IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v87y2016ip1p572-584.html
   My bibliography  Save this article

Lightning field behavior around grounded airborne systems

Author

Listed:
  • Malinga, G.A.
  • Niedzwecki, J.M.

Abstract

A variety of innovative airborne concepts are being developed to capture the wind energy available at higher altitudes, facilitate wireless communication and provide heavy lift capability. These airborne systems will be in close proximity to cloud cover and exposed to increased risk of lightning strikes. A two-dimensional physics based formulation that was recently developed to investigate the potential field behavior about circular sections was used to develop uniform and tapered cylindrical elements. These elements were then combined to approximate the total charge and lightning behavior about an airborne wind turbine and a heavy lift airship. Surface electrical charge and lightning collection area are developed as a function of elevation, body shape, cloud cover and leader properties. The surface charge density is utilized to compute the degree of field intensification on the body periphery in order to determine the level of susceptibility of the airborne system to lightning strikes. It was observed that as airborne bodies move closer to the thundercloud the ambient potential field becomes more highly perturbed and leads to greater risk of lightning strikes. The lightning collection area was shown to increase with elevation of the airborne body and decrease with increase in the leader propagation angle.

Suggested Citation

  • Malinga, G.A. & Niedzwecki, J.M., 2016. "Lightning field behavior around grounded airborne systems," Renewable Energy, Elsevier, vol. 87(P1), pages 572-584.
  • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:572-584
    DOI: 10.1016/j.renene.2015.10.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115304006
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2015.10.047?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. Pei-Sheng Lin, 2014. "Generalized Scan Statistics for Disease Surveillance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 791-808, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saleem, Arslan & Kim, Man-Hoe, 2019. "Performance of buoyant shell horizontal axis wind turbine under fluctuating yaw angles," Energy, Elsevier, vol. 169(C), pages 79-91.

    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. Pei‐Sheng Lin & Yi‐Hung Kung & Murray Clayton, 2016. "Spatial scan statistics for detection of multiple clusters with arbitrary shapes," Biometrics, The International Biometric Society, vol. 72(4), pages 1226-1234, December.
    2. Riechers, Maraja & Barkmann, Jan & Tscharntke, Teja, 2016. "Perceptions of cultural ecosystem services from urban green," Ecosystem Services, Elsevier, vol. 17(C), pages 33-39.
    3. Goel, Varun & Kumar, Naresh & Singh, Paramvir, 2018. "Impact of modified parameters on diesel engine characteristics using biodiesel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2716-2729.
    4. Mohamed-Salem Ahmed & Lionel Cucala & Michaël Genin, 2021. "Spatial autoregressive models for scan statistic," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-20, December.
    5. Pei‐Sheng Lin & Jun Zhu, 2020. "A heterogeneity measure for cluster identification with application to disease mapping," Biometrics, The International Biometric Society, vol. 76(2), pages 403-413, June.

    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:renene:v:87:y:2016:i:p1:p:572-584. 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/renewable-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.