As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the...As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the "thermal damping effect" will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input(NARX) algorithm was used as a novel method to predict the dry-bulb temperature(Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network(ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical "damping coefficient" for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Tdat the bottom of intake shafts.展开更多
通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄...通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄。提出用非线性时序模型与前向神经网络相结合的模型(Nonlinear auto-regressive moving average neural network with exogenousinputs,NARMAX-NN)来辨识热弹性效应。用NARMAX-NN模型对高速进给系统试验台的热动态特性进行建模,获得良好的效果。此方法比多变量回归模型、反馈神经网络模型及广义最小二乘输出误差模型有更好的精度和鲁棒性,能精确地对复杂结构、多热源的时变非线性热误差特性进行建模和预测。展开更多
利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化...利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化的基础上,结合厄尔尼诺/拉尼娜事件对其影响特征,利用线性倾向估计和非线性自回归(nonlinear auto regressive models with exogenous inputs,NARX)神经网络模型分别对沈阳地区2011—2015年的气温进行预测。结果表明,1961—2015年共计660个月中,沈阳地区11月—3月气温的变异系数在20%以上,远大于其他月份。1961—2015年的厄尔尼诺/拉尼娜事件往往在秋冬季达到最大强度,或为导致沈阳地区11月—3月气温变异增强的原因之一。厄尔尼诺事件结束之后的春季,沈阳地区气温偏低的概率逾70%。沈阳地区气温随MEI变化的线性倾向值为0.98,决定系数为0.98且通过了0.01的可信度检验。利用MEI对沈阳地区的气温进行同期和时滞预测,NARX的预测结果均优于一元线性回归模型。当气温滞后MEI16个月时,两者的相关系数达到最大且通过了0.01的显著性检验,此时回归模型预测的相关系数为0.59,较同期预测提升了79%;NARX预测的均方误差(mean-square error,MSE)为0.49,较同期预测降低了36%,相关系数为0.86,较同期预测提升了8%。展开更多
基金funded by National Institute for Occupational Safety and Health (NIOSH) (No. 2014-N-15795, 2014)
文摘As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the "thermal damping effect" will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input(NARX) algorithm was used as a novel method to predict the dry-bulb temperature(Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network(ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical "damping coefficient" for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Tdat the bottom of intake shafts.
文摘通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄。提出用非线性时序模型与前向神经网络相结合的模型(Nonlinear auto-regressive moving average neural network with exogenousinputs,NARMAX-NN)来辨识热弹性效应。用NARMAX-NN模型对高速进给系统试验台的热动态特性进行建模,获得良好的效果。此方法比多变量回归模型、反馈神经网络模型及广义最小二乘输出误差模型有更好的精度和鲁棒性,能精确地对复杂结构、多热源的时变非线性热误差特性进行建模和预测。
文摘极限学习机(Extreme learning machine,ELM)是一种单隐层前馈神经网络(SLFNs),它随机选择网络的隐含层节点及其参数,训练时仅需调节输出层权值,因此ELM以极快的学习速度获得良好的推广性。考虑到ELM的特征映射函数未知时,可以将核矩阵引入到ELM中。针对模型未知的强非线性连续搅拌反应釜(Continuous Stirred Tank Reactor,CSTR),提出一种基于核极限学习机(Extreme Learning Machine with Kernels,KELM)的NARX模型辨识方法。以仿真的CSTR过程实例进行辨识实验,建立基于NARX-KELM的辨识模型。实验结果表明,在相同条件下,与带动量因子的BP神经网络、模糊神经网络(FNN)、GAP-RBF、MGAP-RBF神经网络、回声状态网络(ESN)、ELM等方法相比,KELM能够有效地改进辨识精度,而且性能更好,这表明了所提方法的有效性和应用潜力。
文摘利用1961—2015年国家气象信息中心沈阳站的日平均气温资料、美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的多变量ENSO指数(multivariate ENSO index,MEI)资料等,在分析沈阳地区气温月际变化的基础上,结合厄尔尼诺/拉尼娜事件对其影响特征,利用线性倾向估计和非线性自回归(nonlinear auto regressive models with exogenous inputs,NARX)神经网络模型分别对沈阳地区2011—2015年的气温进行预测。结果表明,1961—2015年共计660个月中,沈阳地区11月—3月气温的变异系数在20%以上,远大于其他月份。1961—2015年的厄尔尼诺/拉尼娜事件往往在秋冬季达到最大强度,或为导致沈阳地区11月—3月气温变异增强的原因之一。厄尔尼诺事件结束之后的春季,沈阳地区气温偏低的概率逾70%。沈阳地区气温随MEI变化的线性倾向值为0.98,决定系数为0.98且通过了0.01的可信度检验。利用MEI对沈阳地区的气温进行同期和时滞预测,NARX的预测结果均优于一元线性回归模型。当气温滞后MEI16个月时,两者的相关系数达到最大且通过了0.01的显著性检验,此时回归模型预测的相关系数为0.59,较同期预测提升了79%;NARX预测的均方误差(mean-square error,MSE)为0.49,较同期预测降低了36%,相关系数为0.86,较同期预测提升了8%。
