• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Heat and Mass Transfer >Predicting effective thermal conductivity in sands using an artificial neural network with multiscale microstructural parameters
【24h】

Predicting effective thermal conductivity in sands using an artificial neural network with multiscale microstructural parameters

机译：使用具有多尺度微结构参数的人工神经网络预测砂中的有效导热性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Accurate and efficient prediction of thermal conductivity of sands is challenging due to the variations in particle size, shape, connectivity and mineral compositions, and external conditions. Artificial Neural Networks (ANN) models have been used to predict the effective thermal conductivity but they have not considered variables related to particle connectivity. This work uses computed tomography (CT) scanned images of four dry sands and network analysis to redress this significant shortcoming. Here sands are represented as networks of nodes (grains) and edges (interparticle contacts or/and small gaps between neighbouring particles) to extract network features that characterise interparticle connectivity. A network feature - weighted coordination number (WCN) capturing both particle connectivity and contact area -was found to be a good predictor of effective thermal conductivity in dry materials. Roundness, sphericity, solid particle thermal conductivity and porosity are other input parameters rigorously selected for an ANN model that predicts well the effective thermal conductivity of sands.
机译：由于粒径，形状，连通性和矿物组合物和外部条件的变化，对沙子的热导率的准确和有效预测是挑战性的。人工神经网络（ANN）模型已被用于预测有效的导热性，但它们并未考虑与粒子连接相关的变量。这项工作采用了计算的断层扫描（CT）扫描的四个干燥沙滩和网络分析，以纠正这一显着缺点。这里的沙子被表示为节点（谷物）和边缘（相邻粒子之间的晶粒触点或/且小间隙），以提取特性连接颗粒状连接的网络特征。捕获粒子连接和接触区域的网络特征加权协调数（WCN）发现是干燥材料中有效导热率的良好预测因子。圆度，球形，固体粒子导热系数和孔隙率是严格选择的其他输入参数，用于安卡模型，其预测井的有效导热率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号