基于PMV指标的列车空调节能研究

目前列车空调基本上是定风量运行的,由于列车空调负荷随时间和地点变化,定风量空调系统无法达到令人满意的热舒适性,而且浪费能源。本文通过分析PMV方程的特点,提出以PMV指标作为控制变量的变风量空调系统应用于列车空调,分析该空调系统的控制原理;通过实例计算分析,验证列车使用变风量空调系统的可行性,得到变风量空调系统的节能潜力。     

基于神经网络的室内热舒适指标PMV的预测

PMV是国际上公认的反映室内热舒适度的指标,由于它与各个影响因素之间存在复杂的非线性关系,不能直接检测,因而使用上受到了限制.而利用神经网络建立了PMV指标的预测模型,可进行实时检测,从而可以解决基于PMV指标的中央空调的实时控制的问题.     

最小二乘支持向量机在热舒适性PMV指标预测中的应用研究

介绍了一种新型的机器学习算法一最小二乘支持向量机的原理，并针对预测PMV指标建立了最小二乘支持向量机预测模型。该模型的预测结果表明，最小二乘支持向量机预测准确度高，计算过程速度快，可以满足以PMV指标作为被控参数的空调系统控制的要求。     

热舒适评价指标PMV-PPD在空调列车上的应用

简要介绍了评价热环境的综合性指标PMV－PPD指标体系，并采用FORTRAN语言编程，计算在不同温湿度情况下夏季列车车厢内的PMV值和PPD值。通过分析比较计算结果，提出了基于热舒适性要求的夏季空调列车车厢内温湿度设定值推荐范围。     

人工神经网络对空调负荷预测过程的优化研究

人工神经网络预测系统可以对空调负荷进行有效的预测。本文通过利用神经网络预测对空调负荷的实际预测过程的研究,讨论了输入参数的选择和预处理、目标误差的确定、网络的学习率和训练次数等与预测效果之间的关系。对目标误差、网络的学习率和训练次数进行了具体的优化。该优化结果对今后开展利用神经网络的空调负荷预测工作有一定的参考作用。     

空调列车夏季行驶热舒适性分析

利用MATLAB／SIMULINK平台,建立了空调硬座列车的围护结构动态传热及PMV的计算模型;其中在PMV的计算中,考虑了人体散湿量、空气相对湿度及内壁温度和平均辐射温度对PMV-PPD值的影响。对空调硬座列车空调双位控制分别采用不同的反馈信号对热舒适性的影响,进行了理论上的分析。本文建立的模型,可用于列车空调相关问题的研究。     

热舒适评价指标应用分析

本文介绍了热舒适评价的几个重要指标,对其适用范围进行归纳分析,为建筑环境舒适性评价指标的选择提供一定参考。     

室内热舒适性指标PMV影响因素分析与研究

服装热阻、空气温度及气流速度是影响人体热舒适性的三个主要因素。本文针对上送上回气流分布方式，采用K-ε湍流模型及热舒适性指标PMV数学分析式，对室内三维湍流流动和传热及PMV指标进行了数值模拟，研究不同送风速度下的室内流场、温度场及PMV值分布变化规律，并分析比较了不同送风速度、送风温度及服装热阻对室内PMV值的影响。研究结果表明：送风速度是影响室内流场、温度场及PMV值分布模式的主要因素，送风温度的改变对PMV值有一定的影响，而室内人员的服装热阻对PMV值的影响较大。本文的研究对如何合理确定空调送风参数，达到满足热舒适性的目的奠定了理论基础，同时促进了以人居舒适为核心的空调技术发展。     

工位空调在现代办公楼的应用展望

简要分析了一下我国现代办公楼空调负荷特点、目前常见的空调设计形式弊端,引进了工位空调的概念、特点,并展望了其在现代办公楼的应用.     

不同气流组织下空调客房热舒适性的数值模拟

基于不同的气流组织利用CFD技术对空调客房室内的速度场、温度场、PMV、PPD值进行数值模拟,通过对3种气流组织速度场、温度场比较,结果显示散流器送风能够得到均匀的温度场、速度场。同时,PMV、PPD值更靠近推荐值范围,热舒适性更好,从而为改善空调客房的热舒适性设计提供了方法和理论依据。     

船舶中央空调热舒适性影响因素及其评价方法

对船舶中央空调热舒适性的影响因素进行了分析,同时对舱室微气候条件、空气品质及船员人体因素对热舒适性的影响进行了研究,并介绍了船舶空调热舒适性的评价和预测。     

Sequential forecast of incident duration using Artificial Neural Network models

This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides multi-period updates of duration time after the incident notification. These two models together provide a sequential forecast of incident duration from the point of incident notification to the incident road clearance. Model inputs include incident characteristics, traffic data, time gap, space gap, and geometric characteristics. The model performance of mean absolute percentage error for forecasted incident duration at each time point of forecast are mostly under 40%, which indicates that the proposed models have a reasonable forecast ability. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an incident is reported. Thereby travelers and traffic management units can better understand the impact of the existing incident. Based on the model effect assessments, this study shows that the proposed models are feasible in the Intelligent Transportation Systems (ITS) context.     

