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.