Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC

Design and test of a new high pressure phase equlibrium apparatus for highly corrosive mixtures of importance for natural gas

A new static analytical apparatus for high-pressure phase equilibrium measurements has been designed and built. The new apparatus enables the measurement of vapor–liquid and liquid–liquid equilibria, which can operate at temperatures ranging from 225 K to 475 K and pressures up to 20 MPa. It is constructed in Titanium and alloy C276, being suitable for highly corrosive systems of interest for the gas industry (e.g., for hydrogen sulfide containing mixtures). The apparatus is equipped with two Rapid Online Sampling Injectors (ROLSI™) enabling the withdrawing of micro-samples without disturbing the equilibrium conditions. A gas chromatograph is connected to the apparatus for direct analysis of the phases' compositions. The quality and performance of the new apparatus has been evaluated by measuring a well reported system (carbon dioxide + methylcyclohexane)