Syngas kinetics inside steady Perfectly Stirred Reactor

We present the dataset and python scripts used in our autoencoder (AE) neural network (NN)-based reduced chemistry work (Zhang and Sankaran, 2022).

1. To train the AE NN, run "python Train_PSR_AE_PCA.py " with Keras 

2. The dataset is about syngas combustion inside 0-D steady perfectly stirred reactor (PSR) at a wide range of parameter conditions.

1. The fuel is CO, H2, N2 with a volume ratio 5:1:4. The oxidizer is O2 and N2 mixed in 1:3 by volume. The inflow temperature is 500 K and combustion occurs at atmospheric pressure.
2. In total, there are 1.63 million samples with equivalence ratio varying from 0.09 to 20.0 and residence time scale varying to cover the entire S-curve.
3. The dataset is in hdf5 format and can be loaded with the python script, load_data_h5.py. Inside the dataset, there 12 entries.

• 1. asciiListtmp = h5f['vars_name'][()] ##name of the 12 thermochemical state variables
• 2. para_Phi = h5f['parameters_Phi'][()] ##equivalence ratio, varying from 0.09 to 20.0
• 3. para_Tin = h5f['parameters_Tin'][()] ##inflow temperature, constant=500 [K]
• 4. para_inv_tau_res = h5f['parameters_inv_tau_res'][()] ##inverse of residence time, varying from 4.53e-09 to 1.54e+04 [1/s]
• 5. x_train_min = h5f['trainset_min'][()] ##minimum value of training set
• 6. x_train_max = h5f['trainset_max'][()] ##maximum value of training set
• 7. x_data = h5f['dataset'][()] ##Thermochemical state variables (temperature, mass fractions of chemical species), normalized with x_train_min and x_train_max to be [-1,1]
• 8. x_src_data = h5f['dataset_src'][()] ##source term * 2/(x_train_max-x_train_min)
• 9. x_del_data = h5f['dataset_del'][()] ##(xinflow-x)* 2/(x_train_max-x_train_min)
• 10. train_ind = h5f['train_dataset_indices'][()] #0.7, sample index of training set 
• 11. test_ind = h5f['test_dataset_indices'][()] #0.3*0.5, sample index of test set
• 12. vali_ind = h5f['valid_dataset_indices'][()] #0.3*0.5, sample index of validation set
• The training/test/validation splitting is used in our reduced chemistry work.