Thermoacoustic stability prediction using classification algorithms

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-04-25 DOI:10.1017/dce.2022.17
R. Gaudron, A. Morgans
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引用次数: 2

Abstract

Abstract 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.
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基于分类算法的热声稳定性预测
预测热声不稳定性的发生在飞机推进、发电和工业加热等各种工程应用中具有重要意义。基于物理方法的预测方法在过去几十年中得到了发展,但是在探索大量设计时具有中等到高的计算成本。在本研究中,研究了四种成熟的分类算法——k近邻、决策树(DT)、随机森林(RF)和多层感知器(MLP)算法的稳定性预测能力和计算成本。这些算法使用内部基于物理的低阶网络模型工具OSCILOS进行训练。所有四种算法都能够以非常高的精度和非常低的运行时间预测哪些结构是热声不稳定的。此外,对于给定的配置,包含不稳定模式的频率区间也可以使用多标签分类进行准确预测。在99.6%和98.3%的配置中,RF算法正确地预测了整体稳定性,并找到了包含不稳定模式的所有频率区间,使其成为在大量训练样例可用时最准确的算法。对于较小的训练集,MLP算法成为最准确的算法。DT算法的准确性略低,但训练速度非常快,运行速度比传统的基于物理的低阶网络模型工具快100万倍。这些发现可以用来设计新一代的燃烧器优化工具,这些工具将比现有代码运行得快得多,同时保持相似的准确性。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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