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  1. README.md +60 -0
  2. models/co2_all_MIOEGPT_meanvarianceuncertainty_0228_00_10_00.pt +3 -0
  3. models/co2_all_MIOEGPT_meanvarianceuncertainty_0228_15_25_04.pt +3 -0
  4. models/co2_all_MIOEGPT_meanvarianceuncertainty_0228_15_31_54.pt +3 -0
  5. models/co2_all_MIOEGPT_meanvarianceuncertainty_0301_16_02_36.pt +3 -0
  6. models/co2_all_MIOEGPT_meanvarianceuncertainty_0301_16_03_28.pt +3 -0
  7. processed_data/test_data_norm.pkl +3 -0
  8. processed_data/train_data_norm.pkl +3 -0
  9. raw_data/unsteady_10.pkl +3 -0
  10. raw_data/unsteady_100.pkl +3 -0
  11. raw_data/unsteady_1000.pkl +3 -0
  12. raw_data/unsteady_1001.pkl +3 -0
  13. raw_data/unsteady_1002.pkl +3 -0
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README.md CHANGED
@@ -1,3 +1,63 @@
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  ---
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  license: cc-by-4.0
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: cc-by-4.0
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  ---
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+
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+ # BEAR-CFD-Dataset
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+
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+ This dataset is released with paper **Data-driven operator learning for energy-efficient and indoor air quality-aware building ventilation control**.
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+
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+ Author: Yuexin Bian, Yuanyuan Shi
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+
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+ The data was generated using **ANSYS FLUENT 2023R2** and includes both steady-state and transient flow simulations. It is designed to support research in scientific machine learning, particularly in learning neural operators and data-driven PDE solvers.
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+
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+ This dataset is released for research purposes only. Please cite the authors if used in published work.
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+
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+
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+ ## 🧪 Dataset Description
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+
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+ ### Steady-State Simulations
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+ - **10 distinct cases** were simulated, each with unique boundary conditions.
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+ - Each simulation converges to a time-independent solution.
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+ - These cases represent equilibrium airflow behavior under varying physical setups.
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+
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+ ### Transient Simulations
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+ - **300 cases**, each initialized from a corresponding steady-state solution.
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+ - Each simulation spans **30 minutes** of physical time.
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+ - Simulations use a **time step size of 10 seconds** and output results every 30 seconds.
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+ - This results in **60 time steps per case**, capturing unsteady, time-varying airflow dynamics under changing boundary conditions.
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+
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+ Transient simulation data is used to train neural operators for modeling the evolution of fluid dynamics in time.
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+
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+ ## 📂 Dataset Structure
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+
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+ The dataset repository contains the following subfolders:
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+
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+ ├── raw_data/
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+ # Transient simulation results in pickle format
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+ ├── processed_data/ # Processed datasets split into train/test sets
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+ ├── models/ # Trained neural operator models
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+ ├── steady_case_data/ # Original ANSYS steady-state result files
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+
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+
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+ - **Pickle Files (`.pkl`)**: Each transient simulation case is stored as a pickle file for convenient loading in Python.
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+ - **ANSYS Data**: The steady-state simulation results are in native ANSYS formats (e.g., `.cas.h5`, `.dat.h5`).
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+
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+ ## 📚 Use Cases
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+
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+ - Training neural operators to learn the temporal dynamics of fluid flow.
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+ - Studying how steady-state solutions influence transient behavior.
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+ - Benchmarking data-driven PDE solvers and physics-informed machine learning models.
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+
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+ ## 📥 Loading Example
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+
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+ ```python
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+ import pickle
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+
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+ with open("raw_data/unsteady_10.pkl", "rb") as f:
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+ data = pickle.load(f)
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+
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+ import ansys.fluent.core as pyfluent
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+
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+ solver = pyfluent.launch_fluent(mode="solver", precision="double",processor_count=6)
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+ solver.file.read_case_data(file_name='steady_case_data/steady_seed0.cas.h5')
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+ solver.settings.file.read_data(file_name = "steady_case_data/steady_seed0.dat.h5")
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