Adam Wilson

This project investigates whether a physically constrained neural network can replace expensive chemistry calculations in simulations of hydrogen flames. Using data from a high-fidelity swirling-flow simulation, the network learns to predict how the chemical composition changes while conserving the elements involved. The aim is to make reacting-flow simulations for applications such as gas turbines faster and more practical.

What were the key results of your research project?

• A complete workflow was developed to turn simulation data into neural-network training examples.
• The network reproduced short-term chemical changes accurately, including in an unseen part of the flow domain.
• Physical constraints helped keep the predictions chemically valid.
• Errors accumulated when the network repeatedly predicted further into the future.
• The results show potential for faster chemistry calculations, but further development is needed before integration into a full flow solver.