Skip to main content
Log in

Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety, Given Manufacturing Imperfections

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Energy harvesting is a component of contemporary offshore and onshore green energy engineering. Rigorous experimental studies, as well as safety and reliability research, being essential for modern green energy design and engineering. In order to evaluate dynamic performance of galloping energy harvesters, this study utilized extensive wind-tunnel tests, performed under realistic in situ windspeed conditions. State of art Gaidai structural reliability approach has been presented, that is particularly well suitable for non-stationary imperfect or damaged multi-dimensional energy harvesting systems. This approach utilizes analog observations made during representative timelapse, producing quasi-ergodic system dynamic record. As shown in the current study, the recommended technique may be utilized to evaluate the risk of damage or failure in dynamic systems. Additionally, high-dimensionality, deterioration, and nonlinear cross-correlations between dynamic system's key components are challenging to handle for standard reliability approaches, dealing with nonstationary, multidimensional systems. The goal of this study was to benchmark novel Gaidai multivariate reliability approach that allows for effective processing of pertinent statistical data even from limited, multivariate non-stationary underlying dataset. Gaidai multivariate reliability approach attempts to assist designers in evaluating risks of failure and hazards for nonlinear multidimensional dynamic energy harvesting systems, when initial manufacturing imperfections being present.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Availability of Data and Materials

Data will be made available on request from corresponding author.

References

  1. Albaladejo, C., Sánchez, P., Iborra, A., Soto, F., López, J. A., & Torres, R. (2010). Wireless sensor networks for oceanographic monitoring: A systematic review. Sensors, 10, 6948.

    Article  Google Scholar 

  2. Mehmood, A., Abdelkefi, A., Hajj, M. R., Nayfeh, A. H., Akhtar, I., & Nuhait, A. O. (2013). Piezoelectric energy harvesting from vortex-induced vibrations of circular cylinder. Journal of Sound and Vibration, 332(19), 4656–4667.

    Article  Google Scholar 

  3. Fazeres-Ferradosa, T., Taveira-Pinto, F., Vanem, E., Reis, M. T., & Neves, L. D. (2018). Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications. Wind Engineering, 42(4), 304–334.

    Article  Google Scholar 

  4. Dai, H., Abdelkefi, A., & Wang, L. (2014). Theoretical modeling and nonlinear analysis of piezoelectric energy harvesting from vortex-induced vibrations. Journal of Intelligent Material Systems and Structures, 25(14), 1861–1874.

    Article  Google Scholar 

  5. Wang, J., Zhou, S., Zhang, Z., & Yurchenko, D. (2019). High-performance piezoelectric wind energy harvester with Y-shaped attachments. Energy Conversion and Management, 181, 645–652.

    Article  Google Scholar 

  6. Zhao, T., Xu, M., Xiao, X., Ma, Y., Li, Z., & Wang, Z. (2021). Recent progress in blue energy harvesting for powering distributed sensors in ocean. Nano Energy, 88, 106199.

    Article  Google Scholar 

  7. Gong, Y., Yang, Z., Shan, X., Sun, Y., Xie, T., & Zi, Y. (2019). Capturing flow energy from ocean and wind. Energies, 12, 2184.

    Article  Google Scholar 

  8. Amin, A. A., & Hussian, A. (2014). A weighted three-parameter weibull distribution. Journal of Applied Sciences Research, 9(13), 6627–6635.

    Google Scholar 

  9. Abdelkefi, A. (2016). Aeroelastic energy harvesting: A review. International Journal of Engineering Science, 100, 112–135.

    Article  Google Scholar 

  10. Yang, K., Wang, J., & Yurchenko, D. (2019). A double-beam piezo-magneto-elastic wind energy harvester for improving the galloping-based energy harvesting. Applied Physics Letters, 115(19), 193901.

