IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v262y2023ipas0360544222021880.html
   My bibliography  Save this article

Predictions and analysis of flash boiling spray characteristics of gasoline direct injection injectors based on optimized machine learning algorithm

Author

Listed:
  • Chang, Mengzhao
  • Park, Suhan

Abstract

The main purpose of this study is to make a machine model that can predict the characteristics of flash boiling spray according to the injector design parameters and the injection conditions, and then use the machine learning model to further analyze the spray characteristics. In order to achieve described purpose, this study uses three decision tree algorithms (random forest (RF), gradient boosted regression tree (GBRT) and extreme gradient boosting (XGB)) to build machine learning models, and utilizes the tree-structured parzen estimator (TPE) for farther improvement of models' performance. The influence of injector design parameters and injection conditions on spray characteristics was analyzed, basing on the constructed machine learning model. Finally, the spray characteristic map was made by predicting the spray characteristics under 1 million conditions, and the method of using the spray characteristic map to design the spray characteristics was proposed. The results have shown that TPE-GBRT has the most accurate prediction result for spray tip penetration (STP), and TPE-XGB has the most accurate result for spray downstream angle (SA_down). The feature importance analysis indicated the greater importance of the injection conditions over the injector design parameters for the spray characteristics. Further, by analyzing the spray characteristics map, the injection conditions have less design freedom, while injector parameters can assume different values to achieve the same spray characteristics.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222021880
    DOI: 10.1016/j.energy.2022.125304
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222021880
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125304?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. Tian, Junjian & Liu, Yu & Bi, Haobo & Li, Fengyu & Bao, Lin & Han, Kai & Zhou, Wenliang & Ni, Zhanshi & Lin, Qizhao, 2022. "Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model," Energy, Elsevier, vol. 239(PA).
    2. Chu, Huaqiang & Han, Weiwei & Cao, Wenjian & Gu, Mingyan & Xu, Guangju, 2019. "Effect of methane addition to ethylene on the morphology and size distribution of soot in a laminar co-flow diffusion flame," Energy, Elsevier, vol. 166(C), pages 392-400.
    3. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    4. Jiang, Changzhao & Parker, Matthew C. & Butcher, Daniel & Spencer, Adrian & Garner, Colin P. & Helie, Jerome, 2019. "Comparison of flash boiling resistance of two injector designs and the consequences on downsized gasoline engine emissions," Applied Energy, Elsevier, vol. 254(C).
    5. Yu, Shenghao & Yin, Bifeng & Bi, Qinsheng & Chen, Chen & Jia, Hekun, 2021. "Experimental and numerical investigation on inner flow and spray characteristics of elliptical GDI injectors with large aspect ratio," Energy, Elsevier, vol. 224(C).
    6. 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).
    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. Chang, Mengzhao & Kim, Huijun & Zhou, Bo & Park, Suhan, 2023. "Spray collapse resistance of GDI injectors with different hole structures under flash boiling conditions," Energy, Elsevier, vol. 268(C).
    2. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors," Energy, Elsevier, vol. 93(P2), pages 1704-1710.
    3. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
    4. Liu, Yang & Cheng, Xiaobei & Qin, Longjiang & Wang, Xin & Yao, Junjie & Wu, Hui, 2020. "Experimental investigation on soot formation characteristics of n-heptane/butanol isomers blends in laminar diffusion flames," Energy, Elsevier, vol. 211(C).
    5. Hazar, Hanbey & Gul, Hakan, 2016. "Modeling analysis of chrome carbide (Cr3C2) coating on parts of combustion chamber of a SI engine," Energy, Elsevier, vol. 115(P1), pages 76-87.
    6. Chen, Zhijie & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & E, Jiaqiang, 2023. "Multi-factor impact mechanism on the performance of high temperature proton exchange membrane fuel cell," Energy, Elsevier, vol. 278(PB).
    7. Zhou, Yifan & Qi, Wenyuan & Zhang, Yuyin, 2020. "Investigation on cyclic variation of diesel spray and a reconsideration of penetration model," Energy, Elsevier, vol. 211(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. Zhang, Zhiqing & Li, Jiangtao & Tian, Jie & Dong, Rui & Zou, Zhi & Gao, Sheng & Tan, Dongli, 2022. "Performance, combustion and emission characteristics investigations on a diesel engine fueled with diesel/ ethanol /n-butanol blends," Energy, Elsevier, vol. 249(C).
    10. Chen, Hao & Wang, Yu & Zuo, Mingsheng & Zhang, Chao & Jia, Ninghong & Liu, Xiliang & Yang, Shenglai, 2022. "A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network," Energy, Elsevier, vol. 239(PC).
    11. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
    12. 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).
    13. Peng, Qingguo & Wu, Yifeng & E, Jiaqiang & Yang, Wenming & Xu, Hongpeng & Li, Zhenwei, 2019. "Combustion characteristics and thermal performance of premixed hydrogen-air in a two-rearward-step micro tube," Applied Energy, Elsevier, vol. 242(C), pages 424-438.
    14. Li, Dexin & Zuo, Wei & Li, Qingqing & Zhang, Guangde & Zhou, Kun & E, Jiaqiang, 2023. "Effects of pulsating flow on the performance of multi-channel cold plate for thermal management of lithium-ion battery pack," Energy, Elsevier, vol. 273(C).
    15. Irfan Javid & Rozaida Ghazali & Waddah Saeed & Tuba Batool & Ebrahim Al-Wajih, 2023. "CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images," Sustainability, MDPI, vol. 16(1), pages 1-13, December.
    16. Ruomiao Yang & Tianfang Xie & Zhentao Liu, 2022. "The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines," Energies, MDPI, vol. 15(9), pages 1-16, April.
    17. Garg, A. & Lam, Jasmine Siu Lee, 2017. "Design of explicit models for estimating efficiency characteristics of microbial fuel cells," Energy, Elsevier, vol. 134(C), pages 136-156.
    18. 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.
    19. 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).
    20. 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.

    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:262:y:2023:i:pa:s0360544222021880. 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.