CTU Prague, Faculty of civil engineering, Department of microenvironmental and building services
prof. Ing., CSc.
CTU Prague, Faculty of civil engineering, Department of microenvironmental and building services
This paper presents a study performed on the Pilgrimage Chapel of Holy Stair in Rumburk, Czech Republic, where high values of air moisture leading to degradation historical interior were detected. In order to understand hydrothermal conditions in the chapel, the monitoring of air temperature and relative humidity of interior and exterior was carried out in a sufficiently long-time period. The measured data were used as the basis for the calibration on the simplified numerical model, which works in simplified form on the physical principles and heat balance method. A suitable algorithm for timing of natural ventilation was applied, and consequently a presumed influence on the indoor environmental quality and overall reduction in the amount of air humidity.
Keywords:Historical interiors, Adaptive ventilation, Regression-based numerical model, Indoor environmental quality
Historically valuable interiors require special attention in terms of the indoor environmental quality, especially hydrothermal parameters play a significant role. Relative humidity value and its fluctuation over the time have a fundamental influence on the preservation of the cultural monuments. Other important parameters are air temperature and surface temperature. Unfortunately, in many cases, these parameters lie far beyond the tolerance zone. In the most common cases, these interiors contend with unacceptably high relative humidity level caused by structural problems, as well as moisture-producing occupants.
Historic interiors contain hygroscopic materials whose moisture content is directly dependent on the relative humidity of the surrounding air . Changes in the relative humidity of the ambient air lead to a decrease or increase of moisture content of the materials and can cause irreversible damage to the material . The ideal problem solving could be to install air conditioning systems that can maintain the exact require parameters. However, this solution is unacceptable in terms of operational cost and often difficult to implement for many objects . In this case, the use of adaptive ventilation is provided. Natural ventilation with correct definition of the algorithm can lead to improvement of the indoor parameters with minimal energy input.
The paper presents a simplified numerical design model describing actual behaviour of the object in the corresponding detail. The goal of this model is to find a suitable algorithm, able to indicate proper time and regulation of natural ventilation (e.g. window opening). The aim of the paper is to verify the hypothesis that the quality of indoor environment can be improved by applying a suitable adaptive ventilation algorithm.
The Pilgrimage Chapel of the Holy Stairs (Figure 1) is part of the important cultural monument called Loreto, in Rumburk in the north of the Czech Republic. Since 2014, it has been included among the significant places on the “Via Sacra” Pilgrims’ Way. Although the chapel has recently undergone extensive reconstruction, the interior shows signs of damage due to the high level of air moisture. In order to understand the overall airflow and moisture movement in the chapel, the monitoring of hydrothermal parameters was carried out between 8 November and 20 June. The results of analysis which evaluated the measures data were described in publication  and confirm the assumption of the moisture supplying from the adjacent corridor. From the previous analysis it is possible to assume that adaptive ventilation would have a beneficial effect on the reduction of moisture in the chapel.
Figure 1. The Pilgrimage chapel of Holy stairs . Frost on the frescoes leading to damages.
The key factor to ensure appropriate internal environment consists mainly in a moisture balance between hydroscopic material in the interior and ambient air, where the change in the specific humidity of materials reflects on their dimension. Exposing the interior to air with high relative humidity causes a volume increase of material and, thus, its destruction. On the other hand, constant natural ventilation leads to significant fluctuation in relative humidity, causing damages due to the frequent shrinkage and increased volume of the materials . Adaptive ventilation takes into account the change in air temperature and level of air moisture in the exterior and interior and, based on the evaluation of the current condition, recommends the supply of fresh air. Adaptive ventilation cannot replace full air control system, which is used for accurate environmental management. It only tries to improve the condition from not satisfying to yet acceptable.
Tolerated zone determination
Parameters of the indoor environment for historical interior are defined in the ASHRAE standard , which divides the indoor environment into several categories according to the degree of preservation risk. An acceptable risk is defined by values with ±10% RH and ±5 K deflection from ideal values of 50% RH and 20°C. Extremely high risk is defined for relative humidity values higher than 75% . At the same time, it is necessary to check the maximum fluctuation during the day, when critical values are considered those with a larger difference than 15% RH throughout the day.
