Alcocer, J.

Clayton, M.

Dixit, M.

Haberl, J.

Jeong, W.

Kim, J.

Kota, S.

Yan, W.

Texas A&amp;M Repository

  ESL-TR-11-12-05  Physical Building Information Modeling for Solar Building Design and Simulation – Annual Report 2011  Dr. Wei Yan, PI, Texas A&M University  Dr. Jeff Haberl, CO-PI, Texas A&M University Dr. Mark J. Clayton, CO-PI, Texas A&M University WoonSeong Jeong, Ph.D. Student, Texas A&M University  Jong Bum Kim, Ph.D. Student, Texas A&M University Sandeep Kota, Ph.D. Student, Texas A&M University Jose Luis Bermudez Alcocer, Ph.D. Student, Texas A&M University  Manish Dixit, Ph.D. Student, Texas A&M University  NSF GRANT CBET – 0967446  December 1, 2011  Project Activities What have been your major research and education activities (experiments, observations, simulations, presentations, etc.)?  1 Major research activities 1.1 Research on physical modeling methods  We have investigated modeling of passive solar thermal with Modelica, daylighting with Radiance and DAYSIM, and BIPV with Building Information Modeling (BIM) Application Programming Interface (API). For solar thermal, we have researched on how to model building objects in Object-Oriented Equation-Based modeling language – Modelica by using the LBL Modelica Buildings Library (Lawrence Berkeley National Laboratory, 2011). We have identified modeling methods using Modelica for building geometry, air volume, room configurations, materials, and building components, which are major factors of solar energy utilization in buildings. For daylighting, we studied how to convert building geometry and material information from BIM to Radiance. For Building Integrated Photovoltaic (BIPV), we have researched on how to build solar models in BIM that can calculate solar position and solar insolation.   1.2 Research on BIM simplification methods, BIM topology, and data modeling We further investigated building object hierarchy and topology in BIM and Modelica-based thermal modeling. Using BIM and its API, we explored modeling and extracting both building object properties (including size, material, and construction data) and building topology, which can represent the connectivity of building objects. We also structured our data modeling method that includes a TAMU 1  Modelica BIM Package as a bridge between BIM and Modelica thermal modeling. We are currently implementing this data modeling structure.  1.3 Research on linking BIM-OOPM (Object-Oriented Physical Modeling) and integrating P-BIM (Physical BIM) We reviewed extensively a Modelica-based building thermal library and its example models (LBL Modelica Buildings Library from Lawrence Berkeley National Laboratory, 2011), and evaluated its applicability to P-BIM. We found both advantages and limitations of the library’s application in our P-BIM project.  The library is the currently most complete building thermal library but still in its early stage of development. We examined various ways to link BIM and LBL Modelica Buildings Library and developed a method that can utilize BIM API and our Modelica BIM Package (under-development) to transfer BIM data to Modelica simulation. For achieving this method, we have experimented sample building models (including a single layer wall, a multi-layer wall, a single room building, a two-room building, and a three-room building) by creating both the BIM models and Modelica Models for each sample. We examined the model data transfer results and simulation results.  In addition, we also conducted literature research on how to utilize Modelica to link BIM and daylighting models. The plan is to use both BIM API and Modelica to integrate building thermal and daylighting (and later BIPV) simulations. Experiments on utilizing Modelica for daylighting and BIPV simulations will be conducted in the next phase of the project.  1.4 Prototyping – P-BIM a) Solar thermal simulation prototypes We have further researched the method of our P-BIM prototype using Autodesk Revit, its API and ASHRAE Loads Toolkit, and found the advantages and limitations. ASHRAE Loads Toolkit is a well-developed building load calculation package. However, it is written in procedural programming language (FORTRAN 90) and this presents a significant challenge for us trying to link Object-based BIM and Object-Oriented programming (OOP) language (C#) used in Revit API. In addition, Loads Toolkit is configured to simulate one (1) day and it would need to be completely rewritten to perform an annual simulation needed for P-BIM. We succeeded in implementing selected loads calculation functions in BIM-OOPM that can directly calculate building infiltration and wall conductivity (using Conduction Transfer Functions  or CTFs), as case studies, but due to the highly complex transfer of data and functions between procedural and OOP methodologies, the implementation was very difficult with significant research and programming efforts. For example, some subroutines in ASHRAE Loads Toolkit had to be modified manually in order to be called by BIM API methods (functions). Figure 1 shows a sample BIM model and calculation results for this prototype. 2   Figure 1. A BIM model and the results of conduction calculation [To submit this report using NSF online form, figures will be moved out of the document and uploaded as a separate PDF file]   In addition, we also researched another method and started to develop our P-BIM prototype using Autodesk Revit, its API, and the Modelica-based thermal modeling (LBL Modelica Buildings Library as a case study). We created a preliminary P-BIM prototype that can transfer BIM data to Modelica building models and directly execute energy simulation in a Modelica development environment (Dymola). The major advantage and challenges of this method are described in Findings #3 below. An initial Modelica BIM Package was built in order to assist in mapping BIM data to Modelica thermal modeling data. Once completed, this approach is expected to allow us to eliminate the redundant, inefficient, and inaccurate process of manually converting BIM data to thermal simulation. For demonstration, we are continuing to perform case studies of heat transfer calculation of the buildings that have more than two rooms. The BIM model and calculation results are shown in Figure 2. This is a promising method and we will continue to research and develop this method in the next phase. 