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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 demonstrates promising accuracy, with a Structural Similarity Index Measure (SSIM) ranging from 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. [Display omitted] • GAN surrogate model for urban CFD simulations processes arbitrary building geometries. • Automated end-to-end training pipeline utilizing Eddy3D produces modular ONNX-based CFD-GAN predictor. • CFD-GAN predictor integrated in Rhino & Grasshopper allows for seamless design workflow. • Achieved SSIMs ranging between 75%–97% based on 564 unique urban geometries. • Provides instantaneous simulation feedback for performance-driven decision-making in early urban design. [ABSTRACT FROM AUTHOR]

People spend approximately 90% of their lives indoors, and thus arguably, the indoor space design can significantly influence occupant well-being. Adequate views to the outside are one of the most cited indoor qualities related to occupant well-being. However, due to urbanization and densification trends, designers may have difficulties in providing vistas and views to the outside with an assortment of content, which can support the needs of their occupants. To better understand occupant view satisfaction and provide reliable design feedback to architects, existing view satisfaction data must be expanded to capture a wider variety of view scenarios and occupants. Most related research remains challenging in architectural practice due to a lack of easy-to-use early-design analysis tools. However, early assessment of view can be advantageous as design decisions in early design, such as building orientation, plan layout, and façade design, can improve the view quality. This paper, hence, presents results from a 181 participant view satisfaction survey with 590 window views. The survey data is used to train a tree-regression model to predict view satisfaction. The prediction performance was compared to an existing view assessment framework through case studies. The result showed that the new prediction (RMSE = 0.65) is more accurate to the surveyed result than the framework (RMSE = 3.78). Further, the prediction performance was generally high (R 2 ≥ 0.64) for most responses, verifying the reliability. To facilitate view analysis in early design, this paper describes integrating the satisfaction prediction model and a ray-casting tool to compute view parameters in the CAD environment. • Window view satisfaction data from 181 participants on diverse views were collected. • A new view parameter computing CAD tool was developed using ray-casting. • Tree-regression models predict occupant's view satisfactions with a high accuracy. • View satisfaction is highly related to sky view parameter and floor level. [ABSTRACT FROM AUTHOR]

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 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 that considers design aiding for the architectural practice. 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. [Display omitted] • We introduce Eddy3D — a toolkit for decoupled outdoor thermal comfort simulation that is motivated by global sensitivity analyses. • Eddy3D has been implemented as a plugin for Rhino & Grasshopper. • We validated both mean radiant temperature and wind velocity simulations with on-campus, real-world measurements — and show good agreement for both. • We discuss how our approach can help extract meaningful metrics such as the outdoor thermal comfort autonomy that help practitioners with performance-based decision aiding. [ABSTRACT FROM AUTHOR]

The Universal Thermal Climate Index (UTCI) has been linked to outdoor activity patterns and used to evaluate the effectiveness of urban interventions to improve thermal comfort. This study investigates how simulating the urban environment at increasing levels of physical accuracy impacts UTCI values along three cycling routes in Cambridge, Massachusetts. Baseline UTCI values are estimated using a local weather file, and the following increments in physical accuracy are considered: wind-scaling, shading from buildings, shading and cooling from trees, computational fluid dynamics simulations for wind speeds, and simulated surface temperatures. With bike ridership data from Bluebikes, Boston's bike-sharing program, the relationship between bike ridership patterns and UTCI values along each route is studied. Supervised machine learning models are applied to predict bike ridership based on UTCI and other predictors. UTCI simulation results show that incorporating the various increments of accuracy influences hourly UTCI values at urban areas and exposed areas differently. Incorporating local wind speeds is especially impactful for urban areas. The statistical models trained to predict hourly bike trip counts based on UTCI and other demand and weather predictors achieved a root-mean-squared error of 1.06 trips. 47% of predictions were correct, and an additional 42% of predictions were off by 1 trip. This study demonstrates the importance of spatial refinement in simulating UTCI, and motivates future research into efficient simulation methods or rules-of-thumb for deriving spatial-temporal UTCI values. Future work into building a robust predictive model would motivate the design of thermally comfortable environments for human-powered transportation in cities. [Display omitted] [ABSTRACT FROM AUTHOR]

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 °C and 2.0 °C, 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. • New algorithm to predict exterior surface temperatures and MRT in an arbitrarily complex urban 3D model. • The algorithm is utilizing K-Means clustering to reduce simulation times by a factor of 80. • We report an RMSE of 1.8°C for surface temperature predictions of a full year. • Importance of long-wave radiation exchange is analyzed. • Largest heterogeneity in external surface temperature is reported for horizontal clusters close to obstacles and south-facing clusters. [ABSTRACT FROM AUTHOR]

• Annual outdoor thermal comfort evaluation workflow is introduced. • 60 typology and density scenarios in three hot climatic contexts were evaluated. • Annual energy balance and outdoor thermal comfort results were recorded iteratively. • The impact of urban form on energy performance and outdoor comfort is discussed. • This method helps integrate environmental quality considerations in urban design. With the rise of awareness of health and well-being in cities, urban environmental analysis should expand from energy performance to new environmental quality-based considerations. The limited potential to annually evaluate outdoor thermal comfort, predominant among these considerations, has restricted the exploration of the interrelations between urban morphology and annual energy performance. This study aims to bridge this gap by capitalizing on the new capabilities of Eddy3D – a Grasshopper plugin which enables effective calculations of hourly microclimatic wind factors via OpenFOAM which in turn are used to generate annual outdoor thermal comfort plots. Using this method, a parametric study was conducted for different typology and density scenarios in three different hot climatic contexts in Israel. The automated analytical workflow evaluated a total of 60 design iterations for their energy balance, outdoor thermal comfort autonomy (OTCA) and self-shading levels using the shade index. The high correlation found here between the annual shade index and the OTCA, across all climatic contexts, shows the potential of the shade index to serve as an effective indicator, in these contexts, for comparative or optimization outdoor comfort studies. Further results are both the superiority of the courtyard typology in both energy and outdoor comfort studies, and the contrasting impact of higher density on the annual energy balance (lower performance) and outdoor thermal comfort (higher performance) in hot climates. The annual plots of both the energy balance and OTCA reveal various seasonal and monthly trends in the three different climatic zones which can lead to localized and seasonal urban design strategies. [ABSTRACT FROM AUTHOR]