Historic buildings are characterized by massive structures with high heat and moisture accumulation capabilities which are characterized by frequent occurrence of condensation. This phenomenon occurs especially during the transition period, when humid exterior air condenses on the still cold surface of the walls.
The total heat energy of the space is influenced by the heat fluxes shown in Figure 2. The law of energy conservation expresses that heat fluxes have to always be in equilibrium with the total heat energy of the space.
Figure 2. Heat flux diagram in space.
Thermal equilibrium is valid at each time step. For this reason, it is possible to say that total energy of space at time τ (x-1) with the count of all heat fluxes is equal to the total thermal energy of the space at time τ (x). The heat balance of the space can be written according to formula (1).
total thermal energy of the space [J]
thermal energy supplied to the interior based of the transmission structure [J]
thermal energy including infiltration and ventilation [J]
thermal energy including heat accumulated in structures [J]
thermal energy including all added heat sources (occupants, heating, …) [J]
thermal energy including heat from solar radiation [J]
Different methods can be used to model buildings’ behaviour. Most of the software available on the market (eg. DesignBuilder, WUFI, etc.) work on the principle of explicit method, when parameters of object properties are known and behaviour of the object can be calculated. In cases when building behaviour is known and some of input parameters are missing, regression method is used. One of the regression methods describing non-liner function is the neural network method.
The task of the regression analyses is to find the appropriate theoretical regression function to describe the dependence, determine the point, interval estimates of regression coefficients, estimation of regression function values for prognostic purposes and verification of compliance between the proposed regression function and experimental data .
In case of examining parameters of hydro-thermal microclimate dependent variable represent Ti, xi, Tm as a function (2), (3) and (4).
Model description and calibration
The simplified numerical model created in MS Excel works on the principle of the regression method. The model was calibrated based on measured data for a period of 10 days and subsequently verified for other period with measured data. Calculation of dependent variables Ti and Tm was determined on the principle heat balance method described in (1). The calculation of dependent variable xi took into account the amount of outdoor humidity by ventilation and infiltration and the ability of the material to absorb moisture. Due to the complexity of the issue related to the accumulation of the moisture in the structures, the parameter of moisture accumulation was determined by the regression method from measured data as a function relative humidity. This assumption was further verified through simulations in DesignBuilder, which generated identical results with simplified model in MS Excel.
The choice of appropriate time step was required for proper model operation. The above described model works with 5-minutes time steps that correspond to the time steps obtained from the dataloggers in object monitoring. Additional source of moisture was applied to the model in an amount that correspond with results of previous analysis published in .
The model is not able to take into account the possibilities and limits of natural ventilation in terms of buoyancy-drive and wind drive ventilation and it was only set to two modes (infiltration: 0,2 ach, or infiltration with natural ventilation 1,5 ach). Determination of air change based on differences in temperature and wind effect is subject to further investigation.
The following conditions were applied in the model (5), (6), (7) and (8). The main aim of these conditions was moving parameters of air temperature and relative humidity closer to the tolerance zone and at the same time prevent significant fluctuation of relative humidity.
In case the model evaluated at least one of the conditions as a “CLOSE” mode, it considered only infiltration.
Results of calibration
The graphs (Figure 3, Figure 4), show the results of the model verification for the period 10 days (10.6. - 19.6.). The air temperature results are almost identical to the measured values in all periods that have been verified after calibration. The specific humidity values show acceptable deviations. These differences can be attributed to the considerable simplification of the extensive problems of moisture accumulation in the masonry and the non-linear influence of the measured values by the additional air humidity coming from the adjacent corridor.
Figure 3. Comparison of measured and calculated air temperature values in the chapel.
Figure 4. Comparison of measured and calculated specific humidity values in the chapel.
