jou rn al hom ep age: www.elsev ier .com/ locate /nucengdes
FD analysis of the temperature field in emergency pump room inoviisa NPP
ommi Rämäa,∗, Timo Toppilaa, Teemu Kelavirtab, Pasi Martinb
Fortum Power and Heat, P.O.B. 100, FI-00048 Fortum, FinlandFortum Power and Heat, Loviisa Power Plant, P.O.B. 23, FI-07901 Loviisa, Finland
i g h l i g h t s
Laser scanned room geometry from Loviisa NPP was utilized for CFD simulation.Uncertainty of CFD simulation was estimated using the Grid Convergence Index.Measured temperature field of pump room was reproduced with CFD simulation.
r t i c l e i n f o
rticle history:eceived 27 February 2014ccepted 6 March 2014
a b s t r a c t
In the Loviisa Nuclear Power Plant (NPP) six emergency pumps belonging to the same redundancy arelocated in the same room. During a postulated accident the cooling of the room is needed as the enginesof the emergency pumps generate heat. Cooling is performed with fans blowing air to the upper partof the room. Temperature limits have been given to the operating conditions of the main componentsin order to ensure their reliable operation. Therefore the temperature field of the room is important toknow.
Temperature measurements were made close to the most important components of the pump roomto get a better understanding of the temperature field. For these measurements emergency pumps andcooling fan units were activated. To simulate conditions during a postulated accident additional warm-airheaters were used.
Computational fluid dynamic (CFD) simulations were made to support plant measurements. For theCFD study one of the pump rooms of Loviisa NPP was scanned with a laser and this data converted todetailed 3-D geometry. Tetrahedral computation grid was created inside the geometry. Grid sensitivitystudies were made, and the model was then validated against the power plant tests. With CFD the detailedtemperature and flow fields of the whole room were produced. The used CFD model was able to reproduce
the temperature field of the measurements.
Two postulated accident cases were simulated. In the cases the operating cooling units were varied. Thetemperature profile of the room changes significantly depending on which units are cooling and whichonly circulating the air. The room average temperature stays approximately the same. The simulationresults were used to ensure the acceptable operating conditions of the important process components.
. Introduction
Please cite this article in press as: Rämä, T., et al., CFD analysis of the tEng. Des. (2014), http://dx.doi.org/10.1016/j.nucengdes.2014.03.002
In the Loviisa NPP six emergency pumps belonging to the sameedundancy are located in the same room. During a postulated acci-ent the engines of the pumps and the cooling fan units generate
heat. Temperature limits have been given for different devices, forexample pump engines and valve actuators, to ensure their oper-ation. Thus the temperature field of the room must be known toensure that maximum allowed temperatures are not exceeded evenin case of the most limiting postulated accident scenarios.
Power plant tests with temperature measurements were madeto get a detailed view of the temperature and flow field in the room.
emperature field in emergency pump room in Loviisa NPP. Nucl.
In addition computational fluid dynamic (CFD) simulations weremade to support experiments and to determine the local tempera-ture and flow conditions. This paper presents these CFD simulationswith results compared with plant measurements.
2 T. Rämä et al. / Nuclear Engineering and Design xxx (2014) xxx–xxx
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Fig. 1. Modeled geometry of the emergency pump room.
. Power plant tests
Six emergency pumps of the same redundancy are located in theame room. During the postulated accident the pumps are operat-ng and the engines generate heat. Some extra heat is also generatedy the engines of the cooling fan units. The air circulation system ofhe room is closed. For one emergency pump room there are fourooling units, two with designed cooling power of about 23 kW andwo with cooling power of about 52 kW. They form two redundantooling systems with 75 kW of cooling power each. Inside the cool-ng fan units a separate water cycle removes heat from the air andirectly out from the room.
The test program consisted of three different cases. Primary tar-et variables of the plant measurements were temperature valuesear important instrumentation and process components. In therst measurements, pumps and engines brought a total of 81 kWf heat to the room. All of the cooling units were operating. In theecond measurements five 8 kW additional warm-air heaters werentroduced, representing the additional heat losses of the enginesn under-voltage situation and bringing 40 kW of additional heat tohe room, resulting in total of 121 kW. In the third measurementhe cooling water circuit of two out of four cooling units was shutown, i.e. the fans were still circulating the air but the air was notooled. Otherwise the third test was similar to the second test.
