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--- planning:calculating_energy_efficiency:dynamic_simulation [2020/08/05 12:11] – [Simulation as an alternative to measuring?] wfeist
+++ planning:calculating_energy_efficiency:dynamic_simulation [2020/08/07 23:26] (current) – [Dynamic Simulation using DYNBIL] wfeist
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 ====== Dynamic simulation of a building's thermal performance ======
-\\
+==== Dynamic Simulation using DYNBIL ====
 |{{:picopen:network_eng_s.png|}}|
 |Fig. 1 A typical room model used in instationary simulation of a buildings \\ thermal performance; this is the model-type used \\ in the program DYNBIL [Feist 1994]|
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-==== Models used for Simulation ====
+Dynbil is a multizone dynamic thermal building simulation program developed at the Passive House Institute. Dynbil also takes into account moisture storage and moisture transport processes. The room model works with one air node and one radiation node, which are clearly separated from each other. Heat transmitted to interior surfaces is calculated depending on the location in the room and the actual temperature difference; for exterior surfaces, the complete solar and infrared radiation balance and the influence of wind speed are taken into account. Heat transfer (radiative and convective/conductive) and g-values are calculated for windows depending on the current temperature and solar radiation in each period of time (nonlinear). The wall model uses not transfer functions but uses a forward difference method, thereby fulfilling the conservation of energy principle even over long periods of time. The program was first validated under Central and North European climatic conditions with a number of construction projects measured in detail.
+A single room ("zone") will be modelled by DYNBIL as shown in fig. 1.
+In the meantime, additional features have been added such as simulations of moisture transport and ventilation models.
+Although DYNBIL models the building components very accurately (see e.g. comparison of simulated and measured temperatures within the wall), the focus is the whole building perspective (fig. 2). The entire building  defines the system boundary. All energy fluxes are part of the model, including all electric appliances. This makes it possible to simulate the internal heat gains. General assumptions on heat gains may be tolerable for energy inefficient buildings, but lead to high differences in the calculated demand as soon as heating and/or cooling demands are very low.
+Another aspect of the whole building approach is the integration of all system components including the consideration of thermal comfort, ventilation, air quality, noise protection, user friendliness, building protection.
+|{{:picopen:Dynbil_multizonal.png|}}|
+|Fig. 2 Several zones will be connected to a building model with air flows between the zones as well as components connecting the different zones.|
+Dynbil has been validated with the detailed measurements in the first Passive House (located in Darmstadt Kranichstein; see fif. 3). It had been possible to predict the energy consumptions, as well as to test several variations of the model itself. DYNBIL is also the basis for the development of stationary tools.
+|{{:picopen:compare_meas_Dynbil.png|}}|
+|Fig. 3 Comparison of measured temperatur developments and the simulation with the DYNBIL model for room air temperature and temperatures at the surface and inside the west facing well.|
+==== General Considerations on Models used for Simulation ====
 The actual task in dealing with the questions of indoor climate and energy balance results from the high level of complexity which the "house and heating" system exhibits in combination with the respective physical theories of the subsystems.
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 Quite often, therefore, even with computer supported simulation models, the "detour" via an abstractly defined mathematical model is no longer described, rather the model is formulated directly as a program. In the first place, too, this is not a fundamental deviation from the path of virtue in scientific theories - after all, not every model has to be a mathematical one and, in addition, computer algorithms are also mathematical models. However, there are some dangers in this shortened procedure:
-  * Unordered List ItemAs a rule, the digital algorithm itself lacks direct clarity (it is mastered by discretion). Therefore, even experienced users often find it difficult to read simple facts that can be generalized from EDP models.
+  * As a rule, the digital algorithm itself lacks direct clarity (it is mastered by discretion). Therefore, even experienced users often find it difficult to read simple facts that can be generalized from EDP models.
 Example:
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 The situation is also comparable with (2): the differences that occur due to different window positions, especially at night in the summer indoor climate, are many times greater than the influence that is actually to be examined.
