Hybrid deep learning for nonlinear acoustic-driven flame dynamics and experimental validation

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Akhtardanesh Mohammad Ali; Alipour Ensieh; Farshchi Mohammad

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Journal Paper

Paper Information

Journal: JOURNAL OF THEORETICAL AND APPLIED VIBRATION AND ACOUSICS Year:2025 | Volume:11 | Issue:1 Page(s): 57-72

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Information Journal Paper

Title

Hybrid deep learning for nonlinear acoustic-driven flame dynamics and experimental validation

Author(s)

Akhtardanesh Mohammad Ali | Alipour Ensieh | Farshchi Mohammad | Issue Writer Certificate

Keywords

Partially premixed flame

acoustic wave

Convolutional neural network

Deep Learning

combustion instability

Abstract

This study proposes a hybrid deep-learning model that combines Convolutional Neural Networks (CNNs) with a Transformer Encoder to investigate the nonlinear dynamics of a laminar, partially premixed counterflow flame under acoustic excitation. The model was trained on experimental data obtained from a combustion instability laboratory. OH* chemiluminescence was employed to measure flame responses across a frequency range of 20 to 350 Hz and pressure amplitudes extending up to the extinction threshold. The research explores the intricate interactions between acoustic waves and flame dynamics, providing insights into how varying amplitudes and frequencies influence heat release rates. Despite inherent limitations in the dataset, the model demonstrated a robust ability to accurately approximate the flame transfer function, successfully replicating chemiluminescence signals and forecasting flame reactions to diverse acoustic excitations. Additionally, the repeatability of the flame structure was rigorously validated through high-speed imaging and image processing techniques, confirming consistent flame characteristics over multiple testing cycles. The results underline the significant promise of the hybrid deep-learning approach as a reliable tool for predicting flame behavior in complex acoustic environments, offering practical implications for mitigating combustion instabilities in various engineering applications. This research represents a significant step forward in applying machine learning techniques to enhance the predictability and control of flame dynamics in real-world systems.

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