428
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Implementation of a PID controller in ANSYS© for noise reduction applications

&
Pages 1579-1587 | Received 05 Nov 2019, Accepted 05 Nov 2019, Published online: 20 Nov 2019
 

Abstract

The present work addresses vibration and noise reduction, using the capabilities of the commercial software program ANSYS© to implement a PID controller. Different possibilities regarding control law implementation are available. However, due to different reasons either additional tools have to be used or the preexisting software tools limit the type of analyses that can be conducted. Additionally, the kind of systems it allows to implement are usually simple (e.g. without frequency dependent properties). In this work we propose a general algorithm to implement PID controllers in ANSYS© that needs only the implementation of a non-linear system of equations algorithm (for instance, Newton-Raphson algorithm). The method is illustrated by computing the sound radiation characteristics of different panels using their radiated sound power, through the Rayleigh integral method. Besides presenting the proposed approach and analyzing its results, some preliminary analysis are also presented in order to analyze the problems associated to the most direct ANSYS© implementation.

Additional information

Funding

This work has been supported by National Funds through Fundação para a Ciência e Tecnologia (FCT), through IDMEC, under LAETA, project UID/EMS/50022/2019.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 423.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.