A feature-independent mesh generation and integrated solution framework

独立于功能的网格生成和集成解决方案框架

基本信息

  • 批准号:
    EP/T009071/1
  • 负责人:
  • 金额:
    $ 54.53万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    已结题

项目摘要

Although computational simulation is extensively employed in industry, its wider use is limited by the complexity of the geometric models involved. This limitation is due to the excessive number of human hours, ranging from days to months, required to transfer information from a computer aided design (CAD) model to a computer aided engineering (CAE) model suitable for simulation. CAD models frequently involve a level of detail much greater than that required to perform a computational simulation with a CAE system. The preparation of CAD models for simulation, including mesh generation, is still a challenging bottleneck that needs to be resolved to enable realisation of the full potential of simulation tools in industry. This challenge is also a crucial factor delaying the industrial uptake of, the often computationally superior, high-order methods.Current research is focused on the development of algorithms for de-featuring complex CAD models. A major drawback of this process is the requirement for human expertise and manual interaction with CAD systems and geometry cleaning tools. Although engineers are aided by the semi-automatic tools that are included in many existing commercial mesh generation packages, such as COMSOL, ANSYS, CATIA, SolidWorks, Patran, MSC, CADfix, ESI Visual Environment, de-featuring cannot be fully automatised. In addition, it is usually not possible to know, a priori, the effect of de-featuring on the results of a simulation because this process depends upon the physical problem and the level of approximation required.At the heart of the problem is the traditional hierarchical paradigm implemented in many commercial mesh generators. The ultimate goal of this project is to develop a new computational environment that includes a feature-independent mesh generation paradigm and plug--and--play libraries to enable direct integration of the meshes into existing commercial and research solvers. The proposed approach is disruptive, as it proposes the development of unconventional computational approaches, not only at the stage of generating suitable meshes for computational simulations but also requires the incorporation of new plug-and-play libraries into existing solvers. The libraries will be delivered as part of this project and it will follow the rationale used in commercial software where the user can select a different type of element depending on the demands of a particular simulation. The advantage of the proposed mesh generation technique is not restricted to removing the bottleneck that has been highlighted by many industries that routinely use computational engineering in their design cycles. In addition, the new meshes will completely remove the uncertainty introduced by de-featuring CAD models. Instead of relying on the opinion of experts, to decide which features might not be relevant in a simulation, the CAD model will not be altered, leading to higher fidelity simulations and more confidence in the results.The proposed research is timely, tackling a problem that has been highlighted in the last three years by independent agencies (e.g. NASA), international associations dedicated to computer modelling (e.g. NAFEMS) and the private sector (e.g. Pointwise Inc.). Since the mid 1990s the research has focused on the development of tools for faster de-featuring. The fact that this issue has not been resolved in over two decades, suggests that the radical new approach proposed here, pursuing an orthogonal research direction, in which no de-featuring is needed, can lead to a breakthrough.
虽然计算模拟在工业中被广泛应用,但其更广泛的应用受到所涉及的几何模型的复杂性的限制。这种限制是由于将信息从计算机辅助设计(CAD)模型传输到适合于仿真的计算机辅助工程(CAE)模型所需的从几天到几个月的过多人工小时。CAD模型通常涉及的详细程度远远大于使用CAE系统执行计算模拟所需的详细程度。为仿真准备CAD模型,包括网格生成,仍然是一个具有挑战性的瓶颈,需要解决,以实现仿真工具在工业中的全部潜力。这一挑战也是延迟工业采用通常在计算上上级的高阶方法的关键因素。目前的研究集中在复杂CAD模型去特征化算法的开发上。该过程的主要缺点是需要人类专业知识和与CAD系统和几何清洁工具的手动交互。虽然工程师可以借助许多现有商业网格生成软件包中包含的半自动工具,例如COMSOL,ANSYS,CATIA,SolidWorks,Patran,MSC,CADfix,ESI Visual Environment,但去特征化不能完全自动化。此外,它通常是不可能知道,先验的,去特征化的效果上的仿真结果,因为这个过程取决于物理问题和所需的近似水平。问题的核心是在许多商业网格生成器中实现的传统的分层模式。这个项目的最终目标是开发一个新的计算环境,其中包括一个功能独立的网格生成范例和即插即用库,使网格直接集成到现有的商业和研究求解器。所提出的方法是破坏性的,因为它提出了非常规计算方法的发展,不仅在计算模拟生成合适的网格的阶段,而且还需要将新的即插即用库纳入现有的求解器。这些库将作为本项目的一部分交付,它将遵循商业软件中使用的基本原理,用户可以根据特定模拟的需求选择不同类型的元素。所提出的网格生成技术的优点并不限于消除瓶颈,已突出了许多行业,经常使用计算工程在他们的设计周期。此外,新的网格将完全消除由去特征化CAD模型引入的不确定性。与其依赖专家的意见来决定哪些特征在模拟中可能不相关,CAD模型将不会被改变,从而导致更高保真的模拟和对结果的更大信心。拟议的研究是及时的,解决了过去三年来独立机构强调的问题(如美国航天局)、专门从事计算机建模的国际协会(如NAFEMS)和私营部门(如Pointwise公司)。自20世纪90年代中期以来,研究一直集中在开发用于更快去特征化的工具上。事实上,这个问题在二十多年来一直没有得到解决,这表明这里提出的激进的新方法,追求一个正交的研究方向,其中不需要去特征化,可以导致突破。

项目成果

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Nonintrusive parametric NVH study of a vehicle body structure
  • DOI:
    10.1080/15397734.2022.2098140
  • 发表时间:
    2022-07
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    F. Cavaliere;S. Zlotnik;R. Sevilla;X. Larráyoz;P. Díez
  • 通讯作者:
    F. Cavaliere;S. Zlotnik;R. Sevilla;X. Larráyoz;P. Díez
A face-centred finite volume method for high-contrast Stokes interface problems
高对比度斯托克斯接口问题的面心有限体积法
An implicit HDG method for linear convection-diffusion with dual time stepping
  • DOI:
    10.1016/j.jcp.2021.110201
  • 发表时间:
    2021-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    R. Sevilla
  • 通讯作者:
    R. Sevilla
The Generation of 3D Surface Meshes for NURBS-Enhanced FEM
NURBS 增强型 FEM 的 3D 表面网格生成
  • DOI:
    10.1016/j.cad.2023.103653
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
    Zou X
  • 通讯作者:
    Zou X
HDGlab: An Open-Source Implementation of the Hybridisable Discontinuous Galerkin Method in MATLAB
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Ruben Sevilla其他文献

The generation of tetrahedral meshes for NURBS-enhanced FEM
  • DOI:
    10.1007/s00366-024-02004-z
  • 发表时间:
    2024-07-05
  • 期刊:
  • 影响因子:
    4.900
  • 作者:
    Xi Zou;Sui Bun Lo;Ruben Sevilla;Oubay Hassan;Kenneth Morgan
  • 通讯作者:
    Kenneth Morgan
A machine learning approach to predict near-optimal meshes for turbulent compressible flow simulations
一种机器学习方法,用于预测湍流可压缩流模拟的近乎最佳网格
  • DOI:
    10.1080/10618562.2024.2306941
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sergi Sanchez;Oubay Hassan;Ruben Sevilla
  • 通讯作者:
    Ruben Sevilla

Ruben Sevilla的其他文献

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