Project Details
Projekt
Prediction and surrogate modelling of thermodynamics properties of mixtures with application to the inverse design under uncertainty
Subject Area
Technical Thermodynamics
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 526254705
The selection of a suitable working fluid represents one of the most important factors in the design of a thermodynamic cycle. As the fluid has to meet manifold criteria, mixtures are gaining increasingly importance in order to obtain the most appropriate solution, i.e. the required combination of properties unattainable by pure compounds. Two different strategies are followed in the literature for the fluid design: 1. A computer-aided model (mixture) design approach in combination with the formulation of a mixed-inter-nonlinear-programming (MINLP) problem - which though usually employs property models with limited accuracy or models, which do not include widely used refrigerants. 2. A screening approach, i.e. performing system simulations of the defined cycle for a large number of fluids described by highly accurate multiparameter Helmholtz equations of state (HEOS) that are considered state of the art for the calculation of thermophysical properties. HEOS though are too computationally demanding to allow for their use in the MINLP. Furthermore, potential working fluid mixtures whose mixing parameters or models for the HEOS are missing need to be excluded from screenings. The aim of the proposed project is to overcome these two main restrictions when using HEOS is the working fluid selection. This will be achieved by the development of dedicated surrogate models based on Gaussian processes (GP) for HEOS of (binary) refrigerant mixtures to enable the efficient calculation of their thermodynamic properties in a MINLP based design approach. Additionally, molecular simulations on mixtures not yet described by optimized HEOS will allow to derive their mixing parameters so that they can also be included in the optimization process. To account for the mismatch between the HEOS and molecular simulations, we will introduce stochastic HEOS models, for which stochastic GP surrogates will be generated. These stochastic surrogate models will be employed in a MINLP to identify a suitable mixture for a specific application. The stochastic approach followed in this proposal will additionally allow for optimization considering uncertainties of the underlying property models.
DFG Programme
Priority Programmes
