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Numerical simulation study on the smoke spread process under the influence of the hollow floorboard in the logistics warehouse
Case Studies in Thermal Engineering 15 (2019) 100517
Contents lists available at ScienceDirect
Case Studies in Thermal Engineering journal homepage: http://www.elsevier.com/locate/csite
Numerical simulation study on the smoke spread process under the influence of the hollow floorboard in the logistics warehouse Shifan Tao, Xuan Dong, Yaxin Tan, Yansong Wei, Bowen Wang, Yufeng Huang * College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, Liaoning, 110136, PR China
A R T I C L E I N F O
A B S T R A C T
Keywords: Logistics warehouse Mezzanine Fire dynamics simulator Smoke spread Obscuration rate
In this paper, the fire hazard of logistics warehouses under the influence of the mezzanine shelves structure is studied. The relationship between the hollowing rate of the mezzanine shelves board and the fire smoke diffuse spread is evaluated, with the hollowing rates of 5%, 10%, 15%, 20%, 25%, 30% and 50% are mentioned. The obscuration rate, HRR (heat release rate) and Talarm (time of fire alarm) are obtained through FDS (Fire Dynamics Simulator) simulation study, and some valuable results are yield. The result shows that the lower level of hollowing rate, the greater variation of the obscuration rate value along with distance. Furthermore, the relationship be tween Talarm and hollowing rate was fitted by the functional relationship Y ¼ a=ða þ bx þ cx2 Þ. Except for smoke detector above the fire source R ¼ 0.69, the fitting results of other positions are better, and R values are all above 0.97. The research provides basic theoretical for the selecting and setting of fire alarm system in the warehouse, and the analysis and evaluation of warehouse fire risk which have decent practical significance and application value.
1. Introduction In recent years, with the rapid development of the new retail economy, the modernization of Chinese logistics industry [1] has been accelerated which brings great convenience to people’s lives. However, the accumulation of goods in the logistics warehouse leads to a higher fire load, and the logistics warehouse also has multiple functions of sorting and packaging which is different from the traditional warehouse with single storage function. It has a large number of lighting and operating equipment result in a certain fire risk. Once electrical failure or improper operation occurs which is easy to cause fire accidents. The fire will cause a great threat to the masses of workers’ personal safety in the logistics warehouse, so that the fire safety in the logistics warehouse cannot be ignored [2]. Meanwhile the logistics enterprises use mezzanine rack system [3] (Fig. 1a) to facilitate the storage and sort of goods, taking into account the structural stability and other factors, mezzanine rack system mostly uses hollow floor panel structure (Fig. 1b), which enhances the complexity of smoke spread process in the logistics warehouse. A large number of studies have been executed by many scholars about the fire hazard of warehouses [4–11], the architectural layout, fire resistance level of structures, fire extinguishing system [4,12], fire evolution and smoke spread [13,14] process. The fire detection alarm system of the three-dimensional elevated warehouse was discussed [7], it is proposed that the detection problems in areas such as elevated warehouses workshops can be solved by using intelligent image type fire detector in the elevated warehouse roadway, and using the distributed optical fiber temperature fire detector inside the shelf. The FDS software is used to carry out
* Corresponding author. E-mail address: [email protected] (Y. Huang). https://doi.org/10.1016/j.csite.2019.100517 Received 7 August 2019; Received in revised form 15 August 2019; Accepted 22 August 2019 Available online 26 August 2019 2214-157X/© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 1. Schematic diagram of mezzanine shelving system.
Fig. 2. Schematic diagram of fire source and detector location in fire simulation.
