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Developments in environmental performance simulation
Automation in Construction 13 (2004) 437 – 445 www.elsevier.com/locate/autcon
Review article
Developments in environmental performance simulation Ali M. Malkawi Department of Architecture, School of Design, University of Pennsylvania, 210 S. 34th Street, Philadelphia, PA 19104, USA Received 7 March 2004; accepted 9 March 2004
Abstract Performance simulation in architectural design has been rapidly evolving over the past decade. Simulation tools have become widely available and this has had a measurable influence on the way in which buildings are being designed, analyzed and constructed. Several new approaches for the development of these tools are emerging, some of which evolved through many years of work based on the area of building simulation. This paper reviews these developments in environmental digital performance simulation as it relates to architectural design. It describes the recent research, development and use of these environments as well as some of the challenges that exist. D 2004 Elsevier B.V. All rights reserved. Keywords: Performance simulation; Decision support; Optimization; Interoperability; Collaborative environments; Design process integration; Simulation tools
1. Introduction Simulating building performance requires specialized expertise which targets the design, engineering, construction, operation and management of buildings. It draws its resources from many diverse disciplines including physics, mathematics, material science and human behavior. Its intention is to predict the behavior of a building from conception to demolition. Most of the fundamental work on building performance simulation algorithms and predictions was developed a few decades ago. However, building simulation continues to evolve and the outcomes of research in this area are now being incorporated into the design and the construction of most buildings and are evident in the recent advancement in build-
E-mail address: [email protected] (A.M. Malkawi). 0926-5805/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.autcon.2004.03.002
ing simulation environments. This progress was driven primarily by funded research efforts (academic and governmental) and commercial kernel encapsulations, both sectors benefiting from advancements in computation such as new programming paradigms and the increased power of computing and the Internet. Funded efforts have involved the research and development of simulation throughout the life cycle of the building. Efforts in commercial development have been primarily purpose driven; taking advantage of the maturing algorithms that are able to resolve particular issues of building performance. These environments focus on providing user-friendly interfaces while allowing for flexibility in modeling and accuracy. Advancements in building simulation environments have been focused on two areas: the structural framework of these environments and the activities they support. The structure of the simulation involves
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algorithm developments, data management and interfacing. Simulation algorithms have historically been designed to predict answers to domain-based questions such as lighting, thermal or structural problems. Each of these domains has subproblems that must be modeled and simulated differently. These algorithms have been maturing and are under rapid development in the areas of code validation, uncertainties and efficiency of representations. To shift the conventional use of such tools from only analysis to analysis and design aid, a renewed research in utilizing advancements in optimization is underway. This research stems from the idea that digital simulation tools can be used to support performance-driven design using optimization and partial automation. Current research is promising to take advantage of advancements in visualization and human computer interaction developments. It is responding to the current needs of the design team which are far from being fulfilled. On the other hand, methods are currently being researched to assist in coupling and data management between algorithms to increase their prediction accuracy. In addition, frameworks and standards are being developed to facilitate their integration with other environments in order to support the design analysis team activities. The following sections illustrate these developments and advancements and discuss the challenges that exist.
2. Single domain tools Many tools have been developed during the past few decades to predict the performance of the building design in areas such as thermal, lighting, acoustic, structures, etc. Because different problems require different simulation algorithms, a variety of computational simulation tools exist. The US Department of Energy (DOE) maintains an online directory of energy-related tools for buildings: http://www.eren.doe. gov/buildings/tools_directory/. These tools vary from simple approximate performance tools to the very precise. The site demonstrates the diversity of the tools for solving problems of energy-related issues in buildings. Axley [1] provided classification of such simulation tools based on their purpose, theoretical basis, and generality. He suggested two classifications in
regard to their purpose: System Sizing Tools—developed to directly size and detail individual components and System Performance Evaluation Tools—which simulate the steady or dynamic response of a technical system to specified excitations. For the theoretical basis, he suggested two classes: Macroscopic Analysis Tools—‘‘programs based on the application of fundamental conservation principles to building idealizations described in terms of discrete control volumes that lead to systems of algebraic and/or ordinary differential equations. Most often these programs are suitable for longer time-period whole-system analysis but do not provide within-room detailed results.’’ And Microscopic Analysis Tools—‘‘programs based on approximate solutions of partial differential continuum conservation equations using relatively fine spatial and/or temporal discretizations of the problem domain where finite element, finite difference, and finite volume approaches are used to effect the approximate solution. These programs are generally not suitable for whole-system or longer time-period analysis but provide within-room detailed results’’. Although refinements and updates took place for many of the single domain environmental simulation tools, the two main developments within the past decade have been the introduction of EnergyPlus and the surge in the use of Computational Fluid Dynamics (CFD). In 1995, the Department of Energy introduced EnergyPlus to replace DOE-2 (sponsored by the US Department of Energy) and BLAST (sponsored by the US Department of Defense), which is based on the best capabilities of both of these tools. It launched its first release in 2001 with a promise of continuing support and development. The tool is a simulation engine without a user-friendly interface that simulates heat and mass energy flows throughout a building. The concept of EnergyPlus is to provide a rigorous Objected Oriented environment that will attract thirdparty developers to create user interfaces and modules and can be linked to other programs such as SPARK [2] and TRANSIS [3] through data and object interaction (described later in this paper) [4]. This concept marked a new era in the way simulation engines will be developed and supported. It also marked a shift from approximate based simulation and simplified interface development for the nonexperts to the support of rigorous engines that can be utilized in frame-
