Experimental Study of LNG Pool Fire on Land in the Field - Industrial

Article Cite This: Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Experimental Study of a Liquefied Natural Gas Pool Fire on Land in the Field Bin Zhang,*,†,‡ Delphine M. Laboureur,‡ Yi Liu,‡ Nirupama Gopalaswami,‡ and M. Sam Mannan*,‡ †

College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States

Ind. Eng. Chem. Res. Downloaded from pubs.acs.org by RMIT UNIV on 10/22/18. For personal use only.

‡

ABSTRACT: This study aims to determine several key parameters of a liquefied natural gas (LNG) pool fire on land through a field test. The mass burning rate was 0.186 kg/(m2 s) using the thermocouple method. Through the analysis of high-speed camera images, puffing frequencies of 0.53 and 0.69 Hz were determined for two consecutive oscillations of the flame. The flame tilt was found to be 58°, and the flame length was estimated as 25.4 m. The flame velocity field was first studied for an LNG field test using a high-speed camera, and the maximum velocity was approximately 6 m/s at the center of the flame. The solid flame model predicted well the thermal radiation at both downwind and crosswind directions, using surface emissive power recommended by the Federal Energy Regulatory Commission. Also, new correlations were developed for mass burning rate, flame length, and tilt against experimental results summarized in this work. burning rate and wind speed.12−16 The flame tilt is also affected by the mass burning rate and wind speed, and many correlations were reported in the literature.14,16−18 The Federal Energy Regulatory Commission (FERC) gathered correlations for burning rate, flame length, tilt, and transmissivity and compared them with reported experimental data to recommend key parameters of the solid flame model for land-based LNG fires.19 There has been a number of studies on hydrocarbon pool fires;20−24 however, the study on an LNG pool fire is still limited. In addition, the test conditions varied among existing LNG pool fire tests, which caused a significant discrepancy among experimental data and made it difficult to interpret the results. In this work, a field test of an LNG pool fire was conducted in a rectangle-shaped concrete pit. This experiment used the thermocouple method to measure the mass burning rate and investigated flame geometry using different types of cameras. The flame velocity field of an LNG pool fire in the field was obtained from postprocessing of high-speed camera images for the first time. This work also studied thermal radiation at both crosswind and downwind directions. The results were systematically analyzed and compared with existing data to enhance the understanding of the main parameters of LNG pool fires for thermal radiation determination. With regard to literature data for comparison of models with experiments from previous studies, the data collected in Table 1 was used.

1. INTRODUCTION With increasing demand and production for natural gas, more facilities are constructed and proposed to handle liquefied natural gas (LNG). In these LNG facilities, thermal radiation of a pool fire is the major hazard in the case of an LNG release.1 A U.S. federal regulation requires these LNG facilities to have a thermal exclusion zone using the criteria specified in NFPA 59 A,2 which is 5 kW/m2.3 Similar radiation intensity is adopted by other organizations as the criteria for people exposure to the radiant heat of a fire.4 LNG vaporizes vigorously once spilled from the primary containment to generate a flammable vapor cloud. A portion of the released LNG flashes into vapor or overtops from bund.5 The rest of the LNG accumulates in the bund to form a pool. The heat input from the ground and surroundings to the pool causes continuous vaporization. A pool fire will occur after the vapor cloud is ignited and burns back to the pool. A number of studies have been conducted to investigate measures that can prevent or mitigate an LNG pool fire; e.g., high expansion foam was studied to mitigate the vapor cloud and prevent fire from happening,6−8 as well as to control the fire hazard after an ignition.9,10 The solid flame model is a good method to predict thermal radiation of fires,11 in which the radiation heat flux is determined by multiplying the flame view factor and surface emissive power by the air atmospheric transmissivity. The view factor takes into account the flame geometry to calculate the radiation at a specific location, which ensures an accurate prediction not only for far field but also for near field.11 For a cylindrical flame, the flame shape is characterized by three parameters, flame diameter, length, and tilt, when wind is present. The flame length is often correlated with the mass © XXXX American Chemical Society

