Lecture

Finite Element Modeling: Introduction

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

MICRO-470: Scaling laws in micro & nanosystems

This class adresses scaling laws in MEMS/NEMS. The dominant physical effects and scaling effects when downsizing sensors and actuators in microsystems are discussed, across a broad range of actuation principles. ...

Description

This lecture introduces the concept of finite element modeling, which allows for numerical solutions to complex problems by dividing a complex geometry into smaller elements and modeling their response with algebraic equations. The instructor explains the importance of FEM in approximating solutions, predicting behavior, and limiting experiments. The lecture covers the concept and terminology of the Finite Element Method, discussing governing equations, boundary conditions, and the discretization process. It also explores the challenges, strengths, and common errors in FEM, emphasizing the importance of verification and validation. Additionally, the history, advantages, disadvantages, and procedural steps of FEM are discussed, providing insights into pre-processing, solution, and post-processing stages.

Instructors (3)

Herbert Shea

Expertise soft robotics, wearable haptics, electrostatic actuators, EHD Mission My lab develops flexible polymer-based actuators for a wide range of applications including haptics and soft robotics. More info at https://www.epfl.ch/labs/lmts/ Links from EPFL-LMTS Lab webpage People Research ActivitiesPublications TeachingPublications and Conference papers Link to my lab Education PhD | Physics 1997 – 1997 Harvard University MA | Physics 1993 – 1993 Harvard University

Philippe Renaud

Philippe Renaud is Professor at the Microsystem Laboratory (LMIS4) at EPFL. He is also the scientific director of the EPFL Center of MicroNanoTechnology (CMI). His main research area is related to micronano technologies in biomedical applications (BioMEMS) with emphasis on cell-chips, nanofluidics and bioelectronics. Ph. Renaud is invloved in many scientifics papers in his research area. He received his diploma in physics from the University of Neuchâtel (1983) and his Ph.D. degree from the University of Lausanne (1988). He was postdoctoral fellow at University of California, Berkeley (1988-89) and then at the IBM Zürich Research Laboratory in Switzerland (1990-91). In 1992, he joined the Sensors and Actuators group of the Swiss Center for Electronics and Microtechnology (CSEM) at Neuchâtel, Switzerland. He was appointed assistant professor at EPFL in 1994 and full professor in 1997. In summer 1996, he was visiting professor at the Tohoku University, Japan. Ph. Renaud is active in several scientific committee (scientific journals, international conferences, scientific advisory boards of companies, PhD thesis committee). He is also co-founder of the Nanotech-Montreux conference. Ph. Renaud is committed to valorization of basic research through his involvement in several high-tech start-up companies.

Niels Quack

Prof. Dr. Niels Quack received the M.Sc. degree from Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, in 2005, and the Dr.Sc. degree from Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland, in 2010. From 2011 to 2015, he was Postdoctoral Researcher and Visiting Scholar with the Integrated Photonics Laboratory, Berkeley Sensor and Actuator Center, University of California, Berkeley, CA, USA. From 2014 to 2015, he was Senior MEMS Engineer with Sercalo Microtechnology, Neuchâtel, Switzerland. He is currently an SNSF Assistant Professor with Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. He has authored and co-authored more than 50 papers in leading technical journals and conferences. His research interests include photonic micro and nanosystems, with an emphasis on diamond photonics and silicon photonic MEMS. He is Steering Committee Member of the IEEE International Conference on Optical MEMS and Nanophotonics (OMN) and served as General Chair of the IEEE OMN 2018 and the Latsis Symposium 2019 on Diamond Photonics. He is a Senior Member of IEEE, Member of The Optical Society (OSA) and life member of SPIE.

Official source

Ontological neighbourhood

Mathematics

Analysis: Numerical analysis

Related concepts (32)

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of numerical methods that attempt at finding approximate solutions of problems rather than the exact ones. Numerical analysis finds application in all fields of engineering and the physical sciences, and in the 21st century also the life and social sciences, medicine, business and even the arts.

Numerical stability

In the mathematical subfield of numerical analysis, numerical stability is a generally desirable property of numerical algorithms. The precise definition of stability depends on the context. One is numerical linear algebra and the other is algorithms for solving ordinary and partial differential equations by discrete approximation. In numerical linear algebra, the principal concern is instabilities caused by proximity to singularities of various kinds, such as very small or nearly colliding eigenvalues.

Robin boundary condition

In mathematics, the Robin boundary condition (ˈrɒbɪn; properly ʁɔbɛ̃), or third type boundary condition, is a type of boundary condition, named after Victor Gustave Robin (1855–1897). When imposed on an ordinary or a partial differential equation, it is a specification of a linear combination of the values of a function and the values of its derivative on the boundary of the domain. Other equivalent names in use are Fourier-type condition and radiation condition.

Dirichlet boundary condition

In the mathematical study of differential equations, the Dirichlet (or first-type) boundary condition is a type of boundary condition, named after Peter Gustav Lejeune Dirichlet (1805–1859). When imposed on an ordinary or a partial differential equation, it specifies the values that a solution needs to take along the boundary of the domain. In finite element method (FEM) analysis, essential or Dirichlet boundary condition is defined by weighted-integral form of a differential equation.

Boundary value problem

In the study of differential equations, a boundary-value problem is a differential equation subjected to constraints called boundary conditions. A solution to a boundary value problem is a solution to the differential equation which also satisfies the boundary conditions. Boundary value problems arise in several branches of physics as any physical differential equation will have them. Problems involving the wave equation, such as the determination of normal modes, are often stated as boundary value problems.

Related lectures (64)

Computational Geomechanics: Unconfined Flow

Explores unconfined flow in computational geomechanics, emphasizing weak form derivation and relative permeability.

Finite Element Analysis: Advanced Mechanisms in Engineering

Provides an overview of advanced mechanisms analysis using Finite Element Method and Finite Element Analysis in engineering applications.

Finite Difference Method: Approximating Derivatives and Equations

Introduces the finite difference method for approximating derivatives and solving differential equations in practical applications.

Numerical Methods for Boundary Value Problems

Covers numerical methods for solving boundary value problems using finite difference, FFT, and finite element methods.

Finite Difference Grids