The shift toward urban decarbonization, which involves transitioning to heat pumps and integrating photovoltaic (PV) energy generation, poses substantial challenges to electricity grids in cities. Ithaca, NY, with its commitment to the Green New Deal, serves as a case study for cities confronted with these challenges. This paper explores how various building electrification scenarios might affect Ithaca’s net-zero ambitions. We show that our reduced-order Urban Building Energy Modeling (UBEM) approach can accurately predict building energy consumption and rooftop solar PV energy generation for the 6114 buildings in the City of Ithaca. Building on that, we present three increasingly ambitious decarbonization scenarios that offer a holistic perspective on Ithaca’s future electrification potential. In automating the reconstruction of building geometry from readily available LiDAR data, along with automated BEM setup and calibration, our methodology offers a robust toolkit to plan holistic decarbonization efforts. We argue that our approach, which forecasts both UBEM and PV for all residential and commercial buildings within a single framework, provides insights that cannot be reached with siloed methods. In the future, we believe that the abundance of readily available urban data sets will allow us to transition our approach to other jurisdictions across the US.

Urban form impacts the airflow patterns in cities and the resulting urban microclimate. This has significant implications for ventilation, overheating, wind chill, and safety concerns such as down drafts from skyscrapers. While Computational Fluid Dynamics (CFD) simulations are the best practice for analyzing urban airflow patterns in design, they are computationally expensive and require a high level of expertise, making them underutilized in the early design process. This paper presents a surrogate model for CFD using a Generative Adversarial Network (GAN) that can process arbitrary building geometries. The model is trained using an automated end-to-end pipeline based on Eddy3D and implemented within the Rhino and Grasshopper environment as an Open Neural Network Exchange (ONNX)-based CFD-GAN predictor. This workflow provides instantaneous simulation feedback within the design software, reduces the risk of user error, and allows for appropriate spatial resolution in early design. The CFD-GAN has demonstrated promising accuracy, with a Structural Similarity Index Measure (SSIM)4 range of 75%–97% on a limited training dataset of 564 unique urban geometries. Although the model currently has limitations regarding accuracy in complex urban wake regions, we show that these are likely not of concern for outdoor thermal comfort analyses. While it cannot replace CFD in later design stages, the CFD-GAN facilitates the incorporation of urban airflow analysis in early design with minimal effort and instantaneous performance feedback.

The architectural community needs holistic, evidence-based planning tools to promote urban resilience in the face of global warming. To ensure maximum impact, simulation-driven microclimate analysis methods must be integrated early in the design process. With Eddy3D, we present a toolkit to simulate outdoor thermal comfort (OTC) metrics with a decoupled approach. We motivate the decoupled systems framework with meteorological measurements and local and global sensitivity analyses of three different climates. For a real-world case study on a university campus, we present results for both wind velocity and mean radiant temperature simulations. Finally, we discuss the advantages and disadvantages of a decoupled simulation approach considering design aiding and the architectural community. Our findings support reduced simulation time and flexibility, with the caveat of reduced accuracy due to neglecting forced convection, albeit this being less relevant in the early stages of design. The framework presented in this manuscript has been implemented and released as Eddy3D, a plugin for Rhino & Grasshopper.

As work environments struggle to reopen during the current COVID-19 pandemic, it is crucial to establish practical decision-aiding tools. While a strong emphasis has been placed on determining generic guidelines to reduce the risk of airborne viral spread, there is a lack of free and easy-to-use simulation workflows to quantify indoor air quality and the risk of airborne pathogens indoors at a spatial resolution that can take into account floor-plan layouts, furniture, and ventilation inlet-outlet positions. This paper describes the development of a new, free, early design tool that allows designers and other stakeholders to simulate and compare viral airborne concentration under different indoor conditions. The tool leverages OpenFOAM-based Computational Fluid Dynamics (CFD) and a passive scalar simulation approach to allow architects and interior designers to quantify airborne pathogens’ exposure. The tool is integrated into the popular Rhino3d & Grasshopper CAD environment to facilitate its application in fast-paced design processes. We demonstrate good agreement compared to a CFD benchmark test. Further, we validate newly developed COVID-19 capabilities by comparing our results to an existing restaurant case study that included tracer gas measurements and validation using Fluent (Ansys). We demonstrate applications of the tool in a comparative study of a restaurant that investigates how plan and furniture layout interventions, ventilation strategies can impact the movement of airborne pathogens in indoor environments.

Outdoor thermal comfort simulation simulations rely on the mean radiant temperature (MRT) seen by pedestrians as an important input that remains difficult to compute. Especially for large urban models, computing relevant surface temperatures and radiation fluxes that make up the MRT is a daunting task in terms of simulation setup and the computational overhead. We propose a new algorithm to estimate exterior surface temperatures of building facades, roofs, and ground surfaces in an arbitrary urban 3D model. The algorithm discretizes all model surfaces and clusters them by material properties and sky and sun exposure to reduce computational complexity. The model setup is fully automated, and the algorithm is implemented in the popular Rhino3d CAD environment. We demonstrate the accuracy of the algorithm by comparing both the resulting external surface temperatures against a high-fidelity simulation and the final MRT against real-world measurements. We report an RMSE of 1.8\degC and 2.0\degC, respectively, while reducing simulation times by a factor of 80. Envisioned applications of the algorithm range from rapid microclimate simulations in fast-paced urban design processes to large scale urban comfort evaluation of existing cities.