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Fire
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Protection of Ships against Fire and Personnel Evacuation
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Protection of Ships against Fire and Personnel Evacuation

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1351

Special Issue Editor

Dr. Shaohua Mao

E-Mail Website
Guest Editor
Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: thermal protection; large-scale pool fire; scaling analysis; ship fire

Special Issue Information

Dear Colleagues,

The study of shipboard fires and personnel evacuation is a critical field of research within maritime safety and disaster management. As the global maritime industry continues to expand and evolve, so too do the challenges associated with ensuring the safety of ships, crew members, passengers, and cargo. Understanding the current research status in this area is paramount for improving emergency response strategies, enhancing safety measures, and preventing catastrophic incidents at sea.

Shipboard fires are a persistent and potentially devastating threat. They can ignite due to various factors, including equipment failures, electrical faults, chemical hazards, and human error. Understanding the ignition sources, fire dynamics, and propagation mechanisms is crucial for effective fire prevention and response. Evacuating individuals from a ship in the event of a fire is a complex endeavor, heavily influenced by factors such as vessel design, onboard infrastructure, fire location, sea conditions, and evacuation protocols. Current research seeks to enhance our comprehension of these variables to improve evacuation efficiency and reduce response time. In conclusion, shipboard fires and personnel evacuation represent a dynamic and evolving research area that plays a pivotal role in safeguarding maritime operations and protecting lives and the environment. Continuous exploration of these topics contributes to safer maritime practices and bolsters the industry's resilience in the face of unforeseen challenges.

This Special Issue aims to invite scholars to conduct research on fire-resistant materials and technologies, human-centred evacuation technologies, and parameters of combustion characteristics of ship fires, as well as to build a predictive model of the fire development process and to contribute to the reduction of fire losses.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: oil pool fires on ships in different wind environments, multiple ignition source fire combustion characteristics of ships, safe evacuation of personnel and so on.

We look forward to receiving your contributions.

Dr. Shaohua Mao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ship fires
  • liquid pool fires
  • combustion characteristic
  • heat transfer mechanism
  • personnel evacuation
  • cross airflow
  • flame interaction
  • heat release rate
  • radiation distribution
  • smoke diffusion pattern

Published Papers (1 paper)

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Research

15 pages, 9672 KiB  
Article
Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Approaches, and Histogram Equalization Techniques
by Aziza Ergasheva, Farkhod Akhmedov, Akmalbek Abdusalomov and Wooseong Kim
Fire 2024, 7(3), 84; https://doi.org/10.3390/fire7030084 - 8 Mar 2024
Viewed by 1112
Abstract
The maritime sector confronts an escalating challenge with the emergence of onboard fires aboard in ships, evidenced by a pronounced uptick in incidents in recent years. The ramifications of such fires transcend immediate safety apprehensions, precipitating repercussions that resonate on a global scale. [...] Read more.
The maritime sector confronts an escalating challenge with the emergence of onboard fires aboard in ships, evidenced by a pronounced uptick in incidents in recent years. The ramifications of such fires transcend immediate safety apprehensions, precipitating repercussions that resonate on a global scale. This study underscores the paramount importance of ship fire detection as a proactive measure to mitigate risks and fortify maritime safety comprehensively. Initially, we created and labeled a custom ship dataset. The collected images are varied in their size, like having high- and low-resolution images in the dataset. Then, by leveraging the YOLO (You Only Look Once) object detection algorithm we developed an efficacious and accurate ship fire detection model for discerning the presence of fires aboard vessels navigating marine routes. The ship fire detection model was trained on 50 epochs with more than 25,000 images. The histogram equalization (HE) technique was also applied to avoid destruction from water vapor and to increase object detection. After training, images of ships were input into the inference model after HE, to be categorized into two classes. Empirical findings gleaned from the proposed methodology attest to the model’s exceptional efficacy, with the highest detection accuracy attaining a noteworthy 0.99% across both fire-afflicted and non-fire scenarios. Full article
(This article belongs to the Special Issue Protection of Ships against Fire and Personnel Evacuation)
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