Evaluating Areal Windspeeds and Wave Heights by Gaidai Risk Evaluation Method
Abstract
This study presents a state-of-the-art risk evaluation approach, designed for spatiotemporal multivariate environmental dynamic wind-wave systems, being either measured or Monte Carlo (MC) numerically simulated over a representative time-lapse. The main objective of this study has been to benchmark/validate the effectiveness and accuracy of the Gaidai multivariate risk evaluation methodology, using the application to in situ raw windspeeds, along with correlated wave-heights measurements, delivered by North Pacific area of the National Oceanic and Atmospheric Administration (NOAA) ocean buoys. The current study outlines a novel risk evaluation methodology, suitable for environmental dynamic systems, that are either MC numerically modeled, or directly physically measured. The intercorrelations between the wind-wave environmental system’s critical/key dimensions and components, along with the high dimensionality of complex environmental systems, are not easily addressed by contemporary classic reliability methods. The objective of this study is to apply a novel reliability/risk evaluation methodology to a combined windspeed and correlated wave-height raw data set, recorded by the NOAA buoys within the North Pacific area, to demonstrate the efficiency of the proposed methodology. By reliability/risk assessment in the current study authors primarily mean probability forecast of certain multivariate hazard event. It is well known that when combined, windspeeds along with correlated wave heights form nonlinear dynamic environmental systems, that are complex, multidimensional, nonstationary, and yet intercorrelated. Global warming is only one of several significant factors that have ongoing impact on ocean windspeeds along with correlated wave heights, and environmental system risk evaluation is essential for marine, naval, and offshore structures, operating within specific in situ offshore areas of interest subject to realistic in situ ocean/sea weather conditions. The main goal of this study had been to benchmark and validate novel multivariate risk analysis methodology, making it possible to extract essential information directly from in situ raw environmental measurements. The methodology presented in this study opens the possibility of efficiently yet accurately assessing global failure/damage and hazard risks for multivariate nonstationary nonlinear environmental sea/ocean wind-wave systems.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Data used in this study is available at NOAA (https://www.ndbc.noaa.gov).
Acknowledgments
This study shares the same methodology as previously published by authors in Gaidai et al. (2023r), however the application case is completely different.
Author contributions: O. Gaidai: Conceptualization. J. Sheng: Methodology. Y. Cao: Data analysis. Y. Zhu: Project management. Z. Liu: Visualization.
References
Christou, M., and K. Ewans. 2014. “Field measurements of rogue water waves.” J. Phys. Oceanogr. 44 (9): 2317–2335. https://doi.org/10.1175/JPO-D-13-0199.1.
Doeleman, M. W. 2021. “Rogue waves in the Dutch North Sea.” Master’s thesis, Dept. of Civil Engineering & Geosciences, TU Delft.
Dong, Y. L., and J. Ki-Cheon. 2006. “Estimation of design wave height for the waters around the Korean Peninsula.” Ocean Sci. J. 41 (4): 245–254. https://doi.org/10.1007/BF03020628.
Ducrozet, G., M. Abdolahpour, F. Nelli, and A. Toffoli. 2021. “Predicting the occurrence of rogue waves in the presence of opposing currents with a high-order spectral method.” Phys. Rev. Fluids 6 (6): 064803. https://doi.org/10.1103/PhysRevFluids.6.064803.
Emanuel, K., and T. Jagger. 2010. “On estimating hurricane return periods.” J. Appl. Meteorol. Climatol. 49 (5): 837–844. https://doi.org/10.1175/2009JAMC2236.1.
Ferreira, J. A., and S. C. Guedes. 2000. “Modelling distributions of significant wave height.” Coast Eng. 40 (4): 361–374. https://doi.org/10.1016/S0378-3839(00)00018-1.
Forristall, G. 1978. “On the distributions of wave heights in a storm.” J. Geophys. Res. 83 (Jun): 2353–2358. https://doi.org/10.1029/JC083iC05p02353.
Gaidai, O. 2024. “Global health risks due to the COVID-19 epidemic by Gaidai reliability method.” Video Article 10: 100366. https://doi.org/10.1016/j.sctalk.2024.100366.
Gaidai, O., A. Ashraf, Y. Cao, J. Sheng, Y. Zhu, and Z. Liu. 2024a. “Lifetime assessment of semi-submersible wind turbines by Gaidai risk evaluation method.” J. Mater. Sci: Mater. Eng. 19 (1): 2. https://doi.org/10.1186/s40712-024-00142-2.
