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EXPERIMENTAL VALIDATION OF A COMPUTATIONAL FLUID DYNAMICS SIMULATION OF A WOOD FIRE HEATED SAUNA WITH FIRE DYNAMICS SIMULATOR FOR FIRE RISKS

ANALYSIS

Corentin Macqueron1 and Perttu Leppänen2

1Corresponding author, Computational Fluid Dynamics Engineer

11 place de la convention, 78 280, Guyancourt, France

+33 6 98 26 43 77

corentin.macqueron@gmail.com

2 PhD Student, Tampere University of Technology, Department of Civil Engineering

Korkeakoulunkatu 10, FI-33720 Tampere, Finland

perttu.leppanen@tut.fi

Abstract – In the framework of fire risks and causes analysis, the traditional wood fire heated sauna is studied with the Fire

Dynamics Simulator (FDS) computational fluid dynamics software. An experimental setup following European standard EN

15821 provided experimental results (air, flue, stones and wall temperatures). Compared to the measurements, FDS performs

well for air, flue and stones temperatures. Flame temperature, mass flow rate and heat fluxes are consistent with the data

from the literature. The FDS performance is less conclusive for stove and wall temperatures.

Keywords – Sauna, Computational Fluid Dynamics, Fire Dynamics Simulator, wood stove, flue, fire, safety, EN 15821

1. INTRODUCTION

The traditional sauna, very popular in Finland and

Scandinavia, is a wood-burning stove-heated insulated

room built for bath activities that have numerous beneficial

physiological effects [1] [2].

During the 2008-2014 period, ~2000 fires broke out

from the sauna buildings in Finland [3] [4] [5],

representing ~5% of all the building fires in Finland. At the

same time, ~700-900 fires ignited from fireplaces and

chimneys every year in Finland. The causes of the fires are

amongst other things too small protection distances and too

high flue gases temperatures. Investigations are required in

order to better understand and suppress these causes. In

order to do so, an experimental sauna setup has been built

in the Tampere University of Technology [6] [7], following

the European standard EN 15821 [8] in order to study flue

gas temperatures and accidental fire risks. One of the major

findings of this experimental study is that the measured

peak flue temperatures can reach values well above the

temporally averaged temperature which is considered in

the EN 15821 testing methodology [8]. This might explain

why fires can so frequently occur even with chimneys

correctly dimensioned according to the EN 15821 standard,

simply because this standard is not conservative enough

and should hence be modified.

Following this experimental investigation, the idea

arose that numerical simulation could also be used for

further inquiry. The experimental sauna setup built in the

Tampere University of Technology hence served as a basis

for numerical simulations performed with FDS (Fire

Dynamics Simulator), a fire-dedicated CFD (computational

fluid dynamics) software [9] [10] [11] [12] [13]. The

numerical results from the sauna simulations were then

compared with the sauna setup measurements and data

from the literature in order to assess the performance of

FDS and thus to what extent this new approach might be

effectively reliable.

2. EXPERIMENTAL SETUP

2.1. Sauna

An experimental sauna setup has been built in the

Tampere University of Technology [6] [7], following the

European standard EN 15821 [8] in order to study flue gas

temperatures and accidental fire risks. This setup is used as

a basis for wood stove and sauna modelling and is shown

on Fig. 1-2-3-4.

The volume of the cabin is 20 m3.

The walls are made of 21 mm of plywood (internal

side) and 75 mm of insulating rock wool (external side).

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As required by EN 15821 [8], there is a forced

extraction of the air inside the cabin at a rate of 6 vol/h

(Fig. 1-2-4).

Figure 1 – Top view of the sauna

Figure 2 – Front view of the sauna

Figure 3 – Side view of the sauna

Figure 4 – Sauna roof with forced extraction system

2.2. Wood stove

The wood stove is a Narvi NC 20 [14], dissipating an

average power of 16 kW. It is shown on Fig. 5-6. It is filled

with 60 kg of stones. The thicknesses of the stove walls are

3 mm for the firebox and pipes (cast iron), 1.5 mm for the

internal walls and 0.75 mm for the external walls (steel).

