Application of FDS to Predicting Fire Spread in a Horizontal Cable Tray

Hyo-Yeon Jang; Ho-Sik Han; Cheol-Hong Hwang; Sang Kyu Lee

doi:10.7731/KIFSE.030659d0

Int J Fire Sci Eng > Volume 36(4); 2022 > Article

Jang, Han, Hwang, and Lee: Application of FDS to Predicting Fire Spread in a Horizontal Cable Tray

Research Paper

International Journal of Fire Science and Engineering 2022;36(4):34-44.

Published online: December 31, 2022

DOI: https://doi.org/10.7731/KIFSE.030659d0

Application of FDS to Predicting Fire Spread in a Horizontal Cable Tray

Hyo-Yeon Jang¹, Ho-Sik Han¹, Cheol-Hong Hwang^2,^†, Sang Kyu Lee³

¹Department of Disaster Prevention, Graduate School, Daejeon University, 62 Daehak-ro, Dong-gu, Daejeon 34520, Republic of Korea

²Department of Fire and Disaster Prevention, Daejeon University, 62 Daehak-ro, Dong-gu, Daejeon 34520, Republic of Korea

³Department of Reactor System Evaluation, Korea Institute of Nuclear Safety, 62 Gwahak-ro, Yuseong-gu, Daejeon 34142, Republic of Korea

^†Corresponding Author, TEL: +82-42-280-2592, FAX: +82-42-280-2596, E-Mail: chehwang@dju.ac.kr

Received December 16, 2022 Revised December 28, 2022 Accepted December 30, 2022

Abstract

With regard to cable tray fires, comparing the prediction results obtained through fire simulations with the measurement results from fire experiments conducted under real-life conditions is essential to ensure the predictive reliability of the model. In determining the validity of the calculation process and procedure performed in a specific fire experiment, the result may vary depending on the user’s capability. This is because of the high user dependence due to the characteristics of the fire model. In this study, we examined whether the actual spread of fire could be similarly predicted when the physical quantities of cable tray fires suggested by previous studies were applied. We also applied a fire modeling method considering the arrangement of cables to predict appropriate fire spread in a nuclear power plant and then performed a comparative analysis of the results and the experimental results. The prediction results, in terms of the fire spread time, varied depending on the arrangement type (whether loosely and tightly arranged) of cables. Finally, we devised a process of accurately predicting the heat release rate (HRR) curve as compared to the experimental results by using a sensitivity analysis of the input parameters (density, specific heat, and thermal conductivity) corresponding to the physical properties considered in the fire model.

Keywords: Fire simulation; FDS; Validation and verification; Model uncertainty; Cable tray fire

1. Introduction

Fire modeling-based research has recently been conducted to quantitatively evaluate the fire-induced damage and the safety of structures and system equipment to ensure the fire safety of nuclear power plants (NPPs) in South Korea as well as worldwide[1]. Depending on the prediction accuracy and uncertainty of the fire model, the prediction results of fire modeling are required to go through comparison and validation stages based on measurement results from various fire experiments[2]. Accordingly, to investigate the fire modeling conditions and prediction accuracy by performing real-scale fire experiments through fire environment simulations of NPPs, the PRISME project involving 20 organizations from 12 countries, led by the French Institute for Radiological Protection and Nuclear Safety (French Institut de Radioprotection et de Sûreté Nucléaire, IRSN), is underway[3,4]. The PRISME project aims to understand the phenomena of fire and heat more substantially on the basis of simulations of environments similar to those of actual NPPs. Ultimately, the project aims to quantitatively analyze and evaluate the NPP fire risks. The objective is to understand and evaluate the propagation and spread of smoke and high-temperature products generated mainly in cable, cabinet, and multi-compartment fires[5,6]. The assessment on whether the procedure in the fire model and calculation process for specific conditions are valid may vary considerably depending on the user’s capability. The result of the validity analysis of fire modeling can vary depending on the fire scenario, predicted physical quantities, and allowed accuracy level, and the scalability of the validity and analysis result of the prediction may vary significantly depending on the user’s capability[7].

