Skip to content

Latest commit

 

History

History
152 lines (121 loc) · 7.54 KB

Approach.md

File metadata and controls

152 lines (121 loc) · 7.54 KB
Raw

Bachelor Thesis

[[TOC]]

Presentation of the project

Task of this project

Quantitative analysis of MODE-S information on the possible turbulence assessment of commercial aircraft

Idea

Processed data from many MODE-S receivers are available. These data comprise a large number of parameters in a high frequency. Using these data, statements on turbulence are to be made with the help of different approaches. Due to the very different aircraft characteristics, a normalized approach has to be used. In a first step, the approach 1 shall be implemented in the following

Problems

  • Turbulence are dynamic in spatial and temporal dimensions

  • Lack of observation to understand the process

  • The resolution of the turbulence model is larger than the range that applies to the effect of an aircraft

Relevant work aspects of this project

  • Automated procedure for data transfer and calculation of approach 1. Increasing the temporal and spatial scale of the example data set.

  • Problem of the size of the database. So far, the data is stored in MySQL. Research on more efficient storage of data in other database formats.

  • Problem of unevenly distributed time series ("unevenly distributed time series"). Research methods to ensure the quality of the analysis.

  • Matching with weather data (Prefer "AMDAR" data - availability to be checked).

  • Evaluation and integration of DILAB data after first measurement flights including the directly calculated "Eddy Dissipation Rate".

Installation

Get the code

Get the source code with the following commands. First you will need git, python and pip install to your pc:

git clone https://git.rz.tu-bs.de/j.bisso-bi-ela/ba_mode-s_analysis.git
cd ba_mode-s_analysis

# Install the Dependencies and setup the app
pip install -r .\requirements.txt
pip install .

Dependencies

Following packaged are required to run the development version of the app. They are automatically installed during the previous installations processes:

  • PySide6==6.3.0
  • numpy==1.21.6
  • matplotlib==3.5.2
  • scipy==1.7.3

Usage

General usage

Run the app using the run.ps1 script

$ .\run.ps1 -h
usage: mode_s.py [-h] [-t] [-it] [-v] [-d] [--local] [-la latitude_minimal]
                 [-LA LATITUDE_MAXIMAL] [-lo longitude_minimal]
                 [-LO LONGITUDE_MAXIMAL] [-bd BDS] [-id id_minimal]
                 [-ID ID_MAXIMAL] [-l LIMIT] [-dl DURATION_LIMIT]
                 [-n MEDIAN_N] [-p [PLOTS [PLOTS ...]]]
                 [-pa [PLOT_ADDRESSES [PLOT_ADDRESSES ...]]]

Framework for automatic Mode-S data tranfer and turbulence prediction.

optional arguments:
  -h, --help            show this help message and exit
  -t, --terminal        Whether the app should run only on the terminal
  -it, --interactive    Whether the app should run interactively on the
                        terminal
  -v, --verbose         Whether the app should run only on the terminal
  -d, --debug           Whether the app should run only on debug mode
  --local               Whether the app should should connect to local
                        database
  -la latitude_minimal, --latitude-minimal latitude_minimal
                        The desired minimal latitude. If not set, all
                        available latitudes are evaluated
  -LA LATITUDE_MAXIMAL, --latitude-maximal LATITUDE_MAXIMAL
                        The desired maximal latitude. If not set, all
                        available latitudes are evaluated
  -lo longitude_minimal, --longitude-minimal longitude_minimal
                        The desired minimal longitude. If not set, all
                        available longitudes are evaluated
  -LO LONGITUDE_MAXIMAL, --longitude-maximal LONGITUDE_MAXIMAL
                        The desired maximal longitude. If not set, all
                        available longitudes are evaluated
  -bd BDS, --bds BDS    The desired bds. If not set, all available bds are
                        evaluated
  -id id_minimal, --id-minimal id_minimal
                        The desired minimal id. If not set, all available ids
                        are evaluated
  -ID ID_MAXIMAL, --id-maximal ID_MAXIMAL
                        The desired maximal id. If not set, all available ids
                        are evaluated
  -l LIMIT, --limit LIMIT
                        The desired limit for the mysql commmands. (default =
                        50000)
  -dl DURATION_LIMIT, --duration-limit DURATION_LIMIT
                        The desired flight duration limit in seconds for the
                        analysis. (default = None)
  -n MEDIAN_N, --median-n MEDIAN_N
                        The desired n for the median filtering. MUST BE ODD.
                        (default: n=3)
  -p [PLOTS [PLOTS ...]], --plots [PLOTS [PLOTS ...]]
                        The desired plots. POSSIBLE VALUES: ['occurrence',
                        'bar_ivv', 'filtered', 'interval', 'std']
  -pa [PLOT_ADDRESSES [PLOT_ADDRESSES ...]], --plot-addresses [PLOT_ADDRESSES [PLOT_ADDRESSES ...]]
                        The addresses of the desired plots.

Example Usage

In the following example, we run the app in terminal mode and pass some relevant arguments, such as -l 2000000 for the query limit, -p filtered std for the plots we want the app to do, and -n 3 for the kernel size of the median filter.

$ .\run.ps1 -t --local -l 2000000 -p filtered std -n 3
Starting the app...
INFO    :: 1:0:24 :: Framework for automatic Mode-S data tranfer and turbulence prediction.
SUCCESS :: 1:0:24 :: Database accessible
INFO    :: 1:0:30 :: Row Count for table tbl_mode_s: 7160557
LOG     :: 1:0:30 :: 1 threads for attributes ['timestamp']
SUCCESS :: 1:0:36 :: Query successfully executed. Attributes were: ['timestamp']
INFO    :: 1:0:36 :: Lattest database db_airdata update: Tue Jul 6 14:08:06 2021
LOG     :: 1:0:36 :: Engine plot status : {'occurrence': False, 'bar_ivv': False, 'filtered': True, 'interval': False, 'std': True}
LOG     :: 1:0:36 :: Setting global query row limit to: 2000000
LOG     :: 1:0:36 :: Setting query filter to:
LOG     :: 1:0:36 :: 1 threads for attributes ['identification', 'address']
LOG     :: 1:0:36 :: 5 threads for attributes ['id', 'address', 'timestamp', 'bds', 'altitude', 'latitude', 'longitude']
LOG     :: 1:0:36 :: 5 threads for attributes ['id', 'address', 'timestamp', 'bds', 'altitude', 'bar', 'ivv']
WARNING :: 1:1:9 :: Query executed. Results lower than expected (2078 < 2000000). Attributes were: ['identification', 'address']
WARNING :: 1:1:9 :: Skipping following addresses: [4661099] :: Already added or invalid identification
INFO    :: 1:1:9 :: Known Addresses: 2077
SUCCESS :: 1:3:44 :: Query successfully executed. Attributes were: ['id', 'address', 'timestamp', 'bds', 'altitude', 'bar', 'ivv']
SUCCESS :: 1:3:47 :: Query successfully executed. Attributes were: ['id', 'address', 'timestamp', 'bds', 'altitude', 'latitude', 'longitude']
INFO    :: 1:3:47 :: Data actualized. Size: 2000000
LOG     :: 1:3:49 :: Plotting for following addresses: [4502584, 4966467, 4223733, 5262923]
LOG     :: 1:3:52 :: Filtering data with n set to: 3
INFO    :: 1:3:53 :: Plotting filtered data and standard deviations
SUCCESS :: 1:5:46 :: Successfully plotted
App existed normally.

The result of the previous command look as follow:

MODE-S___FILTERED___STD_-_BAR___IVV