没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
中北大学学位论文
喷雾干燥制备含能材料的神经网络模拟
摘要
含能材料降感一直是该研究领域的热点问题,其中喷雾干燥是一种有效的降感方
法。研究表明,喷雾干燥可以得到微米级的含能材料且能够实现连续化生产。但是由于
实验具有很大的危险性且实验过程较为复杂。而采用人工神经网络(ANN)对喷雾干燥
制备含能材料(CL-20、RDX 和 HMX)进行模拟,极大的降低了操作风险,对研究喷
雾干燥制备含能材料具有重要的意义。本文的主要研究内容如下:
(1)以气体流量、液体流量、入口温度、质量分数、相对分子质量和粘度为输入,
平均粒径和粒径分布为输出建立喷雾干燥制备含能材料的神经网络模型。
(2)采用不同神经网络类型(前馈反向传播神经网络(FFBPNN)、串级正反向传
播神经网络(CFBPNN)、埃尔曼正反向传播神经网络(EFBPNN)、递归神经网络(LR)
和非线性自回归神经网络(NARX))和不同算法(Levenberg-Marquardt(L-M)算法、
动量梯度下降和自适应学习率算法(GDX)、遗传算法(GA)和粒子群算法(PSO))
优化的神经网络对平均粒径进行预测,得到最佳的预测模型为 GA-LR。
(3)对 GA 中的算子(选择算子、交叉算子和变异算子)进行优化,得到最佳参
数:选择算子为两两竞争法、交叉算子为均匀交叉和变异算子为均匀变异,其中交叉概
率为 0.8,变异概率为 0.1。并基于 Matlab GUI 建立平均粒径和粒径分布的预测界面。
(4)对比 GA-LR 和经验关联式的性能,考察了操作条件(气体流量、液体流量、
入口温度、质量分数和粘度)对平均粒径和粒径分布的影响规律,为定量计算提供理论
基础。
关键词:喷雾干燥,神经网络,含能材料,平均粒径,粒径分布
中北大学学位论文
Artificial Neural Network Simulation of Energetic Materials
Prepared by Spray Drying
Abstract
The desensitization of energetic materials is a hot topic in this field, and spray drying is
an effective desensitization method. Researches show that micron sized energetic materials
could be continuous obtained by spray drying. However, due to the danger of the experiment
and the complexity of the process.Energetic materials (CL-20, RDX and HMX) prepared by
spray drying could be simulated by using artificial neural network (ANN) ,which had greatly
reduced the operational risk, and was of great significance to spray drying. The main research
contents of this article were as follows:
(1) An artificial neural network model for energetic materials prepared by spray drying
was established with gas flow, liquid flow, inlet temperature, mass fraction, relative molecular
weight and viscosity as inputs and average particle size and particle size distribution as
outputs.
(2) Artificial neural networks were optimized by different types (Feed-forward back
propagation neural network (FFBPNN), Cascade-forward back propagation neural network
(CFBPNN), Elman-forward back propagation neural network (EFBPNN), Layers Recurrent
neural network (LR) and Nonlinear autoregressive neural network (NARX)), and different
algorithms (Levenberg-Marquardt algorithm (L-M), Momentum gradient descent and
adaptive learning rate algorithm (GDX), Genetic algorithm (GA) and Particle swarm
optimization algorithm (PSO)), to predict the average particle size. The best model was
GA-LR.
(3) The operators (selection operator, crossover operator and mutation operator) in the
GA were optimized to obtain the best parameters: the selection operator was pairwise
competition method, the crossover operator was uniform crossover and the mutation operator
中北大学学位论文
was uniform mutation, the crossover probability was 0.8 and the mutation probability was 0.1.
Based on the Matlab GUI, a prediction interface for the average particle size and particle size
distribution is established.
(4) By comparing the performance of GA-LR and empirical correlations, the effects of
operating conditions (gas flow, liquid flow, inlet temperature, mass fraction and viscosity) on
average particle size and particle size distribution were investigated, which would provide a
theoretical basis for quantitative calculations.
Key words: Spray drying, Artificial neural network, Energetic materials, Average
particle size, Particle size distribution
中北大学学位论文
I
目 录
1 绪论 ........................................................................................................................................ 1
1.1 研究背景 ..........................................................................................................................1
1.2 喷雾干燥技术在含能材料领域的应用 ..........................................................................1
1.2.1 CL-20 ......................................................................................................................... 1
1.2.2 RDX ........................................................................................................................... 3
1.2.3 HMX ...........................................................................................................................3
1.2.4
其他含能材料
...........................................................................................................4
1.3 喷雾干燥模拟研究 ..........................................................................................................4
1.3.1 Aspen ..........................................................................................................................4
1.3.2 CFD ............................................................................................................................ 5
1.4 神经网络 ..........................................................................................................................5
1.4.1 神经网络概述 ...........................................................................................................5
1.4.2 BP
神经网络模型
......................................................................................................6
1.4.3 BP 神经网络优化 ...................................................................................................... 7
1.4.4 神经网络在喷雾干燥领域的应用 .........................................................................11
1.4.5
神经网络在含能材料领域的应用
.........................................................................12
1.5 课题主要研究内容及意义 ............................................................................................13
1.5.1 研究内容 .................................................................................................................13
1.5.2
研究意义
.................................................................................................................14
2 喷雾干燥制备含能材料的神经网络模型建立 .................................................................. 15
2.1 引言 ................................................................................................................................15
2.2 喷雾干燥的实验流程 ....................................................................................................15
中北大学学位论文
II
2.3 喷雾干燥制备含能材料的神经网络模型 ....................................................................16
2.3.1 数据收集和处理 .....................................................................................................16
2.3.2
网络结构的设计
.....................................................................................................18
2.3.3 神经网络的建模过程 .............................................................................................19
2.4 平均粒径和粒径分布预测系统的建立 ........................................................................24
2.5
小结
................................................................................................................................25
3 喷雾干燥制备含能材料的神经网络模拟 .......................................................................... 26
3.1 引言 ................................................................................................................................26
3.2 神经网络模拟含能材料的平均粒径 ............................................................................26
3.2.1
神经网络类型对含能材料平均粒径的影响
.........................................................26
3.2.2 算法优化对含能材料平均粒径的影响 .................................................................31
3.3 小结 ................................................................................................................................35
4
喷雾干燥制备含能材料的
GA-LR
模拟
............................................................................36
4.1
引言
................................................................................................................................36
4.2 GA-LR 模拟含能材料的平均粒径 ............................................................................... 36
4.2.1 选择算子对含能材料平均粒径的影响 .................................................................36
4.2.2
交叉算子对含能材料平均粒径的影响
.................................................................38
4.2.3 变异算子对含能材料平均粒径的影响 .................................................................41
4.2.4 各输入节点对输出的影响 .....................................................................................44
4.3 GA-LR
模拟含能材料的粒径分布
............................................................................... 44
4.3.1 隐含层神经元个数对粒径分布的影响 .................................................................45
4.3.2 各输入节点对输出的影响 .....................................................................................46
4.4
平均粒径和粒径分布预测系统的建立
........................................................................47
剩余81页未读,继续阅读
资源评论
2201_75761617
- 粉丝: 20
- 资源: 7339
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功