IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1381-d221505.html
   My bibliography  Save this article

Study of Sensitive Parameters on the Sensor Performance of a Compression-Type Piezoelectric Accelerometer Based on the Meta-Model

Author

Listed:
  • Gyoung-Ja Lee

    (Nuclear Materials Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Won-Ju Hwang

    (40, Imi-ro, Uiwang-si, Gyeonggi-do 16006, Korea)

  • Jin-Ju Park

    (Nuclear Materials Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

  • Min-Ku Lee

    (Nuclear Materials Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea)

Abstract

Through a numerical analytical approach based on piezoelectric analysis and meta-modeling, this study investigated the effect of the component design of an accelerometer sensor on sensitivity and resonance frequency. The results of the study confirmed that the resonance frequency obtained from the piezoelectric analysis was almost the same as the experimental value of the resonance frequency obtained from the fabricated sensing module and proved the validity of the piezoelectric analysis using a finite element method. Moreover, the results of examining the influence of the component design on the resonance frequency and electrical potential suggested that the diameter and height of the head (seismic mass) had the greatest influence. As the diameter and height of the head increased, the sensitivity increased, but the resonance frequency decreased, which indicates that it is necessary to select an appropriate mass to optimize the sensor performance. In addition, the increase in tail height and epoxy thickness had a positive effect on both the resonance frequency and electric potential, and the base diameter had a negative effect on both of them.

Suggested Citation

  • Gyoung-Ja Lee & Won-Ju Hwang & Jin-Ju Park & Min-Ku Lee, 2019. "Study of Sensitive Parameters on the Sensor Performance of a Compression-Type Piezoelectric Accelerometer Based on the Meta-Model," Energies, MDPI, vol. 12(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1381-:d:221505
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1381/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/7/1381/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    2. Aneesh Koka & Henry A. Sodano, 2013. "High-sensitivity accelerometer composed of ultra-long vertically aligned barium titanate nanowire arrays," Nature Communications, Nature, vol. 4(1), pages 1-10, 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. Qi Fan & Jiaqiao Hu, 2018. "Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 677-693, November.
    2. Dellino, G. & Lino, P. & Meloni, C. & Rizzo, A., 2009. "Kriging metamodel management in the design optimization of a CNG injection system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2345-2360.
    3. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Other publications TiSEM 8fa8d96f-a086-4c4b-88ab-9, Tilburg University, School of Economics and Management.
    4. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Balancing global and local search in parallel efficient global optimization algorithms," Journal of Global Optimization, Springer, vol. 67(4), pages 873-892, April.
    5. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    6. Chong Li & Xinxin Liao & Zhi-Ke Peng & Guang Meng & Qingbo He, 2023. "Highly sensitive and broadband meta-mechanoreceptor via mechanical frequency-division multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    7. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.
    8. Enlu Zhou & Shalabh Bhatnagar, 2018. "Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 154-167, February.
    9. Swetlana Herbrandt & Uwe Ligges & Manuel Pinho Ferreira & Michael Kansteiner & Dirk Biermann & Wolfgang Tillmann & Claus Weihs, 2018. "Model based optimization of a statistical simulation model for single diamond grinding," Computational Statistics, Springer, vol. 33(3), pages 1127-1143, September.
    10. Diana M. Negoescu & Peter I. Frazier & Warren B. Powell, 2011. "The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 346-363, August.
    11. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    12. Kleijnen, Jack P.C. & Mehdad, E., 2013. "Conditional simulation for efficient global optimization," Other publications TiSEM 52e4860d-9887-4a63-b19a-7, Tilburg University, School of Economics and Management.
    13. Shengguan Xu & Hongquan Chen, 2018. "Nash game based efficient global optimization for large-scale design problems," Journal of Global Optimization, Springer, vol. 71(2), pages 361-381, June.
    14. Majed Hadid & Adel Elomri & Regina Padmanabhan & Laoucine Kerbache & Oualid Jouini & Abdelfatteh El Omri & Amir Nounou & Anas Hamad, 2022. "Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling," IJERPH, MDPI, vol. 19(23), pages 1-34, November.
    15. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Discussion Paper 2008-95, Tilburg University, Center for Economic Research.
    16. Mickaël Binois & David Ginsbourger & Olivier Roustant, 2020. "On the choice of the low-dimensional domain for global optimization via random embeddings," Journal of Global Optimization, Springer, vol. 76(1), pages 69-90, January.
    17. Nestor Queipo & Salvador Pintos & Efrain Nava, 2013. "Setting targets for surrogate-based optimization," Journal of Global Optimization, Springer, vol. 55(4), pages 857-875, April.
    18. Shande Li & Shuai Yuan & Shaowei Liu & Jian Wen & Qibai Huang, 2022. "Research on an Accuracy Optimization Algorithm of Kriging Model Based on a Multipoint Filling Criterion," Mathematics, MDPI, vol. 10(9), pages 1-11, May.
    19. Xiqun (Michael) Chen & Xiang He & Chenfeng Xiong & Zheng Zhu & Lei Zhang, 2019. "A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management," Transportation Science, INFORMS, vol. 53(2), pages 545-565, March.
    20. Julien Marzat & Eric Walter & Hélène Piet-Lahanier, 2013. "Worst-case global optimization of black-box functions through Kriging and relaxation," Journal of Global Optimization, Springer, vol. 55(4), pages 707-727, April.

    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:12:y:2019:i:7:p:1381-:d:221505. 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.