Evaluation of burning rate in microgravity based on the fuel regression, flame area, and spread rate

Xiong, Caiyi; Fan, Haoran; Huang, Xinyan; Fernandez-Pello, Carlos

doi:10.1016/j.combustflame.2021.111846

Cited by 13 publications

(1 citation statement)

References 26 publications

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“…In numerous fire tests and actual fire scenes, closed-circuit television cameras and mobile device cameras are frequently utilized to obtain fire videos, record alterations in flames and smoke, and assess related fire parameters [4][5][6]. The extracted fire frame images from these videos contain data about the behavior and characteristics of the fire, including the size, color, brightness, and oscillation frequency of the flames and smoke, as well as their changes over time.…”

Section: Introductionmentioning

confidence: 99%

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

Xu,

Dong,

Zou

2024

Fire

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The fire heat release rate (HRR) is a crucial parameter for describing the combustion process and its thermal effects. In recent years, some studies have employed fire scene images and deep learning algorithms to predict real-time fire HRR, which has led to the advancement of HRR prediction in terms of both lightweightness and real-time monitoring. Nevertheless, the development of an early-stage monitoring system for fires and the ability to predict future HRR based on current moment data represents a crucial foundation for evaluating the scale of indoor fires and enhancing the capacity to prevent and control such incidents. This paper proposes a deep learning model based on continuous fire scene images (containing both flame and smoke features) and their time-series information to predict the future transient fire HRR. The model (Att-BiLSTM) comprises three bi-directional long- and short-term memory (Bi-LSTM) layers and one attention layer. The model employs a bidirectional feature extraction approach, followed by the introduction of an attention mechanism to highlight the image features that have a critical impact on the prediction results. In this paper, a large-scale dataset is constructed by collecting 27,231 fire scene images with instantaneous HRR annotations from 40 different fire trials from the NIST database. The experimental results demonstrate that Att-BiLSTM is capable of effectively utilizing fire scene image features and temporal information to accurately predict future transient HRR, including those in high-brightness fire environments and complex fire source situations. The research presented in this paper offers novel insights and methodologies for fire monitoring and emergency response.

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Section: Introductionmentioning

confidence: 99%