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works and environments to aid in the building design for both the nonexpert as well as expert user. As computing power became less expensive, the use of computational fluid dynamics, after evolving for several decades, began to increase. CFD applies numerical techniques to solve the Navier – Stokes equations for fluid fields and provides an approach to solve the conservation equations for mass, momentum and thermal energy. CFD has been used in many applications in relation to buildings. These include natural ventilation design [5], the prediction of smoke and fire in buildings [6] and building material emissions for indoor air quality assessment [7]. Using different parameters, CFD is also used in noise prediction related to ducting in buildings [8]. Some other applications are more complicated and may integrate other building simulation models as described later in this paper. Although it has been widely used, the technology of CFD is still under development as applied to the solution methods used [9]. In addition, CFD remains an expert tool and its use requires knowledge in fluid mechanics to set up the simulation model, populate its boundary conditions and interpret the results. 2.1. Performance techniques and methods To increase the utility of the simulation in design, several methods and techniques are being incorporated. Decision support environments and optimization in the models as well as user-friendly interfaces combined with visualization techniques are the main areas of developments in regard to performance-based simulation for architectural design. 2.1.1. Decision support and optimization Research in optimization and decision support environments began two decades ago. Computational algorithms were used to design systems that assisted designers in their activities by providing either guidance through advice or optimization using emerging Artificial Intelligence techniques. Knowledge-based systems and complex problem-solving methods were researched for their potential use as aid for both expert and nonexperts to perform and interpret simulations [10 – 13]. Their contribution has been significant in initiating the use of computational techniques to solve performance-based decision-making problems. In ad-
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dition, this research has inspired renewed efforts in the areas of optimization and decision support environments. This work is dominated by the idea that design is a goal oriented decision-making model where goals are defined by desired performance values. In addition, design decisions can be driven by performance feedback and that design optimization can be represented as a simulation-based design process. The work benefited from rapid improvements and refinements in the field of optimization to solve complex problems in areas of numerical methods, solution strategies, and development of new algorithms. Design optimization using performance simulation and functions has been researched recently to test the applicability of specific algorithms in regard to their effectiveness. During the past few years, stochastic methods such as Simulated Annealing and Genetic Algorithms have become very popular and have been applied to a range of problems for optimizing thermal and lighting performance based on building enclosure, HVAC design, and control schedules [14,15]. They are attractive mainly because they solve a wide range of problems; however, these methods are based on random search and will often derive unreliable results unless used with considerable skill and intuition. Recently, investigations of Gradient-based and derivative-free methods were also conducted. Gradiient-based methods have been shown to be very efficient and reliable except in cases of complex simulation [16,17]. On the other hand, derivative-free deterministic methods have been shown to perform particularly well for problems that suffer from simulation noise, but are suitable for only small problems [18]. Simulation-based optimization can also be time consuming since each design evaluation involves using the simulation. Recent applications [17 – 19] have addressed both these issues by using approximation-based methods that derive simpler functions of the original simulation responses and use them for partial search during the optimization process. Hybrid strategies combining two or more methods have been used to overcome problems associated with one particular method. For example, Mickalek et al. [20] and Monks et al. [21] use the global and versatile nature of stochastic methods with the rigor and efficiency of gradient-based methods in a combined framework. Whetter and Wright [22] propose to combine Genetic Algorithms
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and Pattern Searches to derive a hybrid method that reduces computational run time in problems involving expensive simulations. Although some of these optimization algorithms have been used in variety of ways in building simulation and control, their full potential in the design practice remains to be seen. However, the work done over the past few years demonstrates that with a good understanding of the methods involved, design optimization can be used to improve building performance and provides rigor in the way the simulation tools can be utilized. 2.1.2. Data visualization and interface design Most of the available tools provide a one or twodimensional representation of the data derived from a building performance simulation. This has always been an important challenge as only experts can precisely understand the data and hence are always required to interpret them. Consequently, this introduces the problems of time and cost, not only in terms of hiring these experts, but also in establishing communication among the participants. This communication is not only dependent on their physical presence. It also involves issues of representation as well as of semantics. Advancements in visualization led to new developments in simulation. Different technologies have made it possible to create environments that are virtual or augmented. Although Immersive Building Simulation is still in its research and development stage, Virtual and Augmented Environments have been used in a variety of areas in relation to buildings. This includes the extension of visual perception by enabling the user to see through or into objects [23] such as maintenance support for visualizing electrical wires in a wall or construction grids [24]. Other applications include structural system visualization [25], augmented outdoor visualization [26] and collaborative design process [27]. In the area of immersive building simulation [28], only a few projects have been developed—some of which are related to the post processing of Computational Fluid Dynamics data [29,30], augmented simulations [31], building and data representation [32], building performance visualization [31,33], and immersive visualization for structural analysis [34].