3     Figure 2. A sample building with two rooms modeled in BIM (top) and Modelica (Bottom).  b) Daylighting prototype For daylighting modeling, we have investigated a new method of linking BIM and Radiance – the daylighting simulation software tool. We have planned the implementation methods to 1) extract geometry and material information from the BIM model, 2) convert BIM geometry to Radiance geometry model, and 3) translate BIM material information to Radiance material properties such as illumination levels and reflectance. As an example step for the prototype, Table 1 was created to show material properties of Autodesk Revit and Radiance mapping. We are currently working on using BIM API to implement the link between BIM and Radiance. Also we will continue investigating the use of Modelica to integrate the daylighting modeling with thermal modeling.  4   c) BIPV prototypes We have developed a Building Integrated Photovoltaic (BIPV) prototype in the BIM (Autodesk Revit) platform. Our prototype can calculate solar position and determine the amount of solar insolation from given time and location information (Figure 3) with a graphical user interface. We are continuing to develop BIM API that can analyze diffused and scattered components of solar insolation for more precise calculation of solar insolation. This step involves the calculation of the sky clearness index that depends on the atmospheric condition for which we tested the solar insolation model proposed by Kumar and Umananda (2005) and we are developing an algorithm that can be used and tested in locations in the United States. We plan to compare this prototype with PV F-Chart method. We will also research on the use of Modelica for creating PV component models and integrating the PV modeling into thermal and daylighting modeling.   Figure 1. A prototype of BIPV models and BIM API  1.5 Experiments – Solar thermal and daylighting simulations For integrated solar thermal simulations, we finalized experiments of a high performance office building by using traditional tools (DOE-2.1e, F-Chart, and PV F-Chart) for later P-BIM simulation verification. Table 1. Radiance material properties for BIM (Revit) materials  RevitCategoriesName of the Material Radiance material typeRED GREEN BLUE SPECULARITY ROUGHNESS RED GREEN BLUEAVERAGE-REFLECTANCE(R,G,B)SPECULARITY(0-0.1)ROUGHNESS(0-0.2)Asphalt Plastic 171 171 171 NA NA 0.67 0.67 0.67 0.67 0.05 0.10Roofing-asphalt Shingle Plastic 135 135 135 NA NA 0.53 0.53 0.53 0.53 0.05 0.10Site - Asphalt Plastic 27 27 27 NA NA 0.11 0.11 0.11 0.11 0.05 0.10Masonry - Brick Plastic 85 48 55 NA NA 0.33 0.19 0.22 0.23 0.00 0.10Masonry - Brick Soldier Course Plastic 74 38 42 NA NA 0.29 0.15 0.16 0.19 0.00 0.10Masonry - Tile Glass 72 73 78 NA NA 0.28 0.29 0.31 0.29 0.00 0.10RADIANCE MATRIAL PROPERTIES (INDIVIDULA CHANNELS)RADIANCE MATRIAL PROPERTIES (AVERAGED)ASPHALTBrickREVIT COLOR PROPERTIES5  The building includes various features of  windows, clerestory, photovoltaic panels, solar collectors, etc. (Figure 4). By using the active solar strategies, this solar building can achieve Net-Zero energy consumption.  We documented the modeling process, simulation results, and a validation result of test case models that have different configurations of building features.   Figure 4. Office building with all solar features 2 Major education activities  Our major education activities include: 1) The project provided research subjects and support for Ph.D. studies. Five Ph.D. dissertations in solar building design, daylighting simulation, BIM for urban design, building data modeling, and building embodied energy are in progress. These dissertations are closely related to this project. 2) Graduate level Directed Study courses in related topics are offered to students working in this project.  3) All of the five students working on this project have taken or are currently taking the graduate Building Energy class taught by Dr. Jeff Haberl and BIM class taught by Dr. Wei Yan. 4) Students have attended weekly project meetings with faculty members, in which the students present their research and dissertation work that is related to the project.   5) Students have attended teleconferences with Lawrence Berkeley National Laboratory (developers of the Modelica Buildings Library) for collaborative research.  6  Major Findings What are your major findings from the activities identified above? Our major findings include: 1) We finalized the document that describes benchmark results of the high performance office building model with various configurations of building features. Through the simulation and preliminary analyses, we achieved reasonable simulation results that can be used for the development of integrated P-BIM simulation. 2) We found that there are different structures and modeling concepts between BIM and energy simulation tools, which can make integrating P-BIM very complex. For example, ASHRAE Loads Toolkit, written in procedural programming language FORTRAN, performs loads calculations by using a large number of subroutines that reads building data from an input file multiple times during the calculation (while the ideal situation would be reading the data once into memory and use them in calculation when needed). In addition, some pre-calculated values were hard coded in either subroutines or input files; therefore, accessing and managing building data of energy models are highly complex.  3) We found that the major advantage of using BIM-Modelica method is that both BIM (Revit) API and Modelica modeling are based on OOP, and a link between them can be established with a more direct method that maps objects to objects and topology to topology. However, there are significant differences in terms of the semantics and ontology used in BIM and Modelica thermal modeling (one is specific for architecture domain and the other for engineering domain). For example, building components are categorized by the types of boundary conditions that determine algorithms used in energy simulation, while they are categorized as objects such as walls, slabs, etc. in BIM. Even if two slabs have the same material configurations in BIM, different energy simulation calculations may be needed according to the conditions below the slabs. For example, the slab on the ground involves earth-slab conduction heat transfer, whereas the slab above ground causes air-slab convective heat transfer. The understanding of differences of the data modeling and data access mechanisms will help integrate BIM and energy simulation so that inefficient and error-prone process for managing input files in the current energy simulation tools can be substituted by more organized, structured, efficient, and reliable access of BIM data. In addition, we have also found that when we build Modelica models based on LBL Buildings sample models, there is a need to add a convergence algorithm into the simulation. 