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Vladimir Bazjanac - One of the best experts on this subject based on the ideXlab platform.
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mvd based information exchange between bim and building energy Performance Simulation
Automation in Construction, 2018Co-Authors: Sergio Pinheiro, Vladimir Bazjanac, Reinhard Wimmer, James Odonnell, Sergej Muhic, Tobias Maile, Jerome Frisch, Christoph Van TreeckAbstract:Abstract The process of preparing building energy Performance Simulation (BEPS) models involves repetitive manual operations that often lead to data losses and errors. As a result, BEPS model inputs can vary widely from this time consuming, non-standardised and subjective process. This paper proposes a standardised method of information exchange between Building Information Modelling (BIM) and BEPS tools using the Information Delivery Manual (IDM) and Model View Definition (MVD) methodologies. The methodology leverages a collection of use cases to initiate the identification of exchange requirements needed by BEPS tools. The IDM/MVD framework captures and translates exchange requirements into the Industry Foundation Classes (IFC) schema. The suggested approach aims to facilitate the transfer of information from IFC based BIM to either conventional or advanced BEPS tools (e.g. EnergyPlus and Modelica) through the development of a specific MVD that defines a subset of the IFC data model that deals with building energy Performance Simulation. By doing so, the potential of BIM-based Simulation can be fully unlocked, and a reliable and consistent IFC subset is provided as an input for energy Simulation software.
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space boundary requirements for modeling of building geometry for energy and other Performance Simulation
CIB W78 2010 - Applications of IT in the AEC Industry, 2010Co-Authors: Vladimir BazjanacAbstract:CAD models of buildings represent architects’ views of buildings. Data definitions that represent a given building conform to the internal data structure of the CAD software that is used to define the building and typically include data which facilitate the representation of the building in CAD, but do not necessarily by themselves define anything about the building. CAD representations of buildings are based on detailed definitions of building geometry; those definitions thus contain significant amounts of information needed by CAD tools but not used by other types of tools – those tools need only rudimentary definitions of building geometry to operate.Building energy Performance Simulation tools, as well as many other types of Simulation and analysis tools (like acoustics and fire propagation Simulation tools) have their own internal data models of building geometry. Such internal data models represent views of building geometry typically used by the disciplines served by these Simulation and analysis tools, and are usually much simpler than the geometry data models of CAD tools. Consequently, CAD building geometry representations must be “simplified” and “reduced” before they can be directly used by other (non-CAD) tools.Most Simulation and analysis tools define building geometry as systems of surfaces (i.e. surfaces that delineate walls, slabs, roofs, columns, beams, windows and doors) which are all part of the definition of spaces identified in the model of the building. Such surfaces are called “space boundaries” and are the critical part of the building geometry definitions for non-CAD tools. This paper clarifies and systematizes what a “simplified” building geometry for building energy Performance and similar Simulation and analysis tools must contain, and should help prevent misunderstandings and misrepresentations often encountered in the AECOO industry today. It describes the five “levels” of space boundaries, why and how they are defined, and how they are used by Simulation and analysis tools. The paper discusses the 55 test cases pertinent to semi-automated modeling of building geometry for energy Performance Simulation that were developed to test space boundaries defined and exported in IFC format by model based CAD tools. It also discusses the process and tools that check instances of IFC definitions of building geometry exported by CAD tools.
