Highly Sensitive and Integratable Triboelectric Bionic Lateral Line Sensor for Flow Recognition of Underwater Vehicle
Source abstract
Abstract
Abstract The ability to perceive underwater flow fields is essential for the stable operation and efficient navigation of underwater vehicles. Fish use their lateral lines to detect changes in the surrounding flow field, even in low visibility and harsh environments. Inspired by the lateral line neuromasts of fish, this paper introduces a triboelectric bionic lateral‐line sensor (TBLS) designed to enable underwater vehicles to detect flow fields, including propeller‐generated wakes and oscillating flows. The TBLS comprises a flexible sensing unit and a bionic lateral‐line canal. It has a measurement error of just 5.7% in determining the characteristic thickness of propeller wakes and has a pressure gradient sensitivity of 2.1 mV Pa m−1. The H2O AutoML algorithm significantly enhances the recognition of oscillatory flow signals. Finally, the TBLS is integrated into an underwater vehicle, demonstrating its ability to detect underwater flow fields and transmit signals 117 meters to a receiving end via a wireless transmission module in real‐sea conditions. The TBLS provides a novel approach to enhancing flow field perception and underwater operational capabilities of vehicles.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Cardiovascular and Diving-Related Complications · Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Maritime Technology and Safety Marine Engineering College Dalian Maritime University Dalian 116026 China
School of Engineering Newcastle University Newcastle upon Tyne NE1 7RU UK
Intelligent Biomimetic Design Lab, College of Engineering Peking University Beijing 100871 China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities
References (53)
Show source-provided references
1. 10.1038/s41586-020-03153-z10.1038/s41586-020-03153-z ↗
2. 10.1016/j.oceaneng.2023.11535910.1016/j.oceaneng.2023.115359 ↗
3. 10.1038/s41467-024-50581-w10.1038/s41467-024-50581-w ↗
4. S. A.Fattah F.Abedin M. N.Ansary M. A.Rokib N.Saha C.Shahnaz In2016 IEEE Region 10 Conference (TENCON) IEEE Singapore2016 pp.3280–3283.10.1109/TENCON.2016.7848658 ↗
5. Cao X. · IEEE Trans. Intell. Veh. · 2024 · 1
6. 10.1126/scirobotics.abe190110.1126/scirobotics.abe1901 ↗
7. 10.1016/j.oceaneng.2023.11386110.1016/j.oceaneng.2023.113861 ↗
8. 10.1126/sciadv.adg029210.1126/sciadv.adg0292 ↗
9. 10.1016/j.oceaneng.2024.11775710.1016/j.oceaneng.2024.117757 ↗
10. 10.1109/JOE.2010.209423010.1109/JOE.2010.2094230 ↗
11. K.Iqbal M.Odetayo A.James R. A.Salam A. Z. H.jTalib in2010 IEEE International Conference on Systems Man and Cybernetics IEEE Istanbul Turkey2010 pp.1703–1709.10.1109/ICSMC.2010.5642311 ↗
12. 10.1109/JOE.2019.291144710.1109/JOE.2019.2911447 ↗
13. 10.1021/acssuschemeng.3c0694910.1021/acssuschemeng.3c06949 ↗
14. 10.1016/j.cej.2024.15497210.1016/j.cej.2024.154972 ↗
15. 10.1038/s41467-024-47299-010.1038/s41467-024-47299-0 ↗
16. 10.1007/978-3-319-47500-410.1007/978-3-319-47500-4 ↗
17. 10.1088/1748-3190/11/5/05600710.1088/1748-3190/11/5/056007 ↗
18. 10.1038/ncomms275110.1038/ncomms2751 ↗
19. 10.1002/adfm.20220727410.1002/adfm.202207274 ↗
20. 10.1016/j.sna.2023.11487510.1016/j.sna.2023.114875 ↗
21. 10.1016/j.nanoen.2024.11001110.1016/j.nanoen.2024.110011 ↗
22. 10.1016/j.nanoen.2022.10763310.1016/j.nanoen.2022.107633 ↗
23. 10.1016/j.nanoen.2022.10721010.1016/j.nanoen.2022.107210 ↗
24. Wang T. · arXiv:2307.04370 · 2023
25. 10.1007/s42235-021-0034-y10.1007/s42235-021-0034-y ↗
26. 10.1007/s00542-014-2350-110.1007/s00542-014-2350-1 ↗
27. 10.1038/s42256-019-0044-110.1038/s42256-019-0044-1 ↗
28. 10.1007/s40820-022-00799-410.1007/s40820-022-00799-4 ↗
29. 10.1016/j.device.2023.10017510.1016/j.device.2023.100175 ↗
30. 10.1021/acssensors.4c0066410.1021/acssensors.4c00664 ↗
31. 10.1016/j.jcis.2024.09.07510.1016/j.jcis.2024.09.075 ↗
32. 10.1126/sciadv.ado679310.1126/sciadv.ado6793 ↗
33. 10.1021/nn404614z10.1021/nn404614z ↗
34. 10.1016/j.nanoen.2012.01.00410.1016/j.nanoen.2012.01.004 ↗
35. 10.1002/advs.20230657410.1002/advs.202306574 ↗
36. 10.1002/smll.20230849110.1002/smll.202308491 ↗
37. 10.1002/advs.20230119910.1002/advs.202301199 ↗
38. 10.1021/acsnano.9b1014210.1021/acsnano.9b10142 ↗
39. 10.1039/D1EE01382C10.1039/D1EE01382C ↗
40. 10.1002/adma.20200146610.1002/adma.202001466 ↗
41. 10.1038/s41467-019-10433-410.1038/s41467-019-10433-4 ↗
42. 10.34133/research.006210.34133/research.0062 ↗
43. 10.1016/j.nanoen.2023.10901810.1016/j.nanoen.2023.109018 ↗
44. 10.1002/adfm.20190032710.1002/adfm.201900327 ↗
45. 10.1016/j.mattod.2020.06.00310.1016/j.mattod.2020.06.003 ↗
46. 10.1002/adfm.20230571910.1002/adfm.202305719 ↗
47. 10.1002/smll.20210723210.1002/smll.202107232 ↗
48. 10.1002/aenm.20230038710.1002/aenm.202300387 ↗
49. Zhang Y. · Research · 2022 · 2022
50. 10.1038/s41528-022-00160-010.1038/s41528-022-00160-0 ↗
51. 10.1016/j.oceaneng.2016.07.06110.1016/j.oceaneng.2016.07.061 ↗
52. 10.1109/JSEN.2013.225922710.1109/JSEN.2013.2259227 ↗
53. 10.1063/1.361047010.1063/1.3610470 ↗