The stove is loaded with a batch of 3 kg of wood at the

beginning. Another batch is added 24 min after ignition,

and another one 48 min after ignition.

Figure 5 – Wood stove (Narvi NC 20)

Figure 6 – Internal design of the Narvi NC 20 stove

(simplified)

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The stack is made of a ‘sandwich’ of 50 mm of rock

wool between two layers of 0.5 mm of steel.

As required by EN 15821 [8], the pressure in the stack

of the stove is maintained at -12 Pa (± 2 Pa) (Fig. 7 and

Fig. 14).

Figure 7 – Stack exhaust system with pressure regulation

2.3. Measurements

The temperatures are measured with type K

thermocouples, at the locations indicated in Table 1 and

Fig. 1-2-3.

Thermocouple n°

Cabin 21

Flue 2-3

Walls 24-29, 30-35

Table 1 – Temperature measurements

The pressure in the stack is also measured, at location

4 (Fig. 2-3). Stones temperatures were measured during

additional tests with thermocouples not listed here.

The stove heat release rate has not been measured but

it is known from the manufacturer that its average value is

16 kW [14]. The stove mass flow rate has not been

measured and is hence unknown.

3. MODELLING

3.1. Software

The sauna is modelled with the Fire Dynamics

Simulator (FDS) software (version 5.5.3 [9] [10] [11] [12]

[13]).

FDS is developed by the NIST (National Institute of

Standards and Technology) and its partners (VTT for

instance) and is a fire-dedicated computational fluid

dynamics (CFD) software.

As summarized in reference [15], FDS solves a special

form of the Navier-Stokes equations designed for low-

Mach and thermally-driven flows, focused on smoke and

fires. FDS uses an explicit predictor-corrector scheme

(second order in time and space).

Turbulence is taken into account with Large Eddy

Simulation (LES) (Smagorinsky model) by default.

For combustion, FDS generally uses a single step

chemical reaction whose products are tracked via a two-

parameter mixture fraction model.

Radiative heat transfer is taken into account via a

radiation transport equation for gray gases; solved using a

technique similar to finite volume methods. It uses

approximately 100 discrete angles by default. The

absorption coefficients of the gas-soot mixtures are

computed using the narrow-band model from

Grosshandler [11].

Post-processing is done with the Smokeview

software [12].

The choice of FDS for this wood fire heated sauna

study over other CFD software was driven by the fact that

FDS is specifically designed to handle fire. Sauna

simulation is obviously not the core mission of FDS, as

accidental fires for which FDS has been developed are of

course somewhat different from a wood stove fire in terms

of scales, containment and heat release rates, meaning that

some characteristics of FDS may not apply very well to a

wood fire heated sauna, but the underlying physics remains

the same.

3.2. Geometry

The sauna and stove models are shown on Fig. 8-9-10.

The stove and stones geometries are complex and are

somewhat pushing the limits of the FDS capabilities in

terms of geometry and purely one-dimensional conduction

calculations, which were not designed for this kind of

study. These limits are well acknowledged in the

framework of this study, but it will however be shown in

the following that FDS can still produce relevant results.

Figure 8 – Sauna model

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Figure 9 – Real stove on the left, modelled stove on the right

(metal structure in black, wood blocks in red, stones in dark

grey and stack in light grey)

Figure 10 – Two views of the modelled stones inside the stove

(metal structure in black, wood blocks in red, stones in dark

grey and stack in light grey)

3.3. Mesh

The mesh is made of ~220 000 hexahedral cells (cubes

of 5 cm width) and is shown on Fig. 11-12.

Figure 11 – Mesh (front view)

Figure 12 – Mesh (side view)

As it does not contain any refinement on the walls, the

mesh may seem somewhat coarse, especially for LES

modelling. FDS is however known to work on relatively

coarse meshes, especially because it uses ‘macroscopic’

laws-of-the-wall for velocity profiles and heat exchange

coefficients in the boundary layers [9] [10].