    Article  Google Scholar 

  11. Wang, J., Geng, L., Zhou, S., Zhang, Z., Lai, Z., & Yurchenko, D. (2020). Design, modeling and experiments of broadband tristable galloping piezoelectric energy harvester. Acta Mechanica Sinica, 36, 592–605.

    Article  Google Scholar 

  12. Zhao, L., & Yang, Y. (2018). An impact-based broadband aeroelastic energy harvester for concurrent wind and base vibration energy harvesting. Journal of Applied Energy, 212, 233–243.

    Article  Google Scholar 

  13. Daqaq, M. F. (2015). Characterising the response of galloping energy harvesters using actual wind statistics. Journal of Sound and Vibration, 357, 365–376.

    Article  Google Scholar 

  14. Vanem, E., Fazeres-Ferradosa, T., Rosa-Santos, P., Taveira-Pinto, F. (2019). Statistical description and modelling of extreme ocean wave conditions for marine engineering applications.

  15. Fazeres-Ferradosa, T., Taveira-Pinto, F., Romão, X., Vanem, E., Reis, M. T., & das Neves, L. (2018). Probabilistic design and reliability analysis of scour protections for offshore windfarms. Engineering Failure Analysis, 91, 291–305.

    Article  Google Scholar 

  16. Rugbjerg, M., Sørensen, O. R., & Jacobsen, V. (2006). Wave forecasting for offshore wind farms. In 9th International workshop on wave hindcasting and forecasting, Victoria, B.C. Canada.

  17. Rugbjerg, M, Sørensen, O., & Jacobsen, V, (2006). Wave forecasting for offshore wind farms. In 9th International workshop on wave hindcasting and forecasting (pp. 24–29).

  18. Larsen, X., Kalogeri, C., Galanis, G., & Kallos, G. (2015). A statistical methodology for the estimation of extreme wave conditions for offshore renewable applications. Renewable Energy, 80, 205–218.

    Article  Google Scholar 

  19. Teena, N. V., Sanil, K., Sudheesh, K., & Sajeev, R. (2012). Statistical analysis on extreme wave height. Natural Hazards, 64(1), 223–236.

    Article  Google Scholar 

  20. Franck, M., & Luc, H. (2011). A multi-distribution approach to POT methods for determining extreme waveheights. Coastal Engineering, 58, 385–394.

    Article  Google Scholar 

  21. Mouslim, H, Babarit, A, & Jordana, A. (2008). Project development of a wave energy test site in the French Atlantic Coast. In Proceedings of the 2nd International Conference on Ocean Energy, Brest, France.

  22. Cook, N., & Harris, R. (2004). Exact and general FT1 penultimate distributions of extreme windspeeds drawn from tail-equivalent Weibull parents. Structural Safety, 26, 391–420.

    Article  Google Scholar 

  23. Ewans, K. (2014). Evaluating environmental joint extremes for the offshore industry using the conditional extremes model. Journal of Marine Systems, 130, 124–130.

    Article  Google Scholar 

  24. Heffernan, J., & Tawn, J. (2004). A conditional approach for multivariate extreme values. Journal of the Royal Statistic Society: Series B, 66(3), 497–546.

    Article  MathSciNet  Google Scholar 

  25. Jensen, J., & Capul, J. (2006). Extreme response predictions for jack-up units in second-order stochastic waves by FORM. Probabilistic Engineering Mechanics, 21, 330–337.

    Article  Google Scholar 

  26. Zhao, Y., & Ono, T. (1999). A general procedure for first/second order reliability method (FORM/SORM). Structural Safety, 21(2), 95–112.

    Article  Google Scholar 

  27. Cheng, P. W., van Bussel, G., van Kuik, G., & Vugts, J. (2003). Reliability-based design methods to determine the extreme response distribution of offshore wind turbines. Wind Energy, 6, 1–22.

    Article  Google Scholar 

  28. Li, L., Gao, Z., & Moan, T. (2013). Joint environmental data at FIVE European offshore sites for design of combined wind and wave energy devices. In ASME 32nd international conference on ocean, offshore and arctic engineering (vol. 8).