numerical simulation of different fire scenarios [10], and the relationship between temperature changes, thermal radiation changes and smoke development during fire development and the technology of fire fighting is analyzed. However, modern logistics ware houses mostly use mezzanine rack system, which is different from the traditional warehouses, the hollow floor panels of mezzanine rack system have great influences on the smoke spread process and the distribution of smoke layers. Therefore, this paper studies the influence of the floor panel structure of mezzanine rack system in the logistics warehouse on the smoke spreading process and the smoke obscuration rate. 2. Experiment 2.1. FDS simulation software In this paper, the fire dynamics simulation software FDS [15,16], which is authoritative in the fire safety engineering field, is used to predict the flow state of the smoke. FDS analyzes the process of smoke and heat transfer during combustion by using numerical methods to solve the turbulence equation. FDS [17] includes combustion models, thermal radiation models, pyrolysis models and so on. The combustion model adopts the mixed fraction model, which assumes that combustion is controlled by multiple factors. The mass ratio of all reactants to combustion products can be derived from the equation of state and empirical formula. The core algorithm of FDS partial differential equations is an explicit prediction-correction method. The accuracy of time and space is second-order, and the eddy current processing method is the large eddy simulation in the calculation. For the management of fire smoke flow field, it has a desirable precision.
2
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Table 1 Fire simulation condition statistics. Order
Hollowing rate
Growth rate α
Fire source power
1 2 3 4 5 6 7
5% 10% 15% 20% 25% 30% 50%
0.044
4.0 MW
Fig. 3. Heat release rate curve.
2.2. Models Taking a mezzanine logistics warehouse as example, a simulation is carried out in this paper. The logistics warehouse has a flat roof, the building’s height is 10.0 m, the mezzanine rack system in the warehouse is 9.0 m in height, and the height of the shelf single-layer is 3.0 m. During the experiment, typical combustible materials in the warehouse are investigated. Considering the fire scenarios in which corrugated paper and plastic pallet (polystyrene plastic) are ignited, the maximum heat release rate was 4.0 MW, when the fire reach the steady state according to Table 4.6.7 of Technical standards for smoke management systems in buildings GB51251-2017 [14]. According to fire growth coefficient in Table 4.6.10, “The fire category of filled mail bags, wooden shelf pallets, and foam is fast fire”, set α ¼ 0.044 kW/s2. As illustrated in, the small-scale model has a size of 10 m � 10 m � 10 m and spatial accuracy of 0.1 m � 0.1 m � 0.1 m. As shown in Fig. 2a, the design scene is the process the smoke spread after the fire on the bottom layer in the hollow structure of the second floor panel. Among them, the heights of the floor panel are 3.0 m and 6.0 m, and the floor panel is hollowed out. The hollowing rate are set to 5%, 10%, 15%, 20%, 25%, 30% and 50% respectively in 7 working conditions,as shown in Table 1. The calculate process of the hollowing rate is defined as the proportion of a hollowed-out area in the total area, as shown in Eq. (1). Ratehollow ¼
Shollow � 100% Stotal
(1)
Ratehollow is defined as the hollowing rate, Shollow is the proportion of the hollow-out area, Stotal is the total proportion of hollowing board. Three smoke detectors are set under the ceiling, as shown in Fig. 2a. The vertical height of the detector is 9.0 m, and its horizontal positions are denoted as position 1, position 2 and position 3, respectively. As shown in Fig. 2b, the smoke detector at position 1 is on the central axis of the fire source, and the one at position 2 is 5.0 m away from position 1, position 3 is diagonally from position 1, 5.0 m away from position 2. 2.3. Simulation environment These FDS models are simulated on the DELL computing server R930, which is equipped with two Intel Xeon E7-4809 v3 of 8-core CPU, 64 GB memory of 1333Mhz and 20 TB disk with RIAD (Redundant Array of Inexpensive Disks). The simulation time of the model is 300 s, the environment temperature is 25 � C, and the output parameter contains HRR(kW), obscuration rate (%/m), and so on.
3
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 4. The obscuration rate curve under different hollowing rates.
4
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 5. The obscuration rate reaching threshold curve.
5
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 6. Time of fire alarm curves with multiple hollowing out rates at different locations.
