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Strategies for Deploying Virtual Representations of the Built Environment.
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I suppose I should have been more prepared for how dark and full of despair these dystopian stories would be. It seems Huxley was as disturbed as Zamyatin about the dehumanizing power of mechanization, of the reach and influence of powerful governments, and the trivialization of sex when disconnected to child-rearing.
Like We, this story features a main character out-of-sync with the whole of society, a misfit. There are again those on the outside, not touched by the advances that were supposed to benefit mankind. I agree, it's shocking how much this society is like our own or where ours is heading. I am glad however, that it seems like most of our dystopian societies have people who are disturbed with their society, almost as much as we are. It seems these authors predict that humanity will survive, even when the machines seem to be in control.
Great comments, Pam. We will always have forward thinkers to challenge the status quo, but will they be strong enough to battle an entirely bleak infrastructure? Our world changes because the people and advancements within it change; however, who's to say those changes are for the better?
This article is really fantastic and thanks for sharing the valuable post. Thanks for all your information, Website is very nice and informative content. Thanks for Nice and Informative Post. Backyard Revolution works step by step and shows you specifically a way to source the building. The author conjointly offers the piece of paper to his lineman and considers his Backyard Revolution designed.
Backyard Revolution is sometimes connected to the roof of the building with a set quantity. You will find reviews of different types of digital products such as eBooks, e-Course, Videos and software.
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Hit a key or click anywhere to stay logged in. Oh, There You Are! Brave New World. Mar 7, PM. Save Comment Cancel. Thanks for Info. Check for online 3d model conversions tools for your file format. Shown 1 of 2 pages. Marine Transceiver Icom Receiver Icom R Marine Transceiver and Microphone Icom Icom Icom IC base by fabianomoser Icom IC mount by matix Icom Battery Blank by tnbk Icom IC Stand by Ratrodpedal Icom IC Tilt bail by Dl9rnv Icom A by kevin Icom IDa Desk Stand by muredamitten Mic Hanger for my Icom for Icom ICH remote display holder by lehtojo In projects we may need to adapt existing constructions by selecting alternative materials or creating material variants that match what will be used in a specific project.
The following are what you might have found during that exercise as reasonable fits for this project:. Most simulation suites provide at least a basic interface to define the form and fabric of the building and some have extensive in-built CAD facilities.
Our tactical goal is to use a work-flow which minimizes the number of keystrokes and avoids errors:. Of course you never lose scraps of paper and you always remember the assumptions you made four months ago and you will not be asked to demonstrate that you followed procedures.
Simulation suites typically provide alternative methods for describing common room shapes. The eventual form of a room might be of arbitrary complexity involving hundreds of surfaces but the same underlying rules will apply. The examination room could be built from a floor plan or a rectangular shape into which we insert facade elements. Architectural elements should be included in a model if they are thermally important. Internal glazing, doors, structural elements and furniture may be of marginal interest.
Surfaces are an entity type in all simulation suites. Each vendor has evolved their own arbitrary conventions so although surfaces are ubiquitious they are difficult to equate. ESP-r uses surfaces to represent just about everything: - A surface is a partition, ceiling, window, door, frame or grill if you give it a suitable name, attribute its composition accordingly and set its USE attributes.
All surfaces fully participate in the analysis. You can explicitly wrap frames around glazing or architraves wrapping around doors or aggregate them into a few surfaces of equivalent area and composition. An example rule set for EnergyPlus can be used as a comparison.
Most simulation tools have a user contribution blog which provides hints. All simulation suites overload surface entities with attributes. All include a NAME attribute. Otherwise arbitrary conventions dominate and it takes considerable passion to establish equivalence between simulation suites. The ESP-r surface attribute convention is illustrated by Figure 2. In a well-designed model surface attributes reinforce each other - i.
In later sections we will discuss naming patterns that have been observed to work. Folk in a hurry often accept default names for entites. It might have only taken a few seconds to import an 80 zone model but then we have surfaces with similarly opaque names to work with.
They also record the users intent e. The combinations currently supported are:. The zone geometry menu has been updated to reflect the new surface information. For example the volume of the reception is The text summary includes a synopsis of the geometry of the zone and a list of the attributes and derived values for each of the surfaces.