Many fire models applied to NPP fire modeling may have different accuracies because they are all developed individually based on theories, assumptions, and numerical models suitable for various applications. For instance, Fire Dynamics Tools (FDT), an algebraic model, was developed by the US Nuclear Regulatory Commission (NRC) and is available in the form of a Microsoft Excel spreadsheet created based on basic principles and empirical correlation equations of fire dynamics[8]. Consolidated model of Fire growth And Smoke Transport (CFAST), developed by the US National Institute of Standards and Technology (NIST), is a zone model that divides the fire into a high-temperature upper zone and low-temperature lower zone and considers the correlations of heat and mass flow velocity through their interface while assuming uniform physical properties[9-11]. Although it has many limitations in elucidating the time- and space-based fire characteristics when fire occurs in an NPP, it is a useful approach for roughly predicting the effects of various combustion conditions with a relatively simple numerical technique. The Fire Dynamics Simulator (FDS) provided by the NIST is the most widely applied tool worldwide. As it facilitates numerical calculations for a three-dimensional space, local phenomena can be predicted; however, the influence of physical input information is substantial as in the case of the CFAST. Basic knowledge of computational fluid dynamics is required to appropriately consider additional input information.

The detailed setting of the procedures in the actual fire modeling process relies on the user’s numerical and physical modeling knowledge. Therefore, the minimization of high user dependence (which is inherent in fire modeling) and objective evaluation of fire modeling are the most crucial factors in NPP fire risk assessment. For the accurate use of FDS, it is necessary to evaluate the validity of the numerical model included in the FDS (validation) and, simultaneously, examine whether an appropriate prediction accuracy is ensured within given ranges of numerical and physical input parameters for a “given fire scenario” (verification)[12]. If the input uncertainty (or sensitivity analysis result) and the model uncertainty are thus analyzed, the uncertainty of the fire model can be expressed as the input parameters’ uncertainty and the model error[13]. In the case of input uncertainty, the input parameters are selected from statistical distributions or previous research and, in both cases, the calculation results include uncertainties. The process of determining the extent to which each input parameter affects the calculation result is called sensitivity analysis. As for the model error, the assumption for solving the model equation is simplified in the process of physical phenomena. As complex fire phenomena are numerically simplified, the result may not be accurate[14]. It is impractical to evaluate the error by simply adding the error of each factor, and it must be predicted through the validation and verification procedures[12,15].

To accurately analyze cable fires that can occur in NPPs, NUREG/CR-7010 (CRISTIFIRE) has suggested spread patterns and rates that can be empirically calculated in fire experiments[5]. On this basis, the Flame Spread over Horizontal Cable Trays (FLASH-CAT ) model, which can appropriately implement cable tray fires, has been proposed[16]. However, the FLASH-CAT model is an early model for setting up cable tray fires in fire modeling, and many researchers have studied its application feasibility and proposed a modified FLASH-CAT model[17]. In the Fire Performance of Electric Cable (FIPEC) project, which involves the evaluation of the performance of electric cables, the fire spread phenomenon has been discussed for various types of loading, handling, grouping, and spacing of cables installed in NPPs[18-20]. However, cable tray fires have a multitude of variables to be considered, such as the degree of aging of the cable, loading type, arrangement, use, and size of the ignition source. Therefore, although experimental data have been reported in South Korea, and internationally, based on changes in the conditions of various factors considering the actual NPP environment[21], realizing accurate prediction is challenging. Having similar geometries and settings to the real situation while simplifying complex fire scenarios can contribute to the accuracy of the prediction results. Therefore, the prediction accuracy has been improved by examining the physical quantity predicted according to the setting method of cable arrangement based on experimental data. Furthermore, we previously compared and analyzed the results with certain fire experimental results considering the measurement uncertainty of physical properties presented in the literature[22,23] by applying FDS. To this end, we selected PRISME-2 CFSS-4 as the fire experiment conditions and used a fire model, FDS version 6.7.0, to perform fire modeling[24,25]. Therefore, the arrangement conditions and physical properties of the cable tray used in the experiment were simultaneously considered and, on this basis, we examined whether the predicted data is adequately accurate as compared to the experimental data.

