Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 647-654 |
| Seitenumfang | 8 |
| Fachzeitschrift | Building Simulation Conference Proceedings |
| Jahrgang | 18 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 18th IBPSA Conference on Building Simulation, BS 2023 - Shanghai, China Dauer: 4 Sept. 2023 → 6 Sept. 2023 |
Abstract
The computational effort of CFD simulation remains a prohibitive barrier to real-time prediction for design assistance and fast simulation. This study examines and compares the feasibility of two dimensionality reduction algorithms, proper orthogonal decomposition (POD) and deep-autoencoder (AE), for efficient feature extraction from indoor airflow simulation, and investigates how they contribute to fast and accurate prediction. The performance of these two methods is evaluated in various typical scenarios conducted in a 2-dimensional (2D) rectangular chamber, with air velocity distribution as the dependent variable, and tested using a set of different inlet velocities. The research pipeline involves using computational fluid dynamic (CFD) generated data as input for POD and AE to obtain compressed representation, then fed into a neural network (NN) method to train surrogate models for prediction and reconstruction. The results indicate that the combination of AE and neural network method (AE-NN) has excelled in the robust prediction of flow pattern transition than a combination of POD and neural network method (POD-NN). AE-NN accurately predicted that the leading edge of the airflow gradually moved forward as the inlet velocity increased, and the move displacement distances agreed with CFD results.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Bauwesen
- Ingenieurwesen (insg.)
- Architektur
- Mathematik (insg.)
- Modellierung und Simulation
- Informatik (insg.)
- Angewandte Informatik
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in: Building Simulation Conference Proceedings, Jahrgang 18, 2023, S. 647-654.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Feasibility Analysis of POD and Deep-Autoencoder for Reduced Order Modelling
T2 - 18th IBPSA Conference on Building Simulation, BS 2023
AU - Wang, Shaofan
AU - Chen, Xia
AU - Geyer, Philipp
N1 - Funding Information: The author greatly acknowledges the EU project TheGreeFa, the DFG researcher unit FOR2363 EarlyBIM and the DFG Heisenberg grant with grant on (for TheGreeFa online, EarlyBIM: GE 1652/3-2, Heisenberg: GE 1652/4-1
PY - 2023
Y1 - 2023
N2 - The computational effort of CFD simulation remains a prohibitive barrier to real-time prediction for design assistance and fast simulation. This study examines and compares the feasibility of two dimensionality reduction algorithms, proper orthogonal decomposition (POD) and deep-autoencoder (AE), for efficient feature extraction from indoor airflow simulation, and investigates how they contribute to fast and accurate prediction. The performance of these two methods is evaluated in various typical scenarios conducted in a 2-dimensional (2D) rectangular chamber, with air velocity distribution as the dependent variable, and tested using a set of different inlet velocities. The research pipeline involves using computational fluid dynamic (CFD) generated data as input for POD and AE to obtain compressed representation, then fed into a neural network (NN) method to train surrogate models for prediction and reconstruction. The results indicate that the combination of AE and neural network method (AE-NN) has excelled in the robust prediction of flow pattern transition than a combination of POD and neural network method (POD-NN). AE-NN accurately predicted that the leading edge of the airflow gradually moved forward as the inlet velocity increased, and the move displacement distances agreed with CFD results.
AB - The computational effort of CFD simulation remains a prohibitive barrier to real-time prediction for design assistance and fast simulation. This study examines and compares the feasibility of two dimensionality reduction algorithms, proper orthogonal decomposition (POD) and deep-autoencoder (AE), for efficient feature extraction from indoor airflow simulation, and investigates how they contribute to fast and accurate prediction. The performance of these two methods is evaluated in various typical scenarios conducted in a 2-dimensional (2D) rectangular chamber, with air velocity distribution as the dependent variable, and tested using a set of different inlet velocities. The research pipeline involves using computational fluid dynamic (CFD) generated data as input for POD and AE to obtain compressed representation, then fed into a neural network (NN) method to train surrogate models for prediction and reconstruction. The results indicate that the combination of AE and neural network method (AE-NN) has excelled in the robust prediction of flow pattern transition than a combination of POD and neural network method (POD-NN). AE-NN accurately predicted that the leading edge of the airflow gradually moved forward as the inlet velocity increased, and the move displacement distances agreed with CFD results.
UR - http://www.scopus.com/inward/record.url?scp=85179514937&partnerID=8YFLogxK
U2 - 10.26868/25222708.2023.1227
DO - 10.26868/25222708.2023.1227
M3 - Conference article
AN - SCOPUS:85179514937
VL - 18
SP - 647
EP - 654
JO - Building Simulation Conference Proceedings
JF - Building Simulation Conference Proceedings
SN - 2522-2708
Y2 - 4 September 2023 through 6 September 2023
ER -