Hugues Pretrel

Philippe Querre

Repeatability assessment of large scale fire experiment involving water spray system in a forced ventilated compartment

Keywords: repeatability, uncertainty, large scale fire test, sprinkler, pool fire, ventilation, compartment

Repeatability of large scale fire test remains a key issue for code validation process. Most of the large scale experimental studies are based on single experiment and the influence of repeatability is barely considered in the test analysis process. Due to the substantial cost, reproducing several trials of a given large scale fire scenario is not often performed. In the framework of the OECD PRISME 2 project, this topic has been identified and a specific large scale fire test has been reproduced twice in the final goal of assessing the level of repeatability. The scenario is an oil pool fire in a enclosure mechanically ventilated and during which a water spray system is activated. The analysis consists in identifying a set of variables on which metrics is applied in order to quantify the levels of discrepancy between the two tests. A set of 27 variables are selected such as they characterize the whole fire scenario (the fire source, the gas phase, walls, the ventilation network and the water spray system). The analysis points out that the repeatability levels are different depending on the type of variable. The gas temperature or species concentrations are more repeatable than gas pressure or air flow rate. In addition, a new methodology is proposed in comparing, for each physical variable, the variations due to repeatability (i.e. the precision) and the uncertainty. A new metric is proposed helping modelers in code validation process.

INTRODUCTION

The data accuracy, i.e. the closeness of agreement between a measured value and the true value, and the data repeatability of large scale fire tests remains a key issue for code validation process as well as for the understanding of the physics. Due to the complexity of the fire source (pool, electrical cables), of the fire development (ignition, propagation, flashover, extinction) and of the fire consequences (smoke propagation, doorway flows), the repeatability of large scale fire test is often an answer for explaining discrepancies between data and numerical simulations. It is therefore an important issue to propose an assessment of fire test repeatability.

The repeatability is defined by the International Standardization Organization as the precision, ie. the closeness of agreement between independent measurements or trials, of the same experiment performed at the same place, with the same facility and measurements techniques, the same operators and within a short period of time [START_REF]International Standardisation Organisation, Accuracy (trueness and precision) of measurement methods and result[END_REF]. It differs from reproducibility that specified the difference between trials of the same experiment but performed at different place, with different teams and facility. The repeatability studies are mostly associated to a given apparatus or facility, whereas reproducibility studies are associated to comparisons or round robins between laboratories or tests apparatus.

Repeatability (or reproducibility) can be related to uncertainty. Following the ISO definitions, uncertainty is a range associated to the measured value in which the true value should be in (see illustration in Fig. 1). Error, that can be random (noise, equipment sensitivity or acquisition resolution) or systematic (bias due to calibration), is the difference between one measured value and the true value. In case of identical physical phenomenon (i.e. identical true value for each measurement or trial) and considering only random errors, the uncertainty corresponds to the precision of the measurement and thus to its repeatability. However, there are domains of physic, and it is the case for fire phenomena, where reproducing the same experiment, i.e. the same true value, is barely achievable. For those situations, uncertainties and repeatability may be significantly different. In addition to uncertainty assessment, repeatability studies are then necessary stages for fire experimentalists in the final goal of quantifying the measurements variability that can be use then by the modelers for validating numerical simulations. In the field of fire phenomena, several experimental teams have undergoes repeatability studies focusing on physical phenomenon as ignition [START_REF] Hirschler | Repeatability and reproducibility of fire tests for cigarette ignition of upholstered furniture composites[END_REF] burning rate of furniture [START_REF] Hirschler | Heat release testing of stacked chairs: Analysis of repeatability in a single laboratory[END_REF], or on specific apparatus as the tube furnace [START_REF] Purser | Repeatability and reproducibility of the ISO/TS 19700 steady state tube furnace[END_REF] or the cone calorimeter [START_REF] Urbas | BDMC interlaboratory cone calorimeter test programme[END_REF]. These studies gave the level of confidence of the experimental characterizations. These former studies deal with laboratory scale tests. The repeatability studies for large scale tests are less numerous, due to the high financial costs or technical organization difficulties in performing several trials of the same experiment. Three contributions have been identified and summarized in Table 1. In the eighties, Fang et al [START_REF] Fang | Repeatability of Large-scale Room Fire Tests[END_REF], proposed a repeatability study based on four trials of a furnishing fire scenario in a 11 m 2 room equipped with identical interior furnishings and finish materials and leading to flashover temperature. The repeatability is assessing from mean, standard deviation and coefficients of variation for several quantities, critical times (propagation, steady regime and extinction times), gas temperature and heat fluxes. The results show differences for critical times between 5 to 13% and for gas temperature in the order or 6%. More recently two studies have also presented repeatability assessment of large scale fire tests. A first one analyzed a database produced by students in the framework of academic courses over 6-year period [START_REF] Johansson | A Study of Reproducibility of a Full-Scale Multi-Room Compartment Fire Experiment[END_REF]. They analyzed test repeatability on four scenarios with 10 to 15 trials per scenario. The scenarios consisted in a 1 MW heptane pool fire located in a three-room apartment of 40 m 2 . They based their analysis on mean and standard deviation as well as with more advance metrics as Euclidian relative distance, Euclidian projection coefficient and Secant cosine. The results show that the 95% confidence interval of the measured temperature covered +-7 to 35% around the mean for each scenario. A second study proposed a repeatability analysis with a larger number of trials, from 20 to 25, for three simple scenarios. The scenario consisted in two rooms (one fire room and a corridor) of 28 m 2 with less powerful fires, 60 kW n-hexane pool fire and 100 kW wooden cribs fires. The analysis discussed the repeatability on critical times, fuel mass loss rate, gas temperature, gas concentration and smoke obscuration. They showed that gas temperature and smoke density provide reliable value, whereas, mass loss rate and gas concentrations may show higher level of variation.

