A novel method for prediction of extreme wind speeds across parts of Southern Norway

Gaidai, Oleg; Yan, Ping; Xing, Yihan

doi:10.3389/fenvs.2022.997216

Cited by 34 publications

(22 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter is an unacceptable numerical inaccuracy since only positive numbers may represent distributions. To address this numerical difficulty, the following scaling method has been implemented 12 , 16 . As the pivot value, the lowest positive value of the specified distribution tail is used.…”

Section: Resultsmentioning

confidence: 99%

“…Similar definitions may be provided for the additional MDOF response components using ,…. All MDOF components and their unidimensional maxima are considered to be bigger than zero in this investigation 12 .…”

Section: Methodsmentioning

confidence: 99%

“…So having , one must concurrently and continuously screen for each unidimensional response component temporal maxima, noting its exceedances of MDOF limit vector in any of its unidimensional response components Each unidimensional response component maxima are then combined into a single temporal non-decreasing vector based on the merged time vector . Finally, the unified MDOF limit vector is now introduced with each component being either , or …, depending on which of or , or … corresponds to the current unidimensional response component maxima with the running index , see 12 – 16 .…”

Section: Methodsmentioning

confidence: 99%

“…, Z N Z ,…. All MDOF R(t) components and their unidimensional maxima are considered to be bigger than zero in this investigation 12 .…”

Section: Methodsmentioning

confidence: 99%

“…. , η N ) is now introduced with each component η j being either η X , η Y or η Z …, depending on which of X(t) or Y (t) , or Z(t) … corresponds to the current unidimensional response component maxima with the running index j , see [12][13][14][15][16] .…”

Section: P X Maxmentioning

confidence: 99%

See 4 more Smart Citations

Novel methods for wind speeds prediction across multiple locations

Gaidai

Yan

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…, Z N Z ,…. All MDOF R(t) components and their unidimensional maxima are considered to be bigger than zero in this investigation 12 .…”

Section: Methodsmentioning

confidence: 99%

Section: P X Maxmentioning

confidence: 99%

See 3 more Smart Citations

Novel methods for wind speeds prediction across multiple locations

Gaidai

Yan

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

Novel Reliability Method Validation for Floating Wind Turbines

Gaidai

Wang

Xing

et al. 2023

Adv Energy and Sustain Res

Self Cite

View full text Add to dashboard Cite

Wind turbines and associated parts are susceptible to cyclic stresses, including torque, bending, and longitudinal stress, and twisting moments. Therefore, research on the resilience of dynamic systems under such high loads is crucial for design and future risk‐free operations. The method described in this study is beneficial for multidimensional structural responses that have undergone sufficient numerical simulation or measurement. In contrast to established dependability methodologies, the unique technique does not need to restart the numerical simulation each time the system fails. Herein, it is demonstrated that it is also possible to accurately predict the probability of a system failure in the event of a measurable structural reaction. In contrast to well‐established bivariate statistical methods, which are known to predict extreme response levels for 2D systems accurately, this study validates a novel structural reliability method that is particularly suitable for multidimensional structural responses. In contrast to conventional methods, the novel reliability approach does not invoke a multidimensional reliability function in the Monte Carlo numerical simulation case. As demonstrated in this study, it is also possible to accurately anticipate the likelihood of a system failure in the case of a measurable structural reaction.

show abstract

Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

Self Cite

View full text Add to dashboard Cite

This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.

show abstract

A novel method for prediction of extreme wind speeds across parts of Southern Norway

Cited by 34 publications

References 18 publications

Novel methods for wind speeds prediction across multiple locations

Novel methods for wind speeds prediction across multiple locations

Novel Reliability Method Validation for Floating Wind Turbines

Energy harvester reliability study by Gaidai reliability method

Contact Info

Product

Resources

About