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EMMA - Efficient Methods for Mechanical Analysis

EMMA - 机械分析的有效方法

基本信息

批准号：
257987586
负责人：
Professor Dr.-Ing. Felix Fritzen
金额：
--
依托单位：
Professur für Data Analytics in Engineering
依托单位国家：
德国
项目类别：
Independent Junior Research Groups
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/257987586?language=en
关键词：
EMMA Efficient Methods Mechanical Analysis

项目摘要

Applications in classical engineering discplines or in modern domains such as medical engineering require accurate simulations of complex technological problems. In order to obtain the required precision, elaborate simulation models are necessary. In general, these models consider geometrical and/or material nonlinearities. Simulations of these problems involve high computational costs (CPU time, memory requirement). Further, they imply a high energy consumption.The aim of the Emmy Noether Junior Research Group EMMA (Efficient Methods for Mechanical Analysis) is the development of novel methods for computationally efficient simulations of nonlinear mechanical and multi-field problems. The computational efficiency is achieved by using reduced basis model order reduction (RB-MOR) techniques. In order to accelerate the general RB-MOR ansatz, the physical nature of the underlying fields is analyzed in detail. Then the reduced basis framework is designed such that mechanical considerations can help to significantly accelerate the solution of the reduced form of the transient, nonlinear problem. This is a noteworthy difference to other model reduction strategies such as classical Galerkin subspace projection methods. Another important difference to existing model reduction methods for differential equations is the application to nonlinear optimization problems.Although model reduction can already be effective and ready for application for few realistic problems, its field of application is rather limited today. The investigations of the EMMA group will enable a wide application of high performance model reduction techniques in academia, applied science and, possibly, in industrial applications. The development of a virtual laboratory synthesizing the research results in order to demonstrate the capabilities of modern model reduction strategies is a major objective of EMMA. The massive computational savings can render simulations feasible that are impossible today. Thereby, challenging studies, e.g. in mechanical or medical engineering and in computational materials science, can be realized in the future. Besides the pure reduction of the computing time, the reduced models lead to a major improvement of the energy efficiency. Therefore, ecological importance is attributed to these developments.The EMMA group pursues an interdisciplinary approach, which is exemplified by the integration of methods from computer science, e.g., via GPU acceleration or data mining methods. The special synthesis of mathematics, physics and model reduction allows for computational gains that are unparalleled by existing model reduction algorithms. The activities of EMMA allow for many subsequent scientific and industrial applications and deliver the potential for future research projects.

在经典工程学科或现代领域（如医学工程）中的应用需要对复杂的技术问题进行精确的模拟。为了获得所需的精度，精心设计的仿真模型是必要的。通常，这些模型考虑几何和/或材料非线性。这些问题的模拟涉及高计算成本（CPU时间，内存需求）。Emmy Noether Junior Research Group EMMA（Efficient Methods for Mechanical Analysis）的目标是开发新的方法，用于非线性力学和多场问题的高效计算模拟。计算效率是通过使用减少基模型降阶（RB-MOR）技术实现的。为了加快一般RB-MOR方法的计算速度，本文详细地分析了基本场的物理性质。然后，减少的基础框架的设计，使机械的考虑，可以帮助显着加速的瞬态，非线性问题的简化形式的解决方案。这是一个值得注意的区别，其他模型的减少战略，如经典的Galerkin子空间投影方法。与现有的微分方程模型降阶方法的另一个重要区别是其在非线性优化问题中的应用，尽管模型降阶方法已经可以有效地应用于少数实际问题，但其应用领域目前还相当有限。EMMA小组的研究将使高性能模型简化技术在学术界、应用科学以及可能的工业应用中得到广泛应用。EMMA的一个主要目标是开发一个虚拟实验室，综合研究结果，以展示现代模型简化策略的能力。大量的计算节省可以使模拟成为可能，而这在今天是不可能的。因此，具有挑战性的研究，例如在机械或医学工程和计算材料科学，可以在未来实现。除了纯粹的计算时间的减少，减少的模型导致能源效率的重大改善。因此，生态的重要性归因于这些发展。EMMA集团追求跨学科的方法，这是由计算机科学的方法，例如，通过GPU加速或数据挖掘方法。数学，物理和模型简化的特殊合成允许现有模型简化算法无法比拟的计算增益。EMMA的活动允许许多后续的科学和工业应用，并为未来的研究项目提供潜力。