4) The P-BIM prototype demonstrated that the P-BIM methods and algorithms can extract building data from a BIM model, convert BIM data to what the simulation engine requires, and pass the information to energy simulation models. Different granularity levels of building energy simulation can be conducted in P-BIM, through either calling subroutines like ASHRAE Loads Toolkit or executing Modelica thermal simulations directly from a BIM model. Moreover, building parameter values of energy models can be modified from the BIM interface. This way, BIM becomes a user 7  interface for building energy simulations. Planned for next phase, simulation results should also be monitored in the BIM interface and the feedback can be used for design changes to close the design-simulation loop, which is currently open. 5) The preliminary P-BIM prototype for BIPV demonstrated that the P-BIM methods can perform PV energy calculations within the BIM platform. By using BIM API and parametric modeling in BIM, interactions between BIM models and PV calculation can be achieved. The BIM models provide information for PV calculation, and the results can be used to modify the BIM model parameters for updating the building design.  Contributions 1 Contributions within discipline How have your findings, techniques you developed or extended, or other products from your project contributed to the principal disciplinary field(s) of the project? Please enter or update as appropriate. Our findings demonstrated that the new P-BIM methods have the potential to facilitate the integration of BIM and building energy simulation that is expected to assist informed decision-making of solar building design.  The findings also demonstrated the potentials of the methods for reducing the interoperability problem that exists between building design models and energy simulation models. In addition, our findings can help provide specifications and guidelines for well-designed software systems that integrate design and simulation for sustainable buildings. 2 Contributions to other disciplines How have your findings, techniques you developed or extended, or other products from your project contributed to disciplines other than your own (or disciplines of colleagues and associates not covered under "Contributions within Discipline")? Please enter or update as appropriate. The project is cross-disciplinary in the fields of architecture, building science, and computer science. The contributions of the project help integrate architecture and building science with computer science. In addition, our methods demonstrated that specific domain modeling (in our case, BIM) can be integrated into more general physical simulation (in our case, building energy). In terms of computer science, this research provides a case study of translating one OOP-based model (BIM) to another OOP-based model (Modelica models), with different semantics and ontologies.  3 Contributions to human resource development How have results from your project contributed to human resource development in science, engineering, and technology? Please enter or update as appropriate. 8  Our project helped prepare students in architecture and building science for academic and professional careers in sustainable building design and research. The research also motivated some undergraduate students to study BIM and energy related topics at Texas A&M University. 4 Contributions to resources for research and education How have results from your project contributed to physical, institutional, and information resources for research and education (beyond producing specific products reported elsewhere)? Please enter or update as appropriate. 1) In 2010, with the support from the NSF grant and our department and college, we built a new BIM-SIM (Building Information Modeling and Simulation) research and education lab, where both undergraduate and graduate students can work on research projects, among which P-BIM is one of major projects. We installed software and hardware funded by this NSF grant in the lab. We have also set up web-based project collaboration and management systems. In 2010 and 2011, we have acquired software systems including Intel Visual FORTRAN®, Dymola®, and Understand® for the project. The facility, equipment, and software can be used further for research and education in the fields of sustainable building design. 2) Our project produced preliminary software prototypes of P-BIM (which can be further developed into a complete prototype), daylighting sample models, PV component models, and solar building test models, which can contribute to the information resources for research and education in solar building design and simulation, as well as general building energy simulation.  5 Contributions beyond science and engineering How have results from your project contributed to the public welfare beyond science and engineering (e.g., by inspiring commercialized technology or informing regulatory policy)? Please enter or update as appropriate. The resulting preliminary prototypes, models, and methods, when further developed into more comprehensive prototypes, energy models, and software specifications and guidelines, can be used for creating commercialized software tools for solar building design and simulation. One of the related Ph.D. dissertation research projects – BIM’s application in urban planning, has the potential to inspire urban planning code formation.  Acknowledgement This material is based upon work supported by the National Science Foundation under Grant No. 0967446. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  