The size of the mesh cells (5 cm) has been chosen in

order to represent fairly accurately the sauna and stove

geometries and to allow for reasonable calculation time.

Due to FDS limitations (face-to-face conduction can

only occur in solid obstructions if the obstructions are one

cell thick maximum [9]), it is not easy to perform a

relevant mesh resolution sensitivity study as should be

performed for any CFD calculation, because of the

complex stove geometry relying on solid obstructions

(especially for stones modelling).

Finally, as with any CFD study, it is up to the user to

prove the adequacy of the mesh for the pursued results, and

this will be shown in the following.

3.4. Simulation overview

The entire sauna is modelled, as well as an extra layer

of air around it, where ambient temperature (20°C) and

atmospheric pressure are prescribed.

The physical phenomena taken into account in the

present study are the following:

- wood combustion

- fluid dynamics (free and forced convection) with

turbulence

- heat transfer (conduction, convection and

radiation (with participating gases))

The main parameters and hypotheses of the present

study are the following:

- turbulence is represented with the Large Eddy

Simulation (LES) Smagorinsky model [9] [10]

- wood pyrolysis is not simulated: the total heat

release rate is not a result but an input of the

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calculation and is transformed by FDS into a mass

flow rate of flammable gases on the surface of

idealized wood ‘blocks’ (Fig. 9-10). These gases

are transported by fluid dynamics and are burnt

according to the instantaneous and irreversible

combustion model of FDS [9] [10]

- wood combustion parameters are the following:

o chemical composition: CH1.7O0.74N0.002 [16]

o heat of combustion: 12 600 kJ/kg [17]

o soot yield: 0.015 g/g [16] [17]

o carbon monoxide yield: 0.004 g/g [16] [17]

o average heat release rate per unit area on the

wood blocks: 88.8 kW/m2 (this value is

consistent with the wood fire literature [18])

o the fire duration is assumed to be one hour

and a half

- temporal variations of the heat release rate (Fig.

13) have been fitted in order to reproduce fairly

accurately the flue temperature profile. These

variations are somewhat arbitrary but remain

realistic for the following reasons:

o the average heat release rate value is

maintained at 16 kW in order to be

consistent with the characteristics of the

stove given by the manufacturer [14]

o the variations are following the wood

batches

o the amplitude of the variations is consistent

with the literature (see reference [19] for

instance)

- the negative exhaust pressure prescribed by the

standard [8] is applied as a boundary condition on

the stack outlet

- conduction in the solids is purely one-

dimensional, in the solid surface normal direction

(FDS limitation [9])

- geometry has been simplified, mainly due to FDS

limitations (solids can only be horizontal or

vertical flat objects and face-to-face conduction

can only occur if the solids are one cell thick

maximum [9]) (Fig. 8-9-10)

- stones are modelled as solid obstructions in

perfect contact with the stove internal walls or

‘floating’ in the air (Fig. 9-10). This

approximation is crude but it is difficult to

proceed otherwise, given the limitations of FDS in

terms of geometry. As the conduction is only

occurring in one dimension and in the surface

normal direction, and the stones ‘blocks’ being

sometimes more than one cell thick, heat transfer

can occur multiple times in the same stone

‘block’. The density of the stones material is

modified accordingly in order to represent the

correct total stones inertia

- human beings are represented with solid blocks of

the shape and size of a person (Fig. 33-34), sitting

on the bench. The skin temperature is kept at

40°C, as this is the equilibrium temperature of the

human skin reached in a sauna according to

reference [2]. People hence behave as heat sinks.

The production of water vapour through

perspiration is not modelled. The blocks

emissivity is set to 1 (real human skin emissivity

is ~0.98 [20])

- the gap under the door, which is ~2 cm height on

the whole width of the door (~0.014 m2 in total),

is represented by six small openings of 5 cm

width and 5 cm height at the bottom of the door

(0.015 m2 in total)

Figure 13 – Heat release rate

Fig. 13 shows that the required heat release rate to fit

the flue temperature during the first batch is quite low

compared to the two other batches. This is due to the fact

that the combustion was weak during this period.

