重轨开坯轧制的有限元模拟、优化和应力分析

用ANSYS/LS-DYNA软件对U75V重轨钢280 mm×380 mm铸坯开坯轧制重轨过程进行数值模拟,优化切深孔C孔的孔型,并分析优化前后开坯轧制重轧横截面的应力分布。结果表明,经孔型优化,降低了轧制过程重轨的等效应力,中轴线等效应力状态得到改善,原始孔型应力波动范围为9～44 MPa,优化孔型为8～30 MPa,中轴线纵向最大应力由原来的41.4 MPa降至21.1 MPa。     

Nb-和V-微合金化对高碳钢热加工性的影响

用Gleeble 1500D热模拟机试验了0.03%Nb、0.03%Nb-0.02%V和0.05%V微合金化0.75%～0.78%C高碳钢280 mm×380 mm铸坯上钻取的Φ10 mm×120 mm试样在1 300～800℃的断面收缩率和抗拉强度。结果表明,第1脆性区≥1 200℃和第3脆性区1 000～800℃V-钢的热塑性优于Nb-V钢和Nb钢。扫描电镜和能谱分析表明,Nb-钢铸坯存在Fe-Nb-C共晶体,加入V的Nb-V钢铸坯存在Fe-Nb共晶体。     

两种转炉炼钢终点控制模型的比较

分析了转炉炼钢静态控制与动态控制模型的方法、作用及功能关系。针对中小型炼钢转炉检测设备受限制的现状,从建模原理、方法特点和应用效果方面对建立的基于遗传算法-神经网络混合算法(CA-BP)的转炉炼钢终点优化控制静态模型和转炉炼钢终点预测的准动态控制模型进行了比较,认为混合算法可以提高静态模型的预测效果,而准动态模型可以反映转炉冶炼的动态操作和加料过程对一次倒炉时间和终点的影响。     

钢水样本分析仪分析结果的采集、修正、远程传送及显示系统的开发

介绍一种通过在Boland Delphi 6的软件开发平台上进行二次软件开发,同时结合SQL Server 2000数据库技术以及相应的硬件组态,实现在钢铁冶炼过程中钢水样本的分析仪分析结果从数据采集、修正和远程传送及显示全过程自动化的方案。     

宝钢涂层半工艺无取向电工钢的退火工艺和产品特性

宝钢涂层半工艺无取向电工钢产品B50A700T(≤0.003%C、1.25%～0.65%Si)和B50A1300T(≤0.003%C、0.50%～0.15%Si)由铁水预处理-顶底复吹转炉-RH-连铸-热轧-冷轧-退火工艺生产。试验了成品退火温度550～900℃对该无取向电工钢HV1硬度值的影响和680～800℃消除应力退火对该钢磁性的影响。在采用成品退火温度650～700℃生产的涂层半工艺无取向产品,经750℃×2 h消除应力退火后,牌号B50A700T的铁损为4.10 W/kg,而牌号B50A1300T的铁损为4.70 W/kg。     

陕西双王金矿床八号矿体矿化规律探讨

通过对双王金矿床8号矿体厚度、品位的统计分析,以及对矿化强度指数的计算,试从勘探角度探讨矿体在走向上和垂向上的矿化规律,对于加深认识双王金矿床成矿规律有重要的理论意义,同时对指导该区找矿勘探有重要的实际意义。     

Verification and validation benchmarks

Verification and validation (V&V) are the primary means to assess the accuracy and reliability of computational simulations. V&V methods and procedures have fundamentally improved the credibility of simulations in several high-consequence fields, such as nuclear reactor safety, underground nuclear waste storage, and nuclear weapon safety. Although the terminology is not uniform across engineering disciplines, code verification deals with assessing the reliability of the software coding, and solution verification deals with assessing the numerical accuracy of the solution to a computational model. Validation addresses the physics modeling accuracy of a computational simulation by comparing the computational results with experimental data. Code verification benchmarks and validation benchmarks have been constructed for a number of years in every field of computational simulation. However, no comprehensive guidelines have been proposed for the construction and use of V&V benchmarks. For example, the field of nuclear reactor safety has not focused on code verification benchmarks, but it has placed great emphasis on developing validation benchmarks. Many of these validation benchmarks are closely related to the operations of actual reactors at near-safety-critical conditions, as opposed to being more fundamental-physics benchmarks. This paper presents recommendations for the effective design and use of code verification benchmarks based on manufactured solutions, classical analytical solutions, and highly accurate numerical solutions. In addition, this paper presents recommendations for the design and use of validation benchmarks, highlighting the careful design of building-block experiments, the estimation of experimental measurement uncertainty for both inputs and outputs to the code, validation metrics, and the role of model calibration in validation. It is argued that the understanding of predictive capability of a computational model is built on the level of achievement in V&V activities, how closely related the V&V benchmarks are to the actual application of interest, and the quantification of uncertainties related to the application of interest.     

基于线性光谱混合模型(LSMM)的两种不同端元值选取方法应用与评价——以广州市为例

通过分别采用纯像元指数(PPI)和手动选取端元这两种不同的方法获得了2003年广州市区的植被、水体和不透水层3种端元,然后利用线性波谱分离法得到各个端元的丰度图像和均方根误差图像,从而获得广州市老八区不透水层的分量图。另外,还对比分析了基于线性光谱混合模型（LSMM）两种终端单元的选取方法的优缺点,并从定性的角度对所得结果进行精度评价。结果显示:基于线性光谱混合模型（LSMM）的方法获得广州市老八区不透水层的分量图是可行而有效的;手动选取端元的方法比纯像元指数（PPI）能够得到更高精度的分量图。     

Predicting ENR Construction Cost Index Using Machine-Learning Algorithms

Construction Cost Index (CCI) is calculated monthly and published by Engineering News-Record (ENR). CCI is utilized for capital project budgeting and construction cost estimation, especially in cases where mid- and long-term forecasts are needed. Accurate prediction of CCI helps avoid underestimating and overestimating project costs but the current prevailing time series prediction models do not show promising results, especially in mid- and long-term forecasting. The capability of two machine-learning algorithms, k nearest neighbor (k-NN) and perfect random tree ensembles (PERT), are utilized to enhance CCI forecasting, especially in the mid- and long-term. The proposed machine-learning algorithms are able to significantly enhance the predictability of forecasting CCI in all the scenarios, short-, mid-, and long-term. Data from January 1985 to December 2014 is collected from ENR and bureau of labor statistics to conduct empirical studies and quantitatively measure the performance of the proposed methods. As the outcomes show, the prediction accuracies of both proposed methods are better than those of current prevailing time series models under all the tested scenarios. It is anticipated that cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-precise bids for contractors and developing more-accurate budgets for owners.     

A study of energy performance and efficiency improvement procedures of Government Offices in Hong Kong Special Administrative Region

A study of energy performance in 19 Government Offices in Hong Kong has been carried out. The characteristics of Government Offices, difficulties and results in assessing their building energy performance are discussed and reported. An average Energy Use Index based on unit floor area has been derived for the Government Offices using energy consumption data in the past three years. A number of factors that are likely affect the energy use in buildings, such as year of construction and total gross floor area are reviewed. Energy management opportunities are identified and proposed after carrying out energy audit to the Government Offices. Operational practices to improve energy performance are also recommended.