While analytical solutions of critical (phase) transitions in physical
systems are abundant for simple nonlinear systems, such analysis remains
intractable for real-life dynamical systems. A key example of such a physical
system is thermoacoustic instability in combustion, where prediction or early
detection of an onset of instability is a hard technical challenge, which needs
to be addressed to build safer and more energy-efficient gas turbine engines
powering aerospace and energy industries. The instabilities arising in
combustion chambers of engines are mathematically too complex to model. To
address this issue in a data-driven manner instead, we propose a novel deep
learning architecture called 3D convolutional selective autoencoder (3D-CSAE)
to detect the evolution of self-excited oscillations using spatiotemporal data,
i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory
surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to
learn, in a hierarchical fashion, the complex visual and dynamic features
related to combustion instability. We train the 3D-CSAE on frames of videos
obtained from a limited set of operating conditions. We select the 3D-CSAE
hyper-parameters that are effective for characterizing hierarchical and
multiscale instability structure evolution by utilizing the dynamic information
available in the video. The proposed model clearly shows performance
improvement in detecting the precursors of instability. The machine
learning-driven results are verified with physics-based off-line measures.
Advanced active control mechanisms can directly leverage the proposed online
detection capability of 3D-CSAE to mitigate the adverse effects of combustion
instabilities on the engine operating under various stringent requirements and
conditions.

提出了一种名为 3D 卷积选择性自编码器（3D-CSAE）的深度学习架构，用于检测燃烧不稳定性的演变，通过处理从涡流稳定燃烧器（燃气轮机燃烧器的实验室代用品）拍摄的高速视频，可以提高检测的准确性，为航空航天和能源工业提供更安全和更节能的气轮机发动机。

用于燃烧系统不稳定性检测的 3D 卷积选择自编码器

3D Convolutional Selective Autoencoder For Instability Detection in  Combustion Systems

Incorporating computational fluid dynamics in the design process of jets,
spacecraft, or gas turbine engines is often challenged by the required
computational resources and simulation time, which depend on the chosen
physics-based computational models and grid resolutions. An ongoing problem in
the field is how to simulate these systems faster but with sufficient accuracy.
While many approaches involve simplified models of the underlying physics,
others are model-free and make predictions based only on existing simulation
data. We present a novel model-free approach in which we reformulate the
simulation problem to effectively increase the size of constrained pre-computed
datasets and introduce a novel neural network architecture (called a cluster
network) with an inductive bias well-suited to highly nonlinear computational
fluid dynamics solutions. Compared to the state-of-the-art in model-based
approximations, we show that our approach is nearly as accurate, an order of
magnitude faster, and easier to apply. Furthermore, we show that our method
outperforms other model-free approaches.

本研究提出了一种模型无关的计算流体动力学仿真方法，采用聚类神经网络结构，并与基于模型的方法作对比，结果表明该方法与之几乎同样精确，但速度快一个数量级，易于应用，在仿真简化和加速方面具有潜在的应用价值。