Additional tests have shown much better combustion

during the first batch.

3.5. Materials

The material properties considered for this study are

the following.

Plywood:

Density: 461.9 kg/m3 [21]

Thermal conductivity: 0.13 W/m/K [22]

Specific heat: 1250 J/kg/K [23]

Emissivity: 0.92 [24]

Rock wool:

Density: 20 kg/m3 (assumption from [25])

Thermal conductivity: 0.036 W/m/K [26]

Specific heat: 1250 J/kg/K (assumption from [25])

Emissivity: 1 (assumption)

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Stones:

Density: 2980 kg/m3 [27]

Thermal conductivity: 6.4 W/m/K [27]

Specific heat: 980 J/kg/K [27]

Emissivity: 0.9 (assumption)

The stones properties are for steatite sauna stones,

whereas the stones used for the tests are made of olivine,

because no solid data for olivine sauna stones were found.

It is however believed that this should not have a

significant impact, as different sauna stones are likely to

have quite similar properties, and also because the way the

stones are represented in the FDS model is very crude

anyway.

4. RESULTS AND DISCUSSION

4.1. Stack pressure

The negative static pressure in the stack (measure n°4

in Fig. 2-3) is shown on Fig. 14. It appears that the

numerical regulation of the pressure is more efficient than

the real regulation system. This results in slight differences

between the real and the modelled pressure at the stack

outlet, and this has an impact on the results of the

simulation. For instance, at the end, the experimental

pressure is likely to extract less mass flow than the

simulated one. This, combined with the assumptions about

the heat release rate profile, might explain some of the

differences between experimental and numerical flue

temperatures.

Figure 14 – Static pressure in the stack

4.2. Mass flow rate

The mass flow rate in the 16 kW stove calculated by

the model is ~9 g/s on average. This parameter was not

measured during the test but other experimental data

following European standard EN 13240 [28] from different

stove manufacturers can be found and are in the same order

of magnitude, even though the calculated value might be

considered a little bit low. For instance, references [29],

[30], [31] and [32] indicate 3.3 g/s, 6.3 g/s, 7.2 g/s and

14.5 g/s for 5 kW, 7 kW, 7.8 kW and 16.5 kW stoves

respectively.

4.3. Flame temperature

Fig. 15 shows the gas temperature on a vertical slice in

the middle of the stove. The flame temperature was not

measured during the experiment, but the results are

consistent with the data from the literature, which states

that typical wood flame temperature is expected to be in

the range of 750-1300°C [33] [34] [35] [36].

Figure 15 – Gas temperature (°C) at t ~ 3750 s

4.4. Flue temperature

The flue temperature (measure n°3 in Fig. 2-3) is

shown on Fig. 16. As explained in § 3.4, the heat release

rate has been fitted to reproduce the flue temperature as

accurately as possible, following realistic constraints.

On average, the model underestimates the flue

temperature, but the trends and the peak values are fairly

well reproduced. The values of the peaks are ignored by

the EN 15821 [8] testing methodology, because this

standard only focuses on temporally averaged temperature.

The fact that these peaks can be well above this temporally

averaged temperature is a major safety concern about

chimney fires highlighted in reference [7]: accidental fires

can occur even with correctly dimensioned chimneys

(according to the standard), simply because the standard is

not conservative enough (and should hence be modified).

The model being able to reproduce the peaks indicates that

this kind of numerical simulation could help to produce

relevant results for fire safety analysis and chimney

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dimensioning, using appropriate assumptions for the heat

release rate profile.

Figure 16 – Flue temperature

The same calculation has been performed with a

constant heat release rate (16 kW). The corresponding flue

temperature is shown on Fig. 17. The flue is logically

lower than with the realistic heat release rate profile. This

calculation is interesting because it only requires the stove

manufacturer’s data and does not need any assumption for

the heat release rate profile, but it cannot provide reliable

results for safety, the flue temperature being too low. Still,

the numerical results are very close to the manufacturer’s

data (425°C [14] following EN 15821 [8]), which is

another confirmation of the good model behaviour (and of

the limitations towards safety of the standard).

