Prediction of Delivered and Ideal Specific Impulse using Random Forest Models and Parsimonious Neural Networks

Development of complex aerospace systems often takes decades of research and testing. High  performing propellants are important to the success of rocket propulsion systems. Development  and testing of new propellants can be expensive and dangerous. Full scale tests are often required  to understand the performance of new propellants. Many industries have started using data science  tools to learn from previous work and conduct smarter tests. Material scientists have started using  these tools to speed up the development of new materials. These data science tools can be used to  speed up the development and design better propellants. I approach the development of new solid  propellants through two steps: Prediction of delivered performance from available literature tests,  prediction of ideal performance using physics-based models. Random Forest models are used to  correlate the ideal performance to delivered performance of a propellant based on the composition  and motor properties. I use Parsimonious Neural Networks (PNNs) to learn interpretable models  for the ideal performance of propellants. I find that the available open literature data is too biased  for the models to learn from and discover families of interpretable models to predict the ideal  performance of propellants.