Physics-Based Multi-Fidelity Surrogate Modeling with Entropy-Based Availability Methods

Thermodynamic availability of an engineering system is described in terms of entropy generation and is a common parameter applicable to all processes and subsystems within the system. Availability methods based on this metric can be employed for the characterization of aerospace vehicle performance and mission analysis. Models of varying complexity and computational expense are typically used in vehicle design. This paper introduces an approach to construct cheap and accurate surrogate models for availability methods and analysis. The proposed technique is physics-based and aligns entropy-based metrics of computationally cheap low-fidelity models with the expensive high-fidelity models using implicit space mapping. The resulting surrogates are fast and have good generalization capabilities. The approach is applied to the compressible, viscous flow in a convergent-divergent nozzle modeled with an analytic model and a high-fidelity computational fluid dynamics model. The models are aligned in terms of the governing flow variables by aligning the entropy generation across the normal shock. The proposed approach is compared with standard correction/enhancement techniques.