These three studies demonstrated that some variables may give non negligible variation and therefore performing repeatability analyses are a necessary task. The results also show different levels of repeatability according to the complexity of the scenario, the dimension of the facility, the type and the power of the fire, the type of variables investigated and the mastering of the input conditions. These three studies also point out the difficulties in performing within the same study, a large number of trials in a large scale facility and with a representative number of measured variables.

The present contribution proposes a new example of repeatability assessment for large scale fire test. The scenario consists in hydrocarbon pool fire of about 400 kW in one room of 42 m 2 , closed and forced ventilated and including the activation of a water spray system. The particular feature of this scenario concerns the forced ventilated compartment with water spray system leading to complex flow for which numerical simulations involved complex modeling. This study has been performed in the framework of the OECD PRISME 2 project and only two trials could have been performed. The novelty of this study concerns the fire scenario including water spray system and mechanical ventilation at large scale (400 kW fire in a 42 m 2 enclosure). It permits to focus on the repeatability in measuring variables associated to mechanical ventilation and water flow rate. In addition, assessing the level of repeatability of such scenario is of importance for modelers when performing code validation process. Therefore, the analysis focuses on the comparison between the repeatability of a variables and its uncertainty. A new metric is proposed to enhance the possible difference between uncertainty and repeatability in case of large scale fire tests and to indicate to modelers which variation interval should be considered, the measurement uncertainty or its repeatability.

THE FIRE EXPERIMENT Test facility

The fire scenario, also presented in [START_REF] Pretrel | Efficiency of water spray system for smoke cooling in confined and mechanically ventilated compartment[END_REF] and [START_REF] Pretrel | Interactions between water sprays and smoke filling in case of a fire event in a closed and mechanically ventilated compartment[END_REF], consists in a fire in a closed and mechanically ventilated compartment. The compartment is a room of the DIVA facility of IRSN, rectangular (4.88 m × 8.67 m = 42.3 m 2 ) with a height of 3.90 m (Fig. 2). The walls, the floor and the ceiling are made of concrete and the ceiling is covered with silicate of calcium panels avoiding its thermal degradation. The ventilation system consists in an admission and exhaust lines connected in the upper part of the room at about 0.80 m from the ceiling. The ventilation lines are connected to an industrial network equipped with blowing and exhaust fans. The targeted flow rate of ventilation was 2550 m 3 /h. This value is designed prior to the test from numerical calculations in order to avoid extinction by lack of fuel and thus to ensure combustion of the total mass of fuel.

Fire source

The fire source was a liquid fuel pool located on the North-West corner of the room in order to avoid extinction when the water spray is activated. The objective of this fire scenario is to study the interaction between water droplets and smoke layer without extinguishing the fire. The fuel is lubricant oil, DTE Medium from Mobil, preheated to 120°C before ignition. The pool is a circular 0.7m 2 pan, made of carbon steel with a depth of 130mm and placed on a weighting device.

The mass of fuel is about 25kg before ignition. The pool combustion was ignited by a 20 kW propane gas burner.

Water spray system

The water spray system is the one presented in detail in [START_REF] Pretrel | Interactions between water sprays and smoke filling in case of a fire event in a closed and mechanically ventilated compartment[END_REF]. It is made of two nozzles located 2.97 m from the ground and about 0.85 m from the ceiling and centered in the room as indicated in Fig. 2). The nozzles are connected to a system of pipes equipped with valves, pressure trans- The water flow rate delivered by the water spray system was measured with a ROSEMOUNT© magnetic flowmeter. In addition, the spatial distribution of the water density received at the ground during a fire test was measured with set of collectors distributed over the room floor and equipped with pressure transducers measuring water height [START_REF] Pretrel | Efficiency of water spray system for smoke cooling in confined and mechanically ventilated compartment[END_REF].

The measurement uncertainties of each physical variable are shown in Table 2. They are evaluated either from the sensor characteristics given by the manufacturer or determined experimentally from calibration tests in comparing the responses of the sensor and a reference. These uncertainties correspond to standard uncertainties and are obtained only at ambient temperature without the effect of the fire. They are comparable to uncertainties proposed for fire test by the NIST [START_REF] Hamins | Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications[END_REF]. For each sensor, it is assumed that the uncertainty is characterized by a Gaussian distribution and its value can be assessed as the standard deviation . The standard deviation is obtained from the characteristic of each sensor given in Table 2. Two situations are encountered whether the standard deviation is constant whatever the signal amplitude or proportional to the signal amplitude.

