GUMP: General Urban Area Microclimate Predictions Tool

Grushin, Alexander; Tyagi, Ankit; Gluck, Joshua; Mohseni, Seyedmohammad; Nigam, Nikhil; Klopfenstein, Mark; Lee, Robert

doi:10.2514/6.2020-3213

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…[26][27][28] The General Urban area Microclimate Predictions (GUMP) tool forecasts urban ow and has been validated using simultaneous observations from multiple meteorologically instrumented UA. 3,29 Other mobile platforms include ship-based measurements, 30 crewed (manned) aircra measurements, 31 or dropping radiosondes or launching radiosondes, 32 tethered balloon systems, 33 or pedestrian measurements. 34 All these observation systems need to geo-tag and timestamp each atmospheric sample and aggregate the results into a coherent picture.…”

Section: Suitability Of Vehicle Types For Atmospheric Phenomenamentioning

confidence: 99%

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The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors

Compere,

Adkins,

Muthu Krishnan

et al. 2024

Environ. Sci.: Atmos.

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Section: Suitability Of Vehicle Types For Atmospheric Phenomenamentioning

confidence: 99%

“…26–28 The General Urban area Microclimate Predictions (GUMP) tool forecasts urban flow and has been validated using simultaneous observations from multiple meteorologically instrumented UA. 3,29…”

Section: Literature Reviewmentioning

confidence: 99%

The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors

Compere,

Adkins,

Muthu Krishnan

et al. 2024

Environ. Sci.: Atmos.

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“…CFD simulations are typically used to obtain the required resolution of this application [6]. Usually, the initial and boundary conditions are interpolated from the results produced by a coarser mesoscale prediction, making it necessary to have a proper procedure for the coupling of both models.…”

Section: Introductionmentioning

confidence: 99%

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

García-Gutiérrez

Gonzalo

López

et al. 2022

Fluids

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The feasibility, safety, and efficiency of a drone mission in an urban environment are heavily influenced by atmospheric conditions. However, numerical meteorological models cannot cope with fine-grained grids capturing urban geometries; they are typically tuned for best resolutions ranging from 1 to 10 km. To enable urban air mobility, new now-casting techniques are being developed based on different techniques, such as data assimilation, variational analysis, machine-learning algorithms, and time series analysis. Most of these methods require generating an urban wind field database using CFD codes coupled with the mesoscale models. The quality and accuracy of that database determines the accuracy of the now-casting techniques. This review describes the latest advances in CFD simulations applied to urban wind and the alternatives that exist for the coupling with the mesoscale model. First, the distinct turbulence models are introduced, analyzing their advantages and limitations. Secondly, a study of the meshing is introduced, exploring how it has to be adapted to the characteristics of the urban environment. Then, the several alternatives for the definition of the boundary conditions and the interpolation methods for the initial conditions are described. As a key step, the available order reduction methods applicable to the models are presented, so the size and operability of the wind database can be reduced as much as possible. Finally, the data assimilation techniques and the model validation are presented.

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Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

Adkins

Becker²,

Ayyalasomayajula³

et al. 2023

Drones

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This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations.

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GUMP: General Urban Area Microclimate Predictions Tool

Cited by 3 publications

References 16 publications

The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors

The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

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