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| Hauptseite > Publikationsdatenbank > Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning > print |
| Hauptseite > Publikationsdatenbank > Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning > print |
| 001 | 885828 | ||
| 005 | 20230215201830.0 | ||
| 020 | _ | _ | |a 978-3-030-59850-1 |
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| 024 | 7 | _ | |a 10.1007/978-3-030-59851-8_6 |2 doi |
| 024 | 7 | _ | |a 2128/25963 |2 Handle |
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| 037 | _ | _ | |a FZJ-2020-04119 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Rüttgers, Mario |0 P:(DE-Juel1)177985 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a ISC High Performance 2020 |g ISC 2020 |c Frankfurt |d 2020-06-22 - 2020-06-25 |w Germany |
| 245 | _ | _ | |a Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning |
| 260 | _ | _ | |a Cham |c 2020 |b Springer |
| 295 | 1 | 0 | |a High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science |
| 300 | _ | _ | |a 81-101 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
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| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 490 | 0 | _ | |a Lecture Notes in Computer Science |v 12321 |
| 520 | _ | _ | |a 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 level in a 2D square domain. It is trained by numerical results from up to 20,000 GPU-based lattice-Boltzmann simulations that include randomly distributed rectangular and circular objects, and monopole sources. Types of boundary conditions, the monopole locations, and cell distances for objects and monopoles serve as input to the network. Parameters are studied to tune the predictions and to increase their accuracy. The complexity of the setup is successively increased along three cases and the impact of the number of feature maps, the type of loss function, and the number of training data on the prediction accuracy is investigated. An optimal choice of the parameters leads to network-predicted results that are in good agreement with the simulated findings. This is corroborated by negligible differences of the sound pressure level between the simulated and the network-predicted results along characteristic lines and by small mean errors. |
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| 773 | _ | _ | |a 10.1007/978-3-030-59851-8_6 |v 12321 |
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