(240u) Symbolic Regression for Synthesis of Local Thermo-Physical Models

Local thermodynamic models are practical alternatives to computationally expensive rigorous models that involve implicit computational procures and often complement them to accelerate computation for run time optimization and control. Human-centered strategies for development of these models are based on approximation of theoretical models, are case based, and used limited data. This paper describes a fully data driven automatic self-evolving algorithm that builds appropriate approximating formulae for local model using genetic programming. No a priori information on the type of mixture (ideal/non ideal etc.) or assumption is necessary. At the end, the reliability of the model built by GP is tested.