Gradient-based optimization of micro-scale pressurized volumetric receiver geometry and flow rate

This study focuses on the design optimization of a micro-scale pressurized volumetric receiver by changing geometry and flow rate constrained by the volume, outlet air temperature, and outer surface temperature. The pressurized volumetric receiver model is replicated from an existing model, which assumes constant air pressure and neglects the convection loss from the cavity. The existing model is revised from a solver to a design optimizer. The replicated model is restructured using OpenMDAO (Open-source MultiDisciplinary Analysis and Optimization) framework, and analytical derivatives are implemented for efficient derivative calculation to increase optimization performance. The replicated model is verified, and the maximum outlet air temperature difference is less than 0.05%. Optimization performance, selection of optimizers, the effect of the domain size, and radiative methods are discussed. The combined impact of the design variables is observed by selecting SLSQP (Sequential Least SQuares Programming) and trust-region optimizers. Optimization performance is tested in different domain sizes and compared with a design of experiments analysis. For testing the impact of radiative heat transfer methods to design optimization, the Rosseland approximation, and P1 method are selected. Depending on the design domain, a solution methodology is suggested for future receiver design optimizations applicable for macro-scale pressurized volumetric receivers.