Gaidai, O., Y. Cao, H. Li, Z. Liu, A. Ashraf, Y. Zhu, and J. Sheng. 2024b. “Multivariate Gaidai hazard assessment method in combination with deconvolution scheme to predict extreme wave heights.” Results Eng. 22 (May): 102326. https://doi.org/10.1016/j.rineng.2024.102326.
Gaidai, O., Y. Cao, and S. Loginov. 2023a. “Global cardiovascular diseases death rate prediction.” Curr. Probl. Cardiol. 48 (5): 101622. https://doi.org/10.1016/j.cpcardiol.2023.101622.
Gaidai, O., Y. Cao, Y. Xing, and R. Balakrishna. 2023b. “Extreme springing response statistics of a tethered platform by deconvolution.” Int. J. Nav. Archit. Ocean Eng. 15 (Jan): 100515. https://doi.org/10.1016/j.ijnaoe.2023.100515.
Gaidai, O., Y. Cao, X. Xu, and Y. Xing. 2023c. “Offloading operation bivariate extreme response statistics for FPSO vessel.” Sci. Rep. 13 (1): 4695. https://doi.org/10.1038/s41598-023-31533-8.
Gaidai, O., Q. Hu, J. Xu, F. Wang, and Y. Cao. 2023d. “Carbon storage tanker lifetime assessment.” Glob. Challenges 7 (7): 2300011. https://doi.org/10.1002/gch2.202300011.
Gaidai, O., J. Sheng, Y. Cao, F. Zhang, Y. Zhu, and Z. Liu. 2024c. “Gaidai multivariate risk evaluation method for cargo ship dynamics.” Urban Plann. Transp. Res. 12 (1): 2327362. https://doi.org/10.1080/21650020.2024.2327362.
Gaidai, O., J. Sheng, Y. Cao, F. Zhang, Y. Zhu, and S. Loginov. 2024d. “Public health system sustainability assessment by Gaidai hypersurface approach.” Curr. Probl. Cardiol. 49 (3): 102391. https://doi.org/10.1016/j.cpcardiol.2024.102391.
Gaidai, O., J. Sheng, Y. Cao, Y. Zhu, and S. Loginov. 2024e. “Generic COVID-19 epidemic forecast for Estonia by Gaidai multivariate reliability method.” Franklin Open 6 (Mar): 100075. https://doi.org/10.1016/j.fraope.2024.100075.
Gaidai, O., J. Sheng, Y. Cao, Y. Zhu, K. Wang, and Z. Liu. 2024f. “Limit hypersurface state of art Gaidai reliability approach for oil tankers Arctic operational safety.” J. Ocean Eng. Mar. Energy. 10 (2): 351–364. https://doi.org/10.1007/s40722-024-00316-2.
Gaidai, O., J. Sun, and Y. Cao. 2024g. “FPSO/FLNG mooring system evaluation by Gaidai reliability method.” J. Mar. Sci. Technol. https://doi.org/10.1007/s00773-024-01001-7.
Gaidai, O., F. Wang, Y. Cao, and Z. Liu. 2024h. “4400 TEU cargo ship dynamic analysis by Gaidai reliability method.” J. Ship. Trade 9 (1): 1. https://doi.org/10.1186/s41072-023-00159-4.
Gaidai, O., F. Wang, and J. Sun. 2024i. “Energy harvester reliability study by Gaidai reliability method.” Clim. Resilience Sustainability 3 (1): e64. https://doi.org/10.1002/cli2.64.
Gaidai, O., F. Wang, Y. Wu, Y. Xing, A. Rivera Medina, and J. Wang. 2022a. “Offshore renewable energy site correlated wind-wave statistics.” Probab. Eng. Mech. 68 (Apr): 103207. https://doi.org/10.1016/j.probengmech.2022.103207.
Gaidai, O., F. Wang, Y. Xing, and R. Balakrishna. 2023e. “Novel reliability method validation for floating wind turbines.” Adv. Energy Sustainability Res. 4 (8): 2200177. https://doi.org/10.1002/aesr.202200177.
Gaidai, O., F. Wang, V. Yakimov, J. Sun, and R. Balakrishna. 2023f. “Lifetime assessment for riser systems.” Resilience Sustainability 3 (1): 4. https://doi.org/10.1007/s44173-023-00013-7.
Gaidai, O., Y. Xing, R. Balakrishna, and J. Xu. 2023g. “Improving extreme offshore windspeed prediction by using deconvolution.” Heliyon 9 (2): E13533. https://doi.org/10.1016/j.heliyon.2023.e13533.