  29. Kim, D. H., & Lee, S. G. (2015). Reliability analysis of offshore wind turbine support structures under extreme ocean environmental loads. Renewable Energy, 79, 161–166.

    Article  Google Scholar 

  30. Yang, Y., Mao, S., Cao, W., & Huang, Y. (2022). A novel taper design method for face-milled spiral bevel and hypoid gears by completing process method. International Journal of Precision Engineering and Manufacturing, 23, 1–13. https://doi.org/10.1007/s12541-021-00591-1

    Article  Google Scholar 

  31. Lerra, A., Candido, A., Liverani, E., & Fortunato, A. (2022). Prediction of micro-scale forces in dry grinding process through a FEM—ML hybrid approach. International Journal of Precision Engineering and Manufacturing, 23, 15–29. https://doi.org/10.1007/s12541-021-00601-2

    Article  Google Scholar 

  32. Merghache, S., Hamdi, A., & Ghernaout, M. (2022). Experimental measurement and evaluation of the noise generated by three transmissions by synchronous belts of type AT10, BAT10 and SFAT10. International Journal of Precision Engineering and Manufacturing, 23, 31–43. https://doi.org/10.1007/s12541-021-00599-7

    Article  Google Scholar 

  33. Nghi, H., Nhien, D., & Ba, D. (2022). A LQR neural network control approach for fast stabilizing rotary inverted pendulums. International Journal of Precision Engineering and Manufacturing, 23, 45–56. https://doi.org/10.1007/s12541-021-00606-x

    Article  Google Scholar 

  34. Kim, B., Kang, B., Choi, S., & Kim, G. (2022). Modeling and performance analysis of linear part feeder system actuated by piezoelectric transducers. International Journal of Precision Engineering and Manufacturing, 23, 57–65. https://doi.org/10.1007/s12541-021-00608-9

    Article  Google Scholar 

  35. Lim, J., & Lee, E. (2022). A simplified anisotropic yield function not-requiring parameter optimization for sheet metals. International Journal of Precision Engineering and Manufacturing, 23, 67–78. https://doi.org/10.1007/s12541-021-00579-x

    Article  Google Scholar 

  36. Kim, K., & Lee, J. (2022). Light-weight design and structure analysis of automotive wheel carrier by using finite element analysis. International Journal of Precision Engineering and Manufacturing, 23, 79–85. https://doi.org/10.1007/s12541-021-00595-x

    Article  Google Scholar 

  37. Abdullah, O., Stojanpvich, N., & Grujic, I. (2022). The influence of the braking disc ribs and applied material on the natural frequency. International Journal of Precision Engineering and Manufacturing, 23, 87–97. https://doi.org/10.1007/s12541-021-00597-9

    Article  Google Scholar 

  38. Gaidai, O., Xing, Y., & Xu, X. (2023). Novel methods for coupled prediction of extreme windspeeds and wave heights. Scientific Reports. https://doi.org/10.1038/s41598-023-28136-8

    Article  Google Scholar 

  39. Zhang, J., Gaidai, O., & Gao, J. (2018). Bivariate extreme value statistics of offshore jacket support stresses in Bohai bay. Journal of Offshore Mechanics and Arctic Engineering, 140(4), 041305.

    Article  Google Scholar 

  40. Yu, Y., Rij, J., Coe, R., & Lawson, M. (2015). Preliminary wave energy converters extreme load analysis. Proceedings OMAE, 9, 66.

    Google Scholar 

  41. Aarnes, O., Breivik, O., & Reistad, M. (2012). Wave extremes in the northeast Atlantic. Journal of Climate, 25, 1529–1543.

    Article  Google Scholar 

  42. Battjes, J., & Groenendijk, H. (2000). Wave height distributions on shallow foreshores. Coastal Engineering, 40(3), 161–182.