Received: Revised: Accepted: Published: A

May 12, 2018 September 24, 2018 September 26, 2018 September 26, 2018 DOI: 10.1021/acs.iecr.8b02087 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research Table 1. Literature Survey of Experimental Data of LNG Pool Fires Author 25

Diameter [m]

Length [m]

Burning rate [kg/m2s]

Wind velocity [m/s]

Tilt [degree]

Pit material

Mizner

20

43

0.106

6.15

54

Insulated concrete

Gomez26

1×1

4

0.065

0.5

14

Insulated concrete

Battelle Columbus Laboratories27

1.8 1.8 1.8 1.8 1.8 1.8 6.1 6.1 6.1 6.1 6.1

4.76 3.71 4.04 3.32 3.51 4.60 17.22 13.69 17.07 18.41 18.62

0.066 0.052 0.063 0.063 0.063 0.070 0.073 0.103 0.104 0.101 0.084

3.04 1.98 3.63 2.10 2.89 3.11 4.11 2.84 4.32 6.49 4.19

41 48.4 50.2 41 55.5 54.8 62.3 41 51.2 57.8 66.4

Soil

Figure 1. Schematic of the experimental setup.

2. MATERIALS AND METHODS 2.1. Experimental Setup. The LNG pool fire test was conducted in the largest LNG pit at Brayton Fire Training Field (BFTF) in College Station, TX, USA. The field test consisted of two phases. The LNG free burning fire was conducted in the first phase, which was later mitigated with high expansion foam application in the second phase. The experimental setup in the field is shown in Figure 1. The pit is approximately 10 m long and 6.4 m wide, and its aspect ratio is around 1.5. Because the aspect ratio is not high and only one data point was gathered, the effect of the aspect ratio was not studied in this work. The selection of the largest concrete pit is to ensure that results are more applicable to a real scale fire. The pit bottom is 1.22 m below the ground, and there is no lip above the ground. There were nine radiometers (Medtherm

Corporation, USA) installed in the downwind and crosswind directions. One of the closest radiometers to the fire (R9) in the downwind direction has a range of 120 kW/m2 (Gardon gage type); the others have a range of 30 kW/m2 (SchmidtBoelter type). A thermocouple tower and a thermocouple block were installed in the pit. The thermocouple tower had six thermocouples (type K, shielded, 6.4 mm in diameter, Omega Engineering Inc., USA) installed between 0.5 and 2.5 m above the pit bottom, aiming to measure the temperature profile in the flame. The thermocouple block had 24 thermocouples installed at an elevation ranging from 0.64 to 24.64 cm from the pit bottom, and two adjacent thermocouples were 1 cm apart, aiming to determine the mass burning rate. The foam generator was placed in the upwind direction for the following fire mitigation study. A weather station and several cameras were located in the crosswind direction. Radiometers and B

DOI: 10.1021/acs.iecr.8b02087 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Article

Industrial & Engineering Chemistry Research

Figure 2. High-speed camera image processing steps for flame geometry: (a) original images, (b) processed images with a filter, (c) intensity adjusted images, and (d) BW images using Otsu method.

foam was applied to mitigate the fire for the second phase of this project. The data acquisition was started right before the LNG release. The test parameters, such as the pit dimensions, LNG properties and release conditions, and weather conditions, are summarized in Table 2.

