Applying large-scale PIV to water monitor discharge experiment
Introduction
Industrial fires are one of the most challenging missions for firefighters. In 1996, a fire in a chemical plant broke out in Taoyuan City, Taiwan. Dozens of firefighters rushed to the fire point where numerous Methyl ethyl ketone peroxide (MEKPO) storage tanks were held. After 30 min of painstaking operations, they found the fire was too fierce to approach and started to fall back. However, the fire triggered a large scale MEKPO explosion, killing 10 people (including six firefighters), injuring 47, and damaging two fire trucks. This unprecedented tragedy was the most severe industrial disaster in decades in Taiwan, catching local government entirely by surprise [1].
According to the Emergency Response Guidebook (2016 ed.) [2], the fire involving MEKPO tanks should be flooded with water monitor from a distance. By taking a broad view, numerous historic accidents happened worldwide, and have received a great deal of attention, which are listed in Table 1.
The standard emergency response for fires such as those listed in Table 1 is to use water monitors to fight the fire or cool containers from a maximum distance. Thus, water monitors are a recognized remote solution that increases the safety of firefighters.
Nowadays, there are 32 industrial zones in Taoyuan City. Authority in Jurisdiction may purchase a variety of firefighting apparatus. Large-scale industrial fires remain extremely difficult to reach due to the concern of rapid-fire spreading and explosion. Moreover, the more storage height, the more difficult for delivering extinguishing agents to burning tanks. Unfortunately, the Petroleum Administration Act of Taiwan imposes no limitations on the height of oil tanks [11]. In addition, according to the Standard for Installation of Fire Safety Equipment [12], regulations involving water monitors seem to be over-simplified in Taiwan: the flow rate of a fixed water monitor should be over 1,900 L per minute (LPM), and the horizontal discharge range should be over 30 m. The single-design option does not account for the characteristics of the master stream, such as trajectory and footprint, so the purpose of mitigation may be limited. Fortunately, technological progress now allows computational fluid dynamics (CFD) simulations and virtual reality (VR) training for the disposition and use of water monitors. In particular, at some industrial-firefighting training centers in Europe, VR training has become a featured course for tailored scenarios, reducing the cost of the training courses and making them safer and more ergonomic. However, few studies have investigated water monitors, and little is known about their trajectory pattern, which is vital for their strategic deployment. Hasegawa et al. reported numerous full-scale water-monitor experiments and compared the results with Fireles CFD simulations [13]. Miyashita (2013) et al. [14] proposed a Moving Particle Semi-implicit (MPS) based simulation and validated by full-scale water monitor experiment. Nevertheless, these investigations only compared the trajectory pattern of the simulated master stream with that of the real master stream. Thus, the present work presents a cost-effective procedure to collect trajectory pattern as well as the velocity of the moving water cluster, enriching experimental data to validate the simulation work. Section 2 outlines the method of discharge experiment. Section 3 crystallizes the schematic methods proposed in this research, including digital image processing (DIP) and large-scale particle image velocimetry (LS-PIV). Section 4 gives the experimental results and evaluates their accuracy, and section 5 encapsulates the contribution of this research and its future applications.
Section snippets
Experiment
This paper reports a trajectory experiment in which the trajectory of water discharged from a water monitor was recorded. The experiment was filmed by using SONY X1000V video cameras. All the experimental apparatus are easy to acquire, making the budget to be affordable. Some of the experimental apparatus were supported by Taoyuan Fire Department (TYFD) where the author (H–Y. D.) serves.
Proposed method
To obtain the necessary parameters from the digital video data, such as trajectory pattern and moving velocity, we could have used digital PIV (DPIV). However, conventional DPIV requires illumination, particle seeding, a monoscopic camera, data analysis devices, and is usually restricted to laboratory-scale experiments [15]. A full-scale discharge experiment was thus judged too large to be applied. Fortunately, the development of telemetry in the field of hydrology has led to an extension of
Trajectory pattern
Table 3 lists the temporal trajectory patterns obtained in Section 3 with the wind vector taken into account. and denote the upper edge and medium height of the highest region. and are the front edge and medium range of the farthest region. D is the deviation of the landing point of the stream from the centerline.
As shown in Table 3, the 30-degree discharge has greater range, but lower height than the 45-degree discharge. In addition, with the wind component along the negative
Conclusion
This paper presents the result of using DIP to assess a flow field. We conclude that LS-PIV is a safe, nonintrusive, and cost-effective method to collect data from water-monitor streams. The three main findings of this research are as follows: (1) LS-PIV can be used to measure the moving vectors of specific water clusters. (2) Filter-based DIP can deal with most background situations. If the background has low contrast with respect to the master stream, Segment-based DIP may be used to enhance
CRediT authorship contribution statement
Hao-Yu Dai: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization, Project administration. Masato Hasegawa: Methodology, Software, Validation, Formal analysis, Data curation, Writing - review & editing, Supervision. Nobuyoshi Kawabata: Conceptualization, Methodology, Validation, Investigation, Project administration. Miho Seike: Writing - review & editing, Project administration. Shen-Wen Chien:
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References (46)
- et al.
Thermal explosion analysis of methyl ethyl ketone peroxide by non-isothermal and isothermal calorimetric applications
J. Hazard Mater.
(2009) - et al.
A study of storage tank accidents
J. Loss Prev. Process. Ind.
(2006) - et al.
Lessons learned from recent fuel storage fires
Fuel Processing Technology
(2013) - et al.
Medical image fusion: a survey of the state of the art
Information Fusion
(2014) - et al.
Adaptive histogram equalization and its variations, Comput. Vision, Graph
Image Process.
(1987) - et al.
Application of video imagery techniques for low cost measurement of water surface velocity in open channels
Flow Meas. Instrum.
(2016) - et al.
Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers
J. Hydrol.
(2010) - et al.
Large-Scale Particle Image Velocimetry (LSPIV) of aeolian sand transport patterns
Aeolian Research
(2018) - et al.
Rectification of image velocity results (RIVeR): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (PIV) and particle tracking velocimetry (PTV)
Comput. Geosci.
(2017) - et al.
Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging
J. Hydrol
(2018)