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Impact of the U.S. National Building Information Model Standard (NBIMS) on Building Energy Performance Simulation
Lawrence Berkeley National Laboratory, 2008Co-Authors: Vladimir BazjanacAbstract:IMPACT OF THE U.S. NATIONAL BUILDING INFORMATION MODEL STANDARD (NBIMS) ON BUILDING ENERGY Performance Simulation Vladimir Bazjanac Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA ABSTRACT The U.S. National Institute for Building Sciences (NIBS) started the development of the National Building Information Model Standard (NBIMS). Its goal is to define standard sets of data required to describe any given building in necessary detail so that any given AECO industry discipline application can find needed data at any point in the building lifecycle. This will include all data that are used in or are pertinent to building energy Performance Simulation and analysis. This paper describes the background that lead to the development of NBIMS, its goals and development methodology, its Part 1 (Version 1.0), and its probable impact on building energy Performance Simulation and analysis. professional consortia, the formulation of an open, object oriented, extensible lifecycle data model of buildings – Industry Foundation Classes (IFC) – and, ultimately, to the concept of Building Information Model (BIM). Fundamentally, a BIM (defined as a noun), is an instance of a populated data model of buildings that contains multi-disciplinary data specific to a particular building, which they describe unambiguously. It contains all data that define the building and are pertinent from the point of view of more than one discipline. A BIM includes all relationships and inheritances for each of the building components it describes; in that sense it is “intelligent” (Bazjanac 2004). From a general industry point of view, a BIM is a shared digital representation of a building and its physical and functional characteristics, based on open standards for software interoperability. It contains information supplied by all participants in building design, procurement and operation, and forms a reliable basis for decisions throughout its lifecycle (Figure 1). It facilitates effective collaboration by different stakeholders at all phases of that lifecycle. The basis for the definition and population of any BIM is a data model of buildings that all BIM authors and users agree to apply in the case of a particular building. While proprietary data models of buildings (usually limited to data definitions that represent only a part of the building lifecycle) abound, only one data model of buildings is an open specification that covers the entire lifecycle and is also recognized by the International Standards Organization (ISO/PAS 16739): IFC, developed by the IAI (IAI 2003). The IFC data model itself is too large to implement by any single industry software application. IFC compatible applications implement only those parts of the model that represent industry process or processes and discipline(s) they support. Such model parts are called “model views,” and the IAI has developed a Model View Definition Methodology (MVD) to facilitate their definition (Hietanen 2006). Defining IFC model views involves first the definition of data exchange requirements; the methodology for that was defined as part of the Norwegian Information Delivery Manual project (IDM 2006). KEYWORDS BIM, national standard, data exchange, building energy Performance, software interoperability. INTRODUCTION Information technology (IT) professionals who work in the Architecture/Engineering/Construction/ Operations (AECO) industry often define buildings as enormous collections of data. They treat buildings as data bases; data bases that they find mostly disorganized, with data that are often repetitive, inconsistent, contradictory, and prone to loss over the lifetime of a building. The need to organize, systematize and standardize buildings data, and to make them easily available, reusable and preserved has been long recognized by them. Virtually every discipline in the AECO industry uses software in the conduct of its activities; this includes the building energy Performance (BEP) Simulation and analysis profession. Each is experiencing serious data exchange problems in the use of its software: inability to directly import data generated by other software (this often results in the need to manually reproduce already existing data that in turn results in errors, data omission and misinterpretation), inability to access already existing data, the resulting excessive cost and time needed for preparation of productive work, and delay in generation and delivery of results (Bazjanac 2002). This has lead to the formation of the International Alliance for Interoperability (IAI) and several other industry and
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ifc bim based methodology for semi automated building energy Performance Simulation
International Conference on Information Technology, 2008Co-Authors: Vladimir BazjanacAbstract:Building energy Performance (BEP) Simulation is still rarely used in building design, commissioning and operations. The process is too costly and too labor intensive, and it takes too long to deliver results. Its quantitative results are not reproducible due to arbitrary decisions and assumptions made in Simulation model definition, and can be trusted only under special circumstances. A methodology to semi-automate BEP Simulation preparation and execution makes this process much more effective. It incorporates principles of information science and aims to eliminate inappropriate human intervention that results in subjective and arbitrary decisions. This is achieved by automating every part of the BEP modeling and Simulation process that can be automated, by relying on data from original sources, and by making any necessary data transformation rule-based and automated. This paper describes the new methodology and its relationship to IFC-based BIM and software interoperability. It identifies five steps that are critical to its implementation, and shows what part of the methodology can be applied today. The paper concludes with a discussion of application to Simulation with EnergyPlus, and describes data transformation rules embedded in the new Geometry Simplification Tool (GST).