The few studies conducted in this area illustrate the potential application of such research. Despite the prevalence of challenges related to software, hardware and the knowledge required to apply this to the building design, immersive environments provide opportunities which are not available using current simulation models and interactions and it extends on the 3D performance information visualization of buildings. It presents a new way of interfacing with the built environment and controlling its behavior in real-time. This will become increasingly evident as additional techniques (e.g., optimization) lend more power to these environments as users navigate through them and interact with their elements.
3. The integration quest and collaborative environments In order to overcome the drawback of needing different simulation engines to predict different performance aspects, integration between algorithms provides a means to overcome some of these limitations and increases the efficiency and prediction accuracy of performance tools. Research in this area has predominantly involved issues of intermodel coupling based on engine and equation integration. On the other hand, as the computational environments shifted from procedural to object-oriented, simulation-based environments followed this shift. Object-oriented code that supports modularity and inheritance allowed simulation to be more flexible and expandable. This shift made it ‘‘technically’’ possible to begin work on encapsulating shared and distributed simulations. It also made possible the development of environments and frameworks that can achieve design analysis integration based on semantic representations which support object and data interaction. This includes custom-driven object integration developments, the data and object interoperability development as well as the research in process-driven integration—recently launched in an attempt to better achieve this quest. 3.1. Intermodel-driven integration Attempts to achieve intermodel integration are not new. Axley and Grot [35], for example, sug-
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gested single equations describing the performance of environmental phenomena. Several strategies were recently developed to achieve more effective solutions. Hensen [36] suggested that same environment ‘‘intermodel’’ coupling in cases such as the air and heat flow to be very effective. The integrity of the thermodynamic solution in such cases can be achieved by different techniques such as the decoupled or coupled approaches. These approaches are described by Hensen [36] and Clarke [37]. In the decoupled approach, the different models run in sequence and each model uses the results of the other in a time step. In the coupled approach, the models iterate within one time step until satisfactory error value is achieved. Although several other strategies exist and noticeable progress has been made in this area, it should be noted that the issues of coupling which can be utilized for building design are still in their infancy stages— especially in the area of coupling building energy and CFD [38,39] and energy and lighting [40]. Computing time, challenges of convergence, user expertise and the lack of physical modeling able to describe the diversity of the flows are the main issues regarding integration of Energy simulation programs and CFD [41]. Recently, several studies attempted to investigate this issue using different methods. For example, Clovis [41] proposed several staged couplings to integrate CFD and EnergyPlus. The approach attempted to bridge between the computing time required for CFD and the Energy Simulations [42]. The approach illustrated that such a method can lead to correct and converged solution. The ESP-r program addressed these issues earlier [43] and provided methods for coupling CFD with whole-building simulation. Recently, refinements of the coupling abilities of the CFD and Energy simulation of the ESP-r system were performed and validated [44]. These were related to being able to work with complex geometries, blockages, ventilation opening, etc.
different simulation algorithms is accomplished using data sharing and exchange. The COMBINE project sets an example for such environments in the early 1990s [45]. Several other environments during the past decade were designed to achieve this integration and interoperability. These environments used ‘‘custom’’ building model representation based on objectoriented technology to share information. Examples of recent developments are the Building Design Advisor (BDA) and SEMPER environments. The BDA system supports an integrated environment for multisimulations using a common object data representation and allows concurrent simulations to be executed. The results of some simulations can be the input to others using a logic structure [46]. It capsized on the use of existing simulations using a custom built functionality exchange mechanism but was not able to provide a comprehensive framework that support the design– analysis interaction required within the design process. SEMPER, developed initially at Carnegie Mellon University, uses ‘‘Space-based’’ CAD system to allow it to communicate with a set of performance simulation engines using its own internal data structure. SEMPER, as well as its reengineering version for the Internet SEMPER2 (S2) [47,48], provides prototype environment that supports novice and expert users. It has the capacity to allow users getting feedback for both the performance of their evolving design as well to check the design based on certain performance targets. Although, SEMPER differs from the BDA in the way it represents the design– analysis activities and its internal interfaces, its custom-built environment raise questions regarding its scalability. As interoperability among different software became a necessity, a collective effort by industry, governmental and research organizations to establish data exchange standards for the building industry attributed to a surge in object and data-driven integration described in the next section.
