planning:calculating_energy_efficiency:dynamic_simulation
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planning:calculating_energy_efficiency:dynamic_simulation [2011/11/04 14:42] – Angela | planning:calculating_energy_efficiency:dynamic_simulation [2020/08/07 23:18] – [Dynamic Simulation using DYNBIL] wfeist | ||
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+ | ====== Dynamic simulation of a building' | ||
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+ | ==== Dynamic Simulation using DYNBIL ==== | ||
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+ | |{{: | ||
+ | |Fig. 1 A typical room model used in instationary simulation of a buildings \\ thermal performance; | ||
+ | \\ | ||
+ | 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/ | ||
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+ | A single room (" | ||
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+ | 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 | ||
+ | Another aspect of the whole building approach is the integration of all system components including the consideration of thermal comfort, ventilation, | ||
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+ | |{{: | ||
+ | |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.| | ||
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+ | Dynbil has been validated with the detailed measurements in the first Passive House (located in Darmstadt Kranichstein; | ||
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+ | ==== 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" | ||
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+ | The mathematical models for mapping the subsystems are largely " | ||
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+ | * on the one hand in the complexity of the basic equilibria involved (e.g. with Navier Stokes), | ||
+ | * on the other hand especially in the present boundary conditions, e.g. the generally not simple geometries of components. | ||
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+ | While a solution to the flow problem for a room in a stationary case can be tackled numerically with some effort today using the Navier-Stokes equations, a solution to the overall problem " | ||
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+ | On the contrary, the task is to reduce the complexity again by decisive simplifications in the parts constituting the overall model to such an extent that treatment with reasonable effort is always the path to take. | ||
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+ | It is clear from the task that the model must map all the essential, interacting components of the building and the heating system: each sub-model must be dealt with under the boundary conditions that are significantly determined by the other components. This is one of the theses that will be substantiated at various points in this publication - two examples: | ||
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+ | * The energy balance of a window surface depends not only on the component " | ||
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+ | * The assessment of components for solar energy use (such as transparent heat insulation) with regard to the energy balance to be achieved all year round depends on the (insulation) standard of the building in which the subsystem is used. | ||
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+ | An image of the overall " | ||
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+ | ==== Special features of the computer-aided simulation ==== | ||
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+ | The mathematical model for the treatment of the thermal behavior of buildings is characterized by a high level of complexity as shown in the last section: a whole series of partial models for | ||
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+ | * Instationary heat conduction (Fourier equation), | ||
+ | * The flow of air in space (Navier-Stokes equations), | ||
+ | * The radiation exchange between components (Planck law), | ||
+ | * The reflection, transmission and absorption of solar radiation, | ||
+ | * Control of heating, | ||
+ | * Free heat sources in the room, | ||
+ | * Infiltration and ventilation | ||
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+ | are to be treated and linked. This is done geometrically with generally omplex boundary conditions (geometry of a building) and the climate as an inhomogeneity. | ||
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+ | Even with simple sub-models (such as the connections of different components, e.g. on the eaves), the mathematical sub-model (e.g. Fourier problem with boundary conditions) can no longer be solved analytically. Already here you have to rely on numerical methods for the solution. To implement such numerical methods, computer algorithms are expediently used today. | ||
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+ | Such a computer algorithm can also be understood as a (program-based) model of the mathematical model: in this respect the use of computer simulation does not mean a principient innovation compared to the mathematical models generally introduced in the natural sciences. It goes without saying that discretization errors and numerical errors must be carefully discussed here - they characterize the deviation between the mathematical model and the model formation with the underlying program. In this case, however, the model relationship is comparatively simple: it can be formulated internally between the algorithm of the program and the mathematical model and is therefore even accessible to mathematical methods of proof. | ||
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+ | Quite often, therefore, even with computer supported simulation models, the " | ||
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+ | * 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. | ||
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+ | Example: | ||
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+ | In the numerical solution of simple differential equations (dy/dt = - ω²y) the realization that simple solutions y(t) = a cos (ωt) + b sin (ωt) exist will usually be blurred. This solves the problem numerically, | ||
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+ | * By circumventing the mathematical model formulation under certain circumstances, | ||
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+ | Example: | ||
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+ | Conservation theorems that follow from symmetry groups and that are easy to derive in mathematical field formulation also work in the numerical model | ||
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+ | * Even from the greatest abundance of numerical "case studies", | ||
