Research
Data-Driven Surrogate Modelling for Liquid Ammonia Direct Injection Spray Characteristics
Principal investigator: | Amin PAYKANI |
Funding source(s): | Royal Society |
Start: 31-03-2024 / End: 28-02-2027 | |
Amount: £225,000 |
In this project, we are proposing a direct research and partnership building between QMUL with Kyushu University with the aim of developing data-efficient ML models for the prediction of liquid ammonia DI spray characteristics to tackle the computational and experimental costs and time.