Test scenario and experimental procedure

The test scenario is designed such as it permits in a first stage to characterize the behavior of the fire source in confined and ventilated environment without water spraying and then in a second stage to study the effect of water spray application. Therefore, the test procedure was as follows.

First the targeted ventilation flow rate, 2550 m 3 /h, was stabilized by operating the ventilation network. Then the preheated fuel was poured in the pan and ignited. A steady period of burning is waited in order to reach a stabilized hot layer of smoke (transient period of about 15 minutes). Then the water spraying system was turned (here at t=948 s) during a constant period of 600 s. The test ends with the combustion of the remaining amount of fuel in the pan.

Two trials, named PR2_FES_1 and PR2_FES_1A in the context of the OECD PRISME 2 project and renamed here TEST1 and TEST2, were performed in about one month interval, with the same experimental set-up.

METRICS FOR DATA ANALYSIS

To quantify the differences between tests, a set of variables considered as representative physical quantities was selected. Each variable was measured during the two trials with the same measurement technique and acquisition chain. The analysis is based on the comparison of the time variation of all variables. For both trials, the time t = 0 s corresponds to the instant of ignition. In the following, the two signals of each variable are named and for the tests TEST1 and TEST2 respectively. They are discrete time-dependent signal considered as vectors with i, the time index.

Repeatability assessment is based on the analysis of metrics quantifying the difference between signals. Various metrics can be used according to the specificity of the tests [START_REF] Audouin | Quantifying differences between computational results and measurements in the case of a largescale well-confined fire scenario[END_REF]. In the present study, two metrics are considered.

The first one is defined for comparison between two time-dependant signals, here, the signal of one trial k and the average signal, , which is the mean time-dependent signal over the n trials. This first metric, named NED, expresses the normalized Euclidean distance between this two signals as :

(

with the average time-dependant signal (index i) over the n trials computed as:

. is computed for each trial k and over a given period of time [t1; t2]. It represents the mean discrepancy between one trial and the mean behaviour or the precision, following the ISO definitions [START_REF]International Standardisation Organisation, Accuracy (trueness and precision) of measurement methods and result[END_REF]. In the present study, with only two trials (n=2), the two normalized differences, and are equal and will be named NED in the following. This variable is identical to the Euclidian relative distance ERD defined in [START_REF] Johansson | A Study of Reproducibility of a Full-Scale Multi-Room Compartment Fire Experiment[END_REF].

The second metric, proposes here by the authors, is the ratio between the range due to repeatability (the difference between the signal of one trial k and the mean signal over the n trial, and the range characterized by the uncertainty of the sensor also written (see Fig. 3). This ratio, named is expressed as:

with , ( 2
)
is the mean value of at the time I over the n trials. is the standard deviation of at time i. The aim of this metric is to identify which standard deviation has to be considered when comparing an experimental data and a numerical simulation, either the uncertainty of the sensor indicated in Table 2 or , the standard deviation obtained from n repeatable tests.

The properties of this metric are demonstrated in the following. Let's considered the hypothesis, H, that the physical phenomenon is identical for all repeatable tests and that these tests have a measurement noise following a normal law, the difference follows a normal law with a null mean value equal to zero ( . The standard deviation of a repeatable k test at time i, is defined as and is equal to , the uncertainty of the sensor.

From these two statements, the metric , for a repeatable k test and for a given time i, follows a normal law with a mean value equal to zero and a standard deviation equal to 1, i.e.

(illustration in Fig. 3). For a given , we can compute the probability , that using . If this probability is lower than a fixed probability , the hypothesis H is considered false and . The probability p is here computed as:

(3) This condition permits to characterize sensors. For sensors for which are lower than , the hypothesis H is considered as true, the physical phenomenon is identical for all repeatable test, thus . The uncertainty of the sensor can be considered as the standard deviation when comparing with numerical simulation. For sensors for which are larger than , the hypothesis H is considered as false, the physical phenomenon is not identical for all repeatable test and thus . The standard deviation used for comparison with simulations should be the one obtained from the set of repeatable tests . Here, this ratio is computed at each time step (index i). Again, here with only two trials, the two ratios and are equal and named Ri.

The purpose of this new metric is to highlight possible difference between uncertainty assessed on controlled conditions (calibrator and reference apparatus) and precision assessed from trials of fire tests. If the ratio is lower or equal to a given criterion (here is set to 3 so that prob( is lower than 2.10 -3 as indicated in Table 3), precision is identical to uncertainty and the uncertainty is appropriate to characterize the range in which the true value is asserted to be. If the ratio R is greater than the criterion, precision is larger than uncertainty. In this case, true values are probably different between trials and precision have to be considered to expand the range of uncertainty.