Figure 17 – Flue temperature

(with constant heat release rate)

4.5. Air temperature

The air temperature (measure n°21 in Fig. 1-2-3) is

shown on Fig. 18. The model appears to be able to

reproduce it with a very good accuracy, indicating that this

kind of numerical simulation could help to produce

relevant results for sauna design analysis, as it is the main

temperature felt by the users.

The sudden decrease of the experimental temperature

around t = 5500 s, circled in red on Fig. 18, is due to a brief

opening of the door between the room where the sauna was

installed and the outside, which was very cold, causing the

sauna inlet temperature to decrease. This was not

represented in the modelling, hence the sudden differences

between experimental and numerical results. The

experimental and numerical trends after this artefact are

however very similar, meaning that the model is still

producing relevant results. This artefact is also visible on

some wall temperatures (Fig. 23 for instance).

Figure 18 – Air temperature

Fig. 19 shows the gas temperature on a vertical slice at

the location of the air cabin thermocouple when it reaches

its maximal temperature. The stratification of the hot gases

is clearly visible.

Figure 19 – Gas temperature (°C) at t ~4760 s

The same calculation has been performed with a

constant heat release rate (16 kW). The corresponding air

temperature is shown on Fig. 20. This calculation is

interesting because it only requires the stove

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manufacturer’s data and does not need any assumption for

the heat release rate profile and it provides a reasonably

good prediction for the temperature that can be reached in

the cabin (~90-95°C with both realistic and constant

profiles) (whereas it is completely flawed for flue

temperature prediction as seen earlier (Fig. 17)).

Figure 20 – Air temperature

(with constant heat release rate)

4.6. Wall temperatures

The wall temperatures (measures n°24-29 for the back

wall and measures n°30-35 for the left wall in Fig. 1-2-3)

are shown on Fig. 21-22-23-24 for the back wall and on

Fig. 25-26-27-28 for the left wall.

The model appears to be able to reproduce fairly well

some of the wall temperatures at medium heights above the

stove, but it considerably overestimates the bottom

temperatures and underestimates the top temperatures. The

overestimated wall temperatures are facing the stove, while

the correct or underestimated wall temperatures are above

the stove. This may indicate that the stove temperature is

overestimated by the model, which could cause an

overestimated radiative heat flux towards the walls.

Additional tests and measurements on the stove itself

have confirmed that the model overestimates the wall stove

temperatures close to the firebox and underestimates the

others. This is probably caused by the inevitable and

sometimes crude geometrical approximations of the

modelling of the internal parts of the stove.

It is clear that further experimental and numerical tests

should be performed in order to investigate this matter. It is

also possible that the thermocouples, being installed on the

surface of the sauna walls, are exposed to some sort of

‘bound effects’ (the thermal resistance of the wall-

thermocouple contact might be large and/or the radiative

heat flux towards the small sphere of the thermocouple

might be different than the one towards the plane wall).

Additional tests, with in-depth measurements

(thermocouples engulfed in the rock wool), should be

performed, and may well lead to better agreement with the

simulations. For now, the results tend to show that this kind

of numerical simulation should be considered with caution

for wall temperature prediction. Overestimating wall

temperatures facing the stove is nevertheless on the

conservative side for safety analysis. The literature

provides data for maximal temperatures that should not be

exceeded on wood surfaces in order to avoid ignition: 250-

364°C for short-term/single exposures according to

reference [37], but ignition threshold can be significantly

lower for long term/repetitive exposures as can be expected

in a sauna (150°C according to reference [38] and even as

low as 77°C according to reference [37]). These values

could be compared to the simulation results in order to

assess the risks of accidental fire.