Gaidai, O., Y. Xing, F. Wang, S. Wang, P. Yan, and A. Naess. 2022b. “Improving extreme anchor tension prediction of a 10-MW floating semi-submersible type wind turbine, using highly correlated surge motion record.” Front. Mech. Eng., 8 (Jul): 88849. https://doi.org/10.3389/fmech.2022.888497.
Gaidai, O., Y. Xing, J. Xu, and R. Balakrishna. 2023h. “Gaidai-Xing reliability method validation for 10-MW floating wind turbines.” Sci. Rep. 13 (1): 8691. https://doi.org/10.1038/s41598-023-33699-7.
Gaidai, O., Y. Xing, and X. Xu. 2023i. “Novel methods for coupled prediction of extreme windspeeds and wave heights.” Sci. Rep. 13 (1): 1119. https://doi.org/10.1038/s41598-023-28136-8.
Gaidai, O., J. Xu, V. Yakimov, and F. Wang. 2023j. “Analytical and computational modeling for multi-degree of freedom systems: Estimating the likelihood of an FOWT structural failure.” J. Mar. Sci. Eng. 11 (6): 1237. https://doi.org/10.3390/jmse11061237.
Gaidai, O., J. Xu, V. Yakimov, and F. Wang. 2023k. “Liquid carbon storage tanker disaster resilience.” Environ. Syst. Dec. 43 (4): 746–757. https://doi.org/10.1007/s10669-023-09922-1.
Gaidai, O., J. Xu, P. Yan, Y. Xing, K. Wang, and Z. Liu. 2023l. “Novel methods for reliability study of multi-dimensional non-linear dynamic systems.” Sci. Rep. 13 (1): 3817. https://doi.org/10.1038/s41598-023-30704-x.
Gaidai, O., J. Xu, P. Yan, Y. Xing, Y. Wu, and F. Zhang. 2022c. “Novel methods for windspeeds prediction across multiple locations.” Sci. Rep. 12 (1): 19614. https://doi.org/10.1038/s41598-022-24061-4.
Gaidai, O., X. Xu, A. Naess, Y. Cheng, R. Ye, and J. Wang. 2020. “Bivariate statistics of wind farm support vessel motions while docking.” Ships Offshore Struct. 16 (2): 135–143. https://doi.org/10.1080/17445302.2019.1710936.
Gaidai, O., X. Xu, and Y. Xing. 2023m. “Novel deconvolution method for extreme FPSO vessel hawser tensions during offloading operations.” Results Eng. 17 (Mar): 100828. https://doi.org/10.1016/j.rineng.2022.100828.
Gaidai, O., V. Yakimov, Q. Hu, and S. Loginov. 2024j. “Multivariate risks assessment for complex bio-systems by Gaidai reliability method.” Syst. Soft Comput. 6 (Dec): 200074. https://doi.org/10.1016/j.sasc.2024.200074.
Gaidai, O., V. Yakimov, Y. Niu, and Z. Liu. 2023n. “Gaidai-Yakimov reliability method for high-dimensional spatio-temporal biosystems.” Biosystems 235 (Jan): 105073. https://doi.org/10.1016/j.biosystems.2023.105073.
Gaidai, O., V. Yakimov, J. Sun, and E. J. van Loon. 2023o. “Singapore COVID-19 data cross-validation by the Gaidai reliability method.” NPJ Viruses 1 (1): 9. https://doi.org/10.1038/s44298-023-00006-0.
Gaidai, O., V. Yakimov, and E. van Loon. 2023p. “Influenza-type epidemic risks by spatio-temporal Gaidai-Yakimov method.” Dialogues Health 3 (2): 100157. https://doi.org/10.1016/j.dialog.2023.100157.
Gaidai, O., V. Yakimov, F. Wang, and Y. Cao. 2024k. “Gaidai multivariate reliability method for energy harvester operational safety, given manufacturing imperfections.” Int. J. Precis. Eng. Manuf. 25 (5): 1011–1025. https://doi.org/10.1007/s12541-024-00977-x.
Gaidai, O., V. Yakimov, F. Wang, Q. Hu, and G. Storhaug. 2023q. “Lifetime assessment for container vessels.” Appl. Ocean Res. 139 (Oct): 103708. https://doi.org/10.1016/j.apor.2023.103708.