Physical Building Information Modeling for Solar Building Design and Simulation- Annual Report 2011

  ESL-TR-12-12-08  Physical Building Information Modeling for Solar Building Design and Simulation – Annual Report 2012    Dr. Wei Yan, PI, Texas A&M University  Dr. Jeff Haberl, CO-PI, Texas A&M University Dr. Mark J. Clayton, CO-PI, Texas A&M University WoonSeong Jeong, Ph.D. Student, Texas A&M University  Sandeep Kota, Ph.D. Student, Texas A&M University Jong Bum Kim, Ph.D. Student, Texas A&M University Jose Luis Bermudez Alcocer, Ph.D. Student, Texas A&M University  Manish Dixit, Ph.D. Student, Texas A&M University  NSF CBET – 0967446 December 2, 2012 Project Activities What have been your major research and education activities (experiments, observations, simulations, presentations, etc.)?  1 Major research activities 1.1 Research on methods of integrating BIM and physical modeling  We have continued investigation on modeling of passive solar thermal with Modelica, daylighting with Radiance and DAYSIM, and BIPV with Building Information Modeling (BIM) Application Programming Interface (API). We explored the state-of-the-art of BIM-based energy simulation tools. We reviewed research on linking BIM and energy simulation through (1) data exchange formats, such as the Industry Foundation Classes (IFC) and Green Building XML (gbXML), and (2) graphical user interfaces (GUIs), such as eQUEST, Simergy, etc. We conducted comparative studies on architectural modeling in BIM and energy modeling in Modelica, addressing the mismatches between the two disciplines. Our major questions include: how to formulate energy related information from the wide range of information in BIM; how to abstract BIM topology information for energy modeling; how to translate the connectivity of building components in BIM into a network in a thermal model; how to add required information for energy modeling that is missing from BIM; and how to facilitate such mapping processes in our prototyping. We have developed modeling integration methods and created two corresponding prototypes demonstrating our methods: BIM to Thermal and BIM to Daylighting. We also have continued working on BIM and BiPV integration. The prototypes are created using the BIM authoring tool Autodesk Revit 1  and its API, and simulation tools including Lawrence Berkeley National Laboratory’s Modelica Buildings Library and Radiance.  For BIM to thermal modeling, we have researched on how to build an energy model that holds building information of building topologies and energy properties by using the following tools and datasets: • Modelica as an Object-Oriented Equation-based modeling language • LBL Modelica Buildings Library (LBNL, 2012) as a simulation engine • Dymola as a Modelica modeling and simulation environment • Revit as a BIM authoring tool, and  • Revit Application Programming Interface (API) and C# as a prototype development platform We identified energy modeling methods for (1) basic building components such as walls, roofs, floors, windows, and doors, and (2) diverse building forms including rectangular, non-rectangular, multi-room single story, and multi-story buildings. Based on these methods, we have developed a prototyping framework, a TAMU-PBIM library, and a series of PBIM prototypes.  For daylighting, we studied BIM and daylighting modeling structures. We extracted building geometry and surfaces in the form of triangles through BIM Revit API, and then converted the geometry to Radiance geometry. We also extracted building element materials from BIM to form the Radiance element materials. 1.2 Prototyping The main features of our prototypes are (1) generating a thermal model and a daylighting model from BIM, (2) executing thermal and daylighting simulations, and (3) presenting simulation results. a) BIM to Thermal Modeling We implemented TAMU PBIM Modelica Package as an intermediate data model between BIM and Modelica for building thermal analysis. The use of the package demonstrates our mapping methods between architectural models in Revit and energy models with the existing LBL Modelica library. Mismatches of architecture modeling and energy modeling can be explained in two ways: (1) semantic mismatches between an architectural model in BIM and an energy model in Modelica and (2) behavior mismatches between architectural modeling and energy modeling.  These mismatches can impede data exchange and model translations between architectural models and energy models. For example, a building’s envelope in an architectural model is commonly recognized as walls, roofs, and floors/slabs, while it can be recognized as exterior or interior surface in energy models. Precise geometry information of building components is embedded in architectural models in BIM, whereas surface area is required in energy models for heat transfer calculations. These semantic mismatches between two domains can hinder data exchange of building objects and their attributes. On the other hand, the behavior of installing an interior wall in BIM can be represented as the behavior of 2  splitting one thermal zone into two zones. Such behavior mismatches between two domains cause complex translations between architectural modeling and energy modeling. In our prototype and library developments, we addressed (1) semantic mapping of an architectural model in BIM to an energy model in the Modelica library as well as (2) behavior mapping between architectural modeling in BIM and energy modeling in Modelica. TAMU PBIM library consists of Modelica BIM package and Modelica BIM models. The BIM package holds OOPM-based BIM objects that represent building components, such as rooms, walls, roofs, floors, doors, and windows. In addition, it supports semantic mapping such as translating required energy parameters from building components in BIM to energy model components in Modelica. The Modelica-BIM models are OOPM-based BIM models that present building topologies and configurations of the building components. Five test case models demonstrate how building design features in BIM can be translated into the energy modeling features in Modelica. Simple building design options through changing room geometry, adding an interior wall, or installing windows and doors on the wall are presented in the test case models as shown in Table 1.  Table 1. Test case models Name Description TestCase1  A single room model  TestCase2   A two room model TestCase3   A two room model with two windows TestCase4   A two-room model with two windows and a door TestCase5   A two room, two story building model with two windows  Figure 1 shows TAMU Modelica BIM Package and BIM Model Samples. The energy model diagram is a graphical representation of the TestCase5_TAMU model. The test case model demonstrates a two-story building with two rooms and two windows.   3   Figure 2 shows the process of the prototype from a user’s point of view, and Figure 3 shows the workflow of BIM to Thermal Modeling in PBIM.  Figure 2. Process diagram of the prototype from a user’s point of view.  Figure 1. TAMU Modelica BIM Package and BIM Model Samples  TAMU Modelica BIM Package TAMU Modelica BIM Models  Materials, layers, and constructions 4   Figure 3. Workflow of the prototype Figure 4 shows a sample result of a room’s temperature data (blue) in comparison with the exterior temperature data (red) after running a simulation.  Figure 4. Result data after running the prototype’s simulation To map required information from BIM to energy model, three main data processes are developed in the prototype: retrieving existing parameters directly from BIM, calculating parameters that are needed in the energy models from BIM, and adding missing parameters that are important for energy modeling from BIM.  b) BIM to Daylighting For daylighting modeling, we have developed a prototype for translating a BIM model to Radiance - a daylighting simulation tool. The prototype was developed using Revit Application Program Interface 5  (API) and C Sharp (C#) programming language. We identified that the BIM model’s materials data do not have certain information that is necessary for the daylighting simulation. To bridge the gap we have developed a spreadsheet that contains BIM material names and corresponding parameters necessary for daylighting simulation for each material. Also we have developed a way to add the material property information necessary for daylighting simulation into the BIM model. This was accomplished using a custom add-in module of the prototype. The prototype translates a given BIM model into input files of Radiance. Once the input file is created we can run it in Radiance to perform a daylight simulation   The prototype has four custom programs which were developed using Revit API and C# programing language.   The first add-in program  creates a file to add custome parameters to a BIM model.   The second add-in program adds two custome shared parameters for materials in the BIM material database.   The third add-in program adds values to custome shared parameters of each material from an external database.  Finally, the fourth add-in program translates geometry and material properties into Radiance input files.  