3.2. Custom-driven object integration
3.3. Generic-driven object and data integration
This approach attempts to integrate multi simulation engines in one system that provides interoperability between the various tools using shared custom data objects. These environments differ from the coupled environments in that integration between
This approach targets data sharing among different software applications including simulations to achieve software interoperability. Data input and output of each program can be transferred to other programs. Data sharing is achieved by mapping the
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relevant data within each program to a generic common data model that contains information required by all other programs. The development of this common data model is currently the mission of the International Alliance of Interoperability (IAI), which was formed in 1994 and built on the foundation of previous research projects in data exchange for integrated building models and standards in related areas [49]. The view is that this universal framework and specifications for the common data model—called the Industry Foundation Classes (IFC) for the architectural, engineering, construction, and facilities management industries can facilitate interoperability between the existing and future software tools used by industry participants [50,51]. The IFC is an object-oriented data protocol and specifications of the attributes and relationships between, buildingrelated entities that supports building information exchange. To provide an alternative approach to information sharing, the IFC are published in the EXPRESS language for software development and in eXtensible Markup Language (XML), (a term for a type of structured text data) under the name (ifcXML) for eCommerce and Internet purposes. In addition, collaboration does exist between the IAI and aecXML, which is an XML-based language used to represent information in the Architecture, Engineering and Construction (AEC) industry that enables the automated processing of data. The International Organization of Standardization (ISO) accepted the IFCs as a common language in construction in 2002. Initial implementations of the Industry Foundation Classes (IFC) began appearing in building CAD programs in 2001. Currently, many tools and commercial software are now capable of importing and exporting IFC data [52]. Several implementations for IFC-compatible links to energy simulations such as EnergyPlus mentioned earlier have been developed and are underway [53,54]. Although the latest version of the IFC (IFC2x 2nd Edition) captures significant data information of the complete building model, the goal of promoters and developers to have specifications for every class of building element required to support data sharing between all software tools used by building professionals poses a challenge as it is an extensive task to build such a comprehensive data model.
3.4. Process-driven integration To achieve interoperability among simulation tools during the building design and construction stages, the process-driven integration approach suggests there must be more than just data exchanges supported by a common building representation. Performance simulation needs a framework which allows it be called upon at the right time and for the right design decision. The availability of domain-specific tools by themselves requires additional functionalities and frameworks in order to play a better role in design evolution. This approach focuses on the effective use of performance simulation tools in the design process with full participation from the design team including the expert consultants. Augenbroe [55] illustrated earlier research in this area and how expert intervention is utilized as it relates to simulation tools. In his study, Augenbroe suggests that the longer term objectives are better functional embedding of simulation tools in the design process, increased quality control for building analysis efforts, and exploitation of the opportunities provided by the Internet. In 2001, a team of researchers from Georgia Institute of Technology, Carnegie Mellon University and the University of Pennsylvania began work on the Design Analysis Integration Initiative (DAI) project in an attempt to develop credible solutions for the integration of building performance analysis tools in the building design process [56]. It intended to capitalize on the efforts already invested in the development of building product models—IAIIFC effort, described earlier, without making limiting assumptions about the design process or the logic of the design analysis interaction flow. The DAI development targets a layered support of the interaction between the building design process and a wide array of building performance analysis tools. The study argues that design analysis integration requires a dialogue support system, rather than a data exchange system. A framework for a dialogue has been proposed based on the rigorous application of building system performance theory leading to the introduction of performance indicators as the quantifiable elements of the dialogue. The framework was constructed using EXPRESS and XML technologies. Each performance indicator defines an experiment that is suited to quantify a particular performance aspect. The experiment itself is captured in a formally specified analysis
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function. In addition, the study showed that such an approach can effectively be automated in a workbench. A four-layered workbench was proposed that enables expert teams to respond to an analysis request with the design of configurable analysis scenarios, which can consequently be enacted. The approach of process as the central concept of data sharing has been advocated for not only in design – analysis but also for the management and exchange of information by the International Energy Agency [57]. This process-driven integration approach is still in its early stages and will require more research and development to illustrate its full potential to solve the problem of integration.
4. Conclusions The use of Digital Performance Simulation in architectural design has been on the rise. This is due, mainly, to the increase of computing power and the maturing of the building simulation field. The full integration of simulation within the design process is far from complete. However, the building industry, including architects, is aware of the need for better integration of these tools into the lifecycle of the building. Many of the developments presented in this paper are a testimony to the rapid use of these tools, methods and technology into design. However, challenges related to performance simulation accuracy that is influenced by factors such as user interpretations and interventions to variations in simulation variables and behavioral uncertainties and validations are still being investigated. Although a collective effort to develop standards for integration is underway, the paper illustrates that this is a nontrivial issue. As the demand to advance the field of building simulation increases, new paradigms have recently begun to emerge and will influence building design and construction. These paradigms are indications of the fields’ various directions, some of which are purely demand oriented, while others forecast the needs of the field in an attempt to enhance it. The paper illustrated examples of such paradigms which include issues of process-driven interoperability, the influence of optimization techniques into the design of new tools and new means of interaction between the users and simulations.
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Acknowledgements The author wishes to thank several of his former and current Ph.D. students for their contribution to this publication. Dr. Ruchi Choudhary for her background work on optimization documented in her Ph.D. dissertation; Ravi Srinivasan and Yun Kyu Yi for their feedback and assistance with publication searches.