    Article  Google Scholar 

  43. Ferreira, J., & Guedes, S. C. (2000). Modelling distributions of significant wave height. Coastal Engineering, 40, 361–374.

    Article  Google Scholar 

  44. Bidlot, J., & Janssen, P. (2003). Unresolved bathymetry, neutral winds and new stress tables in WAM, Tech. Rep. ECMWF Research Department Memo R60.9/JB/0400, ECMWF.

  45. Gaidai, O., Yan, P., & Xing, Y. (2023). Future world cancer death rate prediction. Scientific Reports. https://doi.org/10.1038/s41598-023-27547-x

    Article  Google Scholar 

  46. Gaidai, O., Xu, J., Hu, Q., Xing, Y., & Zhang, F. (2022). Offshore tethered platform springing response statistics. Scientific Reports, 12, 66.

    Article  Google Scholar 

  47. Gaidai, O., Xu, X., Wang, J., Ye, R., Cheng, Y., & Karpa, O. (2020). SEM-REV offshore energy site wind-wave bivariate statistics by hindcast. Renewable Energy, 156, 689–695.

    Article  Google Scholar 

  48. Janssen, P. (2000). ECMWF wave modeling and satellite altimeter wave data. In Satellites, oceanography and society (pp. 35–36). Elsevier.

  49. Kallos, G. (1997). The regional weather forecasting system SKIRON. In Proceedings, symposium on regional weather prediction on parallel computer environment, Athens, Greece (p. 9).

  50. Avvari, P., Yang, Y., & Soh, C. (2017). Long-term fatigue behavior of a cantilever piezoelectric energy harvester. Journal of Intelligent Material Systems and Structures, 28(9), 1188–1210.

    Article  Google Scholar 

  51. Soma, A., & De Pasquale, G. (2013). Design of high-efficiency vibration energy harvesters and experimental functional tests for improving bandwidth and tunability, Smart Sensors, Actuators, and MEMS VI. International Society for Optics and Photonics, 8763, 87630U.

    Google Scholar 

  52. Stanton, S., Erturk, A., & Mann, B. (2012). Nonlinear nonconservative behavior and modeling of piezoelectric energy harvesters including proof mass effects. Journal of Intelligent Material Systems and Structures, 23(2), 183–199.

    Article  Google Scholar 

  53. Wilkie, W., High, J., & Bockman, J. (2002). Reliability testing of NASA piezocomposite actuators.

  54. Williams, R., Grimsley, B., & Inman, D. (2004). Manufacturing and cure kinetics modeling for macro fiber composite actuators. Journal of Reinforced Plastics and Composites, 23(16), 1741–1754.

    Article  Google Scholar 

  55. Daue, T, & Kunzmann, J. (2008). Energy harvesting systems using piezo-electric MFCs. In 17th IEEE international symposium on the applications of ferroelectrics (vol. 1, p. 1). IEEE.

  56. Sherrit, S., Lee, H., Walkemeyer, P., Hasenoehrl, J., Hall, J., Colonius, T., Tosi, L., Arrazola, A., Kim, N., Sun, K., & Corbett, G. (2014). Flow energy piezoelectric bimorph nozzle harvester, Active and Passive Smart Structures and Integrated Systems 2014. International Society for Optics and Photonics, 9057, 90570D.

    Google Scholar 

  57. Smith, R., Tawn, J., & Yuen, H. (1990). Statistics of multivariate extremes. International Statistical Review, 58(1), 47–58.

    Article  Google Scholar 

  58. Coles, S., & Tawn, J. (1994). Statistical methods for multivariate extremes: An application to structural design. Journal of the Royal Statistical Society, Series C, 43(1), 1–48.