These are unambiguous without being particularly helpful. Simulation tools offer a mix of options for representing doors and windows at different levels of resolution. Some simulation suites consider doors and windows to be child surfaces which inherit position and orientation from a parent surface.
They may treat them as simplified thermophysical entities and frames as non-geometric attributes of the glazing. Tools may also embed support for alternative treatments of glass near framing or for advanced optical properties. In ESP-r a window, door or frame is just another surface which fully participates in all heat flow paths. Parent-child relationships are derived geometric attributes. Some practitioners will tend to abstract facade elements, for example lump all glass facing south in a room into a single surface as in the left of Figure 2.
Facade engineers will often choose to be literal upper right including explicit representations of all frame extrusions in a 3D conduction assessments whilst others will choose moderate detail.
If glare is of concern in the project then this would point to higher facade resolution. Establishing whether the mix of descriptive and analysis features supports a particular project is a non-trivial task. Check feature comparison sites or compose your own virtual tests to confirm what level of resolution is appropriate for specific simulation goals.
Simulation tools often separate geometric and composition descriptions from directives associated with air flow. For example, ESP-r ONLY tracks air passing between rooms or with the outside if you intentionally create an air flow schedule or include relevant flow components within an air flow network. Some tools may create flow directives with minimal notification so check how your tool approaches this. Simulation tools usually provide a number of options for adding or inserting new surfaces - and they tend to manage the updating of related data structures and relationships.
You will use these frequently so visit the exercise below and try out several of the variants so that you can plan your response to various building details. Some users like to hack the model files but this often introduces subtle errors and breaks the underlying dependencies within model files. Surface attribution is the subject of the next section. Tactically, we want to make it easy for others to understand our models.
Clear names increases the speed and accuracy of selection from lists and clarifies reports. So attribute names of surfaces first. This pattern gives a clue about the location and composition of the surface. The examination room is rectangular in plan, but has a sloped roof and it shares partitions with the reception.
The geometric transforms in Figure 2. Surfaces have attributes which define what happens at the other face in terms of a thermophysical boundary condition. And if there are many surfaces which have the same boundary condition e. If we knew what the temperatures at 1m depth were we could enter these as a user defined ground temperature profile.
This is intentional so that you remain in control of boundary conditions. The scan for nearby surfaces is useful for the most simple models or in the case of a large model where changes have been made and you want to establish boundary conditions for a few selected surfaces. And, more importantly you might miss something. The topology facility scans the polygons of a model looking for surfaces in various zones which are close matches in terms of shape and position and makes inferences from this to complete the boundary condition attribute of each surface.
You control the tolerance and the extent of the search parameters and if the tool is unsure of what to do it will pause and ask for confirmation or allow you to select one of the standard boundary conditions. Up to this point the strategy has been to follow procedures which help us to create correct models. How do we know they are correct?
One of the steps in checking the quality of our models is to generate a QA report and then review this against our initial sketches. If your review indicates that all surfaces are fully attributed then it is possible to proceed to the next step of instantiating the zone construction thermophysical properties.
In order to run a simulation, each zone in a model must include full thermophysical data for each layer in each surface. Simulation tools embed this information in different ways. This is not the only approach we could have taken. Simulation tools may offer alternative schemes for creating the form and fabric of models. The planning stage for creating the surgery via a grid is essentially the same. Have Figure 2. After completing the exercise the two zones on the grid will be similar to Figure 2.
You can also supply your own bitmap of the building plan and click on points found in that image to create one or more thermal zones. Practice is essential as is planning. The facility is intended to be used in a single session points collected are lost when you exit the facility. Which ever simulation suite you are using, with practice you have the option to create create models which the client can recognize and which capture the spacial characteristics of the building.
Two examples show the transition from sources to model:. The eighteenth century theatre shown in Figure 2. The model in Figure 2. Surfaces and points from those dummy zones were then used to build up the model zones. A detailed plan of the zoning and the critical points had been worked out first!
This is an example of what can be accomplished by an experienced user and is just the sort of project which would be cruel and unusual punishment for a novice. The second example involves an urban study, as shown in Figures 2. Some of the buildings are thermophysically complete and the others abstractions for context. In this case the ground topology is treated as a thermal zone. Importing third party model descriptive files involves establishing equivalence between the entities in such files and entities within simulation.
