混杂FRP复合材料混杂效应的研究与进展

混杂效应是某些性能偏离混合定律计算结果的现象,是造成混杂FRP(Fiber Reinforced Plastics)复合材料优异综合性能的根本原因。本文总结国内外关于混杂FRP复合材料混杂效应的最新研究成果,探讨影响混杂效应的因素,重点分析它们的理论模型,并对未来的发展方向做出展望。     

苎麻/玻璃纤维混杂复合材料的老化研究及寿命预测

采用玻璃纤维布与苎麻纤维布混杂增强乙烯基树脂制备复合材料,结合船舶在服役环境下的实际情况,通过人工加速老化的方法,对苎麻纤维/玻璃纤维混杂复合材料进行水浸泡老化、盐雾老化和紫外老化实验,研究混杂复合材料的拉伸强度及弯曲强度等随老化时间、老化温度等的变化情况及性能退化趋势,并根据剩余强度模型对混杂复合材料进行寿命预测。研究表明,老化初期阶段试样吸湿趋势主要以浓度梯度推动的菲克扩散为主。老化环境不同,试样强度的衰减程度不同,水浸泡老化对试样影响最大,盐雾老化次之,紫外老化影响相对较少。根据剩余强度模型预测10年后盐雾试样弯曲强度保留率为78.0%,紫外老化弯曲试样强度保留率为81.89%。     

原位混杂复合材料

新型结构的原位混杂复合材料由纤维、液晶聚合物微纤和热塑性基体树脂组成。它可以用常规的挤出、注射工艺成型,含有直径在两个数量级上的两种增强剂,在结构,加工流变性与力学性能均表现出原位混杂增强形成的优势。同时,叙述了它的制备技术要点。     

低成本多功能超混杂复合材料及其应用

介绍研究、开发的低成本、多功能树脂基超混杂复合材料及其产品的应用前景。     

碳/玻混杂纤维的混杂效应及其受力性能研究

碳纤维与玻璃纤维进行层间混杂后用来进行混凝土结构的加固,可以产生较好的正向混杂效应．就混杂纤维的混杂方式．混杂效应和受力性能进行了研究,结果表明：较之单一纤维,混杂纤维复合材料表现出了明显的混杂效应,还可以降低加固成本,综台效益较好。并提出了混杂纤维复合材料在当前工程应用中存在的问题。     

混杂纤维复合材料的平面剪切性能

研究了基体韧性和铺层方式对玻璃纤维、碳纤维及其混杂纤维复合材料平面剪切性能的影响。结果表明，玻璃、碳及其混杂纤维复合材料的平面剪切应力-应变曲线均具有非线性特征；在脆性基体中选用混杂结构，其复合材料的剪切性能具有正的混杂效应。     

游艇用玻纤–碳纤混杂增强复合材料耐盐雾研究及寿命预测

制备游艇用玻纤–碳纤混杂增强2597PT不饱和树脂复合材料,应用人工加速老化方法,对其进行盐雾老化实验,研究其在三种盐雾条件下(盐度6%,温度50℃;盐度6%,温度25℃;盐度3%,温度50℃)的性能演变规律,建立其对应的寿命模型并进行寿命预测。研究表明:材料盐雾老化后与老化前相比,特征官能团的吸收峰位置未发生变化;随着老化时间的增加,材料力学性能均呈现下降的趋势,且在盐度6%,温度50℃条件下老化最为严重;在G.M. Gunyaev剩余强度理论基础上,采用origin对其进行非线性拟合,得到老化最严重的盐雾环境(即温度50℃、盐度6%)下材料剩余拉伸强度公式,相关系数R2=0.99,结合CCS《材料与焊接规范》 2018版要求,预测此材料在盐度6%,温度50℃条件下可安全使用约2 000 d。     