    Google Scholar 

  59. Gaidai, O., Wang, F., Wu, Y., Xing, Y., Rivera, Medina A., & Wang, J. (2022). Offshore renewable energy site correlated wind-wave statistics. Probabilistic Engineering Mechanics. https://doi.org/10.1016/j.probengmech.2022.103207

    Article  Google Scholar 

  60. Gaidai, O., Wu, Y., Yegorov, I., Alevras, P., Wang, J., & Yurchenko, D. (2022). Improving performance of a nonlinear absorber applied to a variable length pendulum using surrogate optimisation. Journal of Vibration and Control. https://doi.org/10.1177/10775463221142663

    Article  Google Scholar 

  61. Gaidai, O., Wang, K., Wang, F., Xing, Y., & Yan, P. (2022). Cargo ship aft panel stresses prediction by deconvolution. Marine Structures, 88, 66. https://doi.org/10.1016/j.marstruc.2022.103359

    Article  Google Scholar 

  62. Gaidai, O., Xu, J., Xing, Y., Hu, Q., Storhaug, G., Xu, X., & Sun, J. (2022). Cargo vessel coupled deck panel stresses reliability study. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2022.113318

    Article  Google Scholar 

  63. Gaidai, O., & Xing, Y. (2022). A Novel Multi Regional Reliability Method for COVID-19 Death Forecast. Engineered Science. https://doi.org/10.30919/es8d799

    Article  Google Scholar 

  64. Gaidai, O., & Xing, Y. (2022). A novel bio-system reliability approach for multi-state COVID-19 epidemic forecast. Engineered Science. https://doi.org/10.30919/es8d797

    Article  Google Scholar 

  65. Xu, X., Xing, Y., Gaidai, O., Wang, K., Patel, K., Dou, P., & Zhang, Z. (2022). A novel multi-dimensional reliability approach for floating wind turbines under power production conditions. Frontiers in Marine Science. https://doi.org/10.3389/fmars.2022.970081

    Article  Google Scholar 

  66. Gaidai, O., Xing, Y., & Balakrishna, R. (2022). Improving extreme response prediction of a subsea shuttle tanker hovering in ocean current using an alternative highly correlated response signal. Results in Engineering. https://doi.org/10.1016/j.rineng.2022.100593

    Article  Google Scholar 

  67. Cheng, Y., Gaidai, O., Yurchenko, D., Xu, X., & Gao, S. (2022). Study on the dynamics of a Payload Influence in the Polar Ship. In The 32nd international ocean and polar engineering conference, Paper Number: ISOPE-I-22-342.

  68. Gaidai, O., Yan, P., Xing, Y., Xu, J., & Wu, Y. (2022). A novel statistical method for long-term coronavirus modelling. F1000 Research, 6, 66.

    Google Scholar 

  69. Gaidai, O., Xu, J., Yan, P., Xing, Y., Zhang, F., & Wu, Y. (2022). Novel methods for windspeeds prediction across multiple locations. Scientific reports, 12, 19614. https://doi.org/10.1038/s41598-022-24061-4

    Article  Google Scholar 

  70. Gaidai, O., Fu, S., & Xing, Y. (2022). Novel reliability method for multi-dimensional nonlinear dynamic systems. Marine Structures. https://doi.org/10.1016/j.marstruc.2022.103278

    Article  Google Scholar 

  71. Gaidai, O., Yan, P., & Xing, Y. (2022). A novel method for prediction of extreme windspeeds across parts of Southern Norway. Frontiers Environment Science. https://doi.org/10.3389/fenvs.2022.997216

    Article  Google Scholar 

  72. Gaidai, O., Yan, P., & Xing, Y. (2022). Prediction of extreme cargo ship panel stresses by using deconvolution. Frontiers Mechanical Engineering. https://doi.org/10.3389/fmech.2022.992177

    Article  Google Scholar 

  73. Balakrishna, R., Gaidai, O., Wang, F., Xing, Y., & Wang, S. (2022). A novel design approach for estimation of extreme load responses of a 10-MW floating semi-submersible type wind turbine. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2022.112007

