Discrepancies in Peak Temperature Times using

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International Conference – Alternative and Renewable Energy Quest, AREQ 2017, 1-3 February International Conference – Alternative and2017, Renewable Spain Energy Quest, AREQ 2017, 1-3 February 2017, Spain

Discrepancies in Peak Temperature Times using Prolonged CFD The 15th Symposium onTimes District Heating Cooling CFD Discrepancies in International Peak Temperature using and Prolonged Simulations of Housing Thermal Performance Simulations of Housing Thermal Performance Assessing the feasibility of using the heat demand-outdoor Aiman Albataynehaa*, Dariusz Altermanbb, Adrian Pagebb and Behdad Moghtaderibb Aiman Albatayneh *, Dariusz , Adrian Page and Behdad Moghtaderi temperature function for Alterman a long-term district heat demand forecast School of Natural Resources Engineering and Management, German Jordanian University, Amman, Jordan P.O. Box 35247 a

a School of Natural Engineering andTECHNOLOGIES Management, German University, Amman, Jordan P.O. Box 35247 Priority Research CentreResources for FRONTIER ENERGY AND Jordanian UTILISATION, The University of Newcastle, NSW, Australia a,b,c a a b c c b Priority Research Centre for FRONTIER ENERGY TECHNOLOGIES AND UTILISATION, The University of Newcastle, NSW, Australia b

I. Andrić

a

*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b

Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract In this paper, CFD analysis was used to determine the internal air temperature over long periods of four full scale housing test In this paper, CFD analysis was used to determine the internal airThe temperature long periods four(i.e. full15, scale housing test modules in Newcastle, Australia exposed to a moderate climate. influenceover of different time of steps 20 30, 35, 40 45, modules in Newcastle, Australia to aonmoderate climate. The influence of different (i.e. 15, 20 30, 35, 40in45, 60, 80, 100, 120, 150, 180 minuteexposed intervals) the discrepancies in peak temperature for thetime CFDsteps simulations is discussed the Abstract 60, 80,It100, 150, intervals) on theindiscrepancies in peak temperature forwere the CFD simulations discussed paper. was120, found that180 theminute average discrepancies time for internal peak temperatures between one andisfive hours in the paper. It was found thatresponses the average in time for internal peak temperatures were between one and five hours compared with the real of discrepancies the testing modules. District heating are commonly addressed in the literature as one of the most effective solutions for decreasing the compared with thenetworks real responses of the testing modules. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat © 2017 The Authors. Published by Elsevier B.V. ©sales. 2017 Due The Authors. Published by Elsevier B.V. to the changed conditions renovation policies, heat demand in the future could decrease, © 2017 The Authors. Publishedclimate by Elsevier B.V. and building Peer-review under responsibility of the 2016. Peer-review under responsibility ofperiod. the organizing organizing committee committee of of GU AREQ 2017. prolonging the investment return Peer-review under responsibility of the organizing committee of GU 2016. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords: CFD, Building Simulation, Internal air temperature, Long term CFD simulation forecast.CFD, The Building district Simulation, of Alvalade, located in Lisbon Long (Portugal), was used as a case study. The district is consisted of 665 Keywords: Internal air temperature, term CFD simulation buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1. Introduction with results from a dynamic heat demand model, previously developed and validated by the authors. 1.compared Introduction The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Theerror building sector largely climate energyconsidered). consumption through theintroducing operationalrenovation energy (the in annual demand wascontributes lower than to 20% for allchange weatherand scenarios However, after The building sector largely contributes to climate change and energy consumption through the operational energy requirements over the entire life-cycle of a structure and its component materials. Designing low energy buildings scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). requirements over the entire life-cycle of a structure and its component materials. Designing low energy buildings requires accurate building assessment tools/software to predict the energy requirements to maintain acceptable The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the requires accurate assessment energy requirements to maintainof acceptable thermal forbuilding occupants. decreasecomfort in the number of heating hours oftools/software 22-139h duringtothepredict heatingthe season (depending on the combination weather and thermal comfort for occupants. renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

© 2017 The Authors. Published by Elsevier Ltd.

*Peer-review Corresponding author. Tel.: 962-06-of4294444. under responsibility the Scientific Committee of The 15th International Symposium on District Heating and *Cooling. Corresponding Tel.: 962-06- 4294444. E-mail address:author. [email protected] E-mail address: [email protected] 1878-0296 ©Heat 2017demand; The Authors. Published bychange Elsevier B.V. Keywords: Forecast; Climate 1878-0296 2017responsibility The Authors. of Published by Elsevier B.V. of GU 2016. Peer-review©under the organizing committee Peer-review under responsibility of the organizing committee of GU 2016.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of AREQ 2017. 10.1016/j.egypro.2017.05.023

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Aiman Albatayneh et al. / Energy Procedia 115 (2017) 253–264 Albatayneh, Alterman, Page, Moghtaderi/ Energy Procedia 00 (2017) 000–000

Autodesk CFD software may be used to create thermal models and assess and optimize any design concept prior to the construction of a building. Modifications and improvements to an existing building can also be evaluated before any renovations. This tool has the benefits of reducing design risks and avoiding expensive misjudgments while facilitating creativity1. In the past several years CFD has been playing progressively more significant part in building design. The results provided by CFD can be used to analyze whole buildings to find the internal air temperature at any point inside the building but using CFD for long term simulations results in excessive computing time. Usually, CFD is used to evaluate a building’s short term thermal performance and is coupled with other programs to predict prolonged performance, such as Building Energy Simulation (ES), to avoid this excessive computing time2,3,4,5,6. The main issues with CFD analysis for prolonged simulation (several weeks and months) are its lengthy computing time7,8,9,10; smaller fluctuation range and discrepancies in peak temperature time 11. Applying larger time steps (i.e. 60 or 80 minutes interval) will accelerate the simulation process (lower computing time) and will increase the temperature fluctuation range11, but this operation shifts the peak temperature time inside the buildings (discrepancies in peak temperature between the real responses of the testing modules (real data) and the CFD simulation results). This paper describes the effect of the larger time step on the peak temperature time inside the housing test modules. 2. Methodology CFD is used to examine the effect of long term building thermal simulation using larger time steps. This will speed up the simulation time and predicting a higher temperature fluctuation range close to the real responses of the testing modules. In this study the simulations of four full scale housing test modules were simulated then compared with the data obtained from the test modules (each incorporating a different walling system). 2.1. Full Scale Test Modules Continuing research program has been underway in the Priority Research Centre for Energy at the University of Newcastle, Australia on the thermal performance of Australian housing. This has involved the construction of four full scale housing modules (see Fig.1) and continuously monitoring their thermal performance under various weather conditions. All the modules were constructed at the University of Newcastle, Callaghan Campus (longitude 151.7 E and latitude 32.9 S). The modules were selected to represent typical forms of construction in Australia12. a

Albatayneh, Alterman, Page, Moghtaderi/ Energy Procedia 00 (2017) 000–000

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Aiman Albatayneh et al. / Energy Procedia 115 (2017) 253–264

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b

c

Fig.1. a) Full scale test modules, b) North plan, c) South plan13.

All modules have same design and share same construction materials except for their walling system, for this reason all the modules will be named after their walling system12 as shown in Fig. 2.  Cavity Brick Module (CB); walling for CB module consists of two 110mm brickwork skins with 50mm cavity between the walls and 10mm render covered the internal walls.  Insulated Cavity Brick Module (InsCB); two of 110 mm brickwork skins with 50mm cavity insulated by R1 polystyrene insulation and the internal walls covered by 10mm internal render.  Insulated Brick Veneer Module (InsBV); internal walls consist of; internal timber frame with low glare reflective foil and R1.5 glass wool batts covered by 10mm plasterboard while the external walls constructed using 110 mm brickwork skin.

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 Insulated Reverse Brick Veneer Module (InsRBV); the internal walls; 110mm brick skin covered by 10mm internal render. External walls; 2-3mm acrylic render on 7mm fibro-cement sheets fixed on timber stud frame insulated by R1.5 glass wool batts insulation. a

b

c

d

Fig. 2. Walling systems a)CB, b) InsCB, c) InsBV, d)InsRBV

More than 100 sensors were installed in each module to record the external and internal conditions with the data recorded every 5 minutes interval. The paper refers to the “free-floating” stage without any mechanical heating or cooling. 2.2. Computational Fluid Dynamics (CFD) CFD is an established simulation technique that mathematically evaluates fluid flow and heat transfer14. Therefore, CFD analysis can be used to find the internal air temperature at any point inside a building but it is not specifically designed to calculate a module’s internal air temperature over long periods and lengthy computing time is required. The variations of internal air temperature of the four housing test modules with different walling systems could determine the accuracy of CFD results. The CFD modules were created based on the actual physical characteristics and properties of each module to obtain results as close as possible to those recorded from the housing test modules. The simulations were based on the measured outside air temperature and the wind speed and direction recorded from the housing test modules. The simulation results were then compared with the actual air temperature recorded inside each module. An automatic mesh was generated for an analysis of the module (an automatic topological examination of the analysis geometry used to find the distribution on every element and the mesh size). In these analyses, 67,567 elements and 264,534 nodes as well as k-epsilon turbulence modelling for numerical/solver settings were used, then a grid independence test was conducted to ensure that the CFD simulation was correct12. Long period CFD simulations (weeks, months) have typically faced some issues such as; excessive computing times, smaller fluctuation range which can be solved by using larger time steps with a reasonable degree of accuracy but will affect the peak temperature time inside the modules. Using Autodesk CFD simulations for long periods (weeks or months) takes long computing times and the CFD simulation results for the smaller time steps have smaller daily temperature fluctuation ranges compared with the real data. In this section, new ways to solve these issues in a fast and accurate way are discussed. Most weather stations record the temperature every minute or at 5 minute intervals. Simulated for long periods (weeks, months) using CFD, it takes a long computing time (for a normal PC it could takes weeks to simulate several months of temperatures) which is not practical. The main factors controlling the simulation time is the simulation period and the time step size. From the Autodesk CFD program the time step size can be edited through the solver, as shown in Fig. 3.


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Fig. 3. Solve window in the Autodesk CFD program14.

Where;  Solution Mode: steady or transient state. The transient state was used in this simulation.  Time Step Size (seconds): depends on the time scale of the analysis and the type of the analysis.  Stop Time (seconds): for transient analyses, the analysis can be stopped when a specific time has been reached, after a certain number of time steps, or whichever comes first, or “-1” if it is not desired to stop the analysis at a certain time.  Inner Iteration: number of iterations for every time step. We used one inner iteration in this analysis  Save Intervals for how often the results and summary information are saved.  Solver Computer: this enables an analysis to be built on one machine (the local computer is the default).  Continue From: if continuing an analysis, select the iteration or time step to continue from.  Iterations to Run: the number of iterations or time steps to run in the analysis. Increasing the time step size in the CFD simulation will affect the temperature fluctuation range and shift the peak temperature inside the modules, as shown in Fig. 4.

Fig. 4. Discrepancies in the peak internal air temperature time between the real data and the CFD simulation for the InsCB.

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3. 3. Results and Discussions Simulations were carried out for all the modules for one week in summer from 14/01/2010 to 22/01/2010 and one week in winter (southern hemisphere) between 11/06/2009 to 19/06/2009 for different time steps. CFD simulations were run for all modules and the results for InsBV are shown in Fig. 5 and 6.

Fig. 5. Comparison between CFD analysis and the real data for the InsBV for different time steps in summer week.

Fig. 6. Comparison between CFD analysis and the real data for the InsBV for different time steps in winter week.

Note; CFD analysis with one inner iteration using smaller time interval may result in some cases with two temperature peaks during one day. This can be avoided by using larger time steps or 3 inner iterations but this significantly increases the computing time. Higher time step size increases the fluctuation range as shown in the previous research shifting back the peak temperature time (earlier peak temperature with larger time step interval)12. In the real diurnal temperature cycle, the peak temperature inside the buildings depends on the outside weather conditions and does not occur at the similar daily time (fluctuating by more than 3 hours within the analysed period).


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The peak temperature time inside the modules did not occur at the same time every day, and varied from day to day reaching 3 hours or more within the studied period (see the highest differences in peak temperature time in Tables 1 and 2). The following tables showed the exact time when the internal air temperature reached its peak throughout the studied period. Table 1. Observed peak temperature time inside the modules for the winter week. CB - Real

InsCB - Real

InsBV- Real

InsRBV- Real

Daily peak temperature time

12/06/2009 14:00

12/06/2009 14:10

12/06/2009 14:10

12/06/2009 14:10


13/06/2009 13:15

13/06/2009 13:10

13/06/2009 13:40

13/06/2009 13:05


14/06/2009 12:55

14/06/2009 12:55

14/06/2009 12:50

14/06/2009 13:00


15/06/2009 14:20

15/06/2009 14:20

15/06/2009 14:25

15/06/2009 14:15


16/06/2009 13:45

16/06/2009 15:30

16/06/2009 15:25

16/06/2009 15:30


17/06/2009 14:05

17/06/2009 12:15

17/06/2009 14:00

17/06/2009 12:20

Highest differences in peak temperature time

1hr:25 min

3hr:15 min

2hr:30 min

3hr:10 min

Table 2. Observed peak temperature time inside the modules for the summer week. CB - Real

InsCB - Real

InsBV- Real

InsRBV- Real


15/01/2010 16:10

15/01/2010 14:15

15/01/2010 14:10

15/01/2010 13:55


16/01/2010 16:40

16/01/2010 15:25

16/01/2010 15:35

16/01/2010 15:40


17/01/2010 15:50

17/01/2010 12:55

17/01/2010 13:05

17/01/2010 13:00


18/01/2010 14:40

18/01/2010 15:35

18/01/2010 16:05

18/01/2010 13:30


19/01/2010 16:50

19/01/2010 15:40

19/01/2010 15:30

19/01/2010 14:25


20/01/2010 17:40

20/01/2010 15:40

20/01/2010 15:50

20/01/2010 15:40

Highest differences in peak temperature time

3 hr

2hr:45 min

3 hr

2hr:20 min

Simulations were then run for each model for the different time steps to monitor the discrepancies in peak temperature for each time step. The discrepancies are defined here as the differences in time between the simulated and real internal air temperature. In this analysis, the daily peak temperature time also changed with the different time steps for the summer and winter weeks. For example, the highest discrepancies in peak temperature time occurred for the 15 minute time step, with a time lag of between 150 - 330 minutes compared with the real internal air temperature, as shown for the winter week in Fig. 7, 8, 9, 10 and the summer week in Fig. 11, 12, 13, 14.

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Aiman Albatayneh et al. / Energy Procedia 115 (2017) 253–264 Albatayneh, Alterman, Page, Moghtaderi/ Energy Procedia 00 (2017) 000–000 Fig. 7. Discrepancies in the peak temperatures for the Cavity Brick Module using different time steps in winter week.

Fig. 8. Discrepancies in the peak temperatures for the Insulated Cavity Brick Module using different time steps in winter week.

Fig. 9. Discrepancies in the peak temperatures for the Insulated Brick Veneer Module using different time steps in winter week.

Aiman Albatayneh et al. / Energy Procedia 115 (2017) 253–264 Albatayneh, Alterman, Page, Moghtaderi/ Energy Procedia 00 (2017) 000–000 Fig. 10. Discrepancies in the peak temperatures for the Insulated Reverse Brick Veneer Module using different time steps in winter week.

Fig. 11. Discrepancies in the peak temperatures for the Cavity Brick Module using different time steps in summer week.

Fig. 12. Discrepancies in the peak temperatures for the Insulated Cavity Brick Module using different time steps in summer week.

Fig. 13. Discrepancies in the peak temperatures for the Insulated Brick Veneer Module using different time steps in summer week.

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262 10


Fig. 14. Discrepancies in the peak temperatures for the Insulated Reverse Brick Veneer Module using different time steps in summer week.

From the above, the discrepancies in peak temperature lagged by almost a fixed time for each time step. The average discrepancies in peak temperature for all the modules shows that there were 3 - 5 hour discrepancies in the peak temperatures inside the modules for most time steps compared to the real peak air temperature inside the modules as shown in Fig. 15, 16, 17.

Fig. 15. Average discrepancies in peak temperature for all the modules for the summer week.

Fig. 16. Average discrepancies in peak temperature for all the modules for different time steps in winter week.


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Fig. 17. Average discrepancies in peak temperature for winter and summer weeks for all the modules and different time steps.

The 80/100 minute time step lagged between 4 to 5 hours compared to real data for internal air temperature. The best results, in regards to the discrepancies in peak temperatures occurred to be for the 40/45 minutes time step which was almost identical with the real performance of modules. To examine the accuracy of the previous values the peak temperature was shifted by certain period as shown in Table 3 (data from Fig. 17.) where a positive value means shift backward and negative value means shift forward. Table 3. Shifted time for the peak temperatures inside the modules for each time step. Time step (minutes)

15

30

40

45

60

80

100

120

150

180

Shifted time (hours)

4

4

1

-1

4

5

4

3

3

2

For example, the discrepancy in the peak temperatures was 4 hours between the real data and the CFD simulation for the 15minute time step for the InsCB this means that shifting the curve back 4 hours the peak temperature time will match the real data peak time. To examine the accuracy of this study, larger time steps (60, 80, 100, 120, 150, 180 minutes) were applied what shifted the peak temperature inside the module (according to the Table 3). By comparing the percentage of the number of hours with temperature difference of 3ᵒC between real data and CFD simulation for each module indicated that the shifted peak temperature according to the Table 3 provides more accurate results compared to the real data as shown in Table 4. Table 4. Percentages of the number of hours between the real data and the CFD simulation for each module. Time step/ model-season

CFD 60 min

CFD 80 min

90.30%

CFD 60 minshifted 94.55%

CFD 100 min

86.67%


CB summer InsCB summer

98.79%

InsBV summer

CFD 120 min

83.64%


84.85%


100.00%

95.15%

98.76%

88.48%

91.41%

87.27%

89.63%

88.48%

90.91%

87.27%

93.79%

80.00%

91.41%

79.39%

84.15%

InsRBV summer

95.15%

95.15%

88.48%

95.65%

84.85%

CB winter

96.36%

93.33%

93.33%

93.79%

94.55%

88.96%

85.45%

87.20%

94.48%

84.85%

89.02%

InsCB winter

92.12%

89.70%

92.73%

92.55%

90.30%

90.80%

85.45%

89.63%

InsBV winter

50.91%

52.12%

70.30%

72.22%

80.61%

83.44%

83.03%

85.98%

InsRBV winter

76.97%

68.48%

87.88%

82.72%

94.55%

96.32%

89.70%

93.29%

Average

86.14%

85.53%

87.73%

90.41%

87.12%

90.64%

85.00%

88.03%

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4. Conclusions The main issue with CFD building analysis for long period simulation is the excessive computing time. This can be solved at a reasonable degree of precision by applying larger time steps to accelerate the simulation process but this will shift the peak temperature time inside the modules resulting in discrepancies in peak temperature between the real data and the CFD simulation results. The fastest simulation with the most accurate results was for the 80/100 time step where 90% of the results fell within 3ᵒC difference to the real data, with only 1% of the computing time required for 1minute time step simulation. However, this shifts the peak temperature time by an average of 2- 4 hours. The best results, in regards to the discrepancies in peak temperatures were for the 40/45 minutes time step but this may take longer computing time. The study confirmed the CFD analysis can be used to simulate the thermal performance for long periods with reasonable degree of accuracy despite of the discrepancies in peak temperature time inside the modules. This will provide building designers an extra accessible tool to find the building internal air temperature which is essential to design low energy building. Acknowledgements This research project has been supported by the Australian Research Council (LP 120100064) and Think Brick Australia. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

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