In practice, it would have been preferable to work with a larger number or trials. Unfortunately, in the context of the OECD PRISME 2 project, only two trials could have been performed. In the present study, the variable R gives only the behavior of one trial. A larger number of trials should be considered to propose global conclusion. The present study proposes an illustration of application with two trials.

A set of 27 variables are selected such as they are representative of the whole fire scenario.

For the fire source, the variables are the fuel mass burnt (MASS, i.e. the difference between the mass of fuel before ignition and the mass of fuel during the experiment) and the mass loss rate (MLR), two temperatures, one in the liquid fuel (TCOMB_L4_2) and one in the gas just above the fuel surface (TCOMB_L4_5) and the fire heat release rate (HRR) computed by the three methods (oxygen consumption (HRROC), carbon dioxide generation (HRRCDG) and mechanical from the MLR measurement and the effective combustion enthalpy set to 37.3 MJ/kg for DTE MEDIUM oil (HRRM)). For the ventilation, the variables are the pressure in the room (P_L4) and in the ventilation exhaust nod (PEXT_NOD_L3), the admission and exhaust flow rates (DADM_L4, DEXT_L4) and the gas temperature in the admission and exhaust lines (TGADM_L4, TGEXT_L4). For the gas phase, the variables are a mean temperature at the height 3.8 m averaged over the four trees (TZMOY8) and the upper layer temperature (TSUP_2ZG) computed with a regression method from vertical temperature profile [START_REF] Pretrel | New developments in the use of data regression methods for characterization of vertical fire smoke stratification[END_REF]. For the wall's behaviour, the variables are a radiative heat flux (FLR_L4_NE260), a total heat flux (FLT_L4_NC265) and a wall temperatures (TP_L4_NC_255). For the gaseous species concentrations, the variables are concentrations of oxygen (O2_L4_BAS, O2_L4_HAUT), of carbon dioxide (CO2_L4_BAS, CO2_L4_HAUT) and of carbon monoxide (CO_L4_BAS, CO_L4_HAUT) measured in the room and the concentration of water vapour measured in the exhaust line (H2OEXT_L4). For the water spray system, the variables are the water flow rate (D_WATER_SPRAY) and two local density measurements (QWS_7, QWS_18).

Examples of comparison between these variables are presented in Fig. 4 and Fig. 5 and the analysis are presented in the next section. In the two figures, for each variable, the upper plot shows the time variation of the given variable for the two trials and the lower plot the difference between the two variables.

Global behavior of the fire test

The fire shows a typical behavior of a pool fire in an enclosure. The fuel MLR and the fire HRR increase during the fire growth and then reach a steady state at about 400 kW (Fig. 4). This steady regime results from equilibrium between the fuel burning rate, lower than in open atmosphere, and the oxygen feed by the ventilation. The consequence on the gas phase is an increase of the gas temperature (to about 200 °C in the upper part, variable TZMOY8 in Fig. 4) and a decrease of the oxygen concentration (cf. variable O2_L4_HAUT in Fig. 5). As expected for confined and mechanically ventilated fire room scenario, the ventilation flow rate and the pressure in the room are very sensitive to the fire growth and to the increase of gas temperature leading to an increase of the exhaust flow rate and a decrease of the admission flow rate as observed in Fig. 4 (cf. variables P_L4, DADM_L4 and DEXT_L4). After about 950 s of combustion, the water spray system is activated during 600 s (cf. variable D_WATER_SPRAY in Fig. 5) without extinguishing the fire. It leads to a decrease of the gas temperature, to a homogenization of the species concentration but also to an enhancement of the burning rate. This unexpected behaviour is interpreted as an increase of the turbulence level within the compartment, illustrated by large fluctuations of the pressure and ventilation flowrate measurements (variables P_L4, DADM_L4 and DEXT_L4 in Fig. 4). The activation and shut-off of the water spray system lead also to sudden variations of pressure in the room (variable P_L4 in Fig. 4).

Repeatability on the input conditions

First, the differences, prior to ignition are quantified. For 15 variables, for which the mean value is non zero, the mean relative differences NED, i.e. the precision between one trials and the mean signal, are displayed in Table 4 and are illustrated in Fig. 6-a. NED values span from 0.001 for the oxygen concentration to 0.45 for the pressure in the room P_L4. Concerning the fire source, the fuel temperature (TCOMB_L4_2) shows a difference of 4 °C (NED=0.02 in Fig. 6) for a fuel temperature of about 85 °C (the fuel being preheated before ignition). The pan was ignited with the same burner during similar time duration. The settings for the operating conditions of the ventilation prior to ignition are well reproduced. The difference of air flowrates was of 50 m 3 /h for a total flow rate of about 2550 m 3 /h (NED=0.02 in Fig. 6). Only, the initial pressure in the room was not set to the same value -33 Pa and -73 Pa (NED=0.45 in Fig. 6). Regarding the ambient temperature, a difference of a maximum of 5 °C is noticed due to the change of the environment conditions.

