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.
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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’.