超低密度植物纤维材料的吸声特性研究

发布时间：2018-06-11 11:07

本文选题：超低密度植物纤维材料 + 吸声性能　；参考：《福建农林大学》2015年硕士论文

【摘要】：当今,随着噪音污染的日益严重,吸声材料已经越来越引起人们的重视。超低密度植物纤维材料是密度为0.01-0.12g/cm3的生物质材料,具有抗冲击、保温隔热、阻燃防腐、吸音防震等优点。材料内部具有三维开孔网状结构,植物纤维间存在大量结构复杂的微小孔隙,是一种环境友好的多功能吸声材料。本文通过对超低密度植物纤维材料的吸声特性进行系统的理论和试验研究,旨在揭示其吸声机理,建立超低密度植物纤维材料的吸声性能预测模型,推导出材料结构参数与吸声性能的关系,对实际工程设计和应用有一定的指导意义。本文通过驻波管法测试超低密度植物纤维材料的吸声系数,并用图像分析法表征材料内部孔隙结构,探讨材料密度、孔隙率、孔径、厚度、背后空腔等因素对其吸声性能的影响。试验结果表明：材料的密度、孔隙率、孔径过大或过小均会影响其吸声性能,在一定条件下存在一个最佳值；增加材料的厚度,有利于材料吸声性能的提高,但是厚度增加至一定值后对吸声性能的提高不明显；在材料背后设置空腔有助于提高其低频吸声效果,随着空腔厚度的增加材料的吸声性能有所提高。在试验的基础上,分别通过以下两种方式建立超低密度植物纤维材料的吸声性能预测模型：(1)基于多孔材料的圆管理论模型建立材料吸声系数的数学模型,将模型计算值与试验测试值进行比较,结果表明该数学模型能对超低密度植物纤维材料的吸声系数进行较准确的预测,还能正确的反映材料结构参数变量对吸声性能的影响,并可通过该模型计算出吸声性能最佳时材料的厚度和孔径；(2)基于BP神经网络理论,建立以超低密度植物纤维材料结构参数(材料厚度、孔隙率、孔径)为输入,平均吸声系数为输出的预测模型。分析讨论模型网络结构构建、样本数据的选取及预处理、传递函数的选择、训练函数的选择、学习速率和目标误差的设立,并将BP神经网络模型预测值与试验测试值进行比较,结果表明该模型也具备较好的预测精度。
[Abstract]:Nowadays, with the increasing noise pollution, sound absorbing materials have attracted more and more attention. Ultra-low density plant fiber is a biomass material with density of 0.01-0.12g/cm3, which has the advantages of shock resistance, heat insulation, flame retardant and anticorrosion, sound absorption and shockproof. The material has a three-dimensional porous network structure and a large number of micro-pores with complex structure between the plant fibers. It is a kind of environment-friendly multifunctional sound absorption material. In this paper, the sound absorption characteristics of ultra-low density plant fiber materials are systematically studied theoretically and experimentally in order to reveal its sound absorption mechanism and to establish a prediction model of sound absorption properties of ultra-low density plant fiber materials. The relationship between material structure parameters and sound absorption performance is deduced, which is of guiding significance for practical engineering design and application. In this paper, the sound absorption coefficient of ultra-low density plant fiber material was measured by standing wave tube method. The pore structure of the material was characterized by image analysis, and the density, porosity, pore size and thickness of the material were discussed. The influence of the back cavity and other factors on the sound absorption performance. The experimental results show that the density, porosity, pore size of the material will affect its sound absorption performance, and there is an optimum value under certain conditions, and increasing the thickness of the material is beneficial to the improvement of the sound absorption property of the material. However, when the thickness is increased to a certain value, the sound absorption performance of the material is not obviously improved, and the low frequency sound absorption effect can be improved by setting a cavity behind the material, and the sound absorption performance of the material is improved with the increase of the thickness of the cavity. On the basis of experiments, the prediction model of sound absorption performance of ultra-low density plant fiber materials was established by the following two ways: (1) the mathematical model of sound absorption coefficient of materials was established based on the theoretical model of porous tubes. The results show that the model can accurately predict the sound absorption coefficient of ultra-low density plant fiber materials, and it can also correctly reflect the influence of material structure parameter variables on the sound absorption performance. Based on the BP neural network theory, the structural parameters (material thickness, porosity, pore size) of ultra-low density plant fiber materials are established as input. The average sound absorption coefficient is a prediction model of output. The network structure of the model, the selection and preprocessing of the sample data, the selection of the transfer function, the selection of the training function, the establishment of the learning rate and the target error are analyzed and discussed. The prediction value of the BP neural network model is compared with the test value. The results show that the model also has good prediction accuracy.
【学位授予单位】：福建农林大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TQ351.01;TB34

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