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\section{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.  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.   %packaged units that plug into building energy models (BEM) then the adaptation and implementation of dynamic building envelope systems can be streamlined and more widely spread.  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. This seed funding would allow us to position SCOREC, CASE and Rensselaer at the forefront of biophysical modeling of the built environment, towards high impact publications and large scale funding opportunities including NSF, DOE, EPA, NIH, DOD, by supporting our initial aims 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) quantifying uncertainty; (v) determining the optimal placement of sensors; (vi) selecting optimal flow control strategies, and; (vii) providing predictive modeling to ‘scenarios’.