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Review article
Developments in environmental performance simulation Ali M. Malkawi Department of Architecture, School of Design, University of Pennsylvania, 210 S. 34th Street, Philadelphia, PA 19104, USA Received 7 March 2004; accepted 9 March 2004
Abstract Performance simulation in architectural design has been rapidly evolving over the past decade. Simulation tools have become widely available and this has had a measurable influence on the way in which buildings are being designed, analyzed and constructed. Several new approaches for the development of these tools are emerging, some of which evolved through many years of work based on the area of building simulation. This paper reviews these developments in environmental digital performance simulation as it relates to architectural design. It describes the recent research, development and use of these environments as well as some of the challenges that exist. D 2004 Elsevier B.V. All rights reserved. Keywords: Performance simulation; Decision support; Optimization; Interoperability; Collaborative environments; Design process integration; Simulation tools
1. Introduction Simulating building performance requires specialized expertise which targets the design, engineering, construction, operation and management of buildings. It draws its resources from many diverse disciplines including physics, mathematics, material science and human behavior. Its intention is to predict the behavior of a building from conception to demolition. Most of the fundamental work on building performance simulation algorithms and predictions was developed a few decades ago. However, building simulation continues to evolve and the outcomes of research in this area are now being incorporated into the design and the construction of most buildings and are evident in the recent advancement in build-
E-mail address: [email protected] (A.M. Malkawi). 0926-5805/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.autcon.2004.03.002
ing simulation environments. This progress was driven primarily by funded research efforts (academic and governmental) and commercial kernel encapsulations, both sectors benefiting from advancements in computation such as new programming paradigms and the increased power of computing and the Internet. Funded efforts have involved the research and development of simulation throughout the life cycle of the building. Efforts in commercial development have been primarily purpose driven; taking advantage of the maturing algorithms that are able to resolve particular issues of building performance. These environments focus on providing user-friendly interfaces while allowing for flexibility in modeling and accuracy. Advancements in building simulation environments have been focused on two areas: the structural framework of these environments and the activities they support. The structure of the simulation involves
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algorithm developments, data management and interfacing. Simulation algorithms have historically been designed to predict answers to domain-based questions such as lighting, thermal or structural problems. Each of these domains has subproblems that must be modeled and simulated differently. These algorithms have been maturing and are under rapid development in the areas of code validation, uncertainties and efficiency of representations. To shift the conventional use of such tools from only analysis to analysis and design aid, a renewed research in utilizing advancements in optimization is underway. This research stems from the idea that digital simulation tools can be used to support performance-driven design using optimization and partial automation. Current research is promising to take advantage of advancements in visualization and human computer interaction developments. It is responding to the current needs of the design team which are far from being fulfilled. On the other hand, methods are currently being researched to assist in coupling and data management between algorithms to increase their prediction accuracy. In addition, frameworks and standards are being developed to facilitate their integration with other environments in order to support the design analysis team activities. The following sections illustrate these developments and advancements and discuss the challenges that exist.
2. Single domain tools Many tools have been developed during the past few decades to predict the performance of the building design in areas such as thermal, lighting, acoustic, structures, etc. Because different problems require different simulation algorithms, a variety of computational simulation tools exist. The US Department of Energy (DOE) maintains an online directory of energy-related tools for buildings: http://www.eren.doe. gov/buildings/tools_directory/. These tools vary from simple approximate performance tools to the very precise. The site demonstrates the diversity of the tools for solving problems of energy-related issues in buildings. Axley [1] provided classification of such simulation tools based on their purpose, theoretical basis, and generality. He suggested two classifications in
regard to their purpose: System Sizing Tools—developed to directly size and detail individual components and System Performance Evaluation Tools—which simulate the steady or dynamic response of a technical system to specified excitations. For the theoretical basis, he suggested two classes: Macroscopic Analysis Tools—‘‘programs based on the application of fundamental conservation principles to building idealizations described in terms of discrete control volumes that lead to systems of algebraic and/or ordinary differential equations. Most often these programs are suitable for longer time-period whole-system analysis but do not provide within-room detailed results.’’ And Microscopic Analysis Tools—‘‘programs based on approximate solutions of partial differential continuum conservation equations using relatively fine spatial and/or temporal discretizations of the problem domain where finite element, finite difference, and finite volume approaches are used to effect the approximate solution. These programs are generally not suitable for whole-system or longer time-period analysis but provide within-room detailed results’’. Although refinements and updates took place for many of the single domain environmental simulation tools, the two main developments within the past decade have been the introduction of EnergyPlus and the surge in the use of Computational Fluid Dynamics (CFD). In 1995, the Department of Energy introduced EnergyPlus to replace DOE-2 (sponsored by the US Department of Energy) and BLAST (sponsored by the US Department of Defense), which is based on the best capabilities of both of these tools. It launched its first release in 2001 with a promise of continuing support and development. The tool is a simulation engine without a user-friendly interface that simulates heat and mass energy flows throughout a building. The concept of EnergyPlus is to provide a rigorous Objected Oriented environment that will attract thirdparty developers to create user interfaces and modules and can be linked to other programs such as SPARK [2] and TRANSIS [3] through data and object interaction (described later in this paper) [4]. This concept marked a new era in the way simulation engines will be developed and supported. It also marked a shift from approximate based simulation and simplified interface development for the nonexperts to the support of rigorous engines that can be utilized in frame-