Prediction of cold rolling texture of steels using an Artificial Neural Network

We present an Artificial Neural Network based model for the prediction of cold rolling textures of steels. The goal of this work was to design a model capable of fast online prediction of textures in an engineering environment. Our approach uses a feedforward fully interconnected neural network with standard backpropagation error algorithm for configuring the connector weights. The model uses texture data, in form of fiber texture intensities, as well as carbon content, carbide size and amount of rolling reduction as input to the model. The output of the model is in the form of fiber texture data. The available data sets are divided into training and test sets to calibrate and test the network. The predictions of the network provide an excellent match to the experimentally measured data within the bounding box of the training set.     

人工神经网络输入层节点筛选规则的确定

针对目前应用人工神经网络构建定量构效关系模型中输入层节点筛选存在的问题，提出了采用人工神经网络对网络输入层节点进行筛选，归纳出筛选规则，利用此规则可简便、快速地对多氯酚生物毒性预测人工神经网络模型的输入层节点进行筛选，输入层节点由最初的24个筛选到最后的3个。对筛选过程中不同输入层节点构建的网络模型质量和预测能力进行比较，得出含有较少输入层节点的人工神经网络模型的预测能力较高，运算速度较快，该规则的建立有利于进一步开展有机化学品生物毒理学的研究，并且该方法可以推广到人工神经网络应用的其他领域。     

基于神经网络的双辉等离子渗金属工艺预测

将人工神经网络理论及Back propagation(BP)算法应用于双层辉光等离子渗金属工艺的研究,并针对BP神经网络收敛速度慢、易陷入局部极小的缺点,提出一种新的动态退火算法优化网络的训练,进而建立了双层辉光等离子渗金属工艺参数与渗层元素总质量分数、渗层厚度和表面硬度之间的数学模型,最后将模拟预测结果与实验数据进行比较和误差分析, 证明该模型具有较高的预测精度.     

Prediction of mechanical properties of extra deep drawn steel in blue brittle region using Artificial Neural Network

Blue brittle region also known as dynamic strain ageing (DSA) regime is very important in the materials because in this region material properties behave in very unpredictable ways. In this work, Artificial Neural Network (ANN) models are developed for the prediction of mechanical properties such as yield strength (YS), ultimate tensile strength (UTS), % elongation, strength coefficient (K) and strain hardening exponent (n) for the extra deep drawn (EDD) quality steel in blue brittle region. To calculate the mechanical properties at elevated temperatures, experiments were conducted at the interval of 25 °C from room temperature till 700 °C in three rolling directions. Based on the experimental results, the blue brittle region for EDD steel is identified between 350 °C and 450 °C and ANN model is trained in all the three rolling directions. Trained up ANN model is tested with the experimental results at two different temperatures with in blue brittle region. Experimental and modeling errors in the prediction of mechanical properties are found within the permissible range.     

浅谈人工神经网在翅片管换热器性能分析中的应用

本文通过对预测换热器换热量的传统方法进行分析,指出了存在的缺点不足,介绍了人工神经网络在制冷空调换热器设计中的应用.     

A Bayesian Neural Network approach to estimating the Energy Equivalent Speed

To reduce the number and the gravity of accidents, it is necessary to analyse and reconstruct them. Accident modelling requires the modelling of the impact which in turn requires the estimation of the deformation energy. There are several tools available to evaluate the deformation energy absorbed by a vehicle during an impact. However, there is a growing demand for more precise and more powerful tools. In this work, we express the deformation energy absorbed by a vehicle during a crash as a function of the Energy Equivalent Speed (EES). The latter is a difficult parameter to estimate because the structural response of the vehicle during an impact depends on parameters concerning the vehicle, but also parameters concerning the impact. The objective of our work is to design a model to estimate the EES by using an original approach combining Bayesian and Neural Network approaches. Both of these tools are complementary and offer significant advantages, such as the guarantee of finding the optimal model and the implementation of error bars on the computed output. In this paper, we present the procedure for implementing this Bayesian Neural Network approach and the results obtained for the modelling of the EES: our model is able to estimate the EES of the car with a mean error of 1.34 m s(-1). Furthermore, we built a sensitivity analysis to study the relevance of model's inputs.