混杂纤维复合材料的力学性能研究

采用盐酸和乙酸对金属纤维表面进行活化处理后，使之与环氧树脂的粘结性大为改善。研究了玻璃纤维与金属网混杂增强环氧树脂复合材料的力学性能。     

CF/KF混杂纤维复合材料阻尼性能研究

本文采用热动态力学分析测试技术测试材料的损耗因子来评价材料的阻尼性能,研究了CF/KF混杂纤维复合材料的混杂比、铺层顺序、混杂方式对材料阻尼性能和力学性能的影响。实验表明,铺层中外层纤维的种类和含量对材料的阻尼性能和力学性能影响很大,混杂界面数对材料的阻尼性能和力学性能也有一定的影响。     

用人工神经网络预测城市地震火灾损失

利用人工神经网络对城市地震火灾损失进行预测,对指导抗震救灾有一定的帮助。具体做法是根据地震损失与其影响因素之间的非线性映射关系,建立人工神经网络模型,并将其应用于地震火灾损失预测。实例表明该方法有一定的推广价值。     

基于神经网络的大型回转机械运行状态监测

l 弓 言 对大型回转机械故障的诊断与监测,目前国内外均投入了大量的人力和物力。但是,匕经研制出来的专家系统存在很多缺陷,如知识获取困难,系统缺少动态或在线学习能力。化工行业中机器的动态变化大,监测参数多,运行环境变化频繁。神经网络具有很强的跟踪性,文献[fi讨论了人工神经网络用于故障诊断的优越性。目前,人们已经利用神经网络解决了化工炼油厂中的一些典型问题[”]:动态模型的建立、化工系统的过程控制、预报和传感器的故障诊断。本文将神经网络应用于化工过程中的大型回转机械运行状态的监测。 2 物理对象的描述 设不同时刻获取…     

人工神经网络及其在硅酸铝纤维寿命预测中的应用

本文分析了人工神经网络的特点及模型 ,以及其在普通硅酸铝纤维寿命预测中的应用。     

利用人工神经网络预测复相陶瓷材料组分含量的研究

根据人工神经网络（ANN）的BP(back propagation）算法,预测复相陶瓷各组分的体积分数的神经网络模型,模型由三层神经元组成,分别为输入层、隐含层和输出层用以模拟人脑的结构,输入层参数由两部分组成,一部分为抗弯强度、硬度和断裂韧性等力学性能,另一部分包括相应各组分的弹性模量和热膨胀系数,以用来辨识不同的材料系统,输出层参数是复相陶瓷中各组分的体积分数,只要训练样本值足够精确,预测模型就能够预测已有的陶瓷系统的组分含量,同时,模型能够预估未知材料系统的组分含量。计算证明,模型的容错性较好,因此对开发新型复相陶瓷非常有益。     

塑料自然老化力学性能的人工神经网络预测

针对塑料老化过程,利用人工神经网络方法建立自然老化力学性能时间序列的预测模型。该模型可很好地解决老化试验中数据少且统计规律不明显的问题。从给出的拉伸强度和断裂伸长率的预测例子和计算结果表明,该方法具有良好的精度,可满足工程实际对塑料老化性能预测精度的要求。同时可大量缩短试验时间,节约试验费用。     

EXTENSION OF THE NEURAL NETWORKS OPERATING RANGE BY THE APPLICATION OF DIMENSIONLESS NUMBERS IN PREDICTION OF HEAT TRANSFER COEFFICIENTS

The paper presents a study aimed at extending the neural network mapping ability. In traditional modelling, operational process parameters (gas/material temperature, air velocity, etc.) are the inputs and outputs to and from the network. In this approach dimensionless numbers (Re, Ar, H/d) were used as inputs to predict the heat transfer coefficient in a fluidised bed drying process. To produce the data set necessary to train the networks, drying trials of different materials in a fluidised bed were carried out.

A series of simulations were performed and several neural networks structures were tested to find an optimal topology of the network. Training data set contained information only about two materials. The networks were tested using data obtained for the third product.