. CFD model
.1. Computational grid
One of the banana shaped pump rooms (with height of approxi-ately 7 m, width of approximately 8 m and average length of 17 m)
f Loviisa NPP was scanned with a laser to get an accurate 3-Deometry of the room. From the laser scan data two geometriesith different levels of detail were created with a CAD program.
he simpler model included the geometry of pumps, engines, airucts and concrete pillars. In the detailed model the largest pipesf the room were introduced. Detailed geometry of the model isresented in Fig. 1. Details like small pipes and cable shelves wereot included in the model as their effect on the flow field was esti-ated to be small. Air volume of the final model was approximately
00 m3.A 3-D computational grid was created on the geometry with the
ommercial Gambit preprocessor (Ansys, 2010). The grid consistsf tetrahedral cells and was modified with the Ansys Fluent adapt-
Please cite this article in press as: Rämä, T., et al., CFD analysis of the tEng. Des. (2014), http://dx.doi.org/10.1016/j.nucengdes.2014.03.002
on tool so that the largest cell volumes are about 0.002 m3. Theumber of cells of the final model was 4.6 million. About 90% ofhe y+ – values of the cells near the surfaces were between 20 and30, which is inside the recommended range for the wall functions
Fig. 2. Location of the CFD boundaries of the model. Fan03 and fan04 inlets andoutlets are at the opposite sides of the boxes. Inlets are toward the middle of theroom.
used (Ansys, 2010; OECD, 2007). Grid independency of the modelwas studied using the Ansys Fluent adaption tool and following therecommendations of references (OECD, 2007; ERCOFTAC, 2000).
3.2. Physical and numerical models
The commercial Ansys Fluent 13.0 solver was used to solve RANSequations of mass, momentum, energy and turbulence for incom-pressible fluid. The code’s pressure-based segregated solver wasused with the SIMPLE pressure–velocity coupling algorithm. Thespatial discretization method used was the second order upwind forconvection terms, with body force weighted scheme for pressureinterpolation. The standard k–ε turbulence model with standardlogarithmic wall functions was used for turbulence modeling. Amore detailed presentation of numerical models is available in ref-erence Ansys (2010).
In this study the density variations were estimated to be lessthan 10% in all cases. Based on the recommendations of refer-ences (Ansys, 2010; OECD, 2007) the Boussinesq model was usedinstead of the temperature-dependent density. The Boussinesqmodel treats density as a constant value in all equations, exceptfor the buoyancy term in the momentum equation
(� − �0)g ≈ −�0ˇ(T − T0)g, (1)
where �0 is the (constant) density of the flow, ̌ is the thermalexpansion coefficient and T0 the operating temperature.
3.3. Boundary conditions
The boundary conditions were set for walls of the room, surfacesof the pipes and other components and for flow inlets and outletsof process components. Location of the boundaries is presented inFig. 2.
The boundary conditions for cooling unit (fan01, fan02, fan03and fan04) flow inlets were pre-defined mass flows accordingto system specifications, turbulence values given as hydraulicdiameter and turbulence intensity at the boundary and the inlettemperature. Because the mass flow distribution between the nineinlet openings of the fan0102 inlet was not known, the air wasassumed to be evenly distributed. As the fan03 and fan04 with largerair flow were considered to be dominant regarding the flow field,this assumption was considered reasonable. The inlet temperature
emperature field in emergency pump room in Loviisa NPP. Nucl.
Tout was computed dynamically so that the cooling power QAC wasdefined according to Eq. (2)
Table 1Comparison of the cooling powers and the measurement point average tempera-tures of the experiments and the validation cases. The heat losses of the engines ofthe cooling units, Pengine are extracted from the results.
those from the measurements, presented in Table 1. Also theaverage temperature of the measurements simulation cases is pre-sented in Table 1.
ARTICLEED-7760; No. of Pages 5
T. Rämä et al. / Nuclear Engine
here Tout is the air temperature at the cooling unit inlet (CFDodel outlet temperature), TTF the cooling water temperature and
engine the heat loss of the cooling unit engines. ̨ is the heat transferoefficient determined experimentally. The validity of the equationas checked against the cooling capacity during the plant tests.ooling water temperature TTF was achieved from the plant mea-urements.
For the outlets outflow boundary conditions were used. The rel-tive outflow volume fractions for each outlet were given so thathe outflow values corresponded with the given inlet mass flows.
The heat losses of the engines, pumps, pipes and heat exchangerere given as heat fluxes at the component surfaces. The heat losses
f the engines, pumps and heat exchanger were taken from the sys-em specifications and the heat losses of the pipes were calculatedccording to the insulation of the piping and the water temper-ture inside. Heat losses were assumed to be evenly distributedlong the surface in question. The heat losses from the cooling fannit engines were taken into account in the cooling capacity asresented in Eq. (2).