3. Results and discussion 3.1. Heat release rate This paper analyzes whether the difference in the hollowing rate of the building panel will affects the heat release rate. As shown in Fig. 3, the fire source type is set to t2, the heat release rate is set as 4 MW. The difference in heat release rate under different void ratios is not obvious, and it is basically a same curve. As a consequence, the combustion process is not affected by the hollowing of the floor panel. It can be considered that the influence of different hollowing rates on the action of the ceiling smoke alarm system is analyzed under the same situation of fire source. 3.2. Smoke obscuration rate The board hollowing rate affects the smoke velocity and obscuration rate. The formula, which is given in the FDS Technical Reference Guide [17] in page 88–89, for calculating the obscuration rate is shown as follow: � Obscuration½%=m� ¼ 1 e κm ρYc l � 100 (2) Where κm is the mass extinction coefficient, ρ is the density of the external gases in the ceiling jet, and l is the length over which the light is attenuated (1 m in this paper). For most flaming fuels, suggested value for κm is 8700 m2/kg � 1100 m2/kg at the wavelength of 633 nm [13]. dYc Ye ðtÞ Yc ðtÞ ¼ L=u dt
(3)
Where Ye is the mass fraction of smoke in the free stream (kg/kg), L is the characteristic length of the detector geometry (m), and u is the free stream velocity (m/s). The obscuration curve at position 1 is given by numerical simulation, as shown in Fig. 4a. With the decrease of the hollowing rate, the growth rate of its obscuration rate value decreases gradually, meanwhile peak of the obscuration rate that can be reached de creases. When the hollowing rate is 5%, the value of obscuration rate decreases significantly, which is distinct from other curves. While under other hollowing conditions, the obscuration rate curve also shows a decreasing trend with the increase of the hollowing rate. When the mezzanine rack system has a low hollowing rate, it is difficult for the smoke detection system installed in the ceiling to operate in time to achieve early warning due to the reduction of the smoke obscuration rate. As shown in Fig. 4b, when the obscuration rate curve at position 2 is under different hollowing rates, it can be seen that there is an obvious delay along the time axis in the rising section of the curve. The delay of the curve indicates that the amount of smoke passing through the board is different due to various hollowing rates. Thus, it affects the change of the smoke obscuration value. As shown in Fig. 4c, comparing the smoke obscuration change at position 2 with position 3, it can be seen that the obscuration value further decreases as the distance increases, and the smoke obscuration rate value can reach a stable condition only when the hollowing rate is 50%. The results imply that the lower hollowing rate also has an effect on the smoke ceiling jet, and the smoke layer located far away from the fire source is prone to settling. 3.3. Alarm time of fire smoke detection system According to the FDS Technical Reference and the fire smoke detection theory, the obscuration rate value of 3.24%/m is selected as 6
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Table 2 Fitting parameter statistics. Curve’s name
a
b
c
Adj. R-Square
Site1 Site2 Site3
17.8 46.5 55.9
0.0027 0.039 0.050
1.76e-4 2.48e-4 4.11e-4
0.68 0.99 0.97
the response value of the fire smoke detection system. As shown in Fig. 5a, it can be seen that the disturbance at the central axis of the fire source is obvious, and the respond time does not change significantly along with the hollowing rate, except that the hollowing rate is 50%. Furthermore, analyzing the change of the obscuration value away from the central axis of the fire source, and evaluating the time when the obscuration value reaches the response value under different distance conditions, it can be clearly seen that the time which the obscuration rate reaches the response value increases as the hollowing rate decreases. And comparing Fig. 5b with Fig. 5c, it indicates that the increase of distance further increases the time which the obscuration rate reaches the response value. 