Figure 21 – Back wall temperature, n°24

Figure 22 – Back wall temperature, n°25

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Figure 23 – Back wall temperature, n°27

Figure 24 – Back wall temperature, n°29

Figure 25 – Left wall temperature, n°30

Figure 26 – Left wall temperature, n°31

Figure 27 – Left wall temperature, n°33

Figure 28 – Left wall temperatures, n°35

Fig. 29 and 30 show the temperature field on the walls

(with different scales to emphasise on different locations)

at peak temperature. Hot spots are visible on the stove and

on the back wall.

Figure 29 – Wall temperature (°C) at t ~ 3750 s

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Figure 30 – Wall temperature (°C) at t ~ 3750 s

4.7. Stones temperatures

Stones temperatures have been measured in additional

tests and are in the range of 200°C to 500°C at peak

temperature. The calculated temperatures are well within

the same range, except for top stones that are ‘floating’ in

the air (these stones are much cooler, ~100°C, which is

probably well underestimated).

4.8. Heat fluxes

Fig. 31 and 32 show the radiative heat flux on the

walls (with different scales to emphasise on different

locations) at peak temperature. Negative values correspond

to emitting walls while positive values correspond to

absorbing walls.

Figure 31 – Radiative heat flux (kW/m2) at t ~ 3750 s

Figure 32 – Radiative heat flux (kW/m2) at t ~ 3750 s

According to reference [39], a value of 25 kW/m2 can

ignite wood panels. The calculated heat fluxes on the walls

are well under this value, consistent with the fact that no

wall fire was ignited during the experiments, but this

cannot directly be used as a relevant indicator of the

model’s correctness. Heat fluxes were not measured during

the experiments. Data can however be found for heat

fluxes towards the human body in a sauna (~0.3 to

~0.6 kW/m2 according to reference [40]). These values for

a human body might not be of direct interest for fire risks

but they are still interesting for safety (skin burns) and

provide data to which the model can be compared.

Additional calculations have thus been performed with

people inside the sauna (Fig. 33-34). According to the

model, at the chest level, at peak temperature, the radiative

heat flux towards the body is ~0.19 to ~0.31 kW/m2 and

the convective heat flux is ~0.14 to ~0.2 kW/m2, hence a

total heat flux of ~0.33 to ~0.51 kW/m2. These values are

well within the data from the literature (~0.3 to

~0.6 kW/m2 [40]), indicating that the model can produce

relevant results for heat fluxes (at least towards human

bodies). Fig. 33 shows that the spatial variations of the

radiative heat flux on the human bodies are significant.

Convective heat flux is much more homogeneous (Fig. 34).

Figure 33 – Radiative heat flux (kW/m2) at t ~ 3750 s

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Figure 34 – Convective heat flux (kW/m2) at t ~ 3750 s

The results can also be analysed from the pain

threshold perspective. Additional calculations have been

performed with small solid blocks, kept at 40°C [2],

mimicking human parts that would stand very close to the

stove (5 cm) (Fig. 35). Radiative heat fluxes on these

surfaces reach ~8 kW/m2 at peak temperature (~3750 s).

According to the Stoll Curve [41], this value would cause

pain within ~15 s (and 5 kW/m2 is enough to cause blister

burns within 30 s [20]). These results are consistent with

typical sauna experience, as one can encounter severe pain

when standing very close to the stove.

Figure 35 – Small ‘human’ parts circled in red

More validation would be required to conclude (with

heat fluxes measurements), but it seems that this kind of

numerical simulation could be used both for thermal

comfort and safety analysis. This kind of calculation could

determine the spatial limits for discomfort, skin burns and

ignition risk zones.

4.9. Calculation requirements

The calculations were performed with FDS 5.5.3 in

monoprocessor on a single desktop computer, equipped

with an Intel Core i5 4300U CPU and 8 GB of RAM and

running Windows 8.1. It took approximately 500 hours to

simulate the 2 hours of the experimental test. Quasi steady-

state can be reached within 80 hours (calculation is

accelerated by diminishing the thermal inertia of the solids

by several orders of magnitude). These calculation

durations may seem long, but they are still manageable and

huge improvements could probably be made using more

powerful processors and running parallel calculations [42].