thermocouples were connected to the Data Acquisition System (DaqBoard 2005, Omega Engineering Inc., USA), which recorded the experimental data at 1 Hz. Other information, such as weather conditions and videos, was recorded for further analysis. The fire was visualized by two CCD cameras and a high-speed camera (Phantom v4.2, Vision Research, Inc., USA) recording a series of 4000 images at 1000 Hz, as well as a FLIR SC3000 infrared camera with a temperature range from 253 to 2273K. 2.2. Image Processing Methods. The high-speed camera images (Figure 2a) were processed by using the MATLAB Image Processing toolbox28 to extract the time evolution of the pool fire flame length. The images were first processed through a 2D median filter (Figure 2b), adjusted in intensity by saturating the top and bottom 1D of all pixel values (Figure 2c), and finally converted to black and white (BW) using a threshold determined with the Otsu method29 and fixed for all images as 0.2 (Figure 2d). The flame boundary was then detected by the Moore-neighbor tracing algorithm modified by Jacob’s stopping criteria.28,29 The flame height was calculated from the flame boundary as the vertical distance between the pool fire bottom and the highest position of the boundary. For the study of the flame velocity field, high-speed images were processed using the large scale particle image velocimetry (LS-PIV) technique,30 which calculates the flow velocity from an image pair using PIVLab, a cross-correlation algorithms for particle image velocimetry (PIV) developed by Thielicke and Stamhuis.31,32 The images were processed with an initial window size of 32 × 32 pixels, two iterations, 50% overlapping, and an adaptive interrogation window shift was applied. The frame rate was decimated by two to allow a larger displacement between each image pair, allowing the computation of around 1000 flow fields (one flow field requires an image pair). Before being averaged, improperly matched vectors were eliminated by removing all spurious high calculated displacement from a scatter plot through a standard deviation filter with a threshold of 7 times the standard deviation.31,32 In addition, a filtering based on the light intensity of the original images was performed to remove velocity vectors that would correspond to areas where no structures were visible.33 Finally, the resulting averaged velocity fields in pixels/s were converted to m/s using a magnification factor calculated by comparing the distance between two poles surrounding the pit measured from an image with its real dimension. 2.3. Experimental Procedure and Summary. The test was started by releasing LNG into the pit. The release was stopped when the LNG level was approximately 12.6 cm high, which took about 70 min to accumulate approximate 8.2 m3 of LNG in the pit. The pool fire was ignited by the fire fighter on site using a torch. The LNG fire lasted for 75 s to study the behavior in a free burning condition. Then, high expansion

Table 2. Summary of Test Conditions Pit

Area of the pit [m2] Equivalent pit diameter [m] Pit depth [m]

64.38 9.05 1.22

LNG

LNG composition

99.5% methane, 0.5% nitrogen 35,579 12.6 423

Average heating value [kJ/m3] LNG pool height [cm] Density [kg/m3] Weather

Average wind speed for fire [m/s] Air temperature [°C] Wind direction Humidity [%]

4.0 24.9 SE 54

3. RESULTS AND DISCUSSION 3.1. Mass Burning Rate. Mass burning rate is an important parameter for a pool fire study since it is a variable of many flame length correlations, and more importantly, it determines the total combustion heat of the fire. The mass burning rate in this work was determined using the LNG level measurement through thermocouples on the block. The distance of two adjacent thermocouples on the block was 1 cm. The time for the LNG pool surface to move from one thermocouple to the next one below was obtained. In theory, the temperature representing the LNG pool surface should be the boiling point (−161.5 °C). However, taking this value for the burning rate calculation introduced errors as the temperature fluctuates due to either loss of precision of the K-type thermocouple at such low temperature or instability of the pool surface. Therefore, a value of −140 °C was taken. The evolution of the surface position with time is shown in Figure 3. The mass burning rate was therefore calculated by multiplying the slope of the time evolution of the pool surface position with the LNG density (423 kg/m3). The average mass burning rate in this work was 0.186 kg/(m2 s). If the burning rate is calculated between a pair of thermocouples instead of using the linear fit as detailed above, differences from 1% to 10% can be observed. A slightly higher value was reported for the same test because the boiling temperature was used in the analysis.34 C


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be seen that the experimental data are strongly scattered. FERC concluded that their model with an infinite burning rate of 0.14 kg/(m2s) fits better the larger scale pool fires, but 0.11 kg/(m2s) fits better the smaller scales.19 This work developed a new correlation by fitting all the data in Figure 4. This correlation is similar to one of the FERC’s correlations and is proved to be good for both large- and small-scale LNG pool fires because more data, especially from small-scale tests, were used to determine coefficients. The scattered data also proves the effect of wind, lip height, and reservoir material. The burning rate measured in this study is larger than the correlation estimations. The effect of wind can be corrected using the correlation of Babrauskas as shown in eq 2.35 ṁ wind u = 1 + 0.15 ṁ D

Figure 3. Time evolution of pool fire surface.

