A Framework for Modeling Multifunctional Building Systems and Co-Simulating with Whole Building Energy Models
According to the U.S. DOE Building Energy Data Book (2010), the building sector is responsible for 40% of the nation’s primary energy consumption.1 Historically, the building envelope has been tasked with the challenge of neutralizing highly variable solar, air, and moisture conditions, all while maintaining a constant desired interior condition.2 In an attempt to maintain indoor conditions, modern building systems rely heavily on fossil-fueled mechanisms and highly insulated or glazed static building envelopes to reject exterior energy flows.3 To achieve significant progress towards global targets for clean on-site energy self-sufficiency within the building sector, integrating building envelopes with adaptive multifunctional systems could provided a series of benefits such as: electrical generation, hydrothermal collection, daylighting, reduced cooling loads, humid air dehumidification, water recuperation, distributed heating and cooling, and improved human comfort and well being.
In parallel, current energy modeling tools lack the fidelity and adaptivity necessary to validate the multi-functional benefits of next-generation envelope technologies.4 With currently available, conduction dominant tools, it is difficult to express the dynamic multi-functional convection and radiation effect of new strategies as they relate to established building physics models.5 In practice, this leads to models that are often created separately from BEM and then loosely connected through pre- or post-processing of data. Development of active systems is impeded by current modeling workflows which do not provide adequate feedback or facilitate rapid design iteration within the context of build energy modeling (BEM).6 To integrate and characterize emerging climate responsive technologies, an approach to modeling is required that encourages information exchange between different types of models at different scales, such that adaptive, higher-fidelity models can interface with standard BEM frameworks.
Esterly, S., & Gelmen, R. 2010 Renewable Energy Data Book. EERE, 2010.↩
S.E. Selkowitz, E.S. Lee, O. Aschehoug. Perspectives on Advanced Facades with Dynamic Glazings and Integrated Lighting Controls.↩
Krarti, Moncef. Energy Audit of Building Systems: An Engineering Approach. 2nd ed. Boca Raton, FL: CRC, 2011. Print.↩
Wetter, Michael. A View of Future Building System Modeling and Simulation. In Building Performance Simulation for Design and Operation. Abingdon, Oxon; New York, NY: Spon Press, 2011.↩
Kim, D.-W. and Park, C.-S. Difficulties and limitations in performance simulation of a double skin facade with EnergyPlus. Energy and Buildings 43, 12 (2011), 3635-3645.↩
Hensen, Jan, and Roberto Lamberts. Building Performance Simulation for Design and Operation. Abingdon, Oxon: Spon, 2011. Print.↩
To expedite the development and integration of adaptive building technologies for on-site net-zero energy, as well as impact future policy, building codes and design practices, energy models must be easy to manipulate, connected to current-practice methods, and validated. To achieve these goals, energy systems cannot be modeled in isolation from the whole building context. For industrial acceptance, a workflow for particular systems should promote fast design iteration through strong informational feedback to the operator. To enable these qualities, a modeling framework should exhibit adaptivity of modes of operation, hierarchical and modular construction techniques, and ease of co-simulation with other models in other environments.
By integrating the multifunctional device performance, material properties, and multiscale physics of dynamic built environments with conservation laws applied to finite volumes, a medium-fidelity dynamic network model is produced that bridges the gap between high-fidelity computational fluid dynamic (CFD) models and low-fidelity building energy models (BEM) to provide more accuracy simulations of impact, allow faster design iteration through co-simulation, and guide future building policy of dynamic building systems.
This research position SCOREC, CASE and Rensselaer at the forefront of dynamic modeling of the built environment, towards high impact publications and large scale funding opportunities by supporting goals to: (i) integrate measured data with biophysical models; (ii) link to distributed controls systems for feedback and correlating the economic value proposition of improved ecosystems services from better extraction of clean energy and air flows within built environments; (iii) improving resolution of models for use within parametric design; (iv) seamlessly connecting to current practice building energy modeling (BEM); (v) quantifying uncertainty; (vi) determining the optimal placement of sensors; (vii) selecting optimal flow control strategies, and; (viii) providing predictive modeling to ‘scenarios’.
This research will review and outline the methods and results of best practice building energy modeling (BEM) tools used for the simulation of advanced built environment technologies. Each modeling method will be evaluated on criteria of adaptivity, usability, repeatability, and accuracy for building design iteration and economic evaluation. Large sections of this thesis will focus on case studies of previously completed simulations by CASE students and myself. Experience creating and using simulations for Integrated Concentrating Solar (ICS) and Electroactive Dynamic Daylighting System (EDDS) will be leverage to discuss limitations of current methods and areas of improvements. The challenges and limitations faced from previous simulations has and will continue to inform the current hypothesis to produce a method which is adaptive, easy to use, repeatable, and accurate. Additionally, existing best practice projects that use methods such as computational fluid dynamics, raytracing, and moisture modeling, will be discussed and outlined for their precedence and limitations.