Performance of the network was satisfactory, however further improvement of mapping ability may be expected after filtration of the testing data.     

EXTENSION OF THE NEURAL NETWORKS OPERATING RANGE BY THE APPLICATION OF DIMENSIONLESS NUMBERS IN PREDICTION OF HEAT TRANSFER COEFFICIENTS

ABSTRACT

The paper presents a study aimed at extending the neural network mapping ability. In traditional modelling, operational process parameters (gas/material temperature, air velocity, etc.) are the inputs and outputs to and from the network. In this approach dimensionless numbers (Re, Ar, H/d) were used as inputs to predict the heat transfer coefficient in a fluidised bed drying process. To produce the data set necessary to train the networks, drying trials of different materials in a fluidised bed were carried out.

A series of simulations were performed and several neural networks structures were tested to find an optimal topology of the network. Training data set contained information only about two materials. The networks were tested using data obtained for the third product.

Performance of the network was satisfactory, however further improvement of mapping ability may be expected after filtration of the testing data.     

基于BP神经网络的水泥抗压强度预测研究

探讨应用ＢＰ（ｂａｃｋ－ｐｒｏｐａｇａｔｉｏｎ）神经网络进行经２８ｄ抗压强度预测的方法。利用ＢＰ网络很强的非线性映射功能,建立抗压强度相关因素与抗压强度之间的关系。通过对样本的学习,ＢＰ网络将这种非线性映射关系以分布并行的方式存储在网络的联结权矩阵中,从而达到对样本集的非逻辑归纳。本文提出了强度预测模型能够以较高的精度预测水泥２８ｄ抗压强度。作为对比,同时应用回归分析方法预测水泥２８ｄ抗压强度,两     

UNIFICATION OF FRUIT WATER SORPTION ISOTHERMS USING ARTIFICIAL NEURAL NETWORKS

The chemical composition, water activity, temperature and equilibrium moisture content (EMC) for 10 selected fruits were determined. Two methods of water sorption modeling, the GAB equation and the artificial neural network (ANN) method, were compared for their ability to predict water sorption behavior. Unlike the GAB equation, which uses only physical data for modeling, the ANN method uses both physical and chemical compositional data to make predictions. The ANN was superior, in most cases, to that of the GAB equation, in predicting EMC. This superiority was due to the availability of the additional chemical compositional information. The ANN method could predict EMC with a mean relative error of 9.85% and a standard error (S _x ) of 1.59% EMC. The correlation coefficient (r ²) of the relationship between the actual and predicted values of equilibrium moisture content obtained by the ANN was 0.9938. The ANN model was able to show a temperature dependent crossing of water sorption isotherms, due to the dissolution of sugar crystals in the fruit. The ANN was also able to predict the extent of crossing, depending upon differences in the individual fruit chemical composition.     

UNIFICATION OF FRUIT WATER SORPTION ISOTHERMS USING ARTIFICIAL NEURAL NETWORKS

The chemical composition, water activity, temperature and equilibrium moisture content (EMC) for 10 selected fruits were determined. Two methods of water sorption modeling, the GAB equation and the artificial neural network (ANN) method, were compared for their ability to predict water sorption behavior. Unlike the GAB equation, which uses only physical data for modeling, the ANN method uses both physical and chemical compositional data to make predictions. The ANN was superior, in most cases, to that of the GAB equation, in predicting EMC. This superiority was due to the availability of the additional chemical compositional information. The ANN method could predict EMC with a mean relative error of 9.85% and a standard error (S_x) of 1.59% EMC. The correlation coefficient (r²) of the relationship between the actual and predicted values of equilibrium moisture content obtained by the ANN was 0.9938. The ANN model was able to show a temperature dependent crossing of water sorption isotherms, due to the dissolution of sugar crystals in the fruit. The ANN was also able to predict the extent of crossing, depending upon differences in the individual fruit chemical composition.