IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v289y2024ics0360544223031018.html
   My bibliography  Save this article

Numerical study of the effect of ultrasound waves on the turbulent flow with chemical reaction

Author

Listed:
  • shateri, Amirali
  • jalili, Bahram
  • saffar, Saber
  • Jalili, Payam
  • Domiri Ganji, Davood

Abstract

In this study, a numerical analysis of turbulent flow in a combustion chamber with a Reynolds number of 16700 was conducted, focusing on the application of a 20 kHz ultrasound wave from a bowl-shaped diffusion source for 1 s to reduce process temperature within 5 ms. The study employed the k-ω model, with turbulence-chemistry interaction modeled using the eddy dissipation concept to achieve this. Additionally, the relocation of nozzles was explored, increasing CO2 combustion from 0.31 to 0.34 by decreasing the distance between them. The most optimal distance for locating the nozzles was identified as Di 0.0.1 (Di = 4.58 mm), which exhibited a higher burning rate and increased velocity and temperature during the process. Furthermore, a spray angle of 45° was determined as the optimal angle for minimizing fuel film on the walls and improving combustion. The application of ultrasound waves facilitated the transfer of flame concentration from the walls to the chamber's center, effectively reducing unburnt hydrocarbons on the walls. Notably, the propagation of ultrasound waves significantly accelerated the combustion process by 0.5 ms, setting this study apart from previous investigations.

Suggested Citation

  • shateri, Amirali & jalili, Bahram & saffar, Saber & Jalili, Payam & Domiri Ganji, Davood, 2024. "Numerical study of the effect of ultrasound waves on the turbulent flow with chemical reaction," Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223031018
    DOI: 10.1016/j.energy.2023.129707
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223031018
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129707?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Duan, Xiongbo & Liu, Jingping & Yuan, Zhipeng & Guo, Genmiao & Liu, Qi & Tang, Qijun & Deng, Banglin & Guan, Jinhuan, 2018. "Experimental investigation of the effects of injection strategies on cycle-to-cycle variations of a DISI engine fueled with ethanol and gasoline blend," Energy, Elsevier, vol. 165(PB), pages 455-470.
    2. Ehsaniderakhshan, Faeze & Mazaheri, Kiumars & Mahmoudi, Yasser, 2020. "Large eddy simulation on combustion noise in a non-premixed turbulent free flame: Effect of oxygen enhancement," Energy, Elsevier, vol. 210(C).
    3. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    2. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    3. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    4. Szodrai, Ferenc & Lakatos, Ákos & Kalmár, Ferenc, 2016. "Analysis of the change of the specific heat loss coefficient of buildings resulted by the variation of the geometry and the moisture load," Energy, Elsevier, vol. 115(P1), pages 820-829.
    5. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    6. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    7. Masood, Zahid & Khan, Shahroz & Qian, Li, 2021. "Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine," Renewable Energy, Elsevier, vol. 173(C), pages 827-848.
    8. Catapano, Francesco & Di Iorio, Silvana & Magno, Agnese & Vaglieco, Bianca Maria, 2022. "Effect of fuel quality on combustion evolution and particle emissions from PFI and GDI engines fueled with gasoline, ethanol and blend, with focus on 10–23 nm particles," Energy, Elsevier, vol. 239(PB).
    9. Zou, Rongwei & Yang, Qiliang & Xing, Jianchun & Zhou, Qizhen & Xie, Liqiang & Chen, Wenjie, 2024. "Predicting the electric power consumption of office buildings based on dynamic and static hybrid data analysis," Energy, Elsevier, vol. 290(C).
    10. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    11. Zhenbin Chen & Jiaojun Deng & Haisheng Zhen & Chenyu Wang & Li Wang, 2022. "Experimental Investigation of Hydrous Ethanol Gasoline on Engine Noise, Cyclic Variations and Combustion Characteristics," Energies, MDPI, vol. 15(5), pages 1-17, February.
    12. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    13. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    14. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    15. Bordbari, Mohammad Javad & Seifi, Ali Reza & Rastegar, Mohammad, 2018. "Probabilistic energy consumption analysis in buildings using point estimate method," Energy, Elsevier, vol. 142(C), pages 716-722.
    16. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
    17. Xinyan Wang & Hua Zhao, 2022. "Modelling Study of Cycle-To-Cycle Variations (CCV) in Spark Ignition (SI)-Controlled Auto-Ignition (CAI) Hybrid Combustion Engine by Using Reynolds-Averaged Navier–Stokes (RANS) and Large Eddy Simulat," Energies, MDPI, vol. 15(12), pages 1-21, June.
    18. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    19. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    20. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223031018. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.