Figure 5 shows a sample model of wall with a window in the context of a sun path specific to the location of the building. Figure 6 shows the simulation result for the simple model. More complex models including the Stanford Solar Decathlon 2013 project models are being tested.   Figure 5. Revit building component sample with a sun path specific to the location. 6   Figure 6. Radiance rendering of the wall. c) BIM to BiPV We developed a Building Integrated Photovoltaic (BiPV) prototype in a BIM platform (Autodesk Revit) to calculate solar position and determine the amount of solar insolation from given time and location information with a graphical user interface. We have developed a model to calculate the direct component of solar insolation by testing and using a solar insolation model proposed in literature. The proposed model can be integrated into BIM to automatically calculate solar position and the amount of solar insolation based on user inputs of time and location. We plan to compare this prototype with the PV F-Chart method and other PV modeling methods. 1.3 Experiments We have done various experiments with our prototypes. Experiments that validate our thermal simulations are described below. Other experiments for daylighting and BiPV modeling are ongoing. Each test case of PBIM consists of a TAMU model and a LBL model. While the LBL model is created using the LBL Buildings Library only, the TAMU models are developed as follows:  Building topology, such as room configurations, building component geometry, materials, layers, and constructions, is built with the TAMU Modelica BIM Package.  Energy components, such as boundary conditions, weather data, and other external heat gain sources, are built with the LBL Buildings library. The TAMU and the LBL models have different code structures and different components; therefore it is required to validate modeling accuracy of the PBIM prototype. To do so, we have conducted a series of experiments for simulation result comparisons. The experiments yielded identical simulation results from paired two models of each test case. It implies that our energy modeling methods, such as encapsulation 7  of calculation modules and a rearrangement of a model code in Modelica, do not affect the simulation results. Meanwhile, another experiment was performed for the comparison of Modelica and DOE 2.1e results. Based on an existing sample model in the LBL Buildings Library, we generated a simplified building model with the following configurations:  The room is located in Chicago. USA_IL_Chicago-OHare.Intl.AP.725300_TMY3 is used as the weather file.  The room is lifted up from the earth, so the floor slab is not attached to the ground.  This one room model consists of a roof, a floor, and four walls.  No windows and doors are installed.  All building components are made up of a 200mm concrete construction.   Simulation results show that two models yield a similar pattern in indoor temperature changes (Figure 7) and that the factors causing temperature differences need to be investigated. Further model calibrations are ongoing. 2 Major education activities  Our major education activities include: 1) The project provided research subjects and support for Ph.D. studies. Five Ph.D. dissertations in solar building design, daylighting simulation, BIM for urban design, building data modeling, and building embodied energy are in progress. These dissertations are closely related to this project. Most of the students have passed their Preliminary Exams. 2) Graduate level Directed Study courses in related topics are offered to students working in this project.  3) A new BIM class that integrates energy simulation is currently taught by Dr. Mark Clayton.  Figure 7. A simulation result comparison with a Modelica and a DOE model. 8  4) Students have attended weekly project meetings with faculty members, in which the students present their research and dissertation work that is related to the project.   5) We are currently working with Stanford University’s Solar Decathlon 2013 Team. We are testing our prototypes with their building models. The aims are to use the simulation results to inform their design and use their data to validate our modeling and simulation. The collaboration is expected to enhance students’ design and research in both universities. Major Findings What are your major findings from the activities identified above? Our major findings include: 1) BIM can be used as an interface for energy simulations. The project shows that we can  a. automatically or semi-automatically generate energy models from BIM,  b. dynamically change energy models using semantically rich information in BIM,  c. effectively store and manage energy model information in BIM’s parameters,  d. execute external simulation engines using BIM’s API without leaving the BIM interface, and  e. improve visualization of simulation results. 2) With the capabilities of object-oriented programming in Modelica, we could encapsulate third-party energy simulation modules and transfer parameters throughout encapsulated modules of the TAMU PBIM prototype. Also, multiple encapsulations of calculation modules could yield identical simulation results as shown in our experiments with test case models. 3) There is difference in the representation of the building geometry and materials between BIM and Thermal/Daylighting modeling which needs to simplified and mapped correctly. 4) Initial results show that BIM models created in Revit can be converted into Modelica thermal models and Radiance/DAYSIM daylighting models, through automated steps with high efficiency and accuracy. Contributions 1 Contributions within discipline How have your findings, techniques you developed or extended, or other products from your project contributed to the principal disciplinary field(s) of the project? Please enter or update as appropriate. 9  Our findings demonstrated that the new PBIM methods have the potential to facilitate the integration of BIM and building energy simulations that can assist informed decision-making of solar building design.  The findings also demonstrated the capability for reducing the interoperability problem that exists between architecture models and energy simulation models. 2 Contributions to other disciplines How have your findings, techniques you developed or extended, or other products from your project contributed to disciplines other than your own (or disciplines of colleagues and associates not covered under "Contributions within Discipline")? Please enter or update as appropriate. The project is cross-disciplinary in the fields of architecture, building science, and computer science. The contributions of the project help integrate architecture and building science with computer science. In addition, our methods demonstrated that specific domain modeling (in our case, BIM) can be integrated into more general physical simulation (in our case, building energy). In terms of computer science, this research provides a case study of translating one OOP-based model (BIM) into another OOP-based model (Modelica models), with different semantics and ontologies.  3 Contributions to human resource development How have results from your project contributed to human resource development in science, engineering, and technology? Please enter or update as appropriate. Our project helped prepare students in architecture and building science for academic and professional careers in sustainable building design and research. The research also motivated some undergraduate students to study BIM and energy related topics at Texas A&M University. More Ph.D. applicants are applying to join our research group recently.  4 Contributions to resources for research and education How have results from your project contributed to physical, institutional, and information resources for research and education (beyond producing specific products reported elsewhere)? Please enter or update as appropriate. Our project produced software prototypes of PBIM, new daylighting sample models, PV component models, and solar building test models, which can contribute to the information resources for research and education in solar building design and simulation, as well as general building energy simulation.  5 Contributions beyond science and engineering How have results from your project contributed to the public welfare beyond science and engineering (e.g., by inspiring commercialized technology or informing regulatory policy)? Please enter or update as appropriate. 10  The resulting prototypes, models, and methods can be used for creating commercialized software tools for solar building design and simulation. One of the related Ph.D. dissertation research projects – BIM’s application in urban planning, has the potential to inspire urban planning code formation.  Acknowledgement This material is based upon work supported by the National Science Foundation under Grant No. 0967446. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. References LBNL (2012). https://gaia.lbl.gov/bir (Lawrence Berkeley National Lab’s Modelica Buildings Library)   