In this case, this correlation would calculate a burning rate without wind. With the 6% reduction, the mass burning rate is closer to the prediction by correlations of FERC and this work but not sufficiently. Babrauskas reported inconsistent findings about the effect of lip height, which was found to decrease the burning rate in some studies35,39 and to increase in another.40 For the experiments of LNG pool fires on land in the literature, the mass burning rate varied depending on the types of substrate for the pit. In general, the ground pit and insulation concrete pit result in lower mass burning rates. The fire in this work was conducted in a concrete pit and had a larger mass burning rate, which is similar to one previous test conducted in the same pit by Suardin.38 Suardin reported a mass burning rate of 0.16 kg/(m2s), which was the minimum estimated value; the actual burning rate should be larger. Therefore, the mass burning rates for two tests in the same pit are quite similar. 3.2. Flame Geometry. Flame geometry is an important characteristic of a pool fire since it helps to determine the fire model for radiation prediction, such as the solid flame model that requires, for the view factor determination, the flame diameter, length, and tilt. In this study, the flame base is known and represented by the pit dimensions. If LNG is released accidentally, models to determine the pool fire diameter from the release rate exist in the TNO Yellow Book41 but will not be applied in this work since the experiment was performed in a pit. The flame developed in this study was shaped as a tilted cylinder due to the wind condition (4m/s). CCD and IR cameras recorded a similar flame shape as observed in Figure 5. The time evolution of the flame height was processed using the high-speed camera images, as the position of the camera was the closest to be perpendicular to the flame downwind. The time evolution of the flame height was processed by converting the flame image to BW for contour determination.42 The time evolution of the flame height can be observed in Figure 6. The flame has a smaller height at the beginning, which might be attributed to the process that the flame was first developing to its full height. The maximum measured flame height was 17.4 m for the free burning fire; however, the flame occasionally went beyond the images slightly due to oscillation. Even at a steady state condition, diffusive flames oscillate with time; this is called the flickering effect being related to the air entrainment.43 Two flame oscillations can be observed over the duration of the high-speed recording. Looking at the duration of these two oscillations, they can be used to determine a puffing frequency:

Correlations for determining the mass burning rate depend only on the pool diameter and fuel type as described in eq 1.35 For large-scale pool fires, the combustion is turbulent. The burning rate is constant with diameter and mainly controlled by radiation. For middle range fires, the regime is transitional and the importance of the radiation compared to conduction through the reservoir and convection increases with the diameter. ṁ = ṁ∞(1 − e−kβD)

(1)

Different correlations have been found in the literature to describe the burning rate of an LNG pool fire,19,35,36 differing by the value of coefficients (ṁ ∞ and kβ), as shown in Table 3 for LNG. Table 3. Coefficients of Burning Rate Model Rew37 Babrauskas35 FERC19 This work

ṁ ∞

kβ

0.141 0.078 0.11 or 0.14 0.14

0.136 1.1 0.46 0.41

(2)

The burning rate obtained from this experiment is plotted together with those of other experiments performed by Suardin38 and Gomez26 under MKOPSC. Other experimental results available in the literature and the correlations of Rew, Babrauskas, and FERC are also represented in Figure 4. It can

Figure 4. Mass burning rate: Comparison of experiments and models. D


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Figure 5. LNG pool fire flame from CCD camera (left) and IR camera (right).

Figure 6. Time evolution of flame height from high-speed camera.

Figure 7. Fourier transform of the flame height evolution.

