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.

  1. Esterly, S., & Gelmen, R. 2010 Renewable Energy Data Book. EERE, 2010.

  2. S.E. Selkowitz, E.S. Lee, O. Aschehoug. Perspectives on Advanced Facades with Dynamic Glazings and Integrated Lighting Controls.

  3. Krarti, Moncef. Energy Audit of Building Systems: An Engineering Approach. 2nd ed. Boca Raton, FL: CRC, 2011. Print.

  4. 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.

  5. 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.

  6. Hensen, Jan, and Roberto Lamberts. Building Performance Simulation for Design and Operation. Abingdon, Oxon: Spon, 2011. Print.

Hypothesis and Goals

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