Near-Optimal Design of Scalable Energy Harvester for Underwater Pipeline Monitoring Applications With Consideration of Impact to Pipeline Performance


Qureshi F. U. , Muhtaroglu A., TUNCAY K.

IEEE SENSORS JOURNAL, cilt.17, sa.7, ss.1981-1991, 2017 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Konu: 7
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1109/jsen.2017.2661199
  • Dergi Adı: IEEE SENSORS JOURNAL
  • Sayfa Sayıları: ss.1981-1991

Özet

Underwater pipelines are often necessary to transport valuable resources between patches of land, and are expected to have increased utilization across the globe with the steep population increase on one hand, and unbalanced depletion of resources on the other. It is desirable to automate monitoring of various performance parameters associated with the pipelines through wireless sensor networks to ensure longevity of use and reduced running costs. However, such pipelines are not easily accessible, and are subject to harsh environments such as salt water and underwater currents. Therefore, an ideal node in this sort of network is embedded into the pipeline, and does not require batteries with regular replacement provisions. Using an energy harvester as the power source becomes a viable option. A method for near-optimal piezoelectric bimorph energy harvester module design is presented in this paper to enable a self-powered wireless sensor node for in-pipe monitoring using kinetic energy of water flow. A crude analytical model provides a starting point for the design, which is tuned through finite element modeling and simulation. Recently constructed Turkey-Cyprus water pipeline project is considered as a realistic application for determining boundary conditions. With an average water velocity of 1.4 m/s, the designed energy harvester is scalable to produce power between 820 mu W (single) to 12.3 mW (15 in parallel) with a negligible impact of 1.5 mm additional head loss. The method developed to deliver a finely modeled, scalable harvester design with minimum quantified impact to pipe performance is first of its kind to our knowledge.