High-end processors can indeed easily be 5 times faster

than the one used for this study and, with a reasonable

number of cores (16 to 24 for instance), it might be

possible to bring the calculation duration down to a mere

few hours.

5. CONCLUSIONS

This study has shown that it is possible to represent

fairly accurately the physics involved in a wood fire heated

sauna with the Fire Dynamics Simulator (FDS) software.

It has been shown that flue, air and stones

temperatures can be reproduced with good accuracy

compared to our experimental results. Flame temperature,

mass flow rate and heat fluxes appear consistent with the

data from the literature. The stove and sauna wall

temperatures prediction would require further experimental

and numerical investigations, as the simulation results are

less conclusive for these variables but it nevertheless seems

possible to perform fire safety analysis like chimney or

stove-wall distance dimensioning in order to avoid

accidental fires through wood ignition. It would also be

possible to investigate insulation needs, power

requirements, design analysis and to perform comfort zone

optimisation.

The main difficulty of this kind of modelling is the

complexity of the stove geometry (the stones are especially

difficult to represent). The FDS limitations in terms of

geometry and mesh may not apply for all stoves and will

always require some assumptions.

One of the main drawback of this kind of modelling is

that the heat release rate profile has to be foreknown, but it

can easily be constructed from the stove manufacturer’s

data and a few reasonable assumptions for batch frequency

and heat release rate amplitude, as it has been done in the

present study. An average profile can be used, requiring no

assumption at all and producing relevant results for air and

mean flue temperatures, but at the cost of severely

underestimating the peak flue temperatures.

This kind of modelling, like any computational fluid

dynamics analysis, would always require some sort of

preliminary experimental tests in order to check that the

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stove (with its stones) is correctly represented and to

supply data for the heat release rate profile, but once this

step is validated, simulations could be performed with no

other testing. Besides, as the stove is the most cautious part

of the modelling, preliminary tests could be limited to the

stove, without the whole sauna cabin setup, saving space,

time, building material and, consequently, costs. This new

simulation approach would hence allow for performing fire

safety analysis and design optimisation with reduced

experimental tests and costs.

Calculations appear possible on a single desktop

computer within reasonable delays, making this approach

affordable. The potential cost benefits would still need to

be precisely evaluated, especially for a somewhat small

industry like sauna manufacturing, but this combined

experimental and numerical approach has already proven

to be effective in many industries.

The results, both experimental and numerical, have

also shown that European standard EN 15821 [8] is not

conservative enough for chimney fire risks because it only

focuses on the temporally averaged temperature, and

should hence be modified.

To pursue this work, further numerical and

experimental tests should be performed with additional

measurements (such as heat release and mass flow rates,

heat fluxes and thermal imaging) and different sauna and

stove designs. Repeatability should also be demonstrated

over multiple tests.

The production of steam cloud by pouring water on

the hot stones could also be studied using the

sprinkler/nozzle tool included in FDS. The condensation of

the hot steam cloud on the ‘cold’ human skin, which is one

of the major reasons for the ‘hot’ feeling during sauna

bathing, is however not possible to take into account by

default, due to software limitation (FDS was designed for

fire analysis, in which condensation takes no part). Steam

cloud calculations are also interesting for safety analysis,

as burn injuries can be caused by steam.

ACKNOWLEDGMENT

The authors would like to thank Sami Lamminen from

the Tampere University of Technology for his helpful

contribution to the study and Simo Hostikka from VTT for

his helpful comments on the paper.

NOMENCLATURE

CFD: Computational Fluid Dynamics

FDS: Fire Dynamics Simulator

NIST: National Institute of Standards and Technology

VTT: Technical Research Centre of Finland

LES: Large Eddy Simulation

REFERENCES

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incidence of common colds, National Center for

Biology Information (NCBI), 1990

2. M. L. Hannuksela et al., Benefits and Risks of Sauna

Bathing, American Journal of Medicine, vol. 110,

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