2. Description of Simulation and Conditions

2.1 Computational domain and approach

Compartmental fire experiments may differ slightly from fire experiments performed in open spaces[26,27]. In particular, as regards a fire experiment performed without sufficiently setting up openings or performed inside a closed compartment, the numerical analysis result of fire modeling performed with ventilation conditions of insufficient openings or ventilation may include uncertainty[28]. Therefore, analyses were performed to examine the prediction accuracy for the experimental results measured under an open condition. The experimental results of PRISME-2 CFSS-4 were considered to perform fire modeling on a cable tray fire; the experiment was performed with the goal of measuring the heat release rate (HRR, kW) for the fire in an open space. Figure 1(a) shows the computational domain and schematic diagram obtained from the implementation of the experimental space and exhaust hood where CFSS-4 was performed. To minimize the uncertainty in the numerical analysis results due to the boundary conditions, we set the computational domain to 0.05 m based on the sensitivity analysis of the grid size for an open space of 6 m (x) × 6 m (y) × 10 m (z). The cable tray selected as a fire source was set in the center of the exhaust hood and the VENT option was applied at a position of 10 m in the height direction to simulate the suction flow rate of the exhaust hood. The suction flow rate of the exhaust hood was set appropriately using repeated calculations based on the information that the flow rate was in the range of 10,000-25,000 m³/h. A stack of five cable trays with a thickness of 0.05 m and a cable length of 2.3 m was considered, and an insulated wall was installed behind the tray.

The arrangement of the cables was considered for the accurate prediction of HRR arising from the fire in the stack of cable trays. PRISME-2 CFSS-4 provides a brief description of the cable trays that are set up to implement the cable fire. Notably, many cables located in the cable trays are arranged in a “loosely” or “tightly” arranged condition; this experiment corresponds to the “loosely” condition. This is to set up empty gaps between cables to consider the spread, which may be the most important element in a cable tray fire, so that the fire can spread smoothly. In other words, the same geometries and settings as in a real situation are required to model the cable tray fire by using a fire model. In reality, however, the cables installed in the cable trays are not fixed and have irregular arrangements. Therefore, it is difficult to perform modeling through a fire model with the same arrangements and settings of cables as the real situation, and it may incur huge computational costs. Therefore, in the case of CFSS-4, we considered the arrangements (I)-(IV), as shown in Figure 1(b), to simulate the cables arranged “loosely” in the multi-stacked horizontal cable trays. The size of the cable trays setup is as illustrated in the figure, and the fire was ignited using a propane sand burner of approximately 80 kW having an area of 0.09 m² (0.30 m×0.30 m) at the bottom.

2.2 Fire spread approach in the fire model

It is relatively difficult to perform numerical analysis on a cable tray fire, which is a typical fire type that may occur in NPPs, as compared to pool fires. Detailed experimental analyses are also difficult. Therefore, appropriate prediction methods have been suggested to simulate cable fire, which is accompanied by complex phenomena such as pyrolysis reaction, fire spread rate, and fire pattern. The simplest method of predicting fire spread is implemented using the fire spread rate obtained through experiments. It may produce an appropriate prediction result in simple cable tray fire modeling; however, the result may be remarkably different from the actual situation in fire modeling for complex fire environments. In contrast, one may use a pyrolysis model. As various factors for the pyrolysis of the cables are input, it can predict actual cable fire; however, it incurs increasingly high computational costs with improving accuracy. It requires a large amount of input information, including reaction parameters, combustion properties, and thermal properties.