0.53 Hz for the first one and 0.69 Hz for the second one. By computing the Fourier transform of the flame height evolution, the peak shows a frequency of 0.59 Hz in Figure 7, which is close to 0.56 Hz, provided by the correlation of Pagni,44 shown in eq 3.

f=

1.68 D

intermittency contour was obtained from high-speed camera images. The average flame height corresponds to an intermittency of 0.5, which was 13.5 m at an average wind speed of 4 m/s in this work. The flame length can be calculated using the flame tilt, which was estimated from the average picture to be 58° from vertical. The flame length is therefore estimated to be 25.4 m. The correlations in the literature mainly deal with the average flame length. For buoyancy dominated fires, the average flame length depends on the pool diameter (D), mass burning rate (ṁ ) and wind velocity (u) as shown in eqs 4, 5, and 6. This correlation was first proposed by Thomas.46

(3)

The idea of intermittency I(L) was used to determine average flame height by Zukoski and Ferrero,12,45 which is defined as the time fraction that the flame height is larger than L. A flame E


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Industrial & Engineering Chemistry Research Table 4. Pool Fire Flame Length Using Different Correlations Author

a

b

c

L/D

Lflame [m]

Error [%]

Error-lit [%]

Battelle27 Moorhouse14 Wang48 Thomas 116 Thomas 216 This work

1 6.2 86.512 42 55 4.77

−0.19 0.254 0.7466 0.61 0.67 0.096

0.06 −0.044 0.1416

2.24 2.15 4.43 3.49 3.22 2.85

20.4 19.3 39.2 31.0 28.5 25.8

−20 −24 54 22 12 2

18 24 43 23 18 16

−0.21 −0.24

Figure 8. Average flame from high speed camera (left) and IR camera (right).

Lflame D m* = u* =

= a(m*)b (u*)c ṁ gD ρa

the IR camera, as observed in Figure 8. The flame tilt was found to be 58 degree from experimental images. A number of correlations were developed to calculate flame tilt in the form of Eq. 7, and their coefficients are summarized in Table 5.

(4)

(5)

Table 5. Pool Fire Flame Tilt Using Different Correlations

u

Author 1/3

( ) gmD ̇ ρa

17

AGA Thomas16a Thomas16 Moorhouse14 Hauffman49 This work

(6)

The correlation parameters a, b, and c from the different correlations available in the literature were summarized47 as shown in Table 4. The error to the experimental value in this study is provided together with the average error from experimental data available.25−27 Compared with the experimental results in this work, the majority of correlations tend to overestimate the average flame length. Wang’s correlation, even though developed from both LNG pool fire experiments and numerical simulations, is showing the largest overestimation. The Moorhouse correlation can provide a good prediction since they were developed from LNG land fires in a rectangular pit from 6.1 to 13.7 m long, which are very similar to the test conditions in this work. The correlation of Battelle fits well the data as it was also developed for LNG pool fires in a dike with diameters lower than 6.1 m.27 FERC recommends Thomas, which also gives a relatively good prediction. In this study, a new correlation was developed by fitting all data in this work and literature.25−27 This correlation has the best performance to predict flame length for this work, as well as results in the literature as shown in the last column of Table 4 in terms of average error. The tilt in this work was determined through the velocity vector from high-speed video as it was positioned closest to be perpendicular to the tilted flame, which was not the case for

a

p

q

Tilt [deg]

Error [%]

Error-lit [%]

1 0.7 0.7 0.86 eq 9 0.74

−0.50 −0.49 −0.49 −0.25 −0.17

43.9 57.0 59.5 43.1 49.5 49.0

−24 −1.7 2.6 −26 −15 −15

13 24 28 13 15 12

This calculation uses air density rather than vapor density.

Compared with the experimental result, Thomas correlation has the best prediction for flame tilt, which was developed from a wood fire.16 In fact, the AGA correlation was developed for LNG pool fires17 and is supposed to provide the best prediction. However, the error is large using this correlation in this work; the possible reason is that the wind speed of oneminute average from the weather station was used in the calculation. The experimental result was from the high-speed video of a few seconds and the wind speed could be much larger than the average wind speed. The Moorhouse correlation was developed based on cylindrical flames.14 Also, a correlation was developed based on LNG results summarized in this paper.25−27 The overall performance is the best for predicting tilt, and slightly better than AGA correlation as shown in the last column of Table 5. F


