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疲劳研究中负荷评估的多种方法。

Varied approaches to loading assessment in fatigue studies.

作者信息

Gadolina I V, Makhutov N A, Erpalov A V

机构信息

IMASH RAS, Moscow, Russia.

South Ural State University, Chelyabinsk, Russia.

出版信息

Int J Fatigue. 2021 Mar;144:106035. doi: 10.1016/j.ijfatigue.2020.106035. Epub 2020 Dec 3.

DOI:10.1016/j.ijfatigue.2020.106035
PMID:33288971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7711198/
Abstract

In this paper, the authors want to draw readers' attention to one long-standing question: which approach is preferable for estimation longevities in fatigue problem, the time domain approach (Rainflow) or the frequency domain one (Dirlik and others)? This question is important in engineering problems, particularly in problems of prolongation of the guaranteed service life. The discussion here is restricted by the longevity evaluation only at the post-processing stage of unidirectional loaded machine parts. It means the realizations might be recorded. Some experimental and speculative evidence of preferable use the Rainflow method is shown. Taking into account the huge computer's power nowadays, the question of the irrelevance of appellation to the calculation accelerating using the spectral methods is specially discussed. There are areas, where the spectral methods are really necessary. There is only a need to recommend the restriction of their application scope to these special situations. It seems there is no need in inventing new spectral complicated algorithms only to stress out at the end, that their result coincides with the Rainflow outcome. That might be confusing for the practicing engineers. Currently, the main attention among supporters of spectral methods is focused on non-stationary and non-Gaussian random processes. (to estimate the spectral density is impossible for non-stationary processes, according to definition). These researchers seem to have forgotten, that even for these complicated situations the decision has already existed: that is the Rainflow and its analogues. The paper shows extensive laboratory experiment results of random fatigue testing of aluminum flat specimens under regular (to build the fatigue curve) and irregular (random) loading. The fatigue life curve (Gassner curve) has been built. These results allowed to compare the existing computation methods of longevity estimation. In the particular situation of narrow-band process, the methods seem to provide comparative results. Considered are methodological issues related to the assessment of the necessary and sufficient realization length, the influence of RMS, cycle counting methods and some possibilities of computing resources saving when using the Rainflow method. The stability of the Rainflow estimates is confirmed. Some problems with the choice of parameters during the longevity assessment by the Dirlik method were noted.

摘要

在本文中,作者希望引起读者对一个长期存在的问题的关注:在疲劳问题中估计寿命时,哪种方法更可取,是时域方法(雨流法)还是频域方法(迪利克等人的方法)?这个问题在工程问题中很重要,特别是在保证使用寿命延长的问题中。这里的讨论仅限于单向加载机器零件后处理阶段的寿命评估。这意味着可能会记录实现情况。展示了一些支持优先使用雨流法的实验和推测性证据。考虑到如今计算机的强大计算能力,专门讨论了关于名称与使用谱方法加速计算无关的问题。存在一些谱方法确实必要的领域。只需要将其应用范围限制在这些特殊情况。似乎没有必要仅仅为了最后强调其结果与雨流法的结果一致而发明新的复杂谱算法。这可能会让实际操作的工程师感到困惑。目前,谱方法支持者的主要注意力集中在非平稳和非高斯随机过程上。(根据定义,对于非平稳过程不可能估计谱密度)。这些研究人员似乎忘记了,即使对于这些复杂情况,解决方案已经存在:那就是雨流法及其类似方法。本文展示了铝平板试样在常规(构建疲劳曲线)和不规则(随机)载荷下随机疲劳试验的大量实验室实验结果。构建了疲劳寿命曲线(加斯纳曲线)。这些结果使得能够比较现有的寿命估计计算方法。在窄带过程的特定情况下,这些方法似乎能提供比较结果。考虑了与评估必要且充分的实现长度、均方根的影响、循环计数方法以及使用雨流法时节省计算资源的一些可能性相关的方法学问题。证实了雨流估计的稳定性。指出了迪利克方法在寿命评估过程中参数选择的一些问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/360ced45494d/gr16_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/f9f1697ad869/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/eac6bc9e3d1c/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/903f6dd7c182/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/96b3063b1b90/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/39f819316e45/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/c06cbe02445a/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/9145cdd46404/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/0a2ecc3c8ac1/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/cd37de35569c/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/d4f44f741fcf/gr12_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/5614d1a93da6/gr13_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/f46a61620704/gr14_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/6ac472ae0784/gr15_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8e/7711198/360ced45494d/gr16_lrg.jpg

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