The differences in the mass of fuel and ventilation conditions are linked to slight changes in the experimental protocol (ignition process, mass of fuel) and the way the ventilation network was set. The variations in temperature depend in the changes in the environmental conditions. For the other variables, the differences between the two tests are within the measurements uncertainty.

Repeatability during the combustion phase

A first observation of Fig. 4 and Fig. 5 shows good qualitative agreement between the two tests.

The amplitudes as well as changes of variation are very similar. For example, the peaks of pressure are similar (in time and in amplitude) and the time variations of the fuel MLR and the fire HRR follow identical trends. The activation and the shut-off of the water spray system are observed with the same qualitative behaviour for the two trials.

A quantitative analysis based on the metric NED shows that the repeatability or precision may be different according to the type of physical variables (temperature, concentration, flowrate,..) and according to the items concerned (fire, gas phase, walls or ventilation). The values of NED during the combustion phase are presented in Fig. 6-d . These measurements correspond to quantities that can be difficult to measure. The levels obtained for gas temperature are very similar to the levels obtained by Johansson [START_REF] Johansson | A Study of Reproducibility of a Full-Scale Multi-Room Compartment Fire Experiment[END_REF].

Then the pressure in the room, the ambient temperature and the quantities related to water spray give much larger value with a maximum of 0.45 for the pressure in the room ( and ),. For the pressure, this large value is explained by the fact that this quantity is very sensitive to few change of temperature within the room and therefore is much more difficult to reproduce.

The variables related to the water spray system (water flow rate and water density) are also difficult to reproduce (for sensor QWS_18 l/min/m 2 and l/min/m 2 ). This result is attributed to the complex physic associated to process of droplet generation by the nozzle and the spraying pattern in the room. For the measurement of the water density, the results are also explained by a larger uncertainty attributed to the water collector technique. The elaborated variables, as the heat release rate, not directly measured but computed from analytical models with calorimetry methods or the upper and lower temperatures with regression methods, show also different results. The mechanical method for the computation of the HRR is the most repeatable method. In between the two calorimetric methods (Oxygen Consumption, OC and Carbon Dioxide Generation, CDG), the CDG method is the most repeatable one ( and ). This result is in agreement with previous studies mentioning OC methods as less accurate in case of mechanically ventilated compartment.

The effect of the water spraying on the precision is discussed in comparing the NED values on the first period of combustion t=[60;600] s without water spraying and on the period t=[1100;1500] s with the effect of the water spraying. Results are presented in Fig. 6-b and Fig.

6-c and in Table 4. Water spraying affects mainly the repeatability on the pressure in the room (sensor P_L4) and on the ventilation flowrate. The sudden pressure peaks induced by the activation of the water spray is difficult to repeat. Concerning the other variables (temperature, species concentration), similar levels of precision are obtained.

Discussion about repeatability and uncertainty

The second metric, R (relation ( 3)), normalizes the discrepancy due to repeatability with the discrepancy due to sensor uncertainty. Examples of results are given in Fig. 7 showing time variation of the ratio , versus time for twelve representative sensors. For the six first sensors, the ratio is mainly greater than 3 and lower than 3 for the six followers. The choice of three is here set as a criterion fixed for this analysis.

In Fig. 8, the sensors are sorted with the percentage of point having a value of greater than the chosen . The fuel mass loss rate (MLR), some temperature (TGADM_L4, TGZMOY8, TSUP_SZG) and the fire HRR with the CDG methods give very small (nearly zero) percentage which indicated that the ratio is always lower than . For those variables, the difference due to repeatability is lower than the sensor uncertainty. If this former is considered as acceptable then difference due repeatability is included to the sensor uncertainty. Repeatability tests will not give additional information to the uncertainty.

On the opposite there are variables, here the fuel mass (MASS), the pressure in the room (P_L4) for instance, for which the value of is mostly greater than . For these physical quantities, the difference due to repeatability is much larger than the sensor uncertainty and therefore repeatability tests are required to give a better estimate of the range of variation compared to uncertainty.

In the perspective of comparing a numerical simulation with a fire experiment, these results demonstrate that two types of situations can be encountered. The first one concerns variables for which variations due to repeatability are lower than those due to sensor uncertainty (R k < ).

For those measurements, comparison can be performed on one test, using the sensor uncertainty as the critical distance ensuring that the simulation is validated. Having additional trials wil not give more information, since the variation due to repeatability is within the sensor uncertainty. The second situation concerns variables for which variations due to repeatability are greater than those due to sensor uncertainty (R k > ). In this case, comparison between the numerical simulation and the measured signal of one test cannot be based on the sensor uncertainty but on a larger critical distance that takes into account the variation due to repeatability. In this case, several trials of the fire scenario should be available to validate the simulation.

CONCLUSION

This study proposes a repeatability analysis of large scale fire test involving a 400 kW oil pool fire in a 42 m 2 mechanically ventilated enclosure with the activation of water spray system. The study, based on two trials, compares a set of 27 variables directly measured or computed from measurements, selected such as they characterize the whole fire scenario (fire source, gas phase, walls, ventilation network and water spray system). For each variable, metrics are computed and give the level of repeatability of this fire scenario. Conclusions obtained from the results are the followings.