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Industrial & Engineering Chemistry Research l * q o o p(u ) cos θ = o m o o o n1 u u* = 1/3

container and presented the averaged velocity field of 1331 image pairs, close to the 1018 image pairs average in this study. The comparison between the Tieszen’s experiment and this work for the velocity magnitude (U2 + V2)1/2 at Y/D = 0.5 is shown in Figure 10. In this study, the extracted profile was taken along a line perpendicular to the fire centerline to minimize the effect of the tilt. Although the fire was larger in scale than that in Tieszen’s study and LS-PIV processing gives less precision than normal PIV, the values of the nondimensional velocity match well. The only difference is that in Tieszen’s study the shape is not Gaussian and shows two maxima. 3.4. Thermal Radiation. The point source model is widely used due to its simplicity; however, the solid flame model is more suitable for a large-scale pool fire since it has a better prediction of radiation for both near and far field by considering the flame shape and length.11,17,37 The solid flame model is expressed as

u* ≥ 1 u* < 1

( ) gmD ̇ ρv

(7)

(8)

ij ρ yz jj v zz jj zz (9) k ρa { 3.3. Flame Velocity Field. The averaged velocity magnitude computed from the high-speed images using the LS-PIV technique can be observed in Figure 9. The velocity is tan θ ji uD zy = 3.3jjj zzz j υa z cos θ k {

0.07

2 jij u zyz jj zz j gD z k {

0.8

−0.6

(10)

I = SEP × F × τ 2

where SEP is the surface emissive power [kW/m ], F is the view factor, and τ is the transmissivity. There are very few studies that investigated correlations for the surface emissive power of LNG pool fires. The usual expression is a semiempirical correlation based on an optical thickness k [m−1] and a maximum mean SEPmax [kW/m2]. Raj and FERC both proposed such a correlation with the values of the parameters listed in Table 6. D is either the diameter for circular fires or the width or length for rectangular fires. SEP = SEPmax(1 − exp(− kD)) Figure 9. Flame velocity field.

(11)

Table 6. SEP Correlation and Calculation

largest at the center of the pool fire as 6 m/s and decreases as the side extremities. The velocity is also smaller at the base of the fire and accelerates along the centerline to the flame center. Not many studies have been found in the literature about the experimental study of the velocity field for LNG pool fires. A similar study has been performed by Tieszen et al.,50 who performed PIV analysis of methane pool fires in a 1m-diameter

FERC19 Raj 200721

SEPmax

k

Downwind [kW/m2]

Crosswind [kW/m2]

190 325

0.3 0.0725

180.7 168.3

162.1 120.6

Figure 10. Velocity magnitude profile at Y/D = 0.5. G


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Figure 11. SEP computed from IR image.

Figure 12. Thermal radiation: Solid flame model and measurements.

In this study, the IR camera was placed on the 6.4 m side of the pool. Palacios proposed to compute the SEP of fires from IR images using the following expression51 l o σT 4 SEP = o m o o n0

The radiation intensity was monitored for both crosswind and downwind directions. The radiation intensities at the downwind direction were much higher than those at the crosswind direction due to the wind effect. Suardin conducted similar tests in the same pit, and the radiation at a distance of 35 m was reported for free burning fire.10 The maximum radiation intensity at the crosswind direction in this work together with reported values by Suardin are shown in Figure 12. The solid flame model, using the experimental value of the flame length and the SEP calculated with both correlations as displayed in Table 6, is compared with the measurements in Figure 12. It can be observed that the correlation of FERC provides the results the closest to the measurements. It can also be observed that downwind is better estimated than crosswind at far field, but this conclusion is reversed for near field prediction. The most possible reason is that the downwind radiometer at 10 m from the fire was below the flame, and the solid flame model might not work properly.

if T > 800K if T < 800K

(12)

The result of this computation on the average IR image gives a minimum SEP of 23 kW/m2, an average SEP of 49 kW/m2, and a maximum SEP of 86 kW/m2, with the distribution shown in Figure 11. These values are almost half the SEP computed from the two correlations as showed in Table 6. The IR image is therefore providing a much lower SEP. Different correlations exist as well for evaluating the transmissivity as summarized by Raj,21 but both FERC and the TNO Yellow Book recommend using the transmissivity modeled by Atallah,52 as shown in eq 13. The stepwise correlation accounts for water vapor content, with details described in the FERC report.19 ij T yz τ = 1 − εwjjjj a zzzz j Tflame z k {

