Date: 12, November 2019

Dataset Title: A Simulated Wind-field Dataset for Testing Energy Efficient Path-Planning Algorithms for UAVs in Urban Environment

Dataset Creators: D. Baskar, A.A. Gorodetsky

Dataset Contact: Deepika Baskar deepikab@umich.edu

Funding: DARPA


Key Points:
- We computationally simulate the wind field acting around a region in the city of Boston. 
- The primary goal of this work is to advance our understanding of flow physics on path planning in the context of low-level flight by UAVs in urban environments.
- The generated data set of the two-dimensional wind fields at varying inlet angles and Mach numbers using an open-source flow solver will enable other researchers to test their path planning algorithms and to gain an understanding of the challenges involved. 

Research Overview:
Studying the effect of wind on urban air mobility typically requires comprehensive fluid dynamics simulations in a realistic urban geometry. Motivated to enable wide-spread autonomous drone activity in urban centers, such studies have indeed been considered by several authors in the recent literature. However, the accessibility of these approaches to those with less fluid dynamics experience and/or without access to purpose built simulation tools has limited validation and application of the resulting path planning strategies.
 
Methodology:
We simulate a wind field in a region near the city of Boston at coordinates 42°15'17.9"N and 71°08'27.7"W. The simulation of the flow physics in this region exhibits significant variation in local velocity direction and magnitude, and thus can potentially serve as a useful test case for UAM. - The CAD footprints for the region are made available by the Boston Planning and Development Agency. The extracted geometry of the region chosen is rendered using commercially available Autocad package. - Two dimensional numerical simulations are carried out for the generated using open source SU2 suite. Compressible steady Reynold’s Averaged Navier-Stoke’s (RANS) equations are solved for the flow domain with initial boundary conditions chosen to suitably replicate the actual field variables. - An Implicit Euler scheme is set to meet results with residual of 10^-6, and a Lower-Upper Symmetric-Gauss-Seidel (LU-SGS) method is used to increase the convergence speed of the code. - The boundary conditions, no slip wall and farfield, are fixed based on this history of velocity profile local to the area obtained from National Weather Services. Simulations are performed at varying Mach numbers and far field wind angles.

Instrument and/or Software specifications: 
- The .dat files contain the flow variables for each of the 402240 points sampled from the region under study and the raw data could be accessed as such using a text editor like NotePad.
- For flow visualization purposes, the .dat files are readable using Tecplot 360 Software.


Files contained here:
- Google_earth.png   		: Google earth image of the region simulated.

- tecplot_sample.lay  		: Shows a sample Tecplot layout (created using Tecplot 360 Version 2018R2)  for streamline visualization of the .dat file. To visualize a new data click "Load Data-> Tecplot Data" and select the .dat file you wish to visualize.

- case_mxxxxxx.dat  		: Represents the data file as a result of simulation for wind field of Mach number xxxxxx heading at zero degrees from left to right. Example,  “case_m0.01500.dat” represents wind of Mach number 0.01500 heading at angle zero degrees.

- case_axxxxxxx.dat  		: Represents the data file as a result of simulation for wind field of Mach number 0.035 heading at xxxxxxx degrees from left to right. Example,  “case_a10.00000.dat” represents wind of Mach number 0.03500 heading at angle 10 degrees.

Each .dat file contains the following variables for each point sampled.
X   			: X coordinate of the sampled point.
Y    			: Y coordinate of the sampled point.
Density 		: Flow density at point (x,y) (kg/m^3).
X-Momentum 		: X component of momentum at (x,y) (m/s).
Y-Momentum 		: Y component of momentum at (x,y) (m/s). 
Energy 			: Total flow energy at (x,y) (kg m^2/s^2).
Pressure 		: Total pressure at (x,y) Pascal (Pa).
Temperature 		: Total temperature at (x,y) in Kelvin (K).
Mach 			: Local Mach number of the wind field at at (x,y).
C<sub>p</sub> 		: Co-efficient of pressure at (x,y). 
<greek>m</greek> 	: Viscosity at (x,y) (Pa-s).


Related publication(s):
D. Baskar, A.A. Gorodetsky (2019). A Simulated Wind-field Dataset for Testing Energy Efficient Path-Planning Algorithms for UAVs in Urban Environment. Forthcoming. 


Use and Access: 
This data set is made available under a Creative Commons Public Domain license (CC0 1.0).


To Cite Data: 
D. Baskar, A.A. Gorodetsky (2019). A Simulated Wind-field Dataset for Testing Energy Efficient Path-Planning Algorithms for UAVs in Urban Environment [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/pdcv-0x63.