3.4. Establishment of empirical functional model According to the time of the obscuration rate reaching the response value given in Fig. 5,which is defined as Talarm (time of fire alarm). Then the hollowing rate and the the value of the Talarm is analyzed, as shown in Fig. 6a. The fitting curve is shown in Fig. 6b, and it can be seen from the figure that the fitting result is desirable. The functional relation is obtained by polynomial fitting, and the empirical model is established as shown in equation (4). � � Y ¼ a a þ bx þ cx2 (4) Among them, Y is the time when the obscuration rate reaches 3.24%/m, x is the hollowing rate, and a, b and c are the fitting parameters. As shown in Table 2, the time variation of the smoke obscuration rate near the central axis reaching the response value is mainly affected by the smoke disturbance, and the R value of its fitting curve is 0.68, with poor fitting effect (seeTable 1). In the area 5.0 m away from the central axis, the ceiling jet causes its smoke velocity to decrease, and the fitting result is good. The fitting R value at site 2 is 0.99, and that at site 3 is 0.97. Therefore, it can be considered that there is a cordial functional relationship between the time when the obscuration rate reaches the response value and the hollowing rate in the case of designing a fire. 4. Conclusions This paper mainly studied the influence of the logistics warehouse and the mezzanine rack system hollow panel on the action of the automatic fire alarm system at the ceiling position, analyzed the change of the obscuration rate, and gave a functional relationship between the time and the hollowing rate. Based on the results in this paper, the main conclusions show as follow: 1 The lower the hollowing rate level, the greater the variation of the obscuration rate with the distance, and the value of the obscuration rate under the condition of higher hollowing rate is less affected by the hollowing out condition. 2 As the distance from the detector to the central axis of the fire source increases, the effect of the hollowing rate on the value of the obscuration rate increases. 3 In the case of designing the fire source, there is a good functional relationship between the time and the hollowing rate, which isY ¼ a=ða þ bx þ cx2 Þ. The stable area of smoke motion, and the correlation of the functional relationship is good. Acknowledgement This work was supported by the Liaoning Education Department of Science and Technology research project (L201735), National undergraduate innovation and entrepreneurship training program (201810143087/110418087 and 201910143417)and Doctoral Initiation Fund of Shenyang Aerospace University (18YB02). The authors appreciate the supports deeply. References [1] Y. Gao, et al., The correlation between logistics industry and other industries: an evaluation of the empirical evidence from China, Asian J.Shipp.Logist.s 34 (1) (2018) 27–32. [2] S. Cebi, E. Ilbahar, Warehouse risk assessment using interval valued intuitionistic fuzzy ahp, Int. J. Anal.Hierarchy Process 10 (2) (2018). [3] MEZZANINE, General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China Standardization Administration of the People’s Republic of China, 2014. [4] Z.L. Magnone, J. Crocker, P. Pe~ na, Warehouse protection of exposed expanded group-A plastics with electronic sprinkler technology, in: 16th International Conference on Automatic Fire Detection’ (AUBE ’17), 2017. [5] J. D, G. D.T, Fire Detection in Warehouse Facilities, springer briefs in fire, Springer, New York, 2012, p. 67. [6] J. Frank, F. P.E, Preventing warehouse total loss caused by excessive ventilation, in: 16th International Conference on Automatic Fire Detection, 2017. [7] L. Xiangning, Application of fire detection and alarm system in logistics distribution center, Fire Sci. Technol. 35 (11) (2016) 1581–1583, 1592. [8] L.-r. Yang, S. Kao-ding, L. jing, An approach to successful application of Gustav’s method to the fire risk assessment of the E-commerce logistics warehouse, J. Saf. Environ. 18 (6) (2018) 2120–2125.
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[9] D. LI, Performance based fire design of logistics center warehouse, Fire Sci. Technol. (8) (2014) 882–885. [10] L. Chiyuan, Research on fire fighting techniques and tactics of large span and large space warehouse based on PyroSim simulation, J. Wuhan Univ. Technol. (Inf. Manag. Eng.) 40 (1) (2018) 1–4, 15. [11] X. Li, Study on countermeasures of logistics warehouse fire safety, J. Harbin. Univ.Commer.(Nat. Sci. Ed.) 27 (3) (2011) 373–376, 380. [12] J. D, G. D.T, Fire Detection in Warehouse Facilities. Fire Detection in Warehouse Facilities, Springer, New York, 2012, p. 67. [13] G.W. Mulholland, C. Croarkin, Specific extinction coefficient of flame generated smoke, Fire Mater. 24 (5) (2000) 227–230. [14] Technical Standards for Smoke Management Systems in Buildings 2017, Ministry of Housing and Urban-Rural Development of the People’s Repubic of China Standardization Administration of the People’s Republic of China. [15] K.B. McGrattan, G.P. Forney, Fire Dynamics Simulator User’s Guide, 2004. [16] McGrattan, K.B. and G.P. Forney, Fire Dynamics Simulator Technical Reference Guide Volume vol. 2: (Verification). [17] K.B. McGrattan, G.P. Forney, Fire Dynamics Simulator Technical Reference Guide, vol. 1, 2017, pp. 88–89. Mathematical Model.