Gaidai, O., V. Yakimov, F. Wang, J. Sun, and K. Wang. 2024l. “Bivariate reliability analysis for floating wind turbines.” Int. J. Low Carbon Technol. 19 (Apr): 55–64. https://doi.org/10.1093/ijlct/ctad108.
Gaidai, O., V. Yakimov, F. Wang, and F. Zhang. 2023r. “Safety design study for energy harvesters.” Sustainable Energy Res. 10 (1): 15. https://doi.org/10.1186/s40807-023-00085-w.
Gaidai, O., V. Yakimov, F. Wang, F. Zhang, and R. Balakrishna. 2023s. “Floating wind turbines structural details fatigue life assessment.” Sci. Rep. 13 (1): 16312. https://doi.org/10.1038/s41598-023-43554-4.
Gaidai, O., P. Yan, Y. Xing, J. Xu, and Y. Wu. 2023t. “Gaidai reliability method for long-term coronavirus modeling.” F1000Research 11 (Nov): 1282. https://doi.org/10.12688/f1000research.125924.3.
Gaidai, O., P. Yan, Y. Xing, J. Xu, F. Zhang, and Y. Wu. 2023u. “Oil tanker under ice loadings.” Sci. Rep. 13 (1): 8670. https://doi.org/10.1038/s41598-023-34606-w.
Goda, Y., M. Kudaka, and H. Kawai. 2010. “Incorporation of Weibull distribution in L-moments method for regional frequency of peaks-over-threshold wave heights.” In Proc., 32nd Int. Conf. on Coastal Engineering. Reston, VA: ASCE.
Haring, R., A. Osborne, and L. Spencer. 1976. “Extreme wave parameters based on continental shelf storm wave records.” Coastal Eng. https://doi.org/10.1061/9780872620834.010.
Horn, J., and B. Leira. 2019. “Fatigue reliability assessment of offshore wind turbines with stochastic availability.” Reliab. Eng. Syst. Saf. 191 (Nov): 106550. https://doi.org/10.1016/j.ress.2019.106550.
Hui, G., O. Gaidai, A. Naess, G. Storhaug, and X. Xu. 2019. “Improving container ship panel stress prediction, based on another highly correlated panel stress measurement.” Mar. Struct. 64 (Jun): 138–145. https://doi.org/10.1016/j.marstruc.2018.11.007.
Ishihara, T., and A. Yamaguchi. 2015. “Prediction of the extreme windspeed in the mixed climate region by using Monte Carlo simulation and measure-correlate-predict method.” Wind Energy 18 (1): 171–186. https://doi.org/10.1002/we.1693.
Jahns, H., and J. Wheeler. 1973. “Long-term wave probabilities based on hindcasting of severe storms.” J. Petrol. Technol. 25 (04): 473–486. https://doi.org/10.2118/3934-PA.
Karmpadakis, I., C. Swan, and M. Christou. 2020. “Assessment of wave height distributions using an extensive field database.” Coastal Eng. 157 (Apr): 103630. https://doi.org/10.1016/j.coastaleng.2019.103630.
Karmpadakis, I., C. Swan, and M. Christou. 2022. “A new wave height distribution for intermediate and shallow water depths.” Coastal Eng. 175 (Feb): 104130. https://doi.org/10.1016/j.coastaleng.2022.104130.
Kimmoun, O., H. C. Hsu, N. Hoffmann, and A. Chabchoub. 2021. “Experiments on uni-directional and nonlinear wave group shoaling.” Ocean Dyn. 71 (11–12): 1105–1112. https://doi.org/10.1007/s10236-021-01485-6.
Leimeister, M., and A. Kolios. 2021. “Reliability-based design optimization of a spar-type floating offshore wind turbine support structure.” Reliab. Eng. Syst. Saf. 213 (Sep): 107666. https://doi.org/10.1016/j.ress.2021.107666.
Li, Y., S. Draycott, T. A. Adcock, and T. Van Den Bremer. 2021a. “Surface wavepackets subject to an abrupt depth change. Part 2: Experimental analysis.” J. Fluid Mech. 915 (Apr): A72. https://doi.org/10.1017/jfm.2021.49.
Li, Y., S. Draycott, Y. Zheng, Z. Lin, T. Adcock, and T. Van Den Bremer. 2021b. “Why rogue waves occur atop abrupt depth transitions.” J. Fluid Mech. 919 (Jul): R5. https://doi.org/10.1017/jfm.2021.409.
