Downburst simulator

Description

This MATLAB code is designed for simulating horizontal slowly-varying mean wind velocities (both wind speed and direction) caused by a traveling downburst. The model utilizes a parametric-analytical approach for the spatiotemporal reconstruction of downburst events and optimizes the downburst geometric and kinematic parameters based on real measurements using the Teaching-Learning-Based Optimization (TLBO) algorithm. 

The analytical downburst model combines three independent velocity components: the steady radial velocity of an impinging jet, the translating downdraft velocity representing the parent cloud motion, and the atmospheric boundary layer (ABL) wind field (Xhelaj et al., 2020). This model simulates the 2D horizontal slowly-varying mean wind velocity at a generic height above the ground caused by traveling downbursts and reconstructs the space-time evolution of these events. 

The code utilizes the TLBO algorithm to simulate the downburst wind fields related to specific anemometric/lidar time histories uploaded by the user. The objective is to minimize the relative error between recorded and simulated wind speed and direction time histories (Xhelaj and Burlando, 2022). The optimization process involves adjusting the geometric and kinematic parameters to fit real measurement data.

Furthermore, the TLBO algorithm can be used to analyze the variability of downburst kinematic and geometric parameters for a full-scale anemometric downburst record (Xhelaj and Burlando, 2024). An example of thunderstorm outflow is also included. The code allows the analysis and simulation of downburst events and is intended for researchers and engineers working in the field of wind engineering and atmospheric sciences.

Features

• Parametric-Analytical Model: Uses an analytical model to simulate downburst wind fields by integrating radial impinging jet, downburst translational, and ambient ABL wind velocities.
• Optimization: Employs the TLBO algorithm to optimize geometric and kinematic parameters of downbursts based on recorded data.
• Visualization: Includes functionality to plot and analyze the simulation results and optimization process.

Installation

No specific installation is required other than having MATLAB installed on your system. This script was developed and tested in MATLAB R2021a.

Usage

• Load Data: Ensure that the thunderstorm data file Thunderstorm_Romania_MA.mat or a similar dataset is accessible within the MATLAB path or in the same directory as the script.
• Run the Script: Ensure to have downloaded the main Matlab script MultiRunV01.m and all the functions present in the repository. Open the script `MultiRunV01.m` in MATLAB and execute it. Note that the script utilizes parallel computing (parfor), enhancing computational efficiency.
• Adjust Parameters: Users can change simulation settings, refine downburst parameters by adjusting the lower and upper bounds, and specify the number of runs for metaheuristic optimization. Conducting multiple runs ensures robust optimization, leading to the identification of the best solution.

Example Code Structure:

The scipt MultiRunV01.m includes the following major sections:

1. Initialization:  Clears the workspace and sets up the environment for the simulation.
2. Load Data:  Loads the thunderstorm data from a .mat file.
3. Simulation Parameters: Defines the simulation time, station location, and parameter bounds for the downburst and ABL wind conditions.
4. Optimization Algorithm Configuration: Configures the TLBO algorithm, including population size, number of iterations, and multiple run settings.
5. Parallel Loop for Multiple Runs: Executes the optimization algorithm multiple times to ensure robustness and identify the best solution.
6. Post-processing and Analysis of Results: Analyzes and displays the results from multiple runs, including some statistical analysis on converegence curves and plotting.

By following these steps and utilizing the provided script, users can simulate and optimize downburst wind fields efficiently.

Financial support

This work was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 741273) for the THUNDERR project (Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures), supported by an Advanced Grant (AdG; 2016).