8
Contents lists available at ScienceDirect
Case Studies in Thermal Engineering journal homepage: http://www.elsevier.com/locate/csite
Numerical simulation study on the smoke spread process under the influence of the hollow floorboard in the logistics warehouse Shifan Tao, Xuan Dong, Yaxin Tan, Yansong Wei, Bowen Wang, Yufeng Huang * College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, Liaoning, 110136, PR China
A R T I C L E I N F O
A B S T R A C T
Keywords: Logistics warehouse Mezzanine Fire dynamics simulator Smoke spread Obscuration rate
In this paper, the fire hazard of logistics warehouses under the influence of the mezzanine shelves structure is studied. The relationship between the hollowing rate of the mezzanine shelves board and the fire smoke diffuse spread is evaluated, with the hollowing rates of 5%, 10%, 15%, 20%, 25%, 30% and 50% are mentioned. The obscuration rate, HRR (heat release rate) and Talarm (time of fire alarm) are obtained through FDS (Fire Dynamics Simulator) simulation study, and some valuable results are yield. The result shows that the lower level of hollowing rate, the greater variation of the obscuration rate value along with distance. Furthermore, the relationship be tween Talarm and hollowing rate was fitted by the functional relationship Y ¼ a=ða þ bx þ cx2 Þ. Except for smoke detector above the fire source R ¼ 0.69, the fitting results of other positions are better, and R values are all above 0.97. The research provides basic theoretical for the selecting and setting of fire alarm system in the warehouse, and the analysis and evaluation of warehouse fire risk which have decent practical significance and application value.
1. Introduction In recent years, with the rapid development of the new retail economy, the modernization of Chinese logistics industry [1] has been accelerated which brings great convenience to people’s lives. However, the accumulation of goods in the logistics warehouse leads to a higher fire load, and the logistics warehouse also has multiple functions of sorting and packaging which is different from the traditional warehouse with single storage function. It has a large number of lighting and operating equipment result in a certain fire risk. Once electrical failure or improper operation occurs which is easy to cause fire accidents. The fire will cause a great threat to the masses of workers’ personal safety in the logistics warehouse, so that the fire safety in the logistics warehouse cannot be ignored [2]. Meanwhile the logistics enterprises use mezzanine rack system [3] (Fig. 1a) to facilitate the storage and sort of goods, taking into account the structural stability and other factors, mezzanine rack system mostly uses hollow floor panel structure (Fig. 1b), which enhances the complexity of smoke spread process in the logistics warehouse. A large number of studies have been executed by many scholars about the fire hazard of warehouses [4–11], the architectural layout, fire resistance level of structures, fire extinguishing system [4,12], fire evolution and smoke spread [13,14] process. The fire detection alarm system of the three-dimensional elevated warehouse was discussed [7], it is proposed that the detection problems in areas such as elevated warehouses workshops can be solved by using intelligent image type fire detector in the elevated warehouse roadway, and using the distributed optical fiber temperature fire detector inside the shelf. The FDS software is used to carry out
* Corresponding author. E-mail address: [email protected] (Y. Huang). https://doi.org/10.1016/j.csite.2019.100517 Received 7 August 2019; Received in revised form 15 August 2019; Accepted 22 August 2019 Available online 26 August 2019 2214-157X/© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 1. Schematic diagram of mezzanine shelving system.
Fig. 2. Schematic diagram of fire source and detector location in fire simulation.
numerical simulation of different fire scenarios [10], and the relationship between temperature changes, thermal radiation changes and smoke development during fire development and the technology of fire fighting is analyzed. However, modern logistics ware houses mostly use mezzanine rack system, which is different from the traditional warehouses, the hollow floor panels of mezzanine rack system have great influences on the smoke spread process and the distribution of smoke layers. Therefore, this paper studies the influence of the floor panel structure of mezzanine rack system in the logistics warehouse on the smoke spreading process and the smoke obscuration rate. 2. Experiment 2.1. FDS simulation software In this paper, the fire dynamics simulation software FDS [15,16], which is authoritative in the fire safety engineering field, is used to predict the flow state of the smoke. FDS analyzes the process of smoke and heat transfer during combustion by using numerical methods to solve the turbulence equation. FDS [17] includes combustion models, thermal radiation models, pyrolysis models and so on. The combustion model adopts the mixed fraction model, which assumes that combustion is controlled by multiple factors. The mass ratio of all reactants to combustion products can be derived from the equation of state and empirical formula. The core algorithm of FDS partial differential equations is an explicit prediction-correction method. The accuracy of time and space is second-order, and the eddy current processing method is the large eddy simulation in the calculation. For the management of fire smoke flow field, it has a desirable precision.