    Article  Google Scholar 

  74. Gaidai, O., & Xing, Y. (2022). Novel reliability method validation for offshore structural dynamic response. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2022.113016

    Article  Google Scholar 

  75. Rice, S. O. (1944). Mathematical analysis of random noise. Bell System Technical Journal, 23, 282–332.

    Article  MathSciNet  Google Scholar 

  76. Madsen, H. O., Krenk, S., & Lind, N. C. (1986). Methods of structural safety. Prentice-Hall.

    Google Scholar 

  77. Ditlevsen, O., & Madsen, H. O. (1996). Structural reliability methods. Wiley.

    Google Scholar 

  78. Melchers, R. E. (1999). Structural reliability analysis and prediction. Wiley.

    Google Scholar 

  79. Choi, S.-K., Grandhi, R. V., & Canfield, R. A. (2007). Reliability-based structural design. Springer.

    Google Scholar 

  80. Thoft-Christensen, P., & Murotsu, Y. (1986). Application of environmental systems reliability theory. Springer.

    Book  Google Scholar 

  81. Wang, J., Zhang, C., Hu, G., Liu, X., Liu, H., Zhang, Z., & Das, R. (2022). Wake galloping energy harvesting in heat exchange systems under the influence of ash deposition. Energy, 253, 15. https://doi.org/10.1016/j.energy.2022.124175

    Article  Google Scholar 

  82. Zhou, C., Zou, H., Wei, K., & Liu, J. (2019). Enhanced performance of piezoelectric wind energy harvester by a curved plate. Smart Materials and Structures, 28(12), 66. https://doi.org/10.1088/1361-665X/ab525a

    Article  Google Scholar 

  83. He, L., Zhang, C., Zhang, B., et al. (2022). A dual-mode triboelectric nanogenerator for wind energy harvesting and self-powered windspeed monitoring. ACS Nano. https://doi.org/10.1021/acsnano.1c11658

    Article  Google Scholar 

  84. Zhao, L., Zou, H., Yan, G., Liu, F., Tan, T., Zhang, W., Peng, Z., & Meng, G. (2019). A water-proof magnetically coupled piezoelectric-electromagnetic hybrid wind energy harvester. Applied Energy, 239(5), 735–746.

    Article  Google Scholar 

  85. Gaidai, O., Cao, Y., & Loginov, S. (2023). Global cardiovascular diseases death rate prediction. Current Problems in Cardiology. https://doi.org/10.1016/j.cpcardiol.2023.101622

    Article  Google Scholar 

  86. Gaidai, O., Cao, Y., Xing, Y., & Balakrishna, R. (2023). Extreme springing response statistics of a tethered platform by deconvolution. International Journal of Naval Architecture and Ocean Engineering. https://doi.org/10.1016/j.ijnaoe.2023.100515

    Article  Google Scholar 

  87. Gaidai, O., Xing, Y., Balakrishna, R., & Xu, J. (2023). Improving extreme offshore windspeed prediction by using deconvolution. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e13533

    Article  Google Scholar 

  88. Gaidai, O., & Xing, Y. (2023). Prediction of death rates for cardiovascular diseases and cancers. Cancer Innovation. https://doi.org/10.1002/cai2.47

    Article  Google Scholar 

  89. Gaidai, O., Wang, F., & Yakimov, V. (2023). COVID-19 multi-state epidemic forecast in India. Proceedings of the Indian National Science Academy. https://doi.org/10.1007/s43538-022-00147-5

    Article  Google Scholar 

  90. Numerical Algorithms Group. (2010). NAG Toolbox for Matlab. Oxford.

  91. https://mechanicalc.com/reference/fracture-mechanics

  92. Paris, P. C., & Erdogan, F. (1963). A critical analysis of crack propagation laws. Journal of Basic Engineering, 18(4), 528–534. https://doi.org/10.1115/1.3656900