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Impact of the u.s. national building information model standard (NBIMS) on building energy Performance Simulation
IBPSA 2007 - International Building Performance Simulation Association 2007, 2007Co-Authors: Vladimir BazjanacAbstract:The U.S. National Institute for Building Sciences (NIBS) started the development of the National Building Information Model Standard (NBIMS). Its goal is to define standard sets of data required to describe any given building in necessary detail so that any given AECO industry discipline application can find needed data at any point in the building lifecycle. This will include all data that are used in or are pertinent to building energy Performance Simulation and analysis. This paper describes the background that lead to the development of NBIMS, its goals and development methodology, its Part 1 (Version 1.0), and its probable impact on building energy Performance Simulation and analysis.
Ardeshir Mahdavi - One of the best experts on this subject based on the ideXlab platform.
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toward advanced representations of the urban microclimate in building Performance Simulation
Sustainable Cities and Society, 2016Co-Authors: Milena Vuckovic, Kristina Kiesel, Ardeshir MahdaviAbstract:Abstract The present contribution is concerned with the potential of empirically-based methods to capture the microclimate variance across a city and its implications for the Performance of buildings. We explore the possibility to explain microclimatic variance across an urban area based on geometric and semantic attributes of specific locations. We use high-resolution and dynamic weather data streams across numerous urban locations in the city of Vienna, Austria. Using advanced data extraction methods, the values of a number of urban attributes that are hypothesized to contribute to the urban microclimate variance (e.g. morphological factors, semantic properties of urban surfaces) are derived for these locations. The results point to possible correlations between location-based climatic conditions and distinct urban attributes that could be harnessed to formulate empirically-based algorithms for generating customized microclimatic boundary conditions.
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toward advanced representations of the urban microclimate in building Performance Simulation
Energy Procedia, 2015Co-Authors: Milena Vuckovic, Kristina Kiesel, Ardeshir MahdaviAbstract:Abstract The present paper is concerned with the potential of empirically-based methods to capture the microclimate variance across a city and its implications for the Performance of buildings. We explore the possibility to explain microclimatic variance across an urban area based on geometric and semantic attributes of specific locations. We use high-resolution and dynamic weather data streams across numerous urban locations in the city of Vienna, Austria. Using advanced data extraction methods, the values of a number of urban attributes that are hypothesized to contribute to the urban microclimate variance (e.g. morphological factors, semantic properties of urban surfaces) are derived for these locations. The results point to the likelihood that correlations between location-based climatic conditions and distinct urban attributes exist and could be potentially harnessed to formulate empirically- based algorithms for generating customized microclimatic boundary conditions.
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semper ii an internet based multi domain building Performance Simulation environment for early design support
Automation in Construction, 2004Co-Authors: Khee Poh Lam, Ardeshir Mahdavi, Nyuk Hien Wong, K K Chan, Z Kang, S GuptaAbstract:Abstract Many existing building Performance analysis methods and tools do not effectively encourage and facilitate iterative, multi-criteria, and multi-agenda building design development. SEMPER, which was originally developed at Carnegie Mellon University (CMU), is a building design and Performance Simulation environment that attempts to remedy these shortcomings. It is an active, multi-domain, space-based, object-oriented design support tool for integrated building Performance computing. However, it has been developed as a “stand-alone” application. This paper discusses the key findings of a collaborative research project between National University of Singapore, Carnegie Mellon University and Temasek Polytechnic to modify and transform SEMPER prototype 1 into SEMPER-II (S2), as an internet-based computational design support environment in order to facilitate geographically distributed design collaboration. The working details of the multi-domain thermal Simulation environment within S2 called the Thermal Suite were specifically discussed with illustrative case studies.
Yue Wang - One of the best experts on this subject based on the ideXlab platform.