The graph in Figure 5 assesses relative humidity values in three possible ventilation modes for a selected period of 10 days:
· Only infiltration: constant 0,2 ach
· Permanent ventilation: constant 1,5 ach
· Adaptive ventilation: 0,2 ach or 1,5 ach depend on conditions
The relative humidity results with only infiltration mode show the minimal fluctuation, however, these values almost never fall below the critical value of 75% RH. On the other hand, permanent ventilation results show extreme daily fluctuations reaching almost 30% RH. Moreover, in some cases, it also could lead to increase values. Adaptive ventilation represents an acceptable rate of relative humidity decrease and leads to overall decrease relative humidity values.
Figure 5. Comparison of different ventilation modes during the selected period of 10 days.
The adaptive ventilation effect assessment was based on Performance Index (PI) determination, defined as the percentage of time in which the parameters (in this case air temperature and relative humidity) lie within the required (tolerance) parameters . The graphs in Figure 6 represent always one month in winter, middle and summer period (from left to right in Figure 6), where is possible to see the moving of values with adaptive ventilation closer to the tolerated zone.
Figure 6. Comparison of calculated air temperature and relative humidity values (blue dots: with infiltration only, red dots: with adaptive ventilation).
The general knowledge about the need for ventilation of interiors is quite widespread. However, the proper timing of natural ventilation is essential and failure to observe the basic conditions can cause a negative effect for preservation.
As expected, adaptive ventilation is not able to offer significant change in the quality of the indoor environment. However, the results achieve improving parameters with minimal energy input. It can be assumed that the influence of adaptive ventilation on the quality of the indoor environment will not be similar for building with different operations. A positive effect is expected especially in buildings with some added source of moisture, which can be characterized by occupants or structural defect. In this case, adaptive ventilation could provide a way to cheaply achieve still acceptable parameters that would not destruct the interior.
Subsequent development of the numerical model will be address to the issue of natural ventilation limits and value of air change intensity.
The presented simplified numerical model demonstrates that if a suitable algorithm is chosen, adaptive ventilation can have a positive effect on the quality of the indoor environment. The model was created by regression method using the measured data of air temperature and relative humidity in the interior and exterior. The paper describes the boundary conditions ensuring that the interior will not be damaged due to the sudden change of internal parameters and large amount of outside air. The results of the numerical model confirmed the positive effect of adaptive ventilation on buildings with some additional source of moisture (e.g. occupants). At the same time, the assumption of different efficiency in individual periods was verified, proving that in winter the influence on parameters is minimal.
 ASHRAE, Museum, libraries, and archives, ASHRAE Applications Book (SI), chapter 21, 2007.
 ZÍTEK, P., VYHLÍDAL, T., FIŠER J., TORNARI V., BERNIKOLA, E., TSIGARIDA, N., Diffusion – model – based risk assessment of moisture originated wood deterioration in historic building, Energy and Environment 94 (2015) 218 – 230.
 PAVLOGEORGATOS, G., Environmental parameters in museums, Building and Environment 38 (2003) 1457 – 1462.
 FILIPPI, M., Remarks on the green retrofitting of historic buildings in Italy, Energy and Buildings 95 (2015), 15 – 22.
 BALOUNOVA, M., KABELE, K., Vyhodnocení rizika kondenzace a kvality vnitřního prostředí v poutní kapli Svatých schodů, Simulace budov a techniky prostředí 2016, 9. konference IBPSA – CZ.
 ČERNÝ, M., NĚMEČEK, M., Mikroklima v historických interiérech, Národní památkový ústav – odborné a metodické publikace 2011.
 KINDLER, E., KŘÍVÝ, I., Simulace a modelování, Ostravská univerzita – výuková skripta 2011.
 RODE, K, et al, Moisture buffering of building material, Technical University of Denmark 2005.
 CORGNATI, S.P., FILIPPI, M., PERINO, M., A new approach for the IEQ (Indoor Environmental Quality) assessment, Research in Building Physics and Building Engineering, Proceeding of 3 rd International Conference on Research in Building Physics IBPC 2006, Montreal.
 Official website of Loreto Chapel, available from: www.loretarumburk.cz.
List of symbols
Specific heat capacity
Indoor air temperature
Outdoor air temperature
Temperature of the mass
Dew point temperature
Volume of indoor air
Volume of outdoor air
Overall heat transfer coefficient
Convection heat transfer coefficient
Indoor specific humidity
Outdoor specific humidity