Imperial College London
Portrait Picture

Professor Aimee S. Morgans

Faculty of Engineering, Department of Mechanical Engineering

Professor of Thermofluids
 
 
 
//

Contact

 

+44 (0)20 7594 9975a.morgans

 
 
//

Location

 

621City and Guilds BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Gaudron:2022:10.1017/dce.2022.17,
author = {Gaudron, R and Morgans, AS},
doi = {10.1017/dce.2022.17},
journal = {Data-Centric Engineering},
title = {Thermoacoustic stability prediction using classification algorithms},
url = {http://dx.doi.org/10.1017/dce.2022.17},
volume = {3},
year = {2022}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Predicting the occurrence of thermoacoustic instabilities is of major interest in a variety of engineering applications such as aircraft propulsion, power generation, and industrial heating. Predictive methodologies based on a physical approach have been developed in the past decades, but have a moderate-to-high computational cost when exploring a large number of designs. In this study, the stability prediction capabilities and computational cost of four well-established classification algorithms—the K-Nearest Neighbors, Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) algorithms—are investigated. These algorithms are trained using an in-house physics-based low-order network model tool called OSCILOS. All four algorithms are able to predict which configurations are thermoacoustically unstable with a very high accuracy and a very low runtime. Furthermore, the frequency intervals containing unstable modes for a given configuration are also accurately predicted using multilabel classification. The RF algorithm correctly predicts the overall stability and finds all frequency intervals containing unstable modes for 99.6 and 98.3% of all configurations, respectively, which makes it the most accurate algorithm when a large number of training examples is available. For smaller training sets, the MLP algorithm becomes the most accurate algorithm. The DT algorithm is found to be slightly less accurate, but can be trained extremely quickly and runs about a million times faster than a traditional physics-based low-order network model tool. These findings could be used to devise a new generation of combustor optimization tools that would run much faster than existing codes while retaining a similar accuracy.
AU  - Gaudron,R
AU  - Morgans,AS
DO  - 10.1017/dce.2022.17
PY  - 2022///
SN  - 2632-6736
TI  - Thermoacoustic stability prediction using classification algorithms
T2  - Data-Centric Engineering
UR  - http://dx.doi.org/10.1017/dce.2022.17
UR  - https://www.cambridge.org/core/journals/data-centric-engineering/article/thermoacoustic-stability-prediction-using-classification-algorithms/D097EE341733F5A9CF33E54FD38C6F59
UR  - http://hdl.handle.net/10044/1/98125
VL  - 3
ER  -
Download