Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches

• URL: http://arxiv.org/abs/2404.17801v3
• Date: Sun, 24 Nov 2024 10:01:39 GMT
• Title: Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches
• Authors: Weiming Xu, Tao Yang, Peng Zhang,
• Abstract summary: Combustion instability is one of the most challenging problems in combustion research. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems.
• Score: 7.088178570214894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames, which are also influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core take. To facilitate fundamental studies, the present work concerns dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators are generated by fully validated reacting flow simulations. Due to limitations of expertise-based models, a data-driven approach is adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) is used to project the simulation data onto a 2-dimensional latent space. Based on the phase trajectories in latent space, both supervised and unsupervised classifiers are proposed for datasets with well known labeling and without, respectively. For labeled datasets, we establish the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we develop a novel unsupervised classifier (GMM-DTWC) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition of complex combustion problems.

Related papers

• Stabilization Analysis and Mode Recognition of Kerosene Supersonic Combustion: A Deep Learning Approach Based on Res-CNN-beta-VAE [13.734716568876378]
  scramjet engine is a key propulsion system for hypersonic vehicles. Complex interactions between fuel injection, turbulent combustion, and aerodynamic effects are crucial for ensuring combustion stable in scramjet engines. This work introduces an innovative learning framework that combines dimensionality reduction via the Residual Contemporalal Neural Network-beta-volutional Autoencoder (Res-CNN-beta-VAE) model with unsupervised clustering (K-means)
  arXiv  Detail & Related papers  (2025-03-17T03:00:01Z)
• Dynamical Mode Recognition of Turbulent Flames in a Swirl-stabilized Annular Combustor by a Time-series Learning Approach [7.823412530621099]
  This study introduces a two-layer bidirectional long short-term memory variational autoencoder, Bi-LSTM-VAE model, to effectively recognize dynamical modes in annular combustion systems. Specifically, leveraging 16 pressure signals from a swirl-stabilized annular combustor, the model maps complex dynamics into a low-dimensional latent space. Analysis of latent variable distributions reveals distinct patterns corresponding to a wide range of equivalence ratios and premixed fuel and air mass flow rates.
  arXiv  Detail & Related papers  (2025-03-17T02:55:01Z)
• CycleHOI: Improving Human-Object Interaction Detection with Cycle Consistency of Detection and Generation [37.45945633515955]
  We propose a new learning framework, coined as CycleHOI, to boost the performance of human-object interaction (HOI) detection. Our key design is to introduce a novel cycle consistency loss for the training of HOI detector. We perform extensive experiments to verify the effectiveness and generalization power of our CycleHOI.
  arXiv  Detail & Related papers  (2024-07-16T06:55:43Z)
• Learning Flame Evolution Operator under Hybrid Darrieus Landau and Diffusive Thermal Instability [0.0]
  This paper explores the application of novel operator learning methodologies to unravel the dynamics of flame instability. Training datasets encompass a wide range of parameter configurations, enabling the learning of parametric solution advancement operators. Results demonstrate the efficacy of these methods in accurately predicting short-term and long-term flame evolution.
  arXiv  Detail & Related papers  (2024-05-11T18:31:13Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems [41.055298739292695]
  Emergence, a global property of complex adaptive systems, is prevalent in real-world dynamic systems, e.g., network-level traffic congestions. This paper proposes a hierarchical framework with CAS-temporal consistency to solve these two problems by learning the system representation and agent representations. Our method achieves more detection than traditional methods and deep learning methods on three datasets with well-known yet hard-to-detect emergent behaviors.
  arXiv  Detail & Related papers  (2024-01-18T08:55:05Z)
• Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network [7.966402372024724]
  This study proposes a promising method (Bi-LSTM-VAE-WDC) for recognizing dynamical modes in oscillatory combustion systems. Specifically, the Bi-LSTM-VAE dimension reduction model was introduced to reduce the high-dimensional spatial-temporal data of the combustion system to a low-dimensional phase space. The results show that the novel Bi-LSTM-VAE method can produce a non-overlapping distribution of phase points, indicating an effective unsupervised mode recognition and classification.
  arXiv  Detail & Related papers  (2023-12-05T03:25:45Z)
• Causal Temporal Regime Structure Learning [49.77103348208835]
  We present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime. We establish the identifiability of the regimes and DAGs within our framework. Experiments show that CASTOR consistently outperforms existing causal discovery models.
  arXiv  Detail & Related papers  (2023-11-02T17:26:49Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Forecasting through deep learning and modal decomposition in two-phase concentric jets [2.362412515574206]
  This work aims to improve fuel chamber injectors' performance in turbofan engines. It requires the development of models that allow real-time prediction and improvement of the fuel/air mixture.
  arXiv  Detail & Related papers  (2022-12-24T12:59:41Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach [0.0]
  We present an Equation/Variable free machine learning (EVFML) framework for the control of the collective dynamics of complex/multiscale systems. The proposed implementation consists of three steps: (A) from high-dimensional agent-based simulations, machine learning (in particular, non-linear manifold learning (DMs)) We exploit the Equation-free approach to perform numerical bifurcation analysis of the emergent dynamics. We design data-driven embedded wash-out controllers that drive the agent-based simulators to their intrinsic, imprecisely known, emergent open-loop unstable steady-states.
  arXiv  Detail & Related papers  (2022-07-12T18:16:22Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations [58.720142291102135]
  Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract coherent schemes. This paper proposes a strategy to enable DMD to extract from observations with different mesh topologies and dimensions.
  arXiv  Detail & Related papers  (2021-04-28T22:14:25Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.