IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v304y2024ics0360544224019091.html
   My bibliography  Save this article

Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool

Author

Listed:
  • Farhan, Muhammad
  • Chen, Tianhao
  • Rao, Anas
  • Shahid, Muhammad Ihsan
  • Liu, Yongzheng
  • Ma, Fanhua

Abstract

Hydrogen and its careers have a lot of potential in transportation industry due to lesser emissions and better performance. Performance of hydrogen enriched compressed natural gas (HCNG) fueled engine is limited by Knock. The purpose of this study is to investigate knock intensity (KI) by different convective heat transfer models. This study can be used to train electronic control unit (ECU) of engine operating on different loads from low to high speed. In present study, experimentation have been performed on HCNG fueled spark ignition engine at different operating conditions by varying hydrogen amount in HCNG, by varying load of engine, by varying exhaust gas recirculation (EGR) rate in air and by varying speed of engine to calculate in-cylinder pressure, knock intensity (KI) and heat transfer rate (HTR). Six convective heat transfer models (Assanis, Eichelberg, Han, Hohenberg, Nusselt and Woschni) have been incorporated in quasi dimensional combustion model (QDCM) to calculate the parameters of HCNG engine. Comparative and predictive analysis using artificial neural network fitting tool (ANNFT) of aforementioned simulated models have been performed with experimental findings to attain the efficient model for knock intensity of HCNG engine. Woschni model, predicts the minimum error of 0.37 % & 0.86 % b/w experimental and simulated in-cylinder pressure and indicated mean effective pressure respectively. Han model predicts the minimum error of 3.8 % b/w experimental and simulated knock intensity operating at 11.2 % EGR. Maximum error of 20.3 % at 2.4 % EGR is attained by using Woschni model in knock intensity calculation. The best suited algorithm for present data set is Bayesian regularization utilized in ANNFT to predict knock intensity effectively. The findings of this study can be utilized in the development of HCNG engine.

Suggested Citation

  • Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Liu, Yongzheng & Ma, Fanhua, 2024. "Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224019091
    DOI: 10.1016/j.energy.2024.132135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224019091
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132135?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. Suleiman Hassan Otuoze & Dexter V. L. Hunt & Ian Jefferson, 2021. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    2. Chiong, Meng-Choung & Kang, Hooi-Siang & Shaharuddin, Nik Mohd Ridzuan & Mat, Shabudin & Quen, Lee Kee & Ten, Ki-Hong & Ong, Muk Chen, 2021. "Challenges and opportunities of marine propulsion with alternative fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    4. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    5. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    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. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Liu, Yongzheng & Ma, Fanhua, 2024. "Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different l," Energy, Elsevier, vol. 302(C).
    2. Hai, Tao & Hussein Kadir, Dler & Ghanbari, Afshin, 2023. "Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses," Energy, Elsevier, vol. 276(C).
    3. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    4. Rao, Xiang & Sheng, Chenxing & Guo, Zhiwei & Dai, Leyang & Yuan, Chengqing, 2023. "A novel finding on tribological, emission, and vibration performances of diesel engines linking to graphene-attapulgite lubricants additives under hot engine tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    5. Chang, Mengzhao & Park, Suhan, 2023. "Predictions and analysis of flash boiling spray characteristics of gasoline direct injection injectors based on optimized machine learning algorithm," Energy, Elsevier, vol. 262(PA).
    6. Shi, Ting & Wang, Huaiyu & Yang, Wenming & Peng, Xueyuan, 2024. "Mathematical modeling and optimization of gas foil bearings-rotor system in hydrogen fuel cell vehicles," Energy, Elsevier, vol. 290(C).
    7. Ahmed, Shoaib & Li, Tie & Yi, Ping & Chen, Run, 2023. "Environmental impact assessment of green ammonia-powered very large tanker ship for decarbonized future shipping operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    8. Zhou, Mengmeng & Wang, Shuai & Luo, Kun & Fan, Jianren, 2022. "Three-dimensional modeling study of the oxy-fuel co-firing of coal and biomass in a bubbling fluidized bed," Energy, Elsevier, vol. 247(C).
    9. Nicola Baldo & Matteo Miani & Fabio Rondinella & Clara Celauro, 2021. "A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    10. Yuan, Chenheng & Peng, Shizhuo & Zhou, Lifu, 2023. "Multi-field coupling effect of injection on dynamics and thermodynamics of a linear combustion engine generator with slow compression and fast expansion," Energy, Elsevier, vol. 270(C).
    11. Chen, Guisheng & Sun, Min & Li, Junda & Wang, Jiguang & Shen, Yinggang & Liang, Daping & Xiao, Renxin, 2024. "Study on high-altitude ceiling strategy of compression ignition aviation piston engines based on BP-NSGA II algorithm optimization," Energy, Elsevier, vol. 294(C).
    12. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    13. Ma, Yinjie & Yang, Dong & Xie, Deyi & E, Jiaqiang, 2024. "Investigating the effect of fuel properties and environmental parameters on low-octane gasoline-like fuel spray combustion and emissions using machine learning-global sensitivity analysis method," Energy, Elsevier, vol. 306(C).
    14. Pinyi Su & Muhammad Imran & Muhammad Nadeem & Shamsheer ul Haq, 2023. "The Role of Environmental Law in Farmers’ Environment-Protecting Intentions and Behavior Based on Their Legal Cognition: A Case Study of Jiangxi Province, China," Sustainability, MDPI, vol. 15(11), pages 1-22, May.
    15. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    16. Kirankumar, K.R. & Kumar, G.N. & Kamath, Nagaraja & Gangadharan, K.V., 2024. "Experimental investigation and optimization of performance, emission, and vibro-acoustic parameters of SI engine fueled with n-propanol and gasoline blends using ANN-GA coupled with NSGA3-modified TOP," Energy, Elsevier, vol. 306(C).
    17. Yang, Jinxin & Wang, Huaiyu & Ji, Changwei & Chang, Ke & Wang, Shuofeng, 2023. "Investigation of intake closing timing on the flow field and combustion process in a small-scaled Wankel rotary engine under various engine speeds designed for the UAV application," Energy, Elsevier, vol. 273(C).
    18. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.
    19. Hamnas, Amina & Unnikrishnan, G., 2023. "Bio-lubricants from vegetable oils: Characterization, modifications, applications and challenges – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    20. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).

    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:304:y:2024:i:c:s0360544224019091. 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.