 For a fire test scenario including water spraying, the repeatability assessment is similar as in previous studies [START_REF] Fang | Repeatability of Large-scale Room Fire Tests[END_REF], [START_REF] Johansson | A Study of Reproducibility of a Full-Scale Multi-Room Compartment Fire Experiment[END_REF], [START_REF] Melcher | Experimental investigations on the repeatability of real scale fire tests[END_REF] for gas temperature, species concentrations or mass loss rate. The consideration of water spraying in the fire test scenario induces pressure variations that are the most difficult quantity to repeat between trials.

 The study points out that repeatability levels are closely related to the type of variable considered. Oxygen concentration measured with analyser or gas temperature measured with thermocouples gives the most repeatable quantities. On the opposite, water spray characteristics or ventilation features (pressure and air flowrates) are more difficult to reproduce.

 The importance of uncertainty when assessing repeatability is highlighted and discussed from the use of a new parameter R defined for each experimental measurement. This new metric is the ratio between precision assessed from trials of fire test and the uncertainty assessed on controlled conditions (calibrator and reference apparatus). If the ratio is lower or equal to a given criterion, precision is identical to uncertainty and the uncertainty is appropriate to characterize the range in which the true value is asserted to be. If the ratio R is greater than the criterion, precision is larger than uncertainty.

 From the perspective of comparison between a numerical simulation and experimental data, this analysis demonstrates that two types of situations can be encountered. The first one concerns variables for which variations due to repeatability are lower than those due to sensor uncertainty (R k < ). For those measurements, code validation can be based on one test, using the sensor uncertainty as the critical distance considering the simulation as validated. The second situation concerns variables for which variations due to repeatability are greater than those due to sensor uncertainty (R k > ). In this case, comparison between the numerical simulation and the measured signal cannot be based on the sensor uncertainty only but on a larger critical distance that takes into account the variation due to repeatability.

 From the present work, examples of measured variables whom ranges of uncertainty and repeatability are similar, are gas temperature, oxygen concentration, fuel mass loss rate in case of liquid pool fire. On the opposite, measures variables that need repeatable tests in order to assess the range of variability are room pressure, fuel temperature, radiative heat fluxes, variables related to water spray system. Nevertheless, these conclusions have been obtained with a set of only two trials which is not enough to give conclusive remarks about repeatability. Future work should integrate larger number of trials.

The repeatability is an issue when performing large scale fire test. For scenario involving complex phenomenon such as complex fire source (furniture, cables, electrical cabinet,..), or underventilated unstable combustion regime, issue of repeatability have to be treated. The phenomena of fire growth and fire propagation as well as the under-ventilated combustion are difficult to be fully controlled during the experiment. For fire tests dealings with such topics, campaign of repeatability tests should be carried out in the perspective of code validation.

Fig. 1 :

 1 Fig. 1: Illustration of the ISO definitions related to repeatability and uncertainty.

Fig. 2 :

 2 Fig. 2: Sketch of the facility with the location of the measurement points, the fire place and the spray pattern.

  ducer and water flow meter. The water spray is activated and shut off manually with a valve. The two nozzles are Deluge type, Protectospray® D3-HV unit with k nozzle coefficient of 26 l/min/bar 0.5[15]. The targeted total water flow rate is 110.3 l/min. The nozzle has been characterized using the Phase Doppler Anemometer technique giving the droplet diameter and velocity as well as the spray envelope. Each nozzle produces water droplet flow with a maximum 45° angle in relation to the nozzle axis at 55 l/min. The droplet velocity measured 200mm from the nozzle is in the range[5;12] m/s. The droplet size distribution fits a log-normal distribution with a mean mass median diameter (MMD) of about 450 m at 568 kPa.MeasurementsThe fuel mass was measured with a SARTORIUS weighting balance. The fuel mass loss rate was then determined from the time derivative of the mass. The ventilation flow rates were computed from the pressure difference measurements performed at the ports of averaging Pitot tubes located at inlet and exhaust ventilation lines. The gas pressures in the room and in the ventilation network were also measured with pressure transducers.The gas temperatures were measured with set of eight 1.5 mm K-type thermocouples positioned on five vertical trees (Fig.2) named SW, SE, CC, NE and NW. The thermocouples were equipped with metal protective caps to prevent droplets coming into contact with their tips. Temperatures were also measured in the liquid fuel and on the walls of the facility.Oxygen, carbon dioxide and carbon monoxide concentrations were measured at three positions within the room: two on the SE tree, in the lower and upper part and one close to the fire. Concentrations are also measured in the ventilation ducts. Each sample point was connected to sampling system and gas analyzers. Molar fractions are measured with SERVOMEX© XANTRA 4100 apparatus for oxygen and SIEMENS© ULTRAMAT-23 apparatus for carbon dioxide and monoxide. The time delay due to the sampling process was corrected. The vapor content was also measured in the exhaust ventilation duct with the ProCeas© H2O analyzer.Total heat fluxes were measured with four CAPTEC© sensors positioned at the centre of the four side walls. Radiative heat fluxes was measured with four Schmidt-Boelter MEDTHERM© sensors located on the vertical tree positioned in the NE corner of the room.