-The examples dealt with show that questions such as (1) or (2) that are decisive for building planning can only be answered by direct field measurements in question with unacceptable effort or with the usual measurement accuracy. The situation becomes completely absurd when not only the influence of one parameter, but - as usual - a whole range is required (window size, type of window, proportion of frame, shading, curtains, wall color, wall insulation, wall storage capacity, roof insulation, Roof ventilation, roof storage capacity, size of the inner wall, structure of the inner walls, color of the inner surfaces, permeability to joints, ...). Each influence can also be changed continuously: the absorption coefficient of the outer wall of 0.2 b
+The examples dealt with show that questions such as (1) or (2) that are decisive for building planning can only be answered by direct field measurements in question with unacceptable effort or with the usual measurement accuracy. The situation becomes completely absurd when not only the influence of one parameter, but - as usual - a whole range is required (window size, type of window, proportion of frame, shading, curtains, wall color, wall insulation, wall storage capacity, roof insulation, Roof ventilation, roof storage capacity, size of the inner wall, structure of the inner walls, color of the inner surfaces, permeability to joints, ...). Each influence can also be changed continuously: the solar absorption coefficient of the outer wall from 0.2 to 0.8. Wishing to deal with all of these questions on the basis of field measurements with the above-mentioned measurement accuracy requirements would go beyond the budget of any research institute.
+The questions mentioned are thus typical examples of tasks that can be solved with the help of thermal building models (usually EDP-supported)
-===== References =====
+  * considerably faster (an annual simulation run per building variant costs a few secaonds computing time (in 2020; in 1994 it was approx. 1 h))
+  * in greater variety and
+  * with better accuracy and reliability
+as with direct field measurements. The last point may be surprising, so here is a brief explanation:
+Of course, simulations are only more accurate and reliable if the underlying model has been sufficiently validated. How this can be done will be explained later.
+With a validated model, the unchanged parameters and boundary conditions in the treatment of every question can be kept exactly the same ("ceteris paribus"), which is precisely the great difficulty with every field measurement. Even unimportant influencing parameters can therefore be extracted from the noise of difficult to control but important influencing variables with the simulation.
+Now there may be an objection that there is no interest in the effects of "such small" influences if they are lost in the noise of the main parameters. This objection is not valid for two reasons:
+  - The accumulation of some of the individually small influences results in noteworthy changes in building behavior (e.g. the "low energy house" type has approx. 70% less heating heat consumption compared to the type "Swedish building standard 1980" developed from cumulative "small" changes).
+  - In a large group (e.g. of some hundreds of buildings) the small savings of perhaps 5%, which are hidden in individual cases by other parameter influences, emerge significantly from the noise.
+The first reason shows a way for the metrological validation of the models: large differences in cumulative changes can be reliably monitored. However, this does not relieve the need to determine the individual changes, otherwise it could be that a particularly expensive "improvement measure" has no big effect.
+The second reason also shows a basic way for a validation: the measurement in very large samples, in which accidental influences such as different indoor climates average out. To do this, however, the buildings must be sufficiently identical in their entirety - which also means high expenditure. In Sweden (Täby [Blomsterberg 1990], Valdemarsrö [Lange 1990], Taberg [Fredlund 1989]) such measurements were actually carried out in settlements with more than 18 similar residential units. - It is clear that this method is also hardly suitable for answering the multitude of questions - after all, some of the model validations carried out stem from this work.
+From the considerations so far it follows quite clearly:
+|**The method of choice for answering typical questions of structural influences on the indoor climate and heating energy consumption is the use of thermal computer aided building models. - On the other hand, validation of such models thus becomes one of the most urgent tasks of research.**|
+In practice, this finding has long since become established: Computer-aided thermal-technical building simulation is a widely used instrument. Of course, this does not say much about the suitability and accuracy of such procedures. A big part of the validation work has been done in recent research projects especially involving passive houses [Johnston 2020].
+==== References ====
+**[Blomsterberg 1990]** Blomsterberg, Å.: Ventilation and airtightness in lowrise residential buildings; Byggforskningsrådet, Stockholm D10:1990 \\
 **[Feist 1994]** Thermische Gebäudesimulation; 1. Auflage, 366 Seiten, 1994
-Thermal building simulation, first edition,1994 \\
+Thermal building simulation, first edition,1994 (in German)\\
+**[Fredlund 1989]** Fredlund, B.: Blocks of flats with glazed verandas, Taberg; Swedish Coun¬cil for Building Research, Stockholm D3:1989 \\
+**[Johnston 2020]** Johnston, D. et al: Are the energy savings of the passive house standard reliable? A review of the as-built thermal and space heating performance of passive house dwellings from 1990 to 2018. March 2020, Energy Efficiency, DOI: 10.1007/s12053-020-09855-7
+**[Lange 1990]** Lange, E.: Radhus i Valdemarsro, Malmö - En energi- och innklimatanalys, Byggforskningsrådet. Stockholm R1:1990 \\