Li, Y., Y. Zheng, Z. Lin, T. A. Adcock, and T. Van Den Bremer. 2021c. “Surface wavepackets subject to an abrupt depth change. Part 1: Second-order theory.” J. Fluid Mech. 915 (Apr): A71. https://doi.org/10.1017/jfm.2021.48.
Liu, Z., O. Gaidai, Y. Xing, and J. Sun. 2023. “Deconvolution approach for floating wind turbines.” Energy Sci. Eng. 11 (8): 2742–2750. https://doi.org/10.1002/ese3.1485.
Majda, A., M. Moore, and D. Qi. 2019. “Statistical dynamical model to predict extreme events and anomalous features in shallow water waves with abrupt depth change.” Proc. Natl. Acad. Sci. USA 116 (10): 3982–3987. https://doi.org/10.1073/pnas.1820467116.
Mazas, F., and L. Hamm. 2011. “A multi-distribution approach to POT methods for determining extreme wave heights.” Coast Eng. 58 (5): 385–394 https://doi.org/10.1016/j.coastaleng.2010.12.003.
Mendes, S., and J. Kasparian. 2022. “Saturation of rogue wave amplification over steep shoals.” Phys. Rev. E 106 (6): 065101. https://doi.org/10.1103/PhysRevE.106.065101.
Mendes, S., and A. Scotti. 2021. “The rayleigh-haring-tayfun distribution of wave heights in deep water.” Appl. Ocean Res. 113 (Feb): 102739. https://doi.org/10.1016/j.apor.2021.102739.
Mendes, S., A. Scotti, and P. Stansell. 2021. “On the physical constraints for the exceeding probability of deep water rogue waves.” Appl. Ocean Res. 108 (Mar): 102402. https://doi.org/10.1016/j.apor.2020.102402.
Moore, N., C. Bolles, A. Majda, and D. Qi. 2020. “Anomalous waves triggered by abrupt depth changes: Laboratory experiments and truncated KDV statistical mechanics.” J. Nonlinear Sci. 30 (6): 3235–3263. https://doi.org/10.1007/s00332-020-09649-2.
Naess, A., and O. Gaidai. 2009. “Estimation of extreme values from sampled time series.” Struct. Saf. 31 (4): 325–334. https://doi.org/10.1016/j.strusafe.2008.06.021.
NOAA (National Oceanic and Atmospheric Administration). 2024. “National Data Buoy Center.” Accessed January 10, 2024. https://www.ndbc.noaa.gov.
Sun, J., O. Gaidai, F. Wang, A. Naess, Y. Wu, Y. Xing, E. van Loon, A. Medina, and J. Wang. 2022. “Extreme riser experimental loads caused by sea currents in the Gulf of Eilat.” Probab. Eng. Mech. 68 (Apr): 103243. https://doi.org/10.1016/j.probengmech.2022.103243.
Sun, J., O. Gaidai, F. Wang, and V. Yakimov. 2023a. “Gaidai reliability method for fixed offshore structures.” J. Braz. Soc. Mech. Sci. Eng. 46 (1): 27. https://doi.org/10.1007/s40430-023-04607-x.
Sun, J., O. Gaidai, Y. Xing, F. Wang, and Z. Liu. 2023b. “On safe offshore energy exploration in the Gulf of Eilat.” Qual. Reliab. Eng. Int. 39 (7): 2957–2966. https://doi.org/10.1002/qre.3402.
Tayfun, M. A., and F. Fedele. 2007. “Wave-height distributions and nonlinear effects.” Ocean Eng. 34 (11–12): 1631–1649. https://doi.org/10.1016/j.oceaneng.2006.11.006.
Teena, N. V., V. Sanil Kumar, K. Sudheesh, and R. Sajeev. 2012. “Statistical analysis on extreme wave height.” Nat. Hazards 64 (1): 223–236. https://doi.org/10.1007/s11069-012-0229-y.
Thoft-Christensen, P., and Y. Murotsu. 1986. Application of environmental systems reliability theory. Berlin: Springer.
Toffoli, A., T. Waseda, H. Houtani, L. Cavaleri, D. Greaves, and M. Onorato. 2015. “Rogue waves in opposing currents: An experimental study on deterministic and stochastic wave trains.” J. Fluid Mech. 769 (Mar): 277–297. https://doi.org/10.1017/jfm.2015.132.
Trulsen, K., A. Raustøl, S. Jorde, and L. Rye. 2020. “Extreme wave statistics of long-crested irregular waves over a shoal.” J. Fluid Mech. 882 (Jan): R2. https://doi.org/10.1017/jfm.2019.861.
