A combinatorial model reduction method for the finite element analysis of wind instruments

Abstract A high‐fidelity finite element model is proposed for the complete simulation of the time‐harmonic acoustic propagation in wind instruments. The challenge is to meet the extremely high accuracy required by professional musicians, in a complex domain, for all fingerings and over a wide frequency range, within an affordable computational time. Several modelling assumptions are made to limit the numerical complexity of the problem while preserving all relevant physics. A dedicated high‐performance solution strategy is also proposed, based on partitioning, condensation and model order reduction, exploiting the combinatorial nature of wind instrument fingerings. Finally, the proposed approach is applied to the simulation of an alto saxophone. An order of magnitude reduction in memory and computational cost is achieved.

Authors

Institutions

Publication Details

Journal
International Journal for Numerical Methods in Engineering
Published
2024-08-26
DOI
https://doi.org/10.1002/nme.7582
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A combinatorial model reduction method for the finite element analysis of wind instruments

Paul Oumaziz, Jonathan Cottier, Marie Jeanneteau, Vincent Gibiat et al.
1 citations
International Journal for Numerical Methods in Engineering
Model Reduction and Neural Networks
0.28
article

A combinatorial model reduction method for the finite element analysis of wind instruments

Paul Oumaziz, Jonathan Cottier, Marie Jeanneteau, Vincent Gibiat, Jean‐Charles Passieux
article en
1 citations

Abstract

Abstract A high‐fidelity finite element model is proposed for the complete simulation of the time‐harmonic acoustic propagation in wind instruments. The challenge is to meet the extremely high accuracy required by professional musicians, in a complex domain, for all fingerings and over a wide frequency range, within an affordable computational time. Several modelling assumptions are made to limit the numerical complexity of the problem while preserving all relevant physics. A dedicated high‐performance solution strategy is also proposed, based on partitioning, condensation and model order reduction, exploiting the combinatorial nature of wind instrument fingerings. Finally, the proposed approach is applied to the simulation of an alto saxophone. An order of magnitude reduction in memory and computational cost is achieved.

International Journal for Numerical Methods in EngineeringVol. 125(24)
Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Institut Clément Ader (FR)
Affordable and clean energy
Openalex Percentile: Top 46%
Model Reduction and Neural Networks
0.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.