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+ | The advantage of using adequate mathematical models (and the resulting possibility of verifying an computer aided algorithm over the mathematical model) is therefore obvious. But there are also practical advantages in favor of such a route: numerical mathematics often offers different methods for the algorithmic treatment of the same mathematical problem, which are useful depending on the circumstances. In this way, computing time and storage space can be saved. | ||
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+ | As a result of the complexity of the problem, the consequent priority for a clean mathematical formulation is not always observed in this work before it is used in IT algorithms. Closing the remaining gaps remains a task for further investigations. | ||
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+ | ==== Simulation as an alternative to measuring? ==== | ||
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+ | According to the explanations in the last sections, such a question does not arise: simulation and measurement have different functions in the cognitive process and complement each other. The simulation can neither make measurements completely superfluous (validation of models is only possible via measurements) - nor is it practical and sensible to want to answer all questions by measurements (statements that can be generalized can never be obtained by at most a finite number of measurements). | ||
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+ | Especially in the field of research into the thermal behavior of buildings, there is a widespread basic skepticism about simulation: Many practitioners only trust statements about the annual heating requirements of houses if they are validated by " | ||
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+ | * According to the results already available today, the heating requirement is extremely sensitive to a number of parameters that can be changed by the user (eg the internal temperature and the air exchange). Measurements are therefore only to be regarded as usful data source if these parameters are also recorded in a suitable manner (e.g. internal temperature profile, tracer gas measurement of the air exchange) or checked in some other way (e.g. by permanently setting them with control organs) or by measurements within very large samples. (All these paths have been used in the development of passive houses.) | ||
+ | * The dependency on the external climate is also very high: In different years at the same Central European location (e.g. in Frankfurt am Main) the annual consumption of the same building can deviate by more than 50% with the same user behavior. Measured values can therefore only be compared if they relate to the same climate. Realizing this is quite difficult: without resorting to arithmetic corrections (the theoretical models need a basis: the usually used heating degree day correction is a very primitive and a quite questionable model), this can only be done by simultaneous measurements at the same place. | ||
+ | * In addition, inaccuracies in the measurement itself must be expected: "If you measure, you measure manure" | ||
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+ | Typical examples of questions that arise in energy related building planning are the following: | ||
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+ | - Is it worthwhile to increase the proportion of south windows in a building from 50 to 60% in order to save heating energy? | ||
+ | - How is the indoor climate influenced in summer by changing the color of the outer facade? | ||
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+ | Questions of these types can also be asked regarding the window structure, the floor covering, the roof construction and the like. From the parameter studies documented in [Feist 1994], it follows that the influence of each of these individual parameters on the annual heating consumption is not very high (e.g. we expect (1) to relate to a building with Swedish building standard 1980 result in a saving of 5.6% with triple glazing). The change in consumption, | ||
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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. | ||
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+ | 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, | ||
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+ | The questions mentioned are thus typical examples of tasks that can be solved with the help of thermal building models (usually EDP-supported) | ||
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+ | * 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 | ||
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+ | as with direct field measurements. The last point may be surprising, so here is a brief explanation: | ||
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+ | 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. | ||
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+ | With a validated model, the unchanged parameters and boundary conditions in the treatment of every question can be kept exactly the same (" | ||
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+ | 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: | ||
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+ | - 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 " | ||
+ | - 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. | ||
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+ | 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 " | ||
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+ | 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. | ||
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+ | From the considerations so far it follows quite clearly: | ||
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+ | |**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.**| | ||
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+ | In practice, this finding has long since become established: | ||
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+ | ==== References ==== | ||
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+ | **[Blomsterberg 1990]** Blomsterberg, | ||
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+ | **[Feist 1994]** Thermische Gebäudesimulation; | ||
+ | Thermal building simulation, first edition, | ||
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+ | **[Fredlund 1989]** Fredlund, B.: Blocks of flats with glazed verandas, Taberg; Swedish Coun¬cil for Building Research, Stockholm D3:1989 \\ | ||
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+ | **[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/ | ||
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+ | **[Lange 1990]** Lange, E.: Radhus i Valdemarsro, | ||
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planning/calculating_energy_efficiency/dynamic_simulation.txt · Last modified: 2020/08/07 23:26 by wfeist