...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal for Multiscale Computational Engineering >A Tailored Strategy for PDE-Based Design of Hierarchically Structured Porous Catalysts
【24h】

A Tailored Strategy for PDE-Based Design of Hierarchically Structured Porous Catalysts

机译：基于PDE的分层结构多孔催化剂的定制策略

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

Optimization problems involving the solution of partial differential equations (PDE) are often encountered in the context of optimal design, optimal control, and parameter estimation. Based on the reduced-gradient method, a general strategy is proposed to solve these problems by reusing existing software. As an illustration, this strategy was employed to solve a PDE-based optimization problem that arises from the optimal design of the network of pore channels in hierarchically structured porous catalysts. A Fortran implementation was developed by combining a gradient-based optimization package, NLPQL, a multigrid solver, MGD9V, and a limited amount of in-house coding. The value and gradient of the objective function are computed by solving the discretized PDE and another system of linear equations using MGD9V. These are subsequently fed into NLPQL to solve the optimization problem. The PDE was discretized in terms of a finite volume method on a matrix of computational cells. The number of the cells ranged from 129 × 129 to 513 × 513, and the number of the optimization variables ranged from 41 to 201. Numerical tests were carried out on a Dell laptop with a 2.16-GHz Intel Core2 Duo processor. The results show that the optimization typically converges in a limited number (i.e., 9-48) of iterations. The CPU time is from 2.52 to 211.52 s. The PDE was solved 36-201 times in each of the numerical tests. This study calls for the use of our strategy to solve PDE-based optimization problems.
机译：在优化设计，优化控制和参数估计的背景下，经常会遇到涉及偏微分方程（PDE）解的优化问题。基于降梯度法，提出了一种通用策略，通过重用现有软件来解决这些问题。作为说明，此策略用于解决基于PDE的优化问题，该问题是由分层结构的多孔催化剂中孔道网络的优化设计引起的。通过结合基于梯度的优化程序包，NLPQL，多网格求解器，MGD9V和少量内部编码，开发了Fortran实现。通过求解离散的PDE和使用MGD9V的另一组线性方程组，可以计算出目标函数的值和梯度。随后将它们输入NLPQL以解决优化问题。根据有限体积方法，在计算单元矩阵上离散了PDE。单元数的范围为129×129至513×513，优化变量的范围为41至201。在装有2.16 GHz Intel Core2 Duo处理器的Dell笔记本电脑上进行了数值测试。结果表明，优化通常收敛于有限数量的迭代（即9-48）。 CPU时间从2.52到211.52 s。在每个数值测试中，PDE均求解36-201次。这项研究要求使用我们的策略来解决基于PDE的优化问题。

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