2
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Table 1 Fire simulation condition statistics. Order
Hollowing rate
Growth rate α
Fire source power
1 2 3 4 5 6 7
5% 10% 15% 20% 25% 30% 50%
0.044
4.0 MW
Fig. 3. Heat release rate curve.
2.2. Models Taking a mezzanine logistics warehouse as example, a simulation is carried out in this paper. The logistics warehouse has a flat roof, the building’s height is 10.0 m, the mezzanine rack system in the warehouse is 9.0 m in height, and the height of the shelf single-layer is 3.0 m. During the experiment, typical combustible materials in the warehouse are investigated. Considering the fire scenarios in which corrugated paper and plastic pallet (polystyrene plastic) are ignited, the maximum heat release rate was 4.0 MW, when the fire reach the steady state according to Table 4.6.7 of Technical standards for smoke management systems in buildings GB51251-2017 [14]. According to fire growth coefficient in Table 4.6.10, “The fire category of filled mail bags, wooden shelf pallets, and foam is fast fire”, set α ¼ 0.044 kW/s2. As illustrated in, the small-scale model has a size of 10 m � 10 m � 10 m and spatial accuracy of 0.1 m � 0.1 m � 0.1 m. As shown in Fig. 2a, the design scene is the process the smoke spread after the fire on the bottom layer in the hollow structure of the second floor panel. Among them, the heights of the floor panel are 3.0 m and 6.0 m, and the floor panel is hollowed out. The hollowing rate are set to 5%, 10%, 15%, 20%, 25%, 30% and 50% respectively in 7 working conditions,as shown in Table 1. The calculate process of the hollowing rate is defined as the proportion of a hollowed-out area in the total area, as shown in Eq. (1). Ratehollow ¼
Shollow � 100% Stotal
(1)
Ratehollow is defined as the hollowing rate, Shollow is the proportion of the hollow-out area, Stotal is the total proportion of hollowing board. Three smoke detectors are set under the ceiling, as shown in Fig. 2a. The vertical height of the detector is 9.0 m, and its horizontal positions are denoted as position 1, position 2 and position 3, respectively. As shown in Fig. 2b, the smoke detector at position 1 is on the central axis of the fire source, and the one at position 2 is 5.0 m away from position 1, position 3 is diagonally from position 1, 5.0 m away from position 2. 2.3. Simulation environment These FDS models are simulated on the DELL computing server R930, which is equipped with two Intel Xeon E7-4809 v3 of 8-core CPU, 64 GB memory of 1333Mhz and 20 TB disk with RIAD (Redundant Array of Inexpensive Disks). The simulation time of the model is 300 s, the environment temperature is 25 � C, and the output parameter contains HRR(kW), obscuration rate (%/m), and so on.
3
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 4. The obscuration rate curve under different hollowing rates.
4
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 5. The obscuration rate reaching threshold curve.
5
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Fig. 6. Time of fire alarm curves with multiple hollowing out rates at different locations.