Fig. 3 :

 3 Fig. 3: Schematic illustration of the definition of R.

For

  the two tests and all variables, the mean values , and the metric NED are computed over several periods of time [t1;t2] : prior to ignition, during a phase of combustion without a water spraying, during a phase of combustion and water spraying and during the whole test duration. Results are displayed in Table 4 and discussed in the next sections.

Fig. 4 : 1 Fig. 5 :

 415 Fig. 4: Comparison of time variations of the mass of fuel (MASS), the fire HRR (HRRCDG), the fuel MLR (MLR), the pressure in the room (P_L4), the ventilation flow rate (DADM_L4), the gas temperature (TZMOY8), (the upper plot gives the signals of the two sensors and the lower plot the difference between the two sensors with the same unit; solid line is for TEST1 and dotted line for TEST2, solid line of the

  . The variable showing the best repeatability are the oxygen concentration ( and ), the fuel mass burnt ( and ), and some gas temperature (for TZMOY8 and ), with NED values below 0.05. Physical variables such as heat fluxes, flow rates and carbon dioxide concentrations show larger discrepancy with NED values in the interval [0.05;0.1]

Fig. 6 :

 6 Fig. 6: Metric s of each sensor, (a) prior to ignition, (b) during the combustion only, (c) during the phase of water spraying and (d) during the whole test.

Fig. 7 : 2 16Fig. 8 :

 728 Fig. 7: Time variation of the metric R for 12 representative sensors (#Ri> means "number of point having Rt greater than "); the horizontal line corresponds to the limit Ri=

Table 1 :

 1 Characteristics of previous studies.

	Reference	Fang & al. [6]	Johansson & al. [7]	Melcher & al. [8]
	Nb. of scenario	1	4	3
	Nb. of trial per scenario	4	10 -8 -15 -12	25 -20 -20
	Type of scenario	Furniture fire	Heptane pool fire	Hexane pool fire /
				Wood cribs
	Facility dimension	One room 11 m 2	3 rooms ~40 m 2	2 rooms ~28 m 2
	Fire power	Flashover	~1000 kW	60kW -110kW
	Statistical variables	Mean/Std Dev/CV	ERD/EPC/SC	Mean/Std Dev/CV
	Variables studied	Times	Times	Times
		Gas temperature	Mass loss rate	Mass loss rate
		Heat fluxes	Gas temperature	Gas temperature
				Gas concentrations
				Smoke obscuration

Table 2 -

 2 Uncertainty assessment of the measured physical quantities

	Physical variable	Sensor type	Range	Unit	Standard uncertainty
					(absolute or relative)
	Mass	IS 300IGGS-Sartorius©	[0-300]	kg	0.005
	O2 concentration	Paramagnetic-Servomex	[0; 25]	%-vol	0.2 %-FS (0.05 %-vol @ Full scale)
	CO2 concentration	Ultramat-23 Siemens©	[0; 20]	%-vol	2 %-FS (0.4 %-vol @ Full scale)
	CO concentration	Ultramat-23 Siemens©	[0; 10000]	ppm	2 %-FS (200 ppm @ Full scale)
	H2O concentration	AP2E-Proceas©	[0; 10]	%-vol	2 %-FS (0.2 %-vol @ Full scale)
	Pressure	Press. transducers	[-2500;+2500]	Pa	0.50 % (12.5 Pa @ Full scale)
	Air flow rate	Press. transducers	[0;+5000]	m 3 /h	2 % (100 m 3 /h @ Full scale)
	Temperature	K type thermocouple	[0;1300]	K	<2.5K @ 293K
	Radiative heat flux	Schmidt-Boelter/Medtherm© [0;25000]	W/m 2	1 %-FS (250 W/m 2 @ Full scale)
	Convective heat flux	Thermopile CAPTEC©	[0;10000]	W/m 2	1 %-FS (100 W/m 2 @ Full scale)
	Water flow rate	Magnetic flowmeter	[0;400]	l/min	1 % (4 l/min @ Full scale)
	Water density	Press. transducers	[0;20]	l/min/m 2	5 % (1 l/min/m 2 @ Full scale)
	FS : full scale

Table 3 -

 3 Equivalence between the probability , and the metric use in the definition of R

	,	0.2	0.1	0.05	0.02	0.01	0.005	0.002	0.001
	,	1.28	1.64	1.96	2.24	2.58	2.80	3.09	3.29

Table 4 :

 4 Metrics for the variables and for the two tests (the variable x 1 corresponds to TEST1 test and x 2 to TEST2 test) (*) number of points for which Ri is greater 2 than during the whole period of test which corresponds to 2186 instants. 3


Camille Adrien

Aurélien Croc

Juan-Carlos García Hernández

Serge Dreuil

Johann Monte Plagnard

Bénédicte Poumarède

Cindy Le Loirec

Jean-Marc Bordy

Carlo

Monte Carlo software for dose calculation in CT examinations

Développement d'un logiciel Monte Carlo pour le calcul de dose en imagerie scanographique

Auteurs : Camille Adrien

Material and Methods:

Due to the lack of information available in the technical note, the GE VCT Lightspeed 64 tube was modeled using the method proposed by Turner et al. Thanks to HVL and profile measurements, equivalent spectra, inherent filtrations and bowtie filter shapes are obtained.

