2020
DOI: 10.1007/978-3-030-59851-8_6
|View full text |Cite
|
Sign up to set email alerts
|

Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning

Abstract: Using traditional computational fluid dynamics and aeroacoustics methods, the accurate simulation of aeroacoustic sources requires high compute resources to resolve all necessary physical phenomena. In contrast, once trained, artificial neural networks such as deep encoder-decoder convolutional networks allow to predict aeroacoustics at lower cost and, depending on the quality of the employed network, also at high accuracy. The architecture for such a neural network is developed to predict the sound pressure l… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
3
2
1

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 25 publications
0
3
0
Order By: Relevance
“…Some make use of built-in tools of convolutional networks, such as periodic padding to encode simple boundary conditions such as periodic conditions [7]. Other works dealing with more complex geometries employ binary inputs [8] or signed distance functions [9] to encode the geometry information. However, such works only tackle steady-state problems, such as RANS predictions [8], or Sound Pressure Level (SPL) estimation [9].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Some make use of built-in tools of convolutional networks, such as periodic padding to encode simple boundary conditions such as periodic conditions [7]. Other works dealing with more complex geometries employ binary inputs [8] or signed distance functions [9] to encode the geometry information. However, such works only tackle steady-state problems, such as RANS predictions [8], or Sound Pressure Level (SPL) estimation [9].…”
Section: Introductionmentioning
confidence: 99%
“…Other works dealing with more complex geometries employ binary inputs [8] or signed distance functions [9] to encode the geometry information. However, such works only tackle steady-state problems, such as RANS predictions [8], or Sound Pressure Level (SPL) estimation [9].…”
Section: Introductionmentioning
confidence: 99%
“…This approach, however, has limited transferability as it is primarily data-informed and does not encode the underlying physics. Furthermore, Rüttgers et al [33] have applied deep learning methods to the lattice Boltzmann method to predict the sound pressure level caused by objects. They introduced an encoder-decoder convolutional neural network and discussed various learning parameters to accurately forecast the acoustic fields.…”
Section: Introductionmentioning
confidence: 99%