DXF cad files include few attributes as well as many 2D entities that have limited application within simulation. Sucess is dependent on the source of the gbXML file and the nature of design which is embedded within the file. The new model might be incomplete. For example the parser will skip past surfaces or openings or doors which exceed array limits.
Simulation tools tend to describe the composition of surfaces in terms of named constructions which are made up of an ordered list of materials each of which have a set of thermophysical attributes. Each simulation suite has its own approach to implementing compositional information as well as providing user access for selection and management tasks.
For example:. Energy Plus provides a folder with IDF files holding instances of window, frame, opaque wall and shading elements.
The entries in these files are tag:data formatted. Users cut and paste relavent entries into the model file. Third party interfaces would provide these as selection lists.
IES VE provides lists of constructions by building type and, again, these reference named materials with thermophysical attributes. These files include ready to use entities as well as acting as templates for practitioners to populate from their own information sources.
Once selected a list of common management tasks see Figure 3. Details of weather, materials, constructions and optics common data files are discussed in the next sections. Organisations which gather, store and distribute weather data serve a broad audience.
Over time a plethora of file formats have evolved. Simulation tools accommodated this via conversion utilities. The overhead for vendors and users was substantial as well as the risk of error and misunderstanding. The author, with Dr. Dru Crawley, who at the time was managing the EnergyPlus program, designed and proposed the EPW weather file format which has become a widely used standard repository format for the simulation community with over locations available for download.
Details of this and other import facilities are covered in Section 3. Weather data is typically hourly for days of the year but can be a fraction of the year. At each hour the weather data comprises diffuse horizontal solar radiation, dry bulb temperature, direct normal or global horizontal solar radiation wind speed and wind direction with optional columns for atmospheric pressure. There are no entries for cloud type, cloud percentage, dew point, precipitation or snow or ground temperatures.
Seasonal definitions are user specified and typical weeks can be determined via scanning of the weather data and this is the topic of the next section. The use of short weather sequences for model calibration and focused explorations have many advantages.
Do peak summer demands coincide with the hottest day or is it a function of several hot days in sequence? There is little point in using an annual assessment to explore such issues and considerable advantage to identifying and using specific weather patterns for calibration and to support early identification of opportunities.
This is followed by a selection of weather management functions including [select from list] Figure 3. Select an existing weather file such as Birmingham IWEC, look at the summary in the text feedback area and confirm the selection.
After this the main menu of clm looks like the right section of Figure 3. There are a number of options under synoptic analysis Figure 3. To generate a statistical report as in Figure 3.
Near the bottom of the is an option find typical weeks. The provision of different views of the weather data can assist in locating patterns and answering different questions that clients might pose. Time spent exploring this module can provide critical clues as to patterns that may be used in the design process.
An example is the graph of temperatures over the year Figure 3. There are a number of times when it is below freezing, but the graph indicates that these tend to be brief. This might support the use of brief performance assessments for winter heating demands and capacity. It also indicates scope for testing whether a design might be optimized to cope with brief rather than extended cold periods. Another example is Figure 3. These facilities evolved over time based on observations about how practitioners adapted their models and the assessments they commissioned to fit within their computational and staff resources.
For many building types there is a strong correlation between predictions over one or two typical weeks in each season and seasonal heating and cooling demands. This can be demonstrated by commissioning short period assessments and full season assessments and looking at the relationship between the two perfomance predictions.
Commissioning full season assessments to determine scaling for a building does take time and so alternative methods were investigated. Over scores of projects it emerged that there were relationships that could be used to identify suitable short assessment periods. The key to this was in the practitioners definition of the extent of each season and search criteria for typical periods. For many readers seasons are demarcated by four month intervals or by notes in a reference guide.
If we pause to consider what constitutes winter in Hong Kong we might conclude that it is triggered by something other than temperature. The start of spring might be a day on a calendar but often there are cultural clues such as observing cherry blossoms which trigger changes in how we dress and how we operate our buildings. Astute practitioners leveraged this insight when they review weather data.
Before we had access to computers which supported numerical simulation simplified methods such as heating degree day HDD and later cooling degree day CDD methods were used. We observed that practitioners often did a quick check of HDD and CDD to judge, for example the onset of demand for environmental controls or the likely period that the building might be allowed to free float with minimal loss of comfort. Weather data is scanned week by week for the average HDD and CDD for each day and the total for each week as well as the daily mean direct and diffuse solar radiation.