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thermal Performance Simulation of a solar cavity receiver under windy conditions
Solar Energy, 2011Co-Authors: Jiabin Fang, Jinjia Wei, Xunwei Dong, Yue WangAbstract:Abstract Solar cavity receiver plays a dominant role in the light–heat conversion. Its Performance can directly affect the efficiency of the whole power generation system. A combined calculation method for evaluating the thermal Performance of the solar cavity receiver is raised in this paper. This method couples the Monte-Carlo method, the correlations of the flow boiling heat transfer, and the calculation of air flow field. And this method can ultimately figure out the surface heat flux inside the cavity, the wall temperature of the boiling tubes, and the heat loss of the solar receiver with an iterative solution. With this method, the thermal Performance of a solar cavity receiver, a saturated steam receiver, is simulated under different wind environments. The highest wall temperature of the boiling tubes is about 150 °C higher than the water saturation temperature. And it appears in the upper middle parts of the absorbing panels. Changing the wind angle or velocity can obviously affect the air velocity inside the receiver. The air velocity reaches the maximum value when the wind comes from the side of the receiver (flow angle α = 90°). The heat loss of the solar cavity receiver also reaches a maximum for the side-on wind.
Jan Hensen - One of the best experts on this subject based on the ideXlab platform.
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occupant behavior in identical residential buildings a case study for occupancy profiles extraction and application to building Performance Simulation
Building Simulation, 2019Co-Authors: Antonio Muroni, Pieterjan P Hoes, Isabella Gaetani Dellaquila Daragona, Jan HensenAbstract:This study employs a simplified Knowledge Discovery in Database (KDD) to extract occupancy, equipment and light use profiles from a database referred to 12 all-electric prefabricated dwellings in the Netherlands. The profiles are then integrated into a building Performance Simulation (BPS) model using the software TRNSYS v17. The significance of the extracted profiles is verified by comparing the total and end-use yearly electricity consumption of the investigated dwellings as predicted by the Simulation tool with on-site measurements. For the considered dwellings, using standard OB modeling results in an underestimation of the energy use intensity (EUI) by 5.9% to 42.5%, depending on the case. The integration of the occupant behavior (OB) profiles improves the total electricity consumption prediction from an initial 22.9% average deviation from measurements to 1.7%. The results corroborate that the 1.6x discrepancy observed in the buildings’ energy use intensity could be entirely ascribed to OB. Then, the knowledge extracted from the households’ database is used to propose a local electricity market framework to reduce the electricity bill and grid dependency of all households. This study confirms the need for appropriate OB modeling in BPS, it shows the potential of the KDD method for successful OB profiles extraction, and is a first example of data-mined OB profiles integration in BPS, as well as of OB profiles deployment for a practical application other than energy use prediction.
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building Performance Simulation for design and operation
2019Co-Authors: Jan Hensen, Roberto LambertsAbstract:1 Introduction to Building Performance Simulation Jan Hensen (Eindhoven University of Technology) and Roberto Lamberts (Federal University of Santa Catarina) 2 The role of Simulation in Performance based building Godfried Augenbroe (Georgia Institute of Technology, USA) 3 Weather Data for Building Performance Simulation Dru Crawley (Bentley Systems, Inc., USA) and Chip Barnaby (Wrightsoft, USA) 4 People in building Performance Simulation Ardeshir Mahdavi (Vienna University of Technology, Austria) 5 Thermal load and energy Performance prediction Jeffrey Spitler (Oklahoma State University, USA) 6 Ventilation Performance Prediction Jelena Srebric (Pennsylvania State University, USA) 7 Indoor Thermal Quality Performance Prediction Christoph van Treeck (Fraunhofer Institute for Building Physics, Germany) 8 Room Acoustics Performance Prediction Ardeshir Mahdavi (Vienna University of Technology, Austria) 9 Daylight Performance Predictions Christoph Reinhart (Harvard University, USA) 10 Moisture phenomena in whole building Performance prediction Jan Carmeliet (ETH, Zurich, Switzerland) Bert Blocken (Eindhoven University of Technology, The Netherlands), Thijs Defraeye (Katholieke Universiteit Leuven, Belgium) and Dominique Derome (EMPA, Switzerland) 11 HVAC systems Performance prediction Jonathan Wright (Loughborough University, UK) 12 Micro-cogeneration system Performance predicition Ian Beausoleil-Morrison (Carleton University, Canada) 13 Building Simulation for practical operational optimization David Claridge (Texas A&M University, USA) 14 Building Simulation in building automation systems Gregor P Henze (University of Colorado, USA) and Christian Neumann (Fraunhofer Institute, Freiburg, Germany) 15 Integrated resource flow modelling of the urban built environment Darren Robinson (EPFL, Switzerland) 16 Building Simulation for policy support Dru Crawley (Bentley Systems, Inc., USA) 17 A view on future building system modeling and Simulation Michael Wetter (Lawrence Berkeley National Laboratory, USA)