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works and environments to aid in the building design for both the nonexpert as well as expert user. As computing power became less expensive, the use of computational fluid dynamics, after evolving for several decades, began to increase. CFD applies numerical techniques to solve the Navier – Stokes equations for fluid fields and provides an approach to solve the conservation equations for mass, momentum and thermal energy. CFD has been used in many applications in relation to buildings. These include natural ventilation design [5], the prediction of smoke and fire in buildings [6] and building material emissions for indoor air quality assessment [7]. Using different parameters, CFD is also used in noise prediction related to ducting in buildings [8]. Some other applications are more complicated and may integrate other building simulation models as described later in this paper. Although it has been widely used, the technology of CFD is still under development as applied to the solution methods used [9]. In addition, CFD remains an expert tool and its use requires knowledge in fluid mechanics to set up the simulation model, populate its boundary conditions and interpret the results. 2.1. Performance techniques and methods To increase the utility of the simulation in design, several methods and techniques are being incorporated. Decision support environments and optimization in the models as well as user-friendly interfaces combined with visualization techniques are the main areas of developments in regard to performance-based simulation for architectural design. 2.1.1. Decision support and optimization Research in optimization and decision support environments began two decades ago. Computational algorithms were used to design systems that assisted designers in their activities by providing either guidance through advice or optimization using emerging Artificial Intelligence techniques. Knowledge-based systems and complex problem-solving methods were researched for their potential use as aid for both expert and nonexperts to perform and interpret simulations [10 – 13]. Their contribution has been significant in initiating the use of computational techniques to solve performance-based decision-making problems. In ad-
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dition, this research has inspired renewed efforts in the areas of optimization and decision support environments. This work is dominated by the idea that design is a goal oriented decision-making model where goals are defined by desired performance values. In addition, design decisions can be driven by performance feedback and that design optimization can be represented as a simulation-based design process. The work benefited from rapid improvements and refinements in the field of optimization to solve complex problems in areas of numerical methods, solution strategies, and development of new algorithms. Design optimization using performance simulation and functions has been researched recently to test the applicability of specific algorithms in regard to their effectiveness. During the past few years, stochastic methods such as Simulated Annealing and Genetic Algorithms have become very popular and have been applied to a range of problems for optimizing thermal and lighting performance based on building enclosure, HVAC design, and control schedules [14,15]. They are attractive mainly because they solve a wide range of problems; however, these methods are based on random search and will often derive unreliable results unless used with considerable skill and intuition. Recently, investigations of Gradient-based and derivative-free methods were also conducted. Gradiient-based methods have been shown to be very efficient and reliable except in cases of complex simulation [16,17]. On the other hand, derivative-free deterministic methods have been shown to perform particularly well for problems that suffer from simulation noise, but are suitable for only small problems [18]. Simulation-based optimization can also be time consuming since each design evaluation involves using the simulation. Recent applications [17 – 19] have addressed both these issues by using approximation-based methods that derive simpler functions of the original simulation responses and use them for partial search during the optimization process. Hybrid strategies combining two or more methods have been used to overcome problems associated with one particular method. For example, Mickalek et al. [20] and Monks et al. [21] use the global and versatile nature of stochastic methods with the rigor and efficiency of gradient-based methods in a combined framework. Whetter and Wright [22] propose to combine Genetic Algorithms
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and Pattern Searches to derive a hybrid method that reduces computational run time in problems involving expensive simulations. Although some of these optimization algorithms have been used in variety of ways in building simulation and control, their full potential in the design practice remains to be seen. However, the work done over the past few years demonstrates that with a good understanding of the methods involved, design optimization can be used to improve building performance and provides rigor in the way the simulation tools can be utilized. 2.1.2. Data visualization and interface design Most of the available tools provide a one or twodimensional representation of the data derived from a building performance simulation. This has always been an important challenge as only experts can precisely understand the data and hence are always required to interpret them. Consequently, this introduces the problems of time and cost, not only in terms of hiring these experts, but also in establishing communication among the participants. This communication is not only dependent on their physical presence. It also involves issues of representation as well as of semantics. Advancements in visualization led to new developments in simulation. Different technologies have made it possible to create environments that are virtual or augmented. Although Immersive Building Simulation is still in its research and development stage, Virtual and Augmented Environments have been used in a variety of areas in relation to buildings. This includes the extension of visual perception by enabling the user to see through or into objects [23] such as maintenance support for visualizing electrical wires in a wall or construction grids [24]. Other applications include structural system visualization [25], augmented outdoor visualization [26] and collaborative design process [27]. In the area of immersive building simulation [28], only a few projects have been developed—some of which are related to the post processing of Computational Fluid Dynamics data [29,30], augmented simulations [31], building and data representation [32], building performance visualization [31,33], and immersive visualization for structural analysis [34].