Physical Building Information Modeling for Solar Building Design and Simulation- Annual Report 2012

Texas A&M University

  ESL-TR-11-12-05 
 
Physical Building Information Modeling for Solar 
Building Design and Simulation – Annual Report 
2011 
 
Dr. Wei Yan, PI, Texas A&M University  
Dr. Jeff Haberl, CO-PI, Texas A&M University 
Dr. Mark J. Clayton, CO-PI, Texas A&M University 
WoonSeong Jeong, Ph.D. Student, Texas A&M University  
Jong Bum Kim, Ph.D. Student, Texas A&M University 
Sandeep Kota, Ph.D. Student, Texas A&M University 
Jose Luis Bermudez Alcocer, Ph.D. Student, Texas A&M University  
Manish Dixit, Ph.D. Student, Texas A&M University 
 
NSF GRANT CBET – 0967446  
December 1, 2011 
 
Project Activities 
What have been your major research and education activities (experiments, observations, 
simulations, presentations, etc.)?  
1 Major research activities 
1.1 Research on physical modeling methods  
We have investigated modeling of passive solar thermal with Modelica, daylighting with Radiance and 
DAYSIM, and BIPV with Building Information Modeling (BIM) Application Programming Interface 
(API). For solar thermal, we have researched on how to model building objects in Object-Oriented 
Equation-Based modeling language – Modelica by using the LBL Modelica Buildings Library (Lawrence 
Berkeley National Laboratory, 2011). We have identified modeling methods using Modelica for building 
geometry, air volume, room configurations, materials, and building components, which are major factors 
of solar energy utilization in buildings. For daylighting, we studied how to convert building geometry and 
material information from BIM to Radiance. For Building Integrated Photovoltaic (BIPV), we have 
researched on how to build solar models in BIM that can calculate solar position and solar insolation.   
1.2 Research on BIM simplification methods, BIM topology, and data modeling 
We further investigated building object hierarchy and topology in BIM and Modelica-based thermal 
modeling. Using BIM and its API, we explored modeling and extracting both building object properties 
(including size, material, and construction data) and building topology, which can represent the 
connectivity of building objects. We also structured our data modeling method that includes a TAMU 
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Modelica BIM Package as a bridge between BIM and Modelica thermal modeling. We are currently 
implementing this data modeling structure.  
1.3 Research on linking BIM-OOPM (Object-Oriented Physical Modeling) and 
integrating P-BIM (Physical BIM) 
We reviewed extensively a Modelica-based building thermal library and its example models (LBL 
Modelica Buildings Library from Lawrence Berkeley National Laboratory, 2011), and evaluated its 
applicability to P-BIM. We found both advantages and limitations of the library’s application in our P-
BIM project.  The library is the currently most complete building thermal library but still in its early stage 
of development. We examined various ways to link BIM and LBL Modelica Buildings Library and 
developed a method that can utilize BIM API and our Modelica BIM Package (under-development) to 
transfer BIM data to Modelica simulation. For achieving this method, we have experimented sample 
building models (including a single layer wall, a multi-layer wall, a single room building, a two-room 
building, and a three-room building) by creating both the BIM models and Modelica Models for each 
sample. We examined the model data transfer results and simulation results.  
In addition, we also conducted literature research on how to utilize Modelica to link BIM and daylighting 
models. The plan is to use both BIM API and Modelica to integrate building thermal and daylighting (and 
later BIPV) simulations. Experiments on utilizing Modelica for daylighting and BIPV simulations will be 
conducted in the next phase of the project.  
1.4 Prototyping – P-BIM 
a) Solar thermal simulation prototypes 
We have further researched the method of our P-BIM prototype using Autodesk Revit, its API 
and ASHRAE Loads Toolkit, and found the advantages and limitations. ASHRAE Loads Toolkit 
is a well-developed building load calculation package. However, it is written in procedural 
programming language (FORTRAN 90) and this presents a significant challenge for us trying to 
link Object-based BIM and Object-Oriented programming (OOP) language (C#) used in Revit 
API. In addition, Loads Toolkit is configured to simulate one (1) day and it would need to be 
completely rewritten to perform an annual simulation needed for P-BIM. We succeeded in 
implementing selected loads calculation functions in BIM-OOPM that can directly calculate 
building infiltration and wall conductivity (using Conduction Transfer Functions  or CTFs), as 
case studies, but due to the highly complex transfer of data and functions between procedural and 
OOP methodologies, the implementation was very difficult with significant research and 
programming efforts. For example, some subroutines in ASHRAE Loads Toolkit had to be 
modified manually in order to be called by BIM API methods (functions). Figure 1 shows a 
sample BIM model and calculation results for this prototype. 
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Figure 1. A BIM model and the results of conduction calculation 
[To submit this report using NSF online form, figures will be moved out of the document and uploaded as 
a separate PDF file]   
In addition, we also researched another method and started to develop our P-BIM prototype using 
Autodesk Revit, its API, and the Modelica-based thermal modeling (LBL Modelica Buildings Library as 
a case study). 
We created a preliminary P-BIM prototype that can transfer BIM data to Modelica building models and 
directly execute energy simulation in a Modelica development environment (Dymola). The major 
advantage and challenges of this method are described in Findings #3 below. An initial Modelica BIM 
Package was built in order to assist in mapping BIM data to Modelica thermal modeling data. Once 
completed, this approach is expected to allow us to eliminate the redundant, inefficient, and inaccurate 
process of manually converting BIM data to thermal simulation. For demonstration, we are continuing to 
perform case studies of heat transfer calculation of the buildings that have more than two rooms. The BIM 
model and calculation results are shown in Figure 2. This is a promising method and we will continue to 
research and develop this method in the next phase. 
3 
 