Among the various methods, this study used a simple pyrolysis model. It is simple as compared to most cable fire setting methods and can derive appropriate prediction results. As the factor that has the greatest effect on the prediction result among the input parameters of the fire model, the heat release rate per unit area (HRRPUA, in kW/m²), which was suggested in the literature to ensure reliability, was applied. Specifically, a recommended input range of HRRPUA was provided on the basis of the results of the experiments performed on various thermoplastic and thermoset cables[5]. Furthermore, among the input parameters required for accurate cable fire modeling, the ignition temperature is an important parameter that can determine the spread rate and pattern of fire. Therefore, the measurement value for the ignition temperature of various cables suggested by NUREG/CR-6850 was applied. In other words, the fire simulations were set up to ignite and spread fire when the surface temperature of the cable trays is at least 218 °C. After securing the reliability and accuracy of fire modeling for cable fire by applying density, specific heat, and thermal conductivity[5,29] to simultaneously consider the physical properties of cable fire, we performed a comparative analysis. Table 1 lists the main parameters considered in this study by applying the simple pyrolysis model.

3. Results and Discussion

Figure 2 illustrates and compares the prediction results of the simple pyrolysis model and the FLASH-CAT model, which appropriately considers cable tray fire spread, as aforementioned. In previous research, adequate prediction performance was expected through the application of experimentally obtained physical quantities, such as ignition temperature, which can control the fire phenomenon of cables measured in actual experiments to simulate cable tray fire. Therefore, the mean values of the data that can be derived from several experiments were provided, and the inherent values were set according to the cable type (thermoplastic cable). While implementing cables as components of the cable trays, the FLASH-CAT model was set to OBST, which has a block shape. It can accurately predict the fire spread because the fire ignition time and spread time of the cable trays installed in accordance with a height are directly applied at the same time as the ignition of the propane burner set up at the bottom. However, Figure 2 indicates that the complex fire phenomenon occurring under each condition could not be considered because the information used was based on the averaging of the data obtained from various experiments. Hence, in the FLASH-CAT model, the HRR is significantly underestimated and the fire spread time is not predicted properly. In other words, because the user specifies the ignition time and spread rate of cable tray fire, the prediction results are unsuitable for application to the fire modeling of CFSS-4. Therefore, we examined the validity of applying the simple pyrolysis model to obtain the HRR values comparable to those measured in the fire experiment by using various physical quantities obtained through cable fire experiments.

The CFSS-4 fire modeling was performed for various arrangements of the cable trays having varying computational costs and the efficiencies of configuration. Figure 3 shows the HRR results predicted according to the methods of implementing the cable arrangement, respectively, and their comparison with the experimental results. For the input information of the simple pyrolysis model, we used the information provided in NUREG/CR-6850 and NUREG/CR-7010. The results indicate that there is no significant difference in the prediction results between various cable arrangement methods. Furthermore, despite applying the input parameters for cable fire obtained from the NUREG report, there is a general trend of underprediction as compared with the experimental results. First, when Arrangements (II)-(IV), which correspond to a single block shape of OBST and discrete shapes of OBST, are compared, it is found that the maximum values of HRR are similar to the experimental value. However, the prediction results are highly inaccurate when examined from the perspective of cable fire spread. Moreover, the prediction result for Arrangement (III) among the discrete shapes of OBST considering the arrangement of the cables placed in the actual cable trays is relatively closer to the HRR values measured in the experiment than the results of Arrangements (II) and (IV). Furthermore, Arrangement (III) shows the fastest fire spread rate and a similar result to the experiment. Therefore, in this study, Arrangement (III) was selected as the cable arrangement for the cable tray fire modeling of CFSS-4.