    Article  Google Scholar 

  93. Gaidai, O., Wang, F., Xing, Y., & Balakrishna, R. (2023). Novel reliability method validation for floating wind turbines. Advanced Energy and Sustainability Research. https://doi.org/10.1002/aesr.202200177

    Article  Google Scholar 

  94. Gaidai, O., Hu, Q., Xu, J., Wang, F., & Cao, Y. (2023). Carbon storage tanker lifetime assessment. Global Challenges. https://doi.org/10.1002/gch2.202300011

    Article  Google Scholar 

  95. Liu, Z., Gaidai, O., Xing, Y., & Sun, J. (2023). Deconvolution approach for floating wind turbines. Energy Science & Engineering. https://doi.org/10.1002/ese3.1485

    Article  Google Scholar 

  96. Gaidai, O., Yan, P., Xing, Y., Xu, J., Zhang, F., & Wu, Y. (2023). Oil tanker under ice loadings. Scientific Reports. https://doi.org/10.1038/s41598-023-34606-w

    Article  Google Scholar 

  97. Gaidai, O., Xing, Y., Xu, J., & Balakrishna, R. (2023). Gaidai-Xing reliability method validation for 10-MW floating wind turbines. Scientific Reports. https://doi.org/10.1038/s41598-023-33699-7

    Article  Google Scholar 

  98. Gaidai, O., Xu, J., Yakimov, V., & Wang, F. (2023). Analytical and computational modeling for multi-degree of freedom systems: Estimating the likelihood of an FOWT structural failure. Journal of Marine Science and Engineering, 11(6), 1237. https://doi.org/10.3390/jmse11061237

    Article  Google Scholar 

  99. Sun, J., Gaidai, O., Xing, Y., Wang, F., & Liu, Z. (2023). On safe offshore energy exploration in the Gulf of Eilat. Quality and Reliability Engineering International. https://doi.org/10.1002/qre.3402

    Article  Google Scholar 

  100. Gaidai, O., Xu, J., Yakimov, V., & Wang, F. (2023). Liquid carbon storage tanker disaster resilience. Environment Systems and Decisions. https://doi.org/10.1007/s10669-023-09922-1

    Article  Google Scholar 

  101. Yakimov, V., Gaidai, O., Wang, F., Xu, X., Niu, Y., & Wang, K. (2023). Fatigue assessment for FPSO hawsers. International Journal of Naval Architecture and Ocean Engineering. https://doi.org/10.1016/j.ijnaoe.2023.100540

    Article  Google Scholar 

  102. Yakimov, V., Gaidai, O., Wang, F., & Wang, K. (2023). Arctic naval launch and recovery operations, under ice impact interactions. Applications in Engineering Science. https://doi.org/10.1016/j.apples.2023.100146

    Article  Google Scholar 

  103. Gaidai, O., Yakimov, V., Wang, F., Hu, Q., & Storhaug, G. (2023). Lifetime assessment for container vessels. Applied Ocean Research. https://doi.org/10.1016/j.apor.2023.103708

    Article  Google Scholar 

  104. Gaidai, O., Wang, F., Yakimov, V., Sun, J., & Balakrishna, R. (2023). Lifetime assessment for riser systems. Green Technology, Resilience, and Sustainability, 3, 66. https://doi.org/10.1007/s44173-023-00013-7

    Article  Google Scholar 

  105. Gaidai, O., Yakimov, V., & Zhang, F. (2023). COVID-19 spatio-temporal forecast in England. Bio Systems. https://doi.org/10.1016/j.biosystems.2023.105035

    Article  Google Scholar 

  106. Gaidai, O., Liu, Z., Wang, K., & Bai, X. (2023). Current COVID-19 Epidemic Risks in Brazil. Epidemiology International Journal, 7(2), 1–10. https://doi.org/10.23880/eij-16000259

    Article  Google Scholar 

  107. Gaidai, O., Yakimov, V., & Balakrishna, R. (2023). Dementia death rates prediction. BMC Psychiatry, 23(691), 66. https://doi.org/10.1186/s12888-023-05172-2