0.45

4. CONCLUSIONS This work experimentally studied the LNG pool fire at Brayton Fire Training Field (BFTF) in College Station, TX, USA. This study determined several key parameters for a large-scale LNG pool fire, including mass burning rate, flame geometry, flame velocity field, and thermal radiation. New correlations were developed for mass burning rate, flame length, and tilt for LNG

(13)

The view factor is taken following the TNO Yellow Book, which recommends use of cylindrical approximation of the tilted flame. Both horizontal and vertical view factors for crosswind and downwind directions are provided in the method, following the publication of Atallah.52 H


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Industrial & Engineering Chemistry Research pool fires based on the experimental results summarized in this work. The mass burning rate in this work was determined as 0.186 kg/ (m2s) using the thermocouple method, which is the largest burning rate reported for LNG pool fires on land and is similar to that obtained by one previous test in the same pit. The 4 m/ s wind velocity partly explains the high burning rate, and the effect of the dike lip height is still not clear. Further study is needed on all contributing factors to burning rates in largescale pool fires. In terms of flame geometry, the flame length, puffing frequency, and tilt were determined from the high-speed images. The flame length was obtained as 25.4 m, which was best estimated by Battelle, Thomas, or Moorhouse models. Similar conclusions applied to other large-scale experimental data that are summarized in Table 1. The puffing frequency was determined between 0.53 and 0.69 Hz. The Fourier transform analysis of the flame height (0.59 Hz) and the prediction of Pagni correlation (0.56 Hz) fall within this range. Concerning the flame tilt, the Thomas correlation provided the best prediction for this work, whereas AGA and Moorhouse models predicted the best for the literature data. The flame velocity field was studied for a field LNG pool fire for the first time using high-speed camera, which had a similar behavior as a small-scale methane fire. The maximum velocity was approximately 6 m/s at the center of the flame. The thermal radiation was measured at both crosswind and downwind directions and compared with the solid flame model. The best agreement with the measurements was obtained using flame SEP based on the FERC’s correlation. Further studies need to be performed on the prediction of radiation in near field, where the flame encloses the observer.

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kβ = mean beam length corrector extinction coefficient product (m−1) Lf lame = flame length (m) ṁ = mass burning rate (kg m−2 s−1) ṁ ∞ = maximum mass burning rate (kg m−2 s−1) m* = dimensionless mass burning rate p, q = constant for flame tilt correlation T = temperature (K) u = wind speed (m s−1) u* = dimensionless wind speed

■

GREEK LETTERS ρa = air density at ambient conditions (kg m−3) ρv = vapor density at ambient conditions (kg m−3) θ = tilt of flame from vertical (degree) τ = atmospheric transmissivity in solid flame model σ = Stefan−Boltzmann constant (W m−2 K−4) εw = emissivity of water vapor υa = kinematic viscosity of air (m2/s)

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REFERENCES

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (B. Zhang). *E-mail: [email protected] (M.S. Mannan). ORCID

Bin Zhang: 0000-0003-0432-5200 M. Sam Mannan: 0000-0002-4058-2119 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was sponsored by the Mary Kay O’Connor Process Safety Center at Texas A&M Engineering Experiment Station. The authors acknowledge support from Brayton Fire Training Field (BFTF) in College Station, TX, USA. The authors also thank Dr. William S. Saric from the Department of Aerospace Engineering, Texas A&M University, for lending the IR cameras and Dr. Jerrod W. Hofferth for filming the LNG pool fire test.

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NOMENCLATURE a, b, c = constant for flame length correlation D = pool fire diameter (m) f = flame puffing frequency (Hz) F = view factor in the solid flame model g = acceleration due to gravity (m s−2) I = thermal radiation intensity (kW m−2) k = constant for Surface Emissive Power correlation I


Article

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Experimental Study of LNG Pool Fire on Land in the Field - Industrial

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