Figure 4 illustrates the spread of fire for various cable arrangements considered in the modeling the cable trays. The figure shows that the fire spread pattern is considerably different over time. First, as regards the FLASH-CAT model, among various methods for implementing cable tray fire, the method shown in Figure 4(a) can be applied because the user specifies the time at which the fire spreads to the multi-stacked cable trays in the height direction after the ignition of the propane burner set up at the bottom of the cable trays. However, as shown in the figure, upon applying the simple pyrolysis model, in which the fire spread time of the cable trays cannot be artificially specified, the fire can barely spread to the upper part because there is no gap (void) between the cables. Specifically, in the case of the block, the fire does not spread through the multi-stacked cable trays in the height direction but spreads along with explosive combustion toward the top as the size of the fire itself increases. However, in the case of Figure 4(b), it is found that the fire spreads in stages to the upper cables through the void, thus spreading with a V-shaped pattern.

In Figure 3, the HRR results obtained based on the arrangement of cables are compared with the experimental results. As a result, it is found that Arrangement (II) may be the most appropriate; however, the HRR values predicted through fire modeling is relatively small compared with those measured in the experiment. In NUREG/CR-7010, the HRRPUA of thermoplastic cable was suggested through radiant panel experiments, and it has a range of 200-300 kW/m²[5]. Accordingly, the HRRPUA occurring in the cable trays was compared with the prediction result obtained by inputting the mean value (250 kW/m²) and a conservative value (300 kW/m²). Figure 5 shows the HRR predicted according to the change in the HRRPUA, the main input parameter of the simple pyrolysis model. As a result, it is found that when HRRPUA is set to 300 kW/m², the prediction result is the closest to the HRR values measured in the experiment. However, as for the fire growth rate, the results predicted by the fire model was slightly slower than the HRR values measured in the experiment. The parameters that may affect the fire growth rate in the prediction results of fire modeling include HRRPUA, ignition temperature, and physical properties (density, specific heat, and thermal conductivity). In other words, the fire growth rate can be determined on the basis of most of the input parameters entered into the simple pyrolysis model.

Therefore, we performed sensitivity analysis considering the measurement uncertainty of the previously reported physical property values for the input parameters applied to the simple pyrolysis model[22,23]. Determining the measurement uncertainty while performing experiments to measure the density, specific heat, and thermal conductivity applied as input parameters in this study is challenging. Therefore, the sensitivity analysis was performed using the uncertainties of the physical properties of polymers, the main material of the cables, as listed in Table 2[22,23]. For example, when the measurement uncertainty of an input parameter was 10% in the sensitivity analysis, fire modeling was performed by changing the physical quantity by −10% and +10%.

Figure 6 compares and shows the HRR values predicted using the sensitivity analysis of each physical property and the HRR values obtained via the experiment. As a result of sensitivity analysis considering the measurement uncertainties of density, specific heat, and thermal conductivity, it is found that a higher HRR value is shown when a value of +5% is applied in the case of density. When an uncertainty of −10% is considered for specific heat, the predicted HRR is similar to the HRR measured in the experiment. When a value of −10% compared with the measurement value is used in the case of thermal conductivity, the result is closer to the HRR measured in CFSS-4. On the basis of such sensitivity analysis or parameter study, we finally selected the parameters applied to the simple pyrolysis model.

Figure 7 presents a comparison of the HRR values measured in the experiment of PRISME-2 CFSS-4 and the HRR values predicted based on the physical quantities selected through the sensitivity analysis in the simple pyrolysis model. Although a relatively simple pyrolysis model was considered, the experimental HRR was accurately predicted when the input parameters (HRRPUA, ignition temperature, density, specific heat, and thermal conductivity) were selected on the basis of the parameter study. There were some differences in terms of fire growth rate; however, the derived prediction results were appropriate, considering that we only used data from the literature[22,23].

4. Conclusion

In this study, a review of the setting method for the cable tray fire and a study on appropriate utilization were conducted targeting the main setting parameters to ensure the accuracy of nuclear power plant fire modeling. To examine the application validity of fire modeling and improve prediction accuracy, we used the empirical results of the fire experiment performed in the PRISME project. We examined the uncertainties associated with various parameters (loading type, arrangement, and use) of cable trays and the thermal properties of cables.

