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\subsubsection*{Context} \section{Context}  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.\footnote{Esterly, S., \& Gelmen, R. \textit{2013 Renewable Energy Data Book.} EERE, 2014.}  
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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).\footnote{Hensen, Jan, and Roberto Lamberts. Building Performance Simulation for Design and Operation. Abingdon, Oxon: Spon, 2011. Print.}   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.  \subsubsection*{Goals and Hypothesis}  To expedite the development and integration of adaptive 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 able to be 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.  CASE and SCOREC has created an exciting partnership that introduces multiscale, predictive modeling, uncertainty quantification, and pervasive adaptivity into building sciences for simulation and validation of dynamic, ecological, building-integrated systems. By co-simulating adaptive, multi-scale, building envelope systems, 1) the simulation of dynamic energy capture technology, in a whole building context, is streamlined and repeatable for design and 2) the ecological, economic and physiological value of integrated systems are more accurately validated for future applications.  \subsubsection*{Methods}  In my Master's Thesis, due Spring 2015, I will review and outline completed simulations that modeled next-generation adaptive technologies for advantages and disadvantages. Each modeling method will be evaluated on criteria of adaptivity, repeatability, and accuracy to valuation of systems and impact to design. Previous simulations done by CASE and myself of pre- and post-processing Integrated Concentrating Solar and Electroactive Dynamic Daylighting System, respectively, will be used to discuss limitations of current methods. Additionally, existing best practice projects that use methods such as, computational fluid dynamics, raytracing, and moisture modeling, will be discussed and outlined for their advantages and disadvantages.   From current research and projections, future simulations will be dominated by co-simulation of specialized models coupled together through the Functional Mock-up Interface (FMI) Standard.\footnote{"FMI for Model Exchange and Co-Simulation." FMI-Standard.org. Modelica Association, 25 July 2014. Web.}  Research by LBNL's Simulation Research Group is leveraging Modelica, a language used for modeling complex, dynamic systems, and functional mock-up units (FMUs), a standard for packaging models, for use in co-simulation of complex building systems into common practice building simulation software, such as EnergyPlus.\footnote{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.}  This method of creating models and linking to common practice energy simulation software (FMU co-simulation) has exciting but not yet explored potential in the modeling and validation of next-generation adaptive envelope technology.  Adaptive, predictive, uncertainty quantified models of dynamic building envelope technology will be developed in Modelica and exported as an FMU to link with other common practice software, specifically EnergyPlus for our studies.   The capture and transformation of natural climatic energy flows at the building envelope is a vastly complex, interconnected physical problem of heat, mass, electromagnetic, and momentum transport.   To explore the exchange of energy between the building exterior and interior, through a dynamic envelope, a multiscale modeling approach will be designed.   The proposed modeling strategy spans micro (microns to mm), meso (mm to m) and macro (m to km) scales.   At the microscale, we will model the generation and the transfer of heat (convection and radiation), mass (moisture), momentum (airflow), and possibly pollutant and microbe populations through particle-based models.   Our 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 possibly pollutants and microbes, if time permits.   At the macro-scale these will include the state of weather at a given location.\footnote{Yi, Yun Kyu, and Ali M. Malkawi. 2011. ?Integrating Neural Network Models with Computational Fluid Dynamics (CFD) for Site-specific Wind Condition.? \textit{Building Simulation} 4 (3): 245?54. doi:10.1007/s12273-011-0042-7.}  These simulations will allow us to model transport exchange in rooms, facades, and urban landscapes for time periods that extend from minutes to hours. %critically, they will be capable of cross linking data from significant biophysical mass transport parameters to other variables such as air microbiome data for the first time.   When multiscale modeling, or advanced models in general, it is important to not trust the results as accurate but instead review them critically. To better understand and trust results uncertainty quantification can be used to determine where within a model that inaccuracies appear, how they propagate throughout the simulation, and the affect on the results. 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 inter-particle interactions and surface heterogeneities such as physical roughness and chemical composition. 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.   To create a parametric modeling framework that allows for pervasive adaptivity in the analysis and design process, we propose to control error and manage uncertainty in our simulations through adaptivity of all components. Much like the building systems adapt to changing climatic conditions, so must the models adapt to varying inputs and user defined parameters. An adaptive model must have the following criteria to achieve confident results: (a) a mechanism to achieve higher-fidelity solutions, (b) error indicators/estimators, and (c) an adaptive strategy. A framework will be set up that allows parametric adaptation of the models while maintaining accurate results and minimal run-times. When adaptivity is applied to our meso- and macro-scale numerical methods, higher-fidelity solutions are constructed through grid refinement in space, time and probability domains. In a particle-based simulation they are typically obtained through increasing the sample size in space and/or time. In both cases error estimators will be used to measure the difference between an ?exact? solution to the underlying mathematical problem and its numerical approximation. The adaptive strategy utilizes these indicators to adapt the mesh (for meso- and macro-scale simulations) or to increase the sampling domain (micro-scale simulations).   The co-simulation of next-generation adaptive building envelopes within whole building simulations enables fundamental modeling of transport phenomena that is otherwise under represented or that would not be 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 improved results inform current and future energy modeling practices through co-simulation and the growing functional mock-up interface standard, with software like EnergyPlus and MatLab already functional.