    Article  Google Scholar 

  108. Gaidai, O., Yakimov, V., Wang, F., Zhang, F., & Balakrishna, R. (2023). Floating wind turbines structural details fatigue life assessment. Scientific Reports. https://doi.org/10.1038/s41598-023-43554-4

    Article  Google Scholar 

  109. Gaidai, O., Yakimov, V., Wang, F., & Zhang, F. (2023). Safety design study for energy harvesters. Sustainable Energy Research. https://doi.org/10.1186/s40807-023-00085-w

    Article  Google Scholar 

  110. Gaidai, O., Yakimov, V., & van Loon, E. (2023). Influenza-type epidemic risks by spatio-temporal Gaidai-Yakimov method. Dialogues in Health. https://doi.org/10.1016/j.dialog.2023.100157

    Article  Google Scholar 

  111. Gaidai, O., Yakimov, V., Niu, Y., & Liu, Z. (2023). Gaidai-Yakimov reliability method for high-dimensional spatio-temporal biosystems. Bio Systems. https://doi.org/10.1016/j.biosystems.2023.105073

    Article  Google Scholar 

  112. Gaidai, O., Yakimov, V., Sun, J., et al. (2023). Singapore COVID-19 data cross-validation by the Gaidai reliability method. npj Viruses. https://doi.org/10.1038/s44298-023-00006-0

    Article  Google Scholar 

  113. Sun, J., Gaidai, O., Wang, F., et al. (2023). Gaidai reliability method for fixed offshore structures. Journal of the Brazilian Society of Mechanical Sciences and Engineering. https://doi.org/10.1007/s40430-023-04607-x

    Article  Google Scholar 

  114. Gaidai, O., Wang, F., Cao, Y., et al. (2024). 4400 TEU cargo ship dynamic analysis by Gaidai reliability method. Journal of Shipping and Trade, 9, 1. https://doi.org/10.1186/s41072-023-00159-4

    Article  Google Scholar 

  115. Gaidai, O., Wang, F., & Sun, J. (2024). Energy harvester reliability study by Gaidai reliability method. Climate Resilience and Sustainability. https://doi.org/10.1002/cli2.64

    Article  Google Scholar 

  116. Gaidai, O., Sheng, J., Cao, Y., Zhang, F., Zhu, Y., & Loginov, S. (2024). Public health system sustainability assessment by Gaidai hypersurface approach. Current Problems in Cardiology. https://doi.org/10.1016/j.cpcardiol.2024.102391

    Article  Google Scholar 

  117. Gaidai, O., Yakimov, V., Hu, Q., & Loginov, S. (2024). Multivariate risks assessment for complex bio-systems by Gaidai reliability method. Systems and Soft Computing. https://doi.org/10.1016/j.sasc.2024.200074

    Article  Google Scholar 

  118. Gaidai, O., Yakimov, V., Wang, F., Sun, J., & Wang, K. (2024). Bivariate reliability analysis for floating wind turbines. International Journal of Low-Carbon Technologies, 19, 55–64. https://doi.org/10.1093/ijlct/ctad108

    Article  Google Scholar 

  119. Gaidai, O., Yan, P., Xing, Y., Xu, J., & Wu, Y. (2023). Gaidai reliability method for long-term coronavirus modelling". F1000 Research. https://doi.org/10.12688/f1000research.125924.3

    Article  Google Scholar 

Download references

Funding

No funding was received.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed equally. All autrhors read and approved the final manuscript.

Corresponding author

Correspondence to Oleg Gaidai.

Ethics declarations

Competing interests

Authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gaidai, O., Yakimov, V., Wang, F. et al. Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety, Given Manufacturing Imperfections. Int. J. Precis. Eng. Manuf. 25, 1011–1025 (2024). https://doi.org/10.1007/s12541-024-00977-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-024-00977-x

Keywords

Navigation