 
 
 
Figure 2. A sample building with two rooms modeled in BIM (top) and Modelica (Bottom). 
 
b) Daylighting prototype 
For daylighting modeling, we have investigated a new method of linking BIM and Radiance – the 
daylighting simulation software tool. We have planned the implementation methods to 1) extract 
geometry and material information from the BIM model, 2) convert BIM geometry to Radiance geometry 
model, and 3) translate BIM material information to Radiance material properties such as illumination 
levels and reflectance. As an example step for the prototype, Table 1 was created to show material 
properties of Autodesk Revit and Radiance mapping. We are currently working on using BIM API to 
implement the link between BIM and Radiance. Also we will continue investigating the use of Modelica 
to integrate the daylighting modeling with thermal modeling.  
4 
 
 
c) BIPV prototypes 
We have developed a Building Integrated Photovoltaic (BIPV) prototype in the BIM (Autodesk Revit) 
platform. Our prototype can calculate solar position and determine the amount of solar insolation from 
given time and location information (Figure 3) with a graphical user interface. We are continuing to 
develop BIM API that can analyze diffused and scattered components of solar insolation for more precise 
calculation of solar insolation. This step involves the calculation of the sky clearness index that depends 
on the atmospheric condition for which we tested the solar insolation model proposed by Kumar and 
Umananda (2005) and we are developing an algorithm that can be used and tested in locations in the 
United States. We plan to compare this prototype with PV F-Chart method. We will also research on the 
use of Modelica for creating PV component models and integrating the PV modeling into thermal and 
daylighting modeling.  
 
Figure 1. A prototype of BIPV models and BIM API 
 
1.5 Experiments – Solar thermal and daylighting simulations 
For integrated solar thermal simulations, we finalized experiments of a high performance office building 
by using traditional tools (DOE-2.1e, F-Chart, and PV F-Chart) for later P-BIM simulation verification. 
Table 1. Radiance material properties for BIM (Revit) materials 
 
Revit
Categories
Name of the Material 
Radiance material 
type
RED GREEN BLUE SPECULARITY ROUGHNESS RED GREEN BLUE
AVERAGE-
REFLECTANCE
(R,G,B)
SPECULARITY
(0-0.1)
ROUGHNESS
(0-0.2)
Asphalt Plastic 171 171 171 NA NA 0.67 0.67 0.67 0.67 0.05 0.10
Roofing-asphalt Shingle Plastic 135 135 135 NA NA 0.53 0.53 0.53 0.53 0.05 0.10
Site - Asphalt Plastic 27 27 27 NA NA 0.11 0.11 0.11 0.11 0.05 0.10
Masonry - Brick Plastic 85 48 55 NA NA 0.33 0.19 0.22 0.23 0.00 0.10
Masonry - Brick Soldier Course Plastic 74 38 42 NA NA 0.29 0.15 0.16 0.19 0.00 0.10
Masonry - Tile Glass 72 73 78 NA NA 0.28 0.29 0.31 0.29 0.00 0.10
RADIANCE MATRIAL PROPERTIES 
(INDIVIDULA CHANNELS)
RADIANCE MATRIAL PROPERTIES (AVERAGED)
ASPHALT
Brick
REVIT COLOR PROPERTIES
5 
 
The building includes various features of  windows, clerestory, photovoltaic panels, solar collectors, etc. 
(Figure 4). By using the active solar strategies, this solar building can achieve Net-Zero energy 
consumption.  We documented the modeling process, simulation results, and a validation result of test 
case models that have different configurations of building features.  
 
Figure 4. Office building with all solar features 
2 Major education activities  
Our major education activities include: 
1) The project provided research subjects and support for Ph.D. studies. Five Ph.D. dissertations in solar 
building design, daylighting simulation, BIM for urban design, building data modeling, and building 
embodied energy are in progress. These dissertations are closely related to this project. 
2) Graduate level Directed Study courses in related topics are offered to students working in this project.  
3) All of the five students working on this project have taken or are currently taking the graduate 
Building Energy class taught by Dr. Jeff Haberl and BIM class taught by Dr. Wei Yan. 
4) Students have attended weekly project meetings with faculty members, in which the students present 
their research and dissertation work that is related to the project.   
5) Students have attended teleconferences with Lawrence Berkeley National Laboratory (developers of 
the Modelica Buildings Library) for collaborative research. 
 