3. Results and discussion 3.1. Heat release rate This paper analyzes whether the difference in the hollowing rate of the building panel will affects the heat release rate. As shown in Fig. 3, the fire source type is set to t2, the heat release rate is set as 4 MW. The difference in heat release rate under different void ratios is not obvious, and it is basically a same curve. As a consequence, the combustion process is not affected by the hollowing of the floor panel. It can be considered that the influence of different hollowing rates on the action of the ceiling smoke alarm system is analyzed under the same situation of fire source. 3.2. Smoke obscuration rate The board hollowing rate affects the smoke velocity and obscuration rate. The formula, which is given in the FDS Technical Reference Guide [17] in page 88–89, for calculating the obscuration rate is shown as follow: � Obscuration½%=m� ¼ 1 e κm ρYc l � 100 (2) Where κm is the mass extinction coefficient, ρ is the density of the external gases in the ceiling jet, and l is the length over which the light is attenuated (1 m in this paper). For most flaming fuels, suggested value for κm is 8700 m2/kg � 1100 m2/kg at the wavelength of 633 nm [13]. dYc Ye ðtÞ Yc ðtÞ ¼ L=u dt
(3)
Where Ye is the mass fraction of smoke in the free stream (kg/kg), L is the characteristic length of the detector geometry (m), and u is the free stream velocity (m/s). The obscuration curve at position 1 is given by numerical simulation, as shown in Fig. 4a. With the decrease of the hollowing rate, the growth rate of its obscuration rate value decreases gradually, meanwhile peak of the obscuration rate that can be reached de creases. When the hollowing rate is 5%, the value of obscuration rate decreases significantly, which is distinct from other curves. While under other hollowing conditions, the obscuration rate curve also shows a decreasing trend with the increase of the hollowing rate. When the mezzanine rack system has a low hollowing rate, it is difficult for the smoke detection system installed in the ceiling to operate in time to achieve early warning due to the reduction of the smoke obscuration rate. As shown in Fig. 4b, when the obscuration rate curve at position 2 is under different hollowing rates, it can be seen that there is an obvious delay along the time axis in the rising section of the curve. The delay of the curve indicates that the amount of smoke passing through the board is different due to various hollowing rates. Thus, it affects the change of the smoke obscuration value. As shown in Fig. 4c, comparing the smoke obscuration change at position 2 with position 3, it can be seen that the obscuration value further decreases as the distance increases, and the smoke obscuration rate value can reach a stable condition only when the hollowing rate is 50%. The results imply that the lower hollowing rate also has an effect on the smoke ceiling jet, and the smoke layer located far away from the fire source is prone to settling. 3.3. Alarm time of fire smoke detection system According to the FDS Technical Reference and the fire smoke detection theory, the obscuration rate value of 3.24%/m is selected as 6
Case Studies in Thermal Engineering 15 (2019) 100517
S. Tao et al.
Table 2 Fitting parameter statistics. Curve’s name
a
b
c
Adj. R-Square
Site1 Site2 Site3
17.8 46.5 55.9
0.0027 0.039 0.050
1.76e-4 2.48e-4 4.11e-4
0.68 0.99 0.97
the response value of the fire smoke detection system. As shown in Fig. 5a, it can be seen that the disturbance at the central axis of the fire source is obvious, and the respond time does not change significantly along with the hollowing rate, except that the hollowing rate is 50%. Furthermore, analyzing the change of the obscuration value away from the central axis of the fire source, and evaluating the time when the obscuration value reaches the response value under different distance conditions, it can be clearly seen that the time which the obscuration rate reaches the response value increases as the hollowing rate decreases. And comparing Fig. 5b with Fig. 5c, it indicates that the increase of distance further increases the time which the obscuration rate reaches the response value. 