The few studies conducted in this area illustrate the potential application of such research. Despite the prevalence of challenges related to software, hardware and the knowledge required to apply this to the building design, immersive environments provide opportunities which are not available using current simulation models and interactions and it extends on the 3D performance information visualization of buildings. It presents a new way of interfacing with the built environment and controlling its behavior in real-time. This will become increasingly evident as additional techniques (e.g., optimization) lend more power to these environments as users navigate through them and interact with their elements.
3. The integration quest and collaborative environments In order to overcome the drawback of needing different simulation engines to predict different performance aspects, integration between algorithms provides a means to overcome some of these limitations and increases the efficiency and prediction accuracy of performance tools. Research in this area has predominantly involved issues of intermodel coupling based on engine and equation integration. On the other hand, as the computational environments shifted from procedural to object-oriented, simulation-based environments followed this shift. Object-oriented code that supports modularity and inheritance allowed simulation to be more flexible and expandable. This shift made it ‘‘technically’’ possible to begin work on encapsulating shared and distributed simulations. It also made possible the development of environments and frameworks that can achieve design analysis integration based on semantic representations which support object and data interaction. This includes custom-driven object integration developments, the data and object interoperability development as well as the research in process-driven integration—recently launched in an attempt to better achieve this quest. 3.1. Intermodel-driven integration Attempts to achieve intermodel integration are not new. Axley and Grot [35], for example, sug-
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gested single equations describing the performance of environmental phenomena. Several strategies were recently developed to achieve more effective solutions. Hensen [36] suggested that same environment ‘‘intermodel’’ coupling in cases such as the air and heat flow to be very effective. The integrity of the thermodynamic solution in such cases can be achieved by different techniques such as the decoupled or coupled approaches. These approaches are described by Hensen [36] and Clarke [37]. In the decoupled approach, the different models run in sequence and each model uses the results of the other in a time step. In the coupled approach, the models iterate within one time step until satisfactory error value is achieved. Although several other strategies exist and noticeable progress has been made in this area, it should be noted that the issues of coupling which can be utilized for building design are still in their infancy stages— especially in the area of coupling building energy and CFD [38,39] and energy and lighting [40]. Computing time, challenges of convergence, user expertise and the lack of physical modeling able to describe the diversity of the flows are the main issues regarding integration of Energy simulation programs and CFD [41]. Recently, several studies attempted to investigate this issue using different methods. For example, Clovis [41] proposed several staged couplings to integrate CFD and EnergyPlus. The approach attempted to bridge between the computing time required for CFD and the Energy Simulations [42]. The approach illustrated that such a method can lead to correct and converged solution. The ESP-r program addressed these issues earlier [43] and provided methods for coupling CFD with whole-building simulation. Recently, refinements of the coupling abilities of the CFD and Energy simulation of the ESP-r system were performed and validated [44]. These were related to being able to work with complex geometries, blockages, ventilation opening, etc.
different simulation algorithms is accomplished using data sharing and exchange. The COMBINE project sets an example for such environments in the early 1990s [45]. Several other environments during the past decade were designed to achieve this integration and interoperability. These environments used ‘‘custom’’ building model representation based on objectoriented technology to share information. Examples of recent developments are the Building Design Advisor (BDA) and SEMPER environments. The BDA system supports an integrated environment for multisimulations using a common object data representation and allows concurrent simulations to be executed. The results of some simulations can be the input to others using a logic structure [46]. It capsized on the use of existing simulations using a custom built functionality exchange mechanism but was not able to provide a comprehensive framework that support the design– analysis interaction required within the design process. SEMPER, developed initially at Carnegie Mellon University, uses ‘‘Space-based’’ CAD system to allow it to communicate with a set of performance simulation engines using its own internal data structure. SEMPER, as well as its reengineering version for the Internet SEMPER2 (S2) [47,48], provides prototype environment that supports novice and expert users. It has the capacity to allow users getting feedback for both the performance of their evolving design as well to check the design based on certain performance targets. Although, SEMPER differs from the BDA in the way it represents the design– analysis activities and its internal interfaces, its custom-built environment raise questions regarding its scalability. As interoperability among different software became a necessity, a collective effort by industry, governmental and research organizations to establish data exchange standards for the building industry attributed to a surge in object and data-driven integration described in the next section.