Measurements were then performed in static mode with a CdTe detector associated with an unfolding method developed by the LNHB to achieve experimental spectra and validate the tube model.

Ultimately, the axial and helical rotation was implemented in the MC tool. To improve the efficiency of the simulation, two variance reduction techniques were used: a circular and a translational splitting. To validate the calculation, simulated sinograms are compared with the expected ones and the particle distribution along the gantry path is checked. The MC tool and the X-ray tube model were then validated for dosimetric purposes. Measured doses were first obtained in a static mode with a calibrated pencil ionization chamber in a PMMA phantom and compared with simulations. Validations for rotational modes are in progress.

Results: Computed and measured spectra show acceptable discrepancies attributable to a misalignment during measurements. Bowtie filter shapes are in agreement with theoretical expectations. The geometric validations are consistent with the theoretical expectations. Comparisons between measured and simulated integrated doses are good enough, with less than 6% discrepancies for all different acquisition parameters.

Summary:

The first validations obtained for the use of PENELOPE-C++ for CT dose estimations are encouraging. The validation of the rotation motion implementation is part of ongoing research on several phantoms and for several examination procedures in CT exams.

  The significant rise of medical imaging exams in the past few years has led to an increase of collective doses. Despite the numerous tools already available, most of them only provide common dose index (CTDI, PDL) and effective dose rather than absorbed dose to organs. To obtain organ doses, a Monte Carlo (MC) tool, PENELOPE-C++, based on the PENELOPE simulator developed by Salvat et al is adapted. Our finale goal is to develop a predictive tool to obtain the best compromise solution for a CT exam exposure between low organ absorbed doses and high image quality.

	Plagnard	1 , Aurélien Croc , Bénédicte Poumarède 1 1 , Cindy Le Loirec 1 , Juan-Carlos Garcia-Hernandez 1 , Serge Dreuil 1 , Jean-Marc Bordy 1	2 , Johann
	Affiliations :	1 CEA, LIST, F-91191 Gif-sur-Yvette, France;	2 Institut Gustave Roussy, Villejuif, France
	Introduction: L'augmentation significative des examens scanographiques au cours des dernières
	années a conduit à une augmentation de la dose collective. Malgré un grand nombre d'outils
	disponibles, la plupart d'entre eux donnent seulement les valeurs d'indice de dose scanographique
	(IDS), de produit dose-longueur (PDL) et de dose efficace. Afin d'obtenir la dose aux organes, un outil
	Monte Carlo, PENELOPE-C++, basé sur le simulateur PENELOPE développé par Salvat et al a été
	adapté. Le but final de notre travail est de développer un outil prédictif permettant d'obtenir le meilleur
	compromis entre une faible dose aux organes et une bonne qualité image.
	Matériel et Méthodes : Ne disposant que des données fournies dans la note technique du
	constructeur, nous avons modélisé le scanner GE VCT Lightspeed 64 de l'Institut Gustave Roussy à

partir de la méthode expérimentale développée par Turner et al. Grâce aux mesures de CDA et de profils, spectres et filtrations équivalents ont été obtenus. Des mesures ont été réalisées en mode fixe avec un détecteur CdTe associé à une technique en cours de développement au LNHB pour obtenir des spectres expérimentaux et valider le modèle du tube. Les rotations axiales et hélicoïdales ont ensuite été implémentées dans le code. Afin d'améliorer l'efficacité de la simulation MC, deux techniques de réduction de variance ont été ajoutées : une circulaire et une transversale. Pour valider l'implémentation, des sinogrammes ont été comparés avec ceux théoriquement attendus. Finalement des mesures ont été faites en tube fixe à l'aide d'une chambre crayon dans un fantôme de PMMA et ont été comparées aux simulations pour différentes positions et protocoles d'acquisition. Résultats : Les spectres équivalents et les spectres mesurés montrent des différences acceptables qui peuvent être attribuées à un mauvais alignement lors des mesures. La forme des filtres papillons sont en adéquation avec leurs utilisations en clinique. Les sinogrammes simulés correspondent à ceux attendus. La comparaison entre les mesures de dose et la dose simulées est bonne, avec des erreurs inférieures à 10% pour les différents types d'acquisitions. Conclusion : Les premières validations obtenues avec le code pour l'estimation de dose en imagerie scanographique sont encourageantes. La validation de la rotation est actuellement en cours pour différents types de fantômes et d'examens. Introduction: