The Building Simulation Model

Geometric Model

To start, a 3D-building model of part of the building was made using Sketch-up and TRNBuild.

The 3D-model included the whole building site along with the neighbourhood. This was done to quantify the shading effects on solar gain calculations. Only part of the building was modelled, representative of other parts containing a thermal zone for every orientation (north, south, east, west) per floor level. Other geometries were modelled as shading devices. The building geometry was derived from Autocad drawings. The geometric model, which includes the library, consists of four thermal zones.

SUB-MODELLING APPROACHES TO PREDICT DYNAMIC MOISTURE RESPONSE 101

Figure 4.8 illustrates the library and the geometric simulation model. The library was modelled as a single zone with following dimensions: 13.3m×7.24m×3.9m (interior floor area: 96m², interior volume: 372m³).

Because occupancy profile and internal load profile were similar for the adjacent zone, it was assumed that adjacent zones on the same floor level had a similar temperature and therefore they were not modelled. The adjacent adiabatic interior walls were maintained in the building model, since they provide a substantial part of the thermal and hygric inertia [269]. The basement, the room at the ground floor and the attic above the library were modelled as additional zones to include stack effect and the influence of the overheating of the attic in summertime on the library. The geometry and material characteristics in the model were assumed constant and were not changed to achieve a validated model.

Figure 4.7: Illustration of the simulation models. Left: The Site Plantin-Moretus included in the 3D-building model (blue) – Right: Illustration of the model construction. The spaces in purple have the function of

shadow objects, the spaces in yellow is the building model including the library.

Figure 4.8: Interior view of the library showing the many books stored in the room and the building model including the library (located at first floor).

Attic

Library Ground floor

Basement

Material Characteristics

Both interior and exterior walls consist of masonry covered with plaster at the interior. The ceiling and floor consist of wood. It was only possible to measure the total wall thicknesses.

Therefore, for the thickness of the separate material layers (masonry, plaster, wood) assumptions were made. Because the interior walls in the library were covered with bookcases, bookcases were included in the build-up of the interior wall. Windows are single glazed and the frame consists of wood and stone pillars. At the interior site, wooden shutters are presents. They were modelled as an additional thermal resistance of the window. Heat transport properties for these materials are summarized in Table 4.2. Windows and shutters were always closed.

Table 4.2: Material properties used in the building model of the library

Building part Material Thickness [m] λ [W/m.K] ρ [kg/m³] cp [kJ/kg.K] moisture buffering of books by a HAM-model, the characteristics found in the work of Kupczak et al. [140] were used. These authors investigated the buffering effects of paper collections in libraries and proposed a 1D model of a book which can be used to investigate any number of books placed on a bookshelf. To use the HAM-component in TRNSYS, the equation for the moisture sorption isotherm have to be in the same format for all material layers in one composition (e.g.

external wall). Therefore the sorption isotherm as presented by Kupczak et al. [140] is written in the format presented by Hansen [203]²⁴. Figure 4.8 shows the sorption isotherm as presented by

24 c = cmax (1-ln(ϕ)/A)^-1/b

Building part material U-value [W/m²K]

Windows Glazing: Single

Frame: Wood + Stone 4.9

Correction closed shutter 2.9

SUB-MODELLING APPROACHES TO PREDICT DYNAMIC MOISTURE RESPONSE 103

Kupczak et al. [140] and as used in this work. The curve used in this work fitted well with the curve presented in literature, except for low relative humidities. For brick, gypsum and wood, characteristics presented in Annex 14 [203] were used. An overview of the used characteristics is presented in Table 4.3.

Figure 4.9: Sorption isotherm as presented by Kupczak et al. [140] and as used in this work written in the form of the equation presented by Hansen [203].

Table 4.3: Hygric Parameters used for the HAM model.

Characteristic Value

Weather data

The weather data was measured by a weather station providing following outputs; air temperature, relative humidity, wind speed and direction, barometric pressure, global solar radiation. All measurements were instantaneous values sampled at 10-minute intervals. Data was reformed to make hourly averaged weather files. To calculate the diffused radiation, TRNSYS type 16 was used which takes hourly integrated values of total horizontal solar radiation and estimates diffuse fraction internally.

Infiltration

The stack effect and the infiltration were modelled using the TRNSYS add-on TRNFLOW [285]. The air mass flow coefficient cs (kg/s @1Pa), used in the power law equation, is based on the results of passive tracer gas measurements performed in cooperation with Pentiaq²⁵ [286]. The power law equation serves to calculate the instantaneous air flow rate Q based on pressure differences, which were dependent on temperature and wind velocity (eq. 4.18). In the performed measurement, an average infiltration rate of the library of 0.2 h^-1 @2Pa was found. The standard value of 0.65 for the airflow exponent is not changed because most cracks have a mixed flow regime with a flow exponent of 0.6 to 0.7

𝑄 = AC_𝑠∆P^𝑛 (4.18)

For more information about modelling with TRNFLOW, the reader is referred to the manual of TRNSYS and TRNFLOW.