3.4. Establishment of empirical functional model According to the time of the obscuration rate reaching the response value given in Fig. 5,which is defined as Talarm (time of fire alarm). Then the hollowing rate and the the value of the Talarm is analyzed, as shown in Fig. 6a. The fitting curve is shown in Fig. 6b, and it can be seen from the figure that the fitting result is desirable. The functional relation is obtained by polynomial fitting, and the empirical model is established as shown in equation (4). � � Y ¼ a a þ bx þ cx2 (4) Among them, Y is the time when the obscuration rate reaches 3.24%/m, x is the hollowing rate, and a, b and c are the fitting parameters. As shown in Table 2, the time variation of the smoke obscuration rate near the central axis reaching the response value is mainly affected by the smoke disturbance, and the R value of its fitting curve is 0.68, with poor fitting effect (seeTable 1). In the area 5.0 m away from the central axis, the ceiling jet causes its smoke velocity to decrease, and the fitting result is good. The fitting R value at site 2 is 0.99, and that at site 3 is 0.97. Therefore, it can be considered that there is a cordial functional relationship between the time when the obscuration rate reaches the response value and the hollowing rate in the case of designing a fire. 4. Conclusions This paper mainly studied the influence of the logistics warehouse and the mezzanine rack system hollow panel on the action of the automatic fire alarm system at the ceiling position, analyzed the change of the obscuration rate, and gave a functional relationship between the time and the hollowing rate. Based on the results in this paper, the main conclusions show as follow: 1 The lower the hollowing rate level, the greater the variation of the obscuration rate with the distance, and the value of the obscuration rate under the condition of higher hollowing rate is less affected by the hollowing out condition. 2 As the distance from the detector to the central axis of the fire source increases, the effect of the hollowing rate on the value of the obscuration rate increases. 3 In the case of designing the fire source, there is a good functional relationship between the time and the hollowing rate, which isY ¼ a=ða þ bx þ cx2 Þ. The stable area of smoke motion, and the correlation of the functional relationship is good. Acknowledgement This work was supported by the Liaoning Education Department of Science and Technology research project (L201735), National undergraduate innovation and entrepreneurship training program (201810143087/110418087 and 201910143417)and Doctoral Initiation Fund of Shenyang Aerospace University (18YB02). The authors appreciate the supports deeply. References [1] Y. Gao, et al., The correlation between logistics industry and other industries: an evaluation of the empirical evidence from China, Asian J.Shipp.Logist.s 34 (1) (2018) 27–32. [2] S. Cebi, E. Ilbahar, Warehouse risk assessment using interval valued intuitionistic fuzzy ahp, Int. J. Anal.Hierarchy Process 10 (2) (2018). [3] MEZZANINE, General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China Standardization Administration of the People’s Republic of China, 2014. [4] Z.L. Magnone, J. Crocker, P. Pe~ na, Warehouse protection of exposed expanded group-A plastics with electronic sprinkler technology, in: 16th International Conference on Automatic Fire Detection’ (AUBE ’17), 2017. [5] J. D, G. D.T, Fire Detection in Warehouse Facilities, springer briefs in fire, Springer, New York, 2012, p. 67. [6] J. Frank, F. P.E, Preventing warehouse total loss caused by excessive ventilation, in: 16th International Conference on Automatic Fire Detection, 2017. [7] L. Xiangning, Application of fire detection and alarm system in logistics distribution center, Fire Sci. Technol. 35 (11) (2016) 1581–1583, 1592. [8] L.-r. Yang, S. Kao-ding, L. jing, An approach to successful application of Gustav’s method to the fire risk assessment of the E-commerce logistics warehouse, J. Saf. Environ. 18 (6) (2018) 2120–2125.
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S. Tao et al.
[9] D. LI, Performance based fire design of logistics center warehouse, Fire Sci. Technol. (8) (2014) 882–885. [10] L. Chiyuan, Research on fire fighting techniques and tactics of large span and large space warehouse based on PyroSim simulation, J. Wuhan Univ. Technol. (Inf. Manag. Eng.) 40 (1) (2018) 1–4, 15. [11] X. Li, Study on countermeasures of logistics warehouse fire safety, J. Harbin. Univ.Commer.(Nat. Sci. Ed.) 27 (3) (2011) 373–376, 380. [12] J. D, G. D.T, Fire Detection in Warehouse Facilities. Fire Detection in Warehouse Facilities, Springer, New York, 2012, p. 67. [13] G.W. Mulholland, C. Croarkin, Specific extinction coefficient of flame generated smoke, Fire Mater. 24 (5) (2000) 227–230. [14] Technical Standards for Smoke Management Systems in Buildings 2017, Ministry of Housing and Urban-Rural Development of the People’s Repubic of China Standardization Administration of the People’s Republic of China. [15] K.B. McGrattan, G.P. Forney, Fire Dynamics Simulator User’s Guide, 2004. [16] McGrattan, K.B. and G.P. Forney, Fire Dynamics Simulator Technical Reference Guide Volume vol. 2: (Verification). [17] K.B. McGrattan, G.P. Forney, Fire Dynamics Simulator Technical Reference Guide, vol. 1, 2017, pp. 88–89. Mathematical Model.
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