Improving Wind Speed Forecasting for Urban Air Mobility Using Coupled Simulations

Abstract

Hazardous weather, turbulence, wind, and thermals pose a ubiquitous challenge to Unmanned Aircraft Systems (UAS) and electric-Vertical Take-Off and Landing (e-VTOL) aircrafts, and the safe integration of UAS into urban area requires accurate high-granularity wind data especially during landing and takeoff phases. Two models, namely, Open-Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study to simulate airflow over Downtown Oklahoma City, Oklahoma, United States. Results show that computational fluid dynamics wind simulation driven by the atmospheric simulation significantly improves the simulated wind speed because the accurate modeling of the buildings affects wind patterns. The evaluation of different simulations against six Micronet stations shows that WRF-CFD numerical evaluation is a reliable method to understand the complicated wind flow within built-up areas. The comparison of wind distributions of simulations at different resolutions shows better description of wind variability and gusts generated by the urban flows. Simulations assuming anisotropy and isotropy of turbulence show small differences in the predicted wind speeds over Downtown Oklahoma City given the stable atmospheric stratification showing that turbulent eddy scales at the evaluation locations are within the inertial subrange and confirming that turbulence is locally isotropic.

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

“…Similarly, in [64], an unsteady RANS and k-ε-based CFD model is nested within two mesoscale wind models for the development of a real-time turbulence alert system for an area surrounding a Norwegian airport. Authors in [65] use hourly data from WRF to dictate the initial and boundary conditions of a CFD model that is based on LES-filtered equations for replicating wind conditions over a downtown area in Oklahoma, United States. Four WRF schemes, which are diversified based on the simulation domain size (3 km, 1 km, and 400 m) and UBL model, and two coupled WRF+LES schemes each with different coupling methods are compared to the Micronet (weather) station observation data.…”

Using Machine Learning to Predict Wind Flow in Urban Areas

“…Subsequently, machine learning techniques will be utilized to identify the key properties that influence micro-urban flow. Due to the long calculation time of coupled, regional models with CFD models [21], using those properties for machine learning prediction, it is possible to shorten the calculation time in order to obtain downscaled information. Regional models use wind measurements as a data assimilation input to improve their accuracy [22].…”