Warm-air heaters used in the experiments were located at theoor level near the air outlet of the cooling units fan01 and fan02nd at the work surface level near the inlet of the cooling unit fan04.n the simulation model the extra heat of the heaters were takennto account in Eq. (2) of the cooling units. The effect of the floorevel heaters in the cooling power of fan01 and fan02 and the workurface level heaters in the cooling power of the cooling unit fan04.
The walls were modeled as concrete solid structures with thick-ess of 0.7 meters. The ambient temperature at the outside wallsere given as Tamb = 17 ◦C and the heat transfer coefficient between
mbient and wall was defined as 20 W/m2 K.The Reynolds number after the cooling fans is approximately
0,000, which suggests that the flow is turbulent. In other partsf the room the flow velocity is significantly smaller, but as theoom dimensions are large, it is assumed that the flow stays mostlyurbulent.
.4. Material properties
Working fluid in the simulation was air and the walls consist ofoncrete. Constant material properties used for the fluid were setased on reference Incropera et al. (1996).
. Simulations
.1. Numerical studies
Simulations were performed as steady-state calculations. Tem-eratures at the measurement locations were monitored during the
teration process. These temperatures did not fully converge to con-tant values having some oscillatory behavior indicating most likelyime-dependent behavior for the flow field. This was confirmedith one transient simulation and the time-averaged temperatureas about the same as the temperature averaged over the itera-
ions of the steady state simulation. The values presented in theesults were defined as an average over a representative numberf iterations.
As grid sensitivity study for one simulation case was performedith two different grids. The first grid consisted of 4.6 million cells
nd the second of 25 million cells. The high resolution grid wasade using the automatic adaption tool of the Ansys Fluent code.
ome of the wall cells were left out of the adaption process in order
Please cite this article in press as: Rämä, T., et al., CFD analysis of the tEng. Des. (2014), http://dx.doi.org/10.1016/j.nucengdes.2014.03.002
o maintain correct y+ values. The Grid Convergence Index (GCI)s proposed in reference ASME (2009) was calculated and gaven estimation of uncertainty of results due to the grid. CalculatedCI with theoretical p value of 2 and factor of safety (Fs) value 3
estimated values from 0.1% to 10% in the measurement points, withthe average being 2.6% and a standard deviation of 2.0%.
In references (Li and Nielsen, 2011) and (Ramponi and Blocken,2012) it is mentioned that usually RNG k–ε and SST k–ω turbu-lence models give the best results in the air ventilation simulationscompared to other RANS models. In reference OECD (2007) themodeling of the thermal plumes is mentioned as one of the short-comings of the k–ε turbulence models, however in the studiedscenario there are flow inlets with forced flow that are dominantconsidering the flow. In this study the same case was simulatedwith standard k–ε, RNG k–ε and SST k–ω turbulence models. Tem-peratures in the measurement positions were similar in all casesindicating that sensitivity to turbulence model is not significantconsidering the other uncertainties. Standard k–ε was selected forsimulations.
The effect of the level of details in geometry modeling wasstudied comparing the results of the flow field and the tempera-tures. Temperatures had fairly good correspondence but there weresignificant differences in the flow fields. Thus the more detailedgeometry was selected for the simulations.
4.2. Validation
Target variables for validation were temperature values at themeasurement points used in the plant measurements. In additionthe simulated cooling power of the units was compared against
emperature field in emergency pump room in Loviisa NPP. Nucl.
Fig. 3. Comparison between the first power plant test, exp01, and correspondingsimulation, valid01. Error bars show the uncertainty in the individual points accord-ing to the GCI.
4 T. Rämä et al. / Nuclear Engineering and Design xxx (2014) xxx–xxx
Fig. 4. Comparison between the second power plant test, exp02, and correspond-ia
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ng simulation, valid02. Error bars show the uncertainty in the individual pointsccording to the GCI.
The measured and simulated cooling powers of the cooling unitsre fairly close to each other. Because the cooling capacities areependent on the air temperature at the outlets of the cooling units,
t can be said that the model simulated the air temperatures nearhe cooling units correctly.