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review of current status requirements and opportunities for building Performance Simulation of adaptive facades
Journal of Building Performance Simulation, 2017Co-Authors: Rcgm Roel Loonen, Fabio Favoino, Jan Hensen, Mauro OverendAbstract:Adaptive building envelope systems have the potential of reducing greenhouse gas emissions and improving the energy flexibility of buildings, while maintaining high levels of indoor environmental quality. The development of such innovative materials and technologies, as well as their real-world implementation, can be enhanced with the use of building Performance Simulation (BPS). Performance prediction of adaptive facades can, however, be a challenging task and the information on this topic is scarce and fragmented. The main contribution of this review article is to bring together and analyse the existing information in this field. In the first part, the unique requirements for successful modelling and Simulation of adaptive facades are discussed. In the second part, the capabilities of five widely used BPS tools are reviewed, in terms of their ability to model energy and occupant comfort Performance of adaptive facades. Finally, it discusses various ongoing trends and research needs in this field.
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integrated building Performance Simulation progress prospects and requirements
Building and Environment, 2015Co-Authors: J A Clarke, Jan HensenAbstract:This paper is concerned with the role of Building Performance Simulation (BPS) in assisting with the creation of energy efficient habitats. It characterises achievements to date in a non-program-specific manner and in relation to the ultimate goal of providing practitioners with the means to appraise, accurately and rapidly, the multi-variate Performance of built environments of arbitrary complexity. The shortcomings of the state-of-the-art, when assessed against this goal, are used to identify future development priorities.
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selection criteria for building Performance Simulation tools contrasting architects and engineers needs
Journal of Building Performance Simulation, 2012Co-Authors: Shady Attia, Liliana Beltran, Jan Hensen, Andre De HerdeAbstract:This article summarises a study undertaken to reveal potential challenges and opportunities for using building Performance Simulation (BPS) tools. The article reviews current trends in building Simulation and outlines major criteria for BPS tool selection and evaluation based on analysing users' needs for tools capabilities and requirement specifications. The research is carried out by means of a literature review and two online surveys. The findings are based on an inter-group comparison between architects and engineers. The aim is to rank BPS tool selection criteria and compare 10 state-of-the-art BPS tools in the USA market. Five criteria are composed to stack up against theories and practices of BPS. Based on the experience gained during the survey, suggested criteria are critically reviewed and tested. The final results indicate a wide gap between architects' and engineers' priorities and tool ranking. This gap is discussed and suggestions for improvement of current tools are presented.
Jiabin Fang - One of the best experts on this subject based on the ideXlab platform.
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thermal Performance Simulation of a solar cavity receiver under windy conditions
Solar Energy, 2011Co-Authors: Jiabin Fang, Jinjia Wei, Xunwei Dong, Yue WangAbstract:Abstract Solar cavity receiver plays a dominant role in the light–heat conversion. Its Performance can directly affect the efficiency of the whole power generation system. A combined calculation method for evaluating the thermal Performance of the solar cavity receiver is raised in this paper. This method couples the Monte-Carlo method, the correlations of the flow boiling heat transfer, and the calculation of air flow field. And this method can ultimately figure out the surface heat flux inside the cavity, the wall temperature of the boiling tubes, and the heat loss of the solar receiver with an iterative solution. With this method, the thermal Performance of a solar cavity receiver, a saturated steam receiver, is simulated under different wind environments. The highest wall temperature of the boiling tubes is about 150 °C higher than the water saturation temperature. And it appears in the upper middle parts of the absorbing panels. Changing the wind angle or velocity can obviously affect the air velocity inside the receiver. The air velocity reaches the maximum value when the wind comes from the side of the receiver (flow angle α = 90°). The heat loss of the solar cavity receiver also reaches a maximum for the side-on wind.
