投影寻踪在火灾后混凝土性能评价中的应用

在研究高强混凝土高温后性能的基础上,通过对影响火灾后高强混凝土结构性能的主要因素进行分析,选择抗压强度损失系数、耐久性损失系数、爆裂损失系数和裂纹损失系数作为评价指标,以投影寻踪回归理论为基础,提出综合评定高温后损伤混凝土性能的方法,建立了投影寻踪综合评价模型,编制了基于MATLAB的相应程序,采用人口迁移算法寻求最优投影方向,根据投影特征指标值对高强混凝土高温后性能进行综合评价。研究表明,该方法避免了评判专家人为确定权重,评价结果客观准确、方法简单,为混凝土结构火灾损伤的诊断评估与修复加固提供科学依据。     

地震作用下高层钢-混组合结构的抗震性能分析

根据结构抗震设计规范,分别采用精细时程积分法和结构分析软件Midas/gen建模,对高层钢框架-混凝土核心筒的混合结构的动力特性和地震时程反应进行分析,得到自振周期及地震作用下的位移和加速度响应.通过对比可知精细时程分析方法和Midas/gen两种不同建模方法得到的计算结果吻合较好,说明精细积分分析方法和Midas/gen的分析方法都是适用可靠的,均能为此类结构的抗震性能研究提供有效的途径.     

高温冲击下三偏心蝶阀的密封性能

某三偏心金属蝶阀在工作中承受瞬时高温、高压冲击,为探索该环境工况对蝶阀密封性能的影响,采用Ansys软件对蝶阀进行热-结构间接耦合分析,研究高温、高压冲击下蝶阀的变形、应力分布和密封特性.蝶阀的温度分布结果表明瞬间的高温仅造成蝶阀温度升高0.23 K,而3.0 MPa的冲击可使得阀门密封面之间的接触应力平均增大60.0...     

工业控制系统中可靠性设计的研究

本文主要阐述了在工业生产控制系统中,可靠性设计中的热设计、电磁防护设计、机械防震设计,提出了可靠性设计中应当注意的问题,以及一些解决问题的方案和办法.热设计中应当主要注意温度,电磁防护设计中应当主要注意屏蔽和接地,机械防震设计中应当主要注意防震.     

Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set

Traditional methods on creating diesel engine models include the analytical methods like multi-zone models and the intelligent based models like artificial neural network (ANN) based models. However, those analytical models require excessive assumptions while those ANN models have many drawbacks such as the tendency to overfitting and the difficulties to determine the optimal network structure. In this paper, several emerging advanced machine learning techniques, including least squares support vector machine (LS-SVM), relevance vector machine (RVM), basic extreme learning machine (ELM) and kernel based ELM, are newly applied to the modelling of diesel engine performance. Experiments were carried out to collect sample data for model training and verification. Limited by the experiment conditions, only 24 sample data sets were acquired, resulting in data scarcity. Six-fold cross-validation is therefore adopted to address this issue. Some of the sample data are also found to suffer from the problem of data exponentiality, where the engine performance output grows up exponentially along the engine speed and engine torque. This seriously deteriorates the prediction accuracy. Thus, logarithmic transformation of dependent variables is utilized to pre-process the data. Besides, a hybrid of leave-one-out cross-validation and Bayesian inference is, for the first time, proposed for the selection of hyperparameters of kernel based ELM. A comparison among the advanced machine learning techniques, along with two traditional types of ANN models, namely back propagation neural network (BPNN) and radial basis function neural network (RBFNN), is conducted. The model evaluation is made based on the time complexity, space complexity, and prediction accuracy. The evaluation results show that kernel based ELM with the logarithmic transformation and hybrid inference is far better than basic ELM, LS-SVM, RVM, BPNN and RBFNN, in terms of prediction accuracy and training time.