The capture and transformation of natural climatic flows at the building envelope by current and next-generation built environment technologies is a vastly complex, multi-scale, interconnected physical problem of mass, momentum, and energy. To account for the transport phenomena of environmental flows leading to the building’s exterior, through the envelope, into the building’s interior is a inherently multi-scale problem which requires an adaptive multi-scale modeling approach. The proposed modeling strategy spans micro (microns to mm), meso (mm to m) and macro (m to km) scales. At the microscale, computational fluid dynamics (CFD) will be leverage to model the transfer, capture, and generation of moisture and populates (mass), airflow (momentum), and convection and radiation (energy).1 With the help of SCOREC, desiccant material performance from Shane Smith and Mae-Ling Lokko will be used as input to cavity CFD simulations developed by myself and linked to room CFD simulations done by Nina Wilson. Meso-scale model will comprise of numerical simulation of the Navier-Stokes equations for air, and conservation laws for a distribution of heat, mass, radiation, and pollutants and microbes. At the macro-scale, local and satellite measured and forecast data will be used as input to the model as higher resolution data than typical meteorological data for predictive modeling and uncertainty quantification.2,3,4 A modeling strategy is devised which bridges these scales by integrated micro-scale variables and results into a meso-scale model with input and outputs to macro-scale modeling for weather data and build energy modeling.
The envelope model developed thus far is referred to as a dynamic network model with medium-fidelity. The model is constructed as a network of individual modules, each module is a finite volume with conservation laws to account for mass, momentum, and energy transfer within and between connected volumes. The behavior of installed built environment systems within individual modules is derived from physical and experimental data analytics. The system’s behavior affects the mass, momentum, and energy of the individual modules. Individual modules are then constructed to form a wall or building envelope integrated with the energy conversion system. Combining individual volumes, conservation laws, and system behavior, a dynamic network model is produced that bridges the gap between high-fidelity models, such as computation fluid dynamics, with low-fidelity models, such as building energy models, to create a parametric tool for design iteration and predictive modeling.
From current research and projections, future simulations will need to possess a level of abstraction able to adapt and easily model new built environment technologies but accurate enough to have confident results. Device and system models are often created in isolation from the building context and loosely coupled to building energy models (BEM). The Functional Mock-Up Interface (FMI) Standard provides a framework for coupling models that streamlines the connection and exchange of data between device and system models with BEM, also known as co-simulation.5 Current research at LBNL’s Simulation Research Group is leveraging Modelica, a language for modeling complex, dynamic systems, and functional mock-up units (FMUs), a standard for packaging models, for co-simulation of building systems with common practice BEM software, EnergyPlus.6 This method of creating complex models and linking to common practice BEM software has exciting but not yet explored potential in the modeling and validation of next-generation dynamic envelopes. Adaptive, predictive, uncertainty quantified models of dynamic building envelope technologies will be developed in Modelica and Python and connected through the FMI standard with common practice BEM software, specifically EnergyPlus (and possibly Grasshopper).
A large portion of my contribution to the research will be co-simulation of the network model with EnergyPlus through the FMI standard and improving that interface for ease of use. To make the greatest impact on building energy modeling practices the model must be easy to use and provide results which answers questions relevant to architecture and engineering projects. Since EnergyPlus is a widely used building energy modeling tool by practitioners, co-simulation will occur through EnergyPlus’s ExternalInterface:FunctionalMockupUnitImport module with EnergyPlus being the master simulation program and an FMU being the slave. The FMU run by EnergyPlus will be a proxy created with C code that externally calls and interfaces with the network model written in python. At each timestep, EnergyPlus calls the FMU and waits for a response before continuing to the next building model timestep. The FMU proxy will externally call and execute the network model with inputs from the EnergyPlus co-simulation. Once the network model has completed simulation, the results are passed back to the FMU proxy which will co-simulate with EnergyPlus and progress to the next timesetp. This will allow the network model written in python to interface with the FMI standard and EnergyPlus.
Uncertainty quantification will be applied at multiple scales within our built environment energy and air flow models to identify sources of uncertainty at all scales and generate better approximations. At the microscale this will include parameters that determine the conversion of radiation to photovoltaic and thermal power, the absorption of moisture with desiccant material, and the digestion of pollutants and microbes within plant walls. At the meso-scale these will include uncertainties in the boundary conditions for the Navier-Stokes equations. At the macro-scale these will include uncertainties in the state of weather at a given location.
The co-simulation of adaptive, medium-fidelity system models within building energy modeling (BEM) enables fundamental modeling of transport phenomena that is under represented or otherwise not achievable in EnergyPlus alone. Using advanced modeling methods of uncertainty quantification and pervasive adaptivity, critical knowledge gaps in the building sciences can be explored and leveraged to better improve our understanding, provide the best approximation, and project the impact of adaptive building envelopes. Additionally, the framework in development today will produce models that are relevant to today’s BEM practices and future simulation methods through the use of the FMI standard, allowing the simulations to apply to future software that also adapt the standard, already including EnergyPlus and Matlab.
Fluent, I. N. C. “Fluent 6.3 Users Guide.” Fluent documentation (2006).↩
SolarAnywhere, satellite based irradiance and weather data, and solar energy simulation services. https://www.solaranywhere.com/Public/About.aspx↩
WeatherUnderground API, local weather stations for monitoring weather condition. http://www.wunderground.com/weather/api↩
Yi, Yun Kyu, and Ali M. Malkawi. 2011. “Integrating Neural Network Models with Computational Fluid Dynamics (CFD) for Site-specific Wind Condition.” Building Simulation 4 (3): 245.54. doi:10.1007/s12273-011-0042-7.↩
“FMI for Model Exchange and Co-Simulation.” FMI-Standard.org. Modelica Association, 25 July 2014. Web.↩
Nouidui, Thierry Stephane, Michael Wetter, and Wangda Zuo. “Functional Mock-Up Unit Import in EnergyPlus For Co-Simulation.” In Proceedings of BS2013: 13th Conference of, 2013. http://btus.lbl.gov/sites/all/files/lbnl-6413e.pdf.↩