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基于时序参数法的BFRP蠕变行为预测
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作者 刘五祥 张胡靓 吴玥 《力学季刊》 CAS CSCD 北大核心 2022年第2期416-422,共7页
本文研究了基于时序参数法的玄武岩纤维增强复合材料(BFRP)板弯曲蠕变行为.为了提高模型理论预测的精度,文中将整个实验时长的数据进行了合理的细化分组,且每组时长尽可能相同,然后对细化分组的数据组分别进行直接外推法拟合,得到各数... 本文研究了基于时序参数法的玄武岩纤维增强复合材料(BFRP)板弯曲蠕变行为.为了提高模型理论预测的精度,文中将整个实验时长的数据进行了合理的细化分组,且每组时长尽可能相同,然后对细化分组的数据组分别进行直接外推法拟合,得到各数据组对应的拟合方程参数,最后获得了BFRP板弯曲蠕变行为的理论预测模型.对比模型预测值和实验值可以发现:模型预测值随着时间的增长,越来越接近于实验值,最后二者的相对误差几乎为零.该结果表明:文中建立的蠕变预测模型具有足够高的精度,可以应用于BFRP的蠕变行为的预测. 展开更多
关键词 玄武岩纤维增强复合材料 蠕变 时序参数法 预测
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考虑热耦合的全桥功率模块功率循环方法
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作者 赵雨山 邓二平 +4 位作者 刘鉴辉 潘茂杨 张莹 张传云 黄永章 《半导体技术》 CAS 北大核心 2022年第9期704-711,共8页
实际应用中,功率模块桥臂之间的热耦合非常普遍,提出了考虑热耦合的全桥功率模块功率循环方法,但电流路径很难与实际工况下的电流路径保持一致。通过时序电参数法,分析了有、无热耦合条件下功率循环试验中功率模块结温的分布特点。分析... 实际应用中,功率模块桥臂之间的热耦合非常普遍,提出了考虑热耦合的全桥功率模块功率循环方法,但电流路径很难与实际工况下的电流路径保持一致。通过时序电参数法,分析了有、无热耦合条件下功率循环试验中功率模块结温的分布特点。分析了电流路径对考虑热耦合的功率循环试验的影响,并进行了有、无热耦合条件下的功率循环试验。结果显示,考虑热耦合的功率循环试验应考虑电流路径差异造成的影响,热耦合对功率循环寿命有影响。通过有限元仿真,探究了热耦合和电流路径影响功率模块结温分布的机理,确定了考虑热耦合的功率循环方法。 展开更多
关键词 热耦合 全桥功率模块 功率循环试验 时序参数 寿命
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CrMoWV钢的应力松弛行为及其预测 被引量:1
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作者 曹铁山 程从前 +3 位作者 朱月梅 张弘伟 刘松峰 赵杰 《材料工程》 EI CAS CSCD 北大核心 2017年第5期106-111,共6页
以12Cr-1Mo-1W-0.25V耐热钢550,600℃的8760h松弛实验数据作为对象,研究短时间松弛数据准确而有效预测长时间松弛应力的方法。在采用松弛模型对长时间松弛应力进行直接拟合外推时,发现模型参数与所采用拟合数据的时间长度呈规律性的变... 以12Cr-1Mo-1W-0.25V耐热钢550,600℃的8760h松弛实验数据作为对象,研究短时间松弛数据准确而有效预测长时间松弛应力的方法。在采用松弛模型对长时间松弛应力进行直接拟合外推时,发现模型参数与所采用拟合数据的时间长度呈规律性的变化。提出考虑模型参数规律变化的时序参数法,以高精度预测长时间松弛应力。通过对比时序参数法与直接拟合外推法的预测结果,认为时序参数法在用短时间松弛数据预测长时间松弛应力上具有明显优势,预测结果的准确性较直接外推法高。 展开更多
关键词 应力松弛 松弛模型 时序参数法 预测
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DIAGNOSTIC CHECKING FOR TIME SERIES MODELS USING NONPARAMETRIC APPROACH
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作者 钟登华 尼伯伦丁 《Transactions of Tianjin University》 EI CAS 1997年第1期45-49,共5页
In time series modeling, the residuals are often checked for white noise and normality. In practice, the useful tests are Ljung Box test. Mcleod Li test and Lin Mudholkar test. In this paper, we present a nonparame... In time series modeling, the residuals are often checked for white noise and normality. In practice, the useful tests are Ljung Box test. Mcleod Li test and Lin Mudholkar test. In this paper, we present a nonparametric approach for checking the residuals of time series models. This approach is based on the maximal correlation coefficient ρ 2 * between the residuals and time t . The basic idea is to use the bootstrap to form the null distribution of the statistic ρ 2 * under the null hypothesis H 0:ρ 2 * =0. For calculating ρ 2 * , we proposes a ρ algorithm, analogous to ACE procedure. Power study shows this approach is more powerful than Ljung Box test. Meanwhile, some numerical results and two examples are reported in this paper. 展开更多
关键词 BOOTSTRAP diagnostic checking nonparametric approach time series white noise ρ algorithm
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Estimation of Number Of Small Cattle Through ARIMA Models in Turkey
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作者 Senol CELIK 《Journal of Mathematics and System Science》 2015年第11期464-473,共10页
In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series w... In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren't stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the "tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion (AIC) was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA(0,1,1). In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats. 展开更多
关键词 ARIMA Models AUTOCORRELATION the number of sheep the number of goats.
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