Cables are classified into thermoplastic and thermoset to predict the fire spread of cable trays appropriately in fire modeling, and on this basis, a FLASH-CAT model was implemented using experimentally confirmed fire spread factors, such as average HRR and fire spread time and rate. However, accurate prediction may be difficult depending on the change in the conditions, and it is necessary to apply more accurate input parameters using the information on HRR and thermal properties for specific cases. Large uncertainties were noted in the process of simplifying the fire model in complex fire experiments. Therefore, if the setting method of “tightly” or “loosely” is applied appropriately to the arrangement of cables for the same arrangement and setting as the actual situation for the accuracy of fire modeling, the fire spread time measured in the actual experiment can be accurately predicted.

The numerical analysis result of cable tray fire changed significantly depending on the setting of HRRPUA, which most affected the prediction result among the input parameters of the fire simulation. When a conservative result of 300 kW/m² was used among various measurement values of thermal plastics reported in the literature, the HRR measured in the empirical experiment was accurately predicted. Furthermore, when the measurement uncertainties of density, specific heat, and thermal conductivity, which contribute to the physical properties among various input parameters, were considered, it was found that they affect the fire spread time and fire duration from the perspective of fire spread.

In conclusion, when simulating cable tray fire, the information provided in the literature should be used appropriately to ensure the objectivity and accuracy of the input parameters that could affect the fire modeling result. To accurately predict complex cable tray fire phenomena, sequentially reviewing the experimental process is necessary. Ultimately, if accurate input parameter values are not set in a fire simulation, the derived prediction result would remarkably differ from the actual fire phenomenon. Therefore, we improved prediction accuracy based on experimental data by examining the fire scenario setting methods and examining the uncertainties of the experimentally obtained input parameters.

Acknowledgments

This work was supported by the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety (KOFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 1705002).

Notes

Conflicts of Interest

The authors have no conflicts of interest to declare.

Author Contributions

Conceptualization, C.H.H.; methodology, C.H.H. and H.S.H.; software, H.Y.J.; validation, H.Y.J.; formal analysis, H.S.H. and H.Y.J.; investigation, C.H.H.; data curation, H.Y.J.; writing—original draft preparation, H.Y.J.; writing—review and editing, C.H.H.; visualization, H.Y.J.; project administration, C.H.H ; funding acquisition, C.H.H. All authors have read and agreed to the published version of the manuscript.

Figure 1

Schematic diagram of the three-dimensional computational domain and setting method of cables for cable tray fire modeling.

Figure 2

Heat release rate (HRR) based on the application of simple pyrolysis and FLASH-CAT models.

Figure 3

Influence of the heat release rate (HRR) on the modeled arrangement of cables with the simple pyrolysis model applied.

Figure 4

Comparison of fire spread according to modeled arrangement of cables through Smokeview.

Figure 5

Comparison of predicted heat release rate per unit area.

Figure 6

Sensitivity analysis result for input parameters of physical properties applied to cables.

Figure 7

Predicted heat release rate according to selection input parameters through sensitivity analysis.

Table 1

Input Parameters of a Simple Pyrolysis Model Applied in PRISME-2 CFSS-4 Fire Modeling

Input Parameters	Value	Source
Heat Release Rate Per Unit Area (HRRPUA)	300 kW/m²	NUREG/CR-7010
Ignition Temperature	218 °C	NUREG/CR-6850
Density	1,380 kg/m³	NUREG/CR-6850
Specific Heat	1.298 kJ/kg·K	NUREG/CR-6850
Thermal Conductivity	0.192 kW/m·K	NUREG/CR-6850

Table 2

Measurement Uncertainty of the Input Parameters of the Physical Properties Applied to the Cable

Input Parameters	Value	Uncertainty (σ̃_E)
Density	1380 kg/m³	5%
Specific Heat	1.298 kJ/kg·K	10%
Thermal Conductivity	0.192 kW/m·K	10%

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