6 
 
Major Findings 
What are your major findings from the activities identified above? 
Our major findings include: 
1) We finalized the document that describes benchmark results of the high performance office building 
model with various configurations of building features. Through the simulation and preliminary 
analyses, we achieved reasonable simulation results that can be used for the development of 
integrated P-BIM simulation. 
2) We found that there are different structures and modeling concepts between BIM and energy 
simulation tools, which can make integrating P-BIM very complex. For example, ASHRAE Loads 
Toolkit, written in procedural programming language FORTRAN, performs loads calculations by 
using a large number of subroutines that reads building data from an input file multiple times during 
the calculation (while the ideal situation would be reading the data once into memory and use them in 
calculation when needed). In addition, some pre-calculated values were hard coded in either 
subroutines or input files; therefore, accessing and managing building data of energy models are 
highly complex.  
3) We found that the major advantage of using BIM-Modelica method is that both BIM (Revit) API and 
Modelica modeling are based on OOP, and a link between them can be established with a more direct 
method that maps objects to objects and topology to topology. However, there are significant 
differences in terms of the semantics and ontology used in BIM and Modelica thermal modeling (one 
is specific for architecture domain and the other for engineering domain). For example, building 
components are categorized by the types of boundary conditions that determine algorithms used in 
energy simulation, while they are categorized as objects such as walls, slabs, etc. in BIM. Even if two 
slabs have the same material configurations in BIM, different energy simulation calculations may be 
needed according to the conditions below the slabs. For example, the slab on the ground involves 
earth-slab conduction heat transfer, whereas the slab above ground causes air-slab convective heat 
transfer. The understanding of differences of the data modeling and data access mechanisms will help 
integrate BIM and energy simulation so that inefficient and error-prone process for managing input 
files in the current energy simulation tools can be substituted by more organized, structured, efficient, 
and reliable access of BIM data. In addition, we have also found that when we build Modelica models 
based on LBL Buildings sample models, there is a need to add a convergence algorithm into the 
simulation. 
4) The P-BIM prototype demonstrated that the P-BIM methods and algorithms can extract building data 
from a BIM model, convert BIM data to what the simulation engine requires, and pass the 
information to energy simulation models. Different granularity levels of building energy simulation 
can be conducted in P-BIM, through either calling subroutines like ASHRAE Loads Toolkit or 
executing Modelica thermal simulations directly from a BIM model. Moreover, building parameter 
values of energy models can be modified from the BIM interface. This way, BIM becomes a user 
7 
 
interface for building energy simulations. Planned for next phase, simulation results should also be 
monitored in the BIM interface and the feedback can be used for design changes to close the design-
simulation loop, which is currently open. 
5) The preliminary P-BIM prototype for BIPV demonstrated that the P-BIM methods can perform PV 
energy calculations within the BIM platform. By using BIM API and parametric modeling in BIM, 
interactions between BIM models and PV calculation can be achieved. The BIM models provide 
information for PV calculation, and the results can be used to modify the BIM model parameters for 
updating the building design.  
Contributions 
1 Contributions within discipline 
How have your findings, techniques you developed or extended, or other products from your 
project contributed to the principal disciplinary field(s) of the project? Please enter or update as 
appropriate. 
Our findings demonstrated that the new P-BIM methods have the potential to facilitate the integration of 
BIM and building energy simulation that is expected to assist informed decision-making of solar building 
design.  The findings also demonstrated the potentials of the methods for reducing the interoperability 
problem that exists between building design models and energy simulation models. In addition, our 
findings can help provide specifications and guidelines for well-designed software systems that integrate 
design and simulation for sustainable buildings. 
2 Contributions to other disciplines 
How have your findings, techniques you developed or extended, or other products from your 
project contributed to disciplines other than your own (or disciplines of colleagues and associates 
not covered under "Contributions within Discipline")? Please enter or update as appropriate. 
The project is cross-disciplinary in the fields of architecture, building science, and computer science. The 
contributions of the project help integrate architecture and building science with computer science. In 
addition, our methods demonstrated that specific domain modeling (in our case, BIM) can be integrated 
into more general physical simulation (in our case, building energy). In terms of computer science, this 
research provides a case study of translating one OOP-based model (BIM) to another OOP-based model 
(Modelica models), with different semantics and ontologies.  
3 Contributions to human resource development 
How have results from your project contributed to human resource development in science, 
engineering, and technology? Please enter or update as appropriate. 
8 
 
Our project helped prepare students in architecture and building science for academic and professional 
careers in sustainable building design and research. The research also motivated some undergraduate 
students to study BIM and energy related topics at Texas A&M University. 
4 Contributions to resources for research and education 
How have results from your project contributed to physical, institutional, and information 
resources for research and education (beyond producing specific products reported elsewhere)? 
Please enter or update as appropriate. 
1) In 2010, with the support from the NSF grant and our department and college, we built a new BIM-
SIM (Building Information Modeling and Simulation) research and education lab, where both 
undergraduate and graduate students can work on research projects, among which P-BIM is one of 
major projects. We installed software and hardware funded by this NSF grant in the lab. We have also 
set up web-based project collaboration and management systems. In 2010 and 2011, we have 
acquired software systems including Intel Visual FORTRAN®, Dymola®, and Understand® for the 
project. The facility, equipment, and software can be used further for research and education in the 
fields of sustainable building design. 
2) Our project produced preliminary software prototypes of P-BIM (which can be further developed into 
a complete prototype), daylighting sample models, PV component models, and solar building test 
models, which can contribute to the information resources for research and education in solar building 
design and simulation, as well as general building energy simulation.  
5 Contributions beyond science and engineering 
How have results from your project contributed to the public welfare beyond science and 
engineering (e.g., by inspiring commercialized technology or informing regulatory policy)? Please 
enter or update as appropriate. 
The resulting preliminary prototypes, models, and methods, when further developed into more 
comprehensive prototypes, energy models, and software specifications and guidelines, can be used for 
creating commercialized software tools for solar building design and simulation. One of the related Ph.D. 
dissertation research projects – BIM’s application in urban planning, has the potential to inspire urban 
planning code formation.  
Acknowledgement 
This material is based upon work supported by the National Science Foundation under Grant No. 
0967446. Any opinions, findings, and conclusions or recommendations expressed in this material are 
those of the authors and do not necessarily reflect the views of the National Science Foundation. 
 


English

https://oaktrust.library.tamu.edu/bitstream/1969.1/152116/1/ESL-TR-12-12-08.pdf

Physical Building Information Modeling for Solar Building Design and Simulation- Annual Report 2011

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Physical Building Information Modeling for Solar Building Design and Simulation- Annual Report 2011

Abstract

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