In the experiments one and two the average temperature of theoom is achieved with the simulation, but in the third experimenthe average room temperature of the simulation stays 1.5 ◦C abovehe measured value. The possible explanation is the effect of room
Please cite this article in press as: Rämä, T., et al., CFD analysis of the tEng. Des. (2014), http://dx.doi.org/10.1016/j.nucengdes.2014.03.002
alls that have not yet reached the steady-state temperature in thexperiment.
ig. 5. Comparison between the third power plant test, exp03, and correspond-ng simulation, valid03. Error bars show the uncertainty in the individual pointsccording to the GCI.
Fig. 6. Air temperature [◦C] 0.5 m above the floor level in the case01.
The simulated temperatures in the measurement locations werecompared to the experiments and are presented in Figs. 3–5. Thesolid line presents the perfect fit between simulation and experi-ments. The horizontal axis is the measured temperatures and thevertical axis the simulated temperatures.
In order to quantify the correlation between the measurementsand simulations, Pearson’s correlation coefficient r is used (Presset al., 1989). The r-values for the cases valid01, valid02 and valid03are 0.52, 0.56 and 0.56, respectively, which means mediocre corre-lation. When the locations with known large uncertainties in theexperiments are left out, the values improve to 0.69, 0.68 and 0.75,respectively.
4.3. Postulated accident scenario
The postulated accident scenario with varying operating coolingunits was simulated. As an additional heat loss in comparison to thevalidation cases the heat losses from the piping and heat exchangerare introduced to the model. In both cases the fans are operatingand circulating air, but the cooling water circulation has been shutoff for selected units. In case01 the operating cooling units werefan02 and fan04 connected in series. In case02 the operating unitswere fan01 and fan03 connected in series. Results of the postulatedaccident scenario cases are presented in Table 2.
emperature field in emergency pump room in Loviisa NPP. Nucl.
In Figs. 6–9 the temperature fields for case01 and case02 at dif-ferent levels of the room are presented. The pipes included in thesimulations are left out of the figures for the sake of clarity.
Fig. 7. Air temperature [◦C] 0.5 m above the floor level in the case02.
Fig. 8. Air temperature [◦C] 5.3 m above the floor at the cooling fan level in thecase01.
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ig. 9. Air temperature [◦C] 5.3 m above the floor at the cooling fan level in thease02.
The area of influence of the cooling units fan03 and fan04 islearly at different sides of the room. The cooling air of the unitsan01 and fan02 mixes in their shared cooling duct, which dis-ributes the cooling air evenly all over the room. The differenceetween the left and right side of the room is approximately 2 ◦Cepending whether cooling unit fan03 or fan04 is active.
Please cite this article in press as: Rämä, T., et al., CFD analysis of the tEng. Des. (2014), http://dx.doi.org/10.1016/j.nucengdes.2014.03.002
. Summary
One of the emergency pump rooms of Loviisa NPP was scannedith a laser to get accurate 3D geometry. A tetrahedral mesh was
20 – 57 32– 39 – 32
created inside the geometry and flow and temperature fields weresimulated using Ansys Fluent 13.0 computational fluid dynamics(CFD) software. The simulations were used to support measure-ments to verify the acceptable temperatures during the postulatedaccident scenarios.
Two geometries with different levels of detail were com-pared, of which the more detailed geometry was selected forthe final simulations. Even from the more detailed geometrysmall details estimated to have very little effect to the flowfield were left out of the model. Simulations were performedwith standard k–ε turbulence model. Sensitivity to turbulencemodel was not significant considering the other uncertain-ties.
The mesh sensitivity studies were made using two meshes. TheGrid Convergence Index as presented in reference ASME (2009)was calculated and the average uncertainty in the measurementpoints due to the grid was estimated to be below 3%. The accept-able level of numerical convergence was barely achieved withsteady-state simulations, probably due to the time-dependent flowfeatures.
Temperatures in the most important parts of the room wereconfirmed with the plant tests. With CFD the detailed tempera-ture profile of the whole room was achieved. The model can beused to estimate the hot and cold temperature areas in postu-lated accident scenarios, but for the estimation of quantitativelocal temperatures the uncertainties estimated by comparison totests and systematic mesh sensitivity studies must be taken intoaccount.
Finally two postulated accident cases were simulated. In thecases the operating cooling units were varied. The temperature pro-file of the room changes significantly depending on which units arecooling and which only circulating. The room average temperatureis approximately the same.
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Safety Applications. NEA/CSNI/R, pp. 5.Press, W., Flannert, B., Teukolsky, S., Vetterling, W., 1989. Numerical Recipes: The
emperature field in emergency pump room in Loviisa NPP. Nucl.
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