1995•Journal of the Society of Materials Science JapanOpen access

Reliability Analysis on Fatigue Strength under Random Loading with Bimodal PSD.

Shinsuke Sakai, Hiroyuki Okamura

Open full text 0 citations

Abstract

To evaluate the cumulative fatigue damage under Gaussian random loading, the distribution of rainflow range must be evaluated from the random waves.It is considered difficult to evaluate the distribution of range from the geometry of the PSD of the waves except the narrow band random waves, the range distribution of which becomes Rayleigh distribution.As to the random waves with bimodal PSD, however, the authors have already shown that the range can be derived from the PSD analytically.The distribution of range can be represented by the ratio of two dominant frequencies and the ratio of two corresponding power.In this paper, the property of fatigue damage evaluated by the proposed method is examined.And the fundamental equations necessary for reliability analysis are formulated.The derived equations are compared with the results of Monte-Carlo simulations and good agreements are obtained.Finally, several diagrams, which enable us fatigue design easily, are presented.

Open-access reader

About this research paper

What this paper is about

To evaluate the cumulative fatigue damage under Gaussian random loading, the distribution of rainflow range must be evaluated from the random waves.It is considered difficult to evaluate the distribution of range from the geometry of the PSD of the waves except the narrow band random waves, the range distribution of which becomes Rayleigh distribution.As to the random waves with bimodal PSD, however, the authors have already shown that the range can be derived from the PSD analytically.The distribution of range can be represented by the ratio of two dominant frequencies and the ratio of two corresponding power.In this paper, the property of fatigue damage evaluated by the proposed method is examined.And the fundamental equations necessary for reliability analysis are formulated.The derived equations are compared with the results of Monte-Carlo simulations and good agreements are obtained.Finally, several diagrams, which enable us fatigue design easily, are presented.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

To evaluate the cumulative fatigue damage under Gaussian random loading, the distribution of rainflow range must be evaluated from the random waves.It is considered difficult to evaluate the distribution of range from the geometry of the PSD of the waves except the narrow band random waves, the range distribution of which becomes Rayleigh distribution.As to the random waves with bimodal PSD, however, the authors have already shown that the range can be derived from the PSD analytically.The distribution of range can be represented by the ratio of two dominant frequencies and the ratio of two corresponding power.In this paper, the property of fatigue damage evaluated by the proposed method is examined.And the fundamental equations necessary for reliability analysis are formulated.The derived equations are compared with the results of Monte-Carlo simulations and good agreements are obtained.Finally, several diagrams, which enable us fatigue design easily, are presented.

Key concepts: Range (aeronautics), Rayleigh distribution, Monte Carlo method, Gaussian, Reliability (semiconductor), Structural engineering, Vibration fatigue, Spectral density

Related papers

Back to paper searchBrowse research topicsOriginal source
Reliability Analysis on Fatigue Strength under Random Loading with Bimodal PSD. — Research Paper | ScholarLens