(1): 65-76. DOI: https://doi.org/10.2507/ijsimm19-1-502.
- 15. Wang Jie, Dong-Peng Yue. 2010. “The Modal Analysis of Automotive Exhaust Muffler Based on Pro/E and Ansys.” 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE). DOI: https://doi.org/10.1109/icacte.2010.5579259.
- 16. Kashikar Ajay, et al. 2021. “Development of Muffler Design and Its Validation.” Applied Acoustics 180: 108132. DOI: https://doi.org/10.1016/j.apacoust.2021.108132.
- 17. Mishra Prakash Chandra, et al. 2018. “Effect of Perforation on Exhaust Performance of a Turbo Pipe Type Muffler Using Methanol and Gasoline Blended Fuel: A Step to Nox Control.” Journal of Cleaner Production 183: 869-879. DOI: https://doi.org/10.1016/j.jclepro.2018.02.236.
- 18. Łazarz Boguslaw, Grzegorz Wojnar, Henryk Madej, Piotr Czech. 2009. „Evaluation of gear power losses from experimental test data and analytical methods”. Mechanika 6(80): 56-63. ISSN: 1392-1207.
- 19. Pavlenko Ivan, Saga Milan, Kuric Ivan, Kotliar Alexey, Basova Yevheniia, Trojanowska Justyna, Ivanov Vitalii. 2020. “Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks.” Materials 13(23): 5357. DOI: https://doi.org/10.3390/ma13235357
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-66400ae0-b790-4fba-b55a-00cee03d6182