IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1266-d505702.html
   My bibliography  Save this article

Design and Analysis of a While-Drilling Energy-Harvesting Device Based on Piezoelectric Effect

Author

Listed:
  • Jun Zheng

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Bin Dou

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Zilong Li

    (Research Institute No. 717, Shipbuilding Industry Corporation, Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China)

  • Tianyu Wu

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Hong Tian

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Guodong Cui

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

Abstract

A while-drilling energy harvesting device is designed in this paper to recovery energy along with the longitudinal vibration of the drill pipes, aiming to serve as a continuous power supply for downhole instruments during the drilling procedure. Radial size of the energy harvesting device is determined through the drilling engineering field experience and geological survey reports. A piezoelectric coupling model based on the selected piezoelectric material was established via COMSOL Multiphysics numerical simulation. The forced vibration was analyzed to determine the piezoelectric patch length range and their best installation positions. Modal analysis and frequency response research indicate that the natural frequency of the piezoelectric cantilever beam increased monotonously with the increase of the piezoelectric patch’ thickness before reaching an inflection point. Moreover, the simulation results imply that the peak voltage of the harvested energy varied in a regional manner with the increase of the piezoelectric patches. When the thickness of the piezoelectric patches was 1.2–1.4 mm, the designed device gained the best energy harvest performance with a peak voltage of 15–40 V. Works in this paper provide theoretical support and design reference for the application of the piezoelectric material in the drilling field.

Suggested Citation

  • Jun Zheng & Bin Dou & Zilong Li & Tianyu Wu & Hong Tian & Guodong Cui, 2021. "Design and Analysis of a While-Drilling Energy-Harvesting Device Based on Piezoelectric Effect," Energies, MDPI, vol. 14(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1266-:d:505702
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1266/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/5/1266/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cui, Guodong & Pei, Shufeng & Rui, Zhenhua & Dou, Bin & Ning, Fulong & Wang, Jiaqiang, 2021. "Whole process analysis of geothermal exploitation and power generation from a depleted high-temperature gas reservoir by recycling CO2," Energy, Elsevier, vol. 217(C).
    2. Hsiao, Y.Y. & Chang, W.C. & Chen, S.L., 2010. "A mathematic model of thermoelectric module with applications on waste heat recovery from automobile engine," Energy, Elsevier, vol. 35(3), pages 1447-1454.
    3. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    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. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    2. Fei Teng & Yafei Song & Xinpeng Guo, 2021. "Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model," Mathematics, MDPI, vol. 9(19), pages 1-21, September.
    3. Aljaghtham, Mutabe & Celik, Emrah, 2020. "Design optimization of oil pan thermoelectric generator to recover waste heat from internal combustion engines," Energy, Elsevier, vol. 200(C).
    4. Yu Wang & Tianfu Xu & Yuxiang Cheng & Guanhong Feng, 2022. "Prospects for Power Generation of the Doublet Supercritical Geothermal System in Reykjanes Geothermal Field, Iceland," Energies, MDPI, vol. 15(22), pages 1-15, November.
    5. Srivastava, Raj Shekhar & Kumar, Anuruddh & Thakur, Harishchandra & Vaish, Rahul, 2022. "Solar assisted thermoelectric cooling/heating system for vehicle cabin during parking: A numerical study," Renewable Energy, Elsevier, vol. 181(C), pages 384-403.
    6. Leszek Pająk & Anna Sowiżdżał & Paweł Gładysz & Barbara Tomaszewska & Maciej Miecznik & Trond Andresen & Bjørn S. Frengstad & Anna Chmielowska, 2021. "Multi-Criteria Studies and Assessment Supporting the Selection of Locations and Technologies Used in CO 2 -EGS Systems," Energies, MDPI, vol. 14(22), pages 1-18, November.
    7. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    8. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    9. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    10. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    11. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
    12. Jia, Xiao-Dong & Wang, Yuan-Jing & Gao, Yuan-Wen, 2017. "Numerical simulation of thermoelectric performance of linear-shaped thermoelectric generators under transient heat supply," Energy, Elsevier, vol. 130(C), pages 276-285.
    13. Luo, Ding & Yan, Yuying & Li, Ying & Wang, Ruochen & Cheng, Shan & Yang, Xuelin & Ji, Dongxu, 2023. "A hybrid transient CFD-thermoelectric numerical model for automobile thermoelectric generator systems," Applied Energy, Elsevier, vol. 332(C).
    14. Kanitta Yarak & Apichon Witayangkurn & Kunnaree Kritiyutanont & Chomchanok Arunplod & Ryosuke Shibasaki, 2021. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
    15. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    16. Fu, Jianqin & Liu, Jingping & Ren, Chengqin & Wang, Linjun & Deng, Banglin & Xu, Zhengxin, 2012. "An open steam power cycle used for IC engine exhaust gas energy recovery," Energy, Elsevier, vol. 44(1), pages 544-554.
    17. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    18. Young Hoo Cho & Jaehyun Park & Naehyuck Chang & Jaemin Kim, 2020. "Comparison of Cooling Methods for a Thermoelectric Generator with Forced Convection," Energies, MDPI, vol. 13(12), pages 1-19, June.
    19. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).
    20. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.

    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:gam:jeners:v:14:y:2021:i:5:p:1266-:d:505702. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.