The weekly data are then compared with the average and total values for the season and the week with the least deviation is suggested as the best fit. When working with a weather file which is already selectable in the Project Manager weather list it will already have meta data defining the duration of each season as well as a typical week in each season which can be used for short duration assessments. The concept of a season can be leveraged in several ways.
The clm module provides a number of views of weather data, including statistics, which can help you identify weather patterns which conform to your own definition of seasons. When you have completed the exercise you will a seasonal display such as Figure 3. A good source of weather data is the United States DoE web site which is located at the following site. Common materials files hold information on material thermophysical properties.
As seen in Figure 2. Brick, Concrete, Metal. A category such as Concrete, contains general types of concrete such as heavy mix concrete and adapted materials such as which painted heavy mix which has adjusted surface properties to represent painted surfaces. Thus a single brick material might be used in several constructions with different thicknesses. Normally, a simulation group would seek to protect their common materials from corruption. In the next section Figure 3.
If we needed to include this in a model we would review the specification of the materials, see if there was an equivalent material. The product is made up of 2mm steel, 28mm of high density particle board and 2mm steel. Generally carpet of 8mm is laid on top. The materials database only has medium density boarding so we must search for data on high density particle board. K and conductivity of 0.
The above examples relate to homogeneous layers used in constructions. Often we need to represent layers which are a mix i. We have the option to define a new material which is a mix of two other solid materials, or by converting an air gap to an equivalent conductivity we can also represent walls which include voids. A common constructions file holds categories of constructions such as opaque walls, partitions, frames, doors, fittings, earth etc.
Vendors often include region-specific construction files so it is useful to spend time exploring their contents. In ESP-r, several hundred constructions can be accommodated and thus the interface includes a number of management facilities. Files are located in the databases folder of ESP-r but may also be placed in the model dbs folder. It includes support categories as well as longer names and documentation for each construction also referenced as a MLC.
You may notice that reports about constructions include U values for the case of horizontal flow, vertical and sloped heat flow. These are derived values following conventions of ISO Similarly glazing is often described with a G value which is a common reporting convention. Physically thick layers e. For example, in the list in Figure 2. Splitting a material in to several layers is a common technique for massive materials or insulation materials.
It results in more nodes so the numerical solution is more efficient as well as providing higher resolution reporting of conditions within constructions. Constructions are defined from the outside the other face working inwards towards the room face. If the air space acts as a supply or return plenum then it might need to be represented explicitly as a thermal zone.
In this case there are additional constructions listed below would be required:. It is important to name constructions in a way which will allow reliable selections. In the above cases if we consider the observers position and look at the construction the names below might work:. Constructions which are transparent require additional attributes to describe how solar radiation passes through or is absorbed in the layers of a construction.
Common optics files hold a list of optical property sets. Each optical set has the following attributes:. For each layer the angular solar absorption factors are held at the same five angles as direct solar transmission. Populating common optics files requires the use of third party software such as LBL Window 7. It is up to the user to create a matching construction. Care is requied to ensure that any air gaps in the construction have appropriate resistances defined so that the reported U value matches the documentation in the optical data set.
One optical set might represent the blind in an open state and another with the blind partly closed. Both optical states should have the same number of layers. A classic case is a blind which is mounted in the room with an air gap between itself and the glass. Without explicitly representing the gap as a zone it is difficult to represent the heat transfer at the blind.
CFC2 in the attributes of the surface. The figures above and below show views of the CFC database, categories of entities, entity lists and the attributes of a typical entry. There are hundreds of different manufacturers data sets which have been extracted from the IGDB. Constructions when they are built up point to these layer entries and the solver uses their attributes.
When a room surface uses a CFC related construction the CFC solver is used rather than the standard optical code and the surface attribute shows up as a.
Below are two typical entities. The first is an office chair which is made from 8 vertices, two pairs of thermal mass and 13 visual objects. These are intended to be imported into a thermal zone as required to populate the space for purposes of both thermal and visual assessments. The second is a residential stair which, when imported into a model forms a complete thermal zone there is a matching entity for the space above the treads.
The zone file can then be imported and documentation added to the entry. As an Architect and consultant the author has also noted the complexity of the built environment:.
Unfortunately, some building sections form explicit instructions for future failure e. With good pattern matching skills Architects can identify and correct inappropriate building sections prior to construction and contractors can identify and avoid faults. Simulation models often reflect little of this complexity and are thus unlikely to provide numerical clues as to future failures.