3.2. Custom-driven object integration
3.3. Generic-driven object and data integration
This approach attempts to integrate multi simulation engines in one system that provides interoperability between the various tools using shared custom data objects. These environments differ from the coupled environments in that integration between
This approach targets data sharing among different software applications including simulations to achieve software interoperability. Data input and output of each program can be transferred to other programs. Data sharing is achieved by mapping the
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relevant data within each program to a generic common data model that contains information required by all other programs. The development of this common data model is currently the mission of the International Alliance of Interoperability (IAI), which was formed in 1994 and built on the foundation of previous research projects in data exchange for integrated building models and standards in related areas [49]. The view is that this universal framework and specifications for the common data model—called the Industry Foundation Classes (IFC) for the architectural, engineering, construction, and facilities management industries can facilitate interoperability between the existing and future software tools used by industry participants [50,51]. The IFC is an object-oriented data protocol and specifications of the attributes and relationships between, buildingrelated entities that supports building information exchange. To provide an alternative approach to information sharing, the IFC are published in the EXPRESS language for software development and in eXtensible Markup Language (XML), (a term for a type of structured text data) under the name (ifcXML) for eCommerce and Internet purposes. In addition, collaboration does exist between the IAI and aecXML, which is an XML-based language used to represent information in the Architecture, Engineering and Construction (AEC) industry that enables the automated processing of data. The International Organization of Standardization (ISO) accepted the IFCs as a common language in construction in 2002. Initial implementations of the Industry Foundation Classes (IFC) began appearing in building CAD programs in 2001. Currently, many tools and commercial software are now capable of importing and exporting IFC data [52]. Several implementations for IFC-compatible links to energy simulations such as EnergyPlus mentioned earlier have been developed and are underway [53,54]. Although the latest version of the IFC (IFC2x 2nd Edition) captures significant data information of the complete building model, the goal of promoters and developers to have specifications for every class of building element required to support data sharing between all software tools used by building professionals poses a challenge as it is an extensive task to build such a comprehensive data model.
3.4. Process-driven integration To achieve interoperability among simulation tools during the building design and construction stages, the process-driven integration approach suggests there must be more than just data exchanges supported by a common building representation. Performance simulation needs a framework which allows it be called upon at the right time and for the right design decision. The availability of domain-specific tools by themselves requires additional functionalities and frameworks in order to play a better role in design evolution. This approach focuses on the effective use of performance simulation tools in the design process with full participation from the design team including the expert consultants. Augenbroe [55] illustrated earlier research in this area and how expert intervention is utilized as it relates to simulation tools. In his study, Augenbroe suggests that the longer term objectives are better functional embedding of simulation tools in the design process, increased quality control for building analysis efforts, and exploitation of the opportunities provided by the Internet. In 2001, a team of researchers from Georgia Institute of Technology, Carnegie Mellon University and the University of Pennsylvania began work on the Design Analysis Integration Initiative (DAI) project in an attempt to develop credible solutions for the integration of building performance analysis tools in the building design process [56]. It intended to capitalize on the efforts already invested in the development of building product models—IAIIFC effort, described earlier, without making limiting assumptions about the design process or the logic of the design analysis interaction flow. The DAI development targets a layered support of the interaction between the building design process and a wide array of building performance analysis tools. The study argues that design analysis integration requires a dialogue support system, rather than a data exchange system. A framework for a dialogue has been proposed based on the rigorous application of building system performance theory leading to the introduction of performance indicators as the quantifiable elements of the dialogue. The framework was constructed using EXPRESS and XML technologies. Each performance indicator defines an experiment that is suited to quantify a particular performance aspect. The experiment itself is captured in a formally specified analysis
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function. In addition, the study showed that such an approach can effectively be automated in a workbench. A four-layered workbench was proposed that enables expert teams to respond to an analysis request with the design of configurable analysis scenarios, which can consequently be enacted. The approach of process as the central concept of data sharing has been advocated for not only in design – analysis but also for the management and exchange of information by the International Energy Agency [57]. This process-driven integration approach is still in its early stages and will require more research and development to illustrate its full potential to solve the problem of integration.
4. Conclusions The use of Digital Performance Simulation in architectural design has been on the rise. This is due, mainly, to the increase of computing power and the maturing of the building simulation field. The full integration of simulation within the design process is far from complete. However, the building industry, including architects, is aware of the need for better integration of these tools into the lifecycle of the building. Many of the developments presented in this paper are a testimony to the rapid use of these tools, methods and technology into design. However, challenges related to performance simulation accuracy that is influenced by factors such as user interpretations and interventions to variations in simulation variables and behavioral uncertainties and validations are still being investigated. Although a collective effort to develop standards for integration is underway, the paper illustrates that this is a nontrivial issue. As the demand to advance the field of building simulation increases, new paradigms have recently begun to emerge and will influence building design and construction. These paradigms are indications of the fields’ various directions, some of which are purely demand oriented, while others forecast the needs of the field in an attempt to enhance it. The paper illustrated examples of such paradigms which include issues of process-driven interoperability, the influence of optimization techniques into the design of new tools and new means of interaction between the users and simulations.
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Acknowledgements The author wishes to thank several of his former and current Ph.D. students for their contribution to this publication. Dr. Ruchi Choudhary for her background work on optimization documented in her Ph.D. dissertation; Ravi Srinivasan and Yun Kyu Yi for their feedback and assistance with publication searches.
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