HVAC

The library is heated by four radiators, located next to the exterior wall. To avoid low relative humidity due to heating, two mobile humidifiers are placed in the library which start working in case the relative humidity drops below 40%. The heating system and humidifiers are continuously in operation. This includes that there is no setback during night of for the summer season. The radiators were modelled in a simplified way using TRNSYS type 1231²⁶. The heat released to the room by one radiator is 800W and was modelled as a heat gain of 70% by convection and 30%

by radiation. Radiators were controlled by a room thermostat. The setpoint of the room temperature is 18.5°C. The radiators switched of at a room temperature of 20.5°C.

Table 4.4: Input values of the radiator.

T rad, inlet

26 This type is not available in the standard library of TRNSYS, but is available in the TESS Libraries.

SUB-MODELLING APPROACHES TO PREDICT DYNAMIC MOISTURE RESPONSE 105

The humidifiers were defined as a moisture gain with a gain of 1kg/hr per humidifier using type 641. The operation of the humidifier was controlled by a hygrostat controller by an on/off controller. The deadband was -3/+3%RH.

Other Internal Gains and Losses

Other defined internal gains were the presence of visitors and the light equipment. The adopted values for these are summarized in Table 4.5. For their presence the following weekly scheme was implemented: from Tuesday till Sunday 10h-17h. (On Mondays, the museum is closed.). The amount of visitors was based on the monthly sold tickets. Furthermore, a daily occupancy profile was used as suggested by Schito and Testi [287].

Heat gain Moisture gain

People 80W/pers 0.07kg/(h.pers)

Lights 4.12W/m² -

Table 4.5: Internal gains used in the building model of the library.

4.4.3 Model Calibration

Monitored indoor temperature and relative humidity over a full annual cycle were used to calibrate the model in order to accurately predict zone temperature. Data-loggers inside the building were located approximately 2m above the floor and all data loggers were placed out of draft or direct sunlight. Criteria used during the validation procedure are presented in Table 4.1.

These criteria were applied to the hourly and monthly average temperature and absolute humidity. Because daily and seasonal temperature and relative humidity fluctuations are needed to estimate the preservation conditions, it was strived to achieve an as low as possible error for the hourly values. Input parameters which were varied in the model are illustrated in Table 4.6.

Table 4.6: Modified parameters in the simulation model.

Parameter Range Initial Value

Occupancy (heat gain and moist gain) [-10%; +10%] Monthly average

Infiltration rate [-10%; +10%] 0.2 h^-1

Setpoint heating [-10%; +10%] 18.5

Walls: λ-value brick [-10%; +10%] 1.00 W/(m.K)

jan/12 feb/12 mrt/12 apr/12 mei/12 jun/12 jul/12 aug/12 sep/12 okt/12 nov/12 dec/12

Daily visitors[-]

After the completion of iterations, analysis of results showed satisfying consistency between calculations and measurements. Hourly and monthly MBE, RMSE and CV(RMSE) were calculated for each hour, month and for the total period. Table 4.7 shows the MBE, RMSE and CV(RMSE) values for temperature, absolute humidity and relative humidity on annual basis. MBE and CV(RMSE) values were below the predefined tolerance range for all parameters.

Figure 4.10 depicts the monitored and simulated temperature and relative humidity for the entire calibration period of 1 year. MBE and CV(RMSE) values for individual months showed that for the coldest months, temperature was underestimated, while in summer, temperatures were overestimated. Hence, the deviation was never more than 5%.

Table 4.7: MBE, RMSE and CV(RMSE) for the hourly and monthly average temperature, absolute humidity and relative humidity.

MBE ^hour RMSE ^hour CV^hour MBE ^month RMSE ^month CV ^month

T 0.6% 0.7°C 3.5% 0.1% 0.4°C 1.7%

RH -0.9% 2.6%RH 5.4% -0.8% 1.0%RH 1.9%

AH -0.7% 0.00048kg/kg 6.5% -0.7% 0.00021kg/kg 2.6%

Figure 4.10: Simulated monthly average temperature and relative humidity for the entire calibration period of one year and hourly MBE and CV(RMSE) values for individual months.

0.0%

SUB-MODELLING APPROACHES TO PREDICT DYNAMIC MOISTURE RESPONSE 107

Because the yearly relative humidity lies in the mid-range and the building is conditioned (heating system and humidifier), the guidelines of ASHRAE were used to assess the preservation class²⁷. Most important parameters are yearly average value and short and seasonal fluctuations.

Figure 4.11 shows the measured and simulated percentages of time a preservation class was reached. For the highest preservation classes, AA, As or A, the simulated results did not predict the outcome as well as for the other preservation classes. This was due to the error on the daily temperature fluctuations, but mainly due to the error on daily relative humidity fluctuations (Figure 4.12). As for the highest preservation classes smaller short- and long-term fluctuations are allowed compared to the other preservation classes, errors for prediction the daily fluctuation have a higher impact for the higher preservation classes and as a consequence, the simulation result is more uncertain.

Figure 4.11: Comparison between the measured (blue) and simulated (green) percentages of time a preservation class is reached.

Figure 4.12: Histogram of absolute errors on hourly average temperature and relative humidity values.