The geometric forms discussed thus far use polygons as the building blocks of our virtual built environment. While modern thin constructions, such as those used in Figure 1. Focusing on Figure 4. The list of bullet points could be much longer. We need strategies for ranking thermophysical issues and deciding what needs to be included in our model s.
A tactical approach to simulation uses the planning phase to constrain options. The following is one possible ranking of what to preserve while abstracting a design into a model:.
The thickness of the insulation in Figure 4. Do we choose to ignore the ceiling thickness when defining geometry? It might also include linear thermal bridge descriptions if these had been calculated. A medium resolution model might subdivide the surfaces to represent full thickness and the partial thickness insulation and extend the roof zone to allow it to form a boundary at the upper wall section as well as including an air channel from the soffet to the roof space.
It is a user choice whether to set the bounds of the zones at the inside face of the facade right of Figure 4. A low resolution model might treat the overhang as a solar obstruction and ignore the different thickness of the insulation. It might assume the air is well mixed within the roof space i. It would also not explicitly represent the overhang as a boundary condition for the upper portion of the wall and would omit the internal reveal of the window right of Figure 4.
This is visually crude and the height of the building is not correct. The lower portion of Figure 4. It is worth exploring models at different levels of resolution to test the sensitivity of predictions.
Geometry digitized from CAD drawings will tend to use the latter. Such differences typically have little or no impact on predicted performance. Note that the surfaces forming the overhang do not in the current version shade the wall.
Shading requires the use of shading obstruction blocks as included in the earlier figures. The overhang, as drawn in the building section is in contact with the upper portion of the wall. Consider Figure 4. The ceiling void includes lighting fixtures as well as contact with the structural mass. It is not clear whether the raised floor is also being used as a supply plenum but it certainly acts to isolate the occupied space from the structural mass.
At the lower edge the spandrel extrusion is in direct contact with the occupied space in the lower level.
A minimal abstraction would represent three facade sections at the glazing, at the fan coil cabinet and at the spandrel. The depth of the ceiling void and the internal heat generation would suggest separate zoning of the ceiling void. To identify spandrel performance the spandrel space probably needs to be treated as a separate zone. Explicit treatment — would allow testing of alternatives such as single glazing of the spandrel.
One might also explore conditions in the raised floor via explicit treatment. Is an explicit zoning worth the effort? Creating an explicit model of a small section of the building would identify the benefits as well as the resources needed for the additional resolution.
Recent empirical validation projects such as IEA Annex 58, which focused on a pair of test buildings in Holzkirchen, Germany indicate that external and internal thermal bridges, dynamic representations of infiltration, heat exchanges with duct work and basement heat exchanges, which are normally treated as optional can have a notable impact on the fit between measured and simulated performance.
A degree of scepticism about thermophysical resolution usual suspects is thus warranted. Whether differences in predictions are significant within the context of a specific simulation project is one of many reality checks that need to be carried out. A review of simulation suites by the Author and Dru Crawley indicates considerable diversity in thermophysical resolution.
This stems, in part, from the diverse preferences of simulation practitioners in different markets, the evolutionary path of the simulation suites.
Fixing infiltration rates or heat transfer coefficients relfects an age of scarse computational resources. Design teams need decision points for adjusting thermophysical resolution. For example, moving beyond scheduled air flows via the inclusion of a mass flow network adds a computational burden but provides a wealth of additional performance information. In other cases, adjusting thermophysical resolution involves specifying directives to the simulation tool so that existing information e.
These are calculated prior to assessments and the computational burden is roughly a function of the number of surfaces in the zone. Historic perceptions of paint drying are rarely applicable and thus the choice to invoke the facility is related to curiosity within a project to explore radiant asymmetry discomfort or radiant exchanges. For many practitioners this is a niche topic.
Lets revisit Table 1. The design team want to deploy ceiling radiant heating panels in scores of private and small wards of a new hospital. One design was claimed to be less costly to install rectangular panel parallel with the facade and the other design was claimed to provide better comfort for the doctor and patient at a lower operating temperature. The assessment needs to provide suitable evidence for the design team to make a decision and main contractor to proceed.