Trulsen, K., H. Zeng, and O. Gramstad. 2012. “Laboratory evidence of freak waves provoked by non-uniform bathymetry.” Phys. Fluids 24 (9): 097101. https://doi.org/10.1063/1.4748346.
Wu, Y., D. Randell, M. Christou, K. Ewans, and P. Jonathan. 2016. “On the distribution of wave height in shallow water.” Coastal Eng. 111 (Apr): 39–49. https://doi.org/10.1016/j.coastaleng.2016.01.015.
Xu, X., O. Gaidai, V. Yakimov, Y. Xing, and F. Wang. 2023. “FPSO offloading operational safety study by a multidimensional reliability method.” Ocean Eng. 281 (Aug): 114652. https://doi.org/10.1016/j.oceaneng.2023.114652.
Xu, X., F. Wang, O. Gaidai, A. Naess, Y. Xing, and J. Wang. 2022. “Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions.” Ocean Eng. 257 (Aug): 111657. https://doi.org/10.1016/j.oceaneng.2022.111657.
Yakimov, V., O. Gaidai, F. Wang, and K. Wang. 2023a. “Arctic naval launch and recovery operations, under ice impact interactions.” Appl. Eng. Sci. 15 (Sep): 100146. https://doi.org/10.1016/j.apples.2023.100146.
Yakimov, V., O. Gaidai, F. Wang, X. Xu, Y. Niu, and K. Wang. 2023b. “Fatigue assessment for FPSO hawsers.” Int. J. Nav. Archit. Ocean Eng. 15 (Jan): 100540. https://doi.org/10.1016/j.ijnaoe.2023.100540.
Yayık, A., Y. Kutlu, and G. Altan. 2019. Regularized HessELM and inclined entropy measurement for congestive heart hazard/failure prediction. Ithaca, NY: Cornell Univ.
Zhang, H., R. Reynolds, and J. Bates. 2006. “Blended and gridded high resolution global sea surface windspeed and climatology from multiple satellites: 1987 - present.” In Proc., 2006 Annual Meeting. Boston: American Meteorological Society.
Zhang, J., M. Benoit, O. Kimmoun, A. Chabchoub, and H. C. Hsu. 2019. “Statistics of extreme waves in coastal waters: Large scale experiments and advanced numerical simulations.” Fluids 4 (2): 99. https://doi.org/10.3390/fluids4020099.
Zhang, J., O. Gaidai, H. Ji, and Y. Xing. 2023. “Operational reliability study of ice loads acting on oil tanker bow.” Heliyon 9 (4): E15189. https://doi.org/10.1016/j.heliyon.2023.e15189.
Information & Authors
Information
Published In
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 23, 2024
Accepted: May 28, 2024
Published online: Aug 2, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 2, 2025
ASCE Technical Topics:
- Analysis (by type)
- Business management
- Continuum mechanics
- Disaster risk management
- Dynamic loads
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Federal government
- Field tests
- Fluid mechanics
- Government
- Hydraulic engineering
- Hydrologic engineering
- Models (by type)
- Numerical analysis
- Numerical models
- Organizations
- Practice and Profession
- Risk management
- Solid mechanics
- Structural dynamics
- Structural engineering
- Tests (by type)
- Water and water resources
- Waves (fluid mechanics)
- Wind engineering
- Wind loads
- Wind waves
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Gaidai Oleg, Gaidai risks evaluation method for lifetime assessment for offshore floating wind turbine gearbox, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 10.1115/1.4066410, (1-16), (2024).
- Oleg Gaidai, Alia Ashraf, Yu Cao, Jinlu Sheng, Yan Zhu, ocean windspeeds forecast by Gaidai multivariate risk assessment method, utilizing deconvolution scheme, Results in Engineering, 10.1016/j.rineng.2024.102796, 23, (102796), (2024).
- Oleg Gaidai, Alia Ashraf, Yu Cao, Jinlu Sheng, Yan Zhu, Hongchen Li, Panamax cargo-vessel excessive-roll dynamics based on novel deconvolution method, Probabilistic Engineering Mechanics, 10.1016/j.probengmech.2024.103676, 77, (103676), (2024).
- Oleg Gaidai, Alia Ashraf, Yu Cao, Yan Zhu, Jinlu Sheng, Hongchen Li, Zirui Liu, Multivariate ocean waves dynamics in North Sea and Norwegian Sea by Gaidai reliability method, Energy Reports, 10.1016/j.egyr.2024.08.040, 12, (2346-2355), (2024).