DOI: 10.5281/zenodo.17790216
title: Datasets for "Evolutionary Steering of Fluidized Bed Hydrodynamics via Spatial Modulation of Inlet Gas Velocities"

description: This repository contains numerical datasets generated for the evolutionary steering of inlet gas velocities in quasi-two-dimensional fluidized beds (FBs). All simulations apply a sinusoidal, time-dependent perturbation to the mean inlet velocity. The oscillatory inlet profiles are fully generated by evolutionary algorithms (EAs), requiring only the mean operating velocity as input.

The dataset includes single-objective (bubble-size control) and multi-objective (simultaneous bed-expansion and bubble-diameter control) optimization studies. All cases were performed using a static bed height of 0.10 m, an operating gas flow rate of 16 L min⁻¹, and video acquisition at 50 fps.

EA settings:





Single-objective hyperparameter study: population size 10, 10 generations




Multi-objective optimization (NSGA-II, R-NSGA-II): population size 10, 20 generations




Random-walk baseline: 1 generation, 200 candidates



 

Repository Structure

```Main_Repository/├── Hyperparameter_Study/│   ├── Final_Screening/│   │   ├── Bubble_Dynamics/│   │   │   └── cxpb{value}_mutpb{value}/│   │   │       └── run{value}.csv│   │   └── EA_Stats/│   │       └── cxpb{value}_mutpb{value}/│   │           └── run{value}.csv│   └── Preliminary_Screening/│       ├── Bubble_Dynamics/│       │   └── cxpb{value}_mutpb{value}/│       │       └── run{value}.csv│       └── EA_Stats/│           └── cxpb{value}_mutpb{value}/│               └── run{value}.csv└── Multi-objective_Steering/    └── Algorithm: NSGA-II, R-NSGA-II, Random walk/        ├── Bubble_Dynamics/        │   └── run{value}.csv        └── EA_Stats/            └── run{value}.csv```

Bubble_Dynamics/

Stores the processed bubble-tracking results for each oscillatory inlet profile.Each file is named run{value}.csv, representing an independent EA evaluation.

Fields:





video_name – User-defined video identifier




gen – EA generation




frame_number – Frame index




time [s] – Time stamp




num_contours_per_frame – Number of detected bubbles




ID – Unique bubble ID




cx_pos [px], cy_pos [px] – Bubble centroid (pixels)




cx_pos [m], cy_pos [m] – Bubble centroid (SI units)




size [px^2], size [m^2] – Bubble area




perimeter [px], perimeter [m] – Bubble perimeter




approx_diameter [px], approx_diameter [m] – Estimated bubble diameter




x_velocity [m/s], y_velocity [m/s] – Bubble velocities between frames




valve1 … valve5 – Inlet velocities per valve (L/min)





EA_Stats/

Contains the complete EA history for each individual of each generation, using the same run{value}.csv filenames to allow direct correlation with Bubble_Dynamics.

Fields:





gen – EA generation




v1 … v5 – Valve-specific inlet velocities (L/min)




F, F1, F2 – Fitness values





F for single-objective




F1 (bed-expansion deviation) and F2 (bubble-size deviation) for multi-objective






CV – Total constraint violation




H – Equality constraint




G1, G2 – Inequality constraints





Hyperparameter Study

Two subdirectories explore crossover and mutation probabilities:





Preliminary_Screening:7 independent runs for each pair(pc,pm)(p_c, p_m)(pc,pm), wherepc∈{0.1,0.2,…,1.0}p_c \in \{0.1, 0.2, \dots, 1.0\}pc∈{0.1,0.2,…,1.0} andpm∈{0.0,0.001,0.01,0.05,0.1}p_m \in \{0.0, 0.001, 0.01, 0.05, 0.1\}pm∈{0.0,0.001,0.01,0.05,0.1}




Final_Screening:Selected (pc,pm)(p_c, p_m)(pc,pm) pairs with 31 independent runs each



Both include full bubble dynamics and EA histories.



Multi-objective Steering

Contains the same data structure as above but for three algorithms:





NSGA-II




R-NSGA-II




Random-walk baseline





Funding

This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), SPP 2364, project number 504548777.