Thermal comfort is a key issue. Radiant asymmetry at specific locations in the room would clarify the performance claims. The layout of the rooms are identical so we know where patients will be and where attending staff will likely be deployed. Radiant heat distribution is a key issue. The shape and operating temperature ranges of the two designs must be represented in the model as well as the distribution of radiation to surfaces in the room. Heat losses from panels into the ceiling void might result in a warmer ceiling surface.
The glazing may result in cold surfaces which the panel may not fully counteract. Control response is a key issue.
Comfort delivery across a range of boundary conditions e. Will such a system be viable for extreme cold weather as well as periods of minimal demand?
How often are panel temperatures near their maximum? Weather patterns need to be identified which will clarify performance at the two extremes. The assessment should clearly indicate the response of the room to the proposed heating regime. The essential characteristics of the radiant panels should be represented. Heat injected into a panel results in a temperature rise and has several paths of escape.
How quickly might panels respond to changes in demand? Although the specifics of the central plant were not yet decided a range of operating temperatures and heat injection densities were available.
The model, shown in Figure 4. The design was constrained by the need to complete the model in a single session while the client was available to supply details and evaluate what was found in the assessments. Given the focus of the study, there is no rational basis for excluding full thermophysical treatment of the beds and its inclusion provides a visual clue to the client.
The radiant panels form boundary surfaces in the rooms so both radiant and convective heat transfer are explicitly represented. This approach allows changes in the composition of the panels to be modified and assessed. The adjacent wards were assumed to be at the same temperature as the rooms being investigated. In order to provide a suitable boundary condition the ceiling void below the room was also represented as a zone at the same temperature as the void above the panel.
See section 6. To represent heat injection into the panels, geometrically thin zones were created to represent each radiant pannel see Figure 4. The lower surface is metal and the upper surface and sides are an insulated metal panel. High heat transfer coefficients are set so that any heat injected will be transferred to the bounding surfaces.
Model calibration assessments were run to tune the heat injection so that it matched the expected panel surface temperatures. This approach was used because setting up system components would have required additional time and supporting details were not available. For purposes of this assessment the approach was seen to represent the expected panel temperatures and response time while the explicit shape of the pannel surfaces worked well in the subsequent detailed comfort assessments.
The critical performance metrics were the comfort for a tall doctor standing at the window and for the patient in the bed as well as the frequency the panels were on and how well they were able to control temperatures within the room on moderately cold days. Radiant sensors are discussed in the next Section. And to ensure that solar ingress does not compromise comfort, facilities to predict the patterns of insolation are also enabled. This model supports reports of:.
To support understanding of the performance and allow for quick calibration, assessments, typical weeks in each season as well as an extreme winter period were used. In this project assessments are live and open to immediate feedback by the design team and contractor in order to determine:. Overall the model was up and running during the visit to the clients offices and the predictions and fine tuning were carried out with feedback from the client.
Assessments indicated that both designs resulted in substantially similar comfort levels for the doctor as well as the patient see Figures 4. There was a slightly increased risk of radiant asymmetry discomfort for the case of the mm wide panel design. It was also clear, however that the mm wide panel was ON more often and tended to work at a higher temperature than the mm wide panel approach.
Some tools report energy balances at the surface level so that the magnitude of radiant transfer can be judged in comparison with other heat flux paths. This assumption is appropriate for highly cluttered spaces, where a limited range of surface temperatures is expected or where comfort is not an assessment criteria.
As rooms depart from simple rectangular shapes, employ radiant heating and cooling or where furniture is located near the face a diffuse distribution assumption becomes less valid. Project goals may also suggest increasing resolution. The process places a grid on each surface in order to compute visibility and is generally robust. However, the calculation parameters may need to be adjusted to ensure all parts of complex surfaces include grid points.
The pre-processing computing resource is related to the number of surfaces in the zone i. One of the classic challenges of numerical assessments is that the form and composition of building facades and site obstructions entities varies considerably in scale, complexity and optical characteristics.
The resulting patterns of solar radiation traversing facades can result in significant temperautre variations across facades. For transparent facade elements these incident patterns become part of the source radiation entering the room and may be further perturbed by reflection and absorption while passing through the glazing or interception by facade framing.
The calculation of solar position and the angular relationship between the sun and surfaces within models are classic features.
Gaps in logic persist even as new facilities have been added. Current simulation tools are thus able to derive many of these relationships.
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