Abstract
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.
Highlights
This article is an open access articleIn response to global warming caused in part by the presence of excessive greenhouse gases in the environment, the world community decided to reduce greenhouse gas emissions and contain the rise in global average temperatures within 2 o C (Paris AgreementCOP21) [1]
A machine learning model based on the deep neural network (DNN) is trained using 600 data sets (Supplementary Table S1)
A deep neural network (DNN) is developed for training the machine learning (ML) model, as the structure and hyperparameters of DNN are observed appropriate for problems identical to those studied in the current work [32]
Summary
In response to global warming caused in part by the presence of excessive greenhouse gases in the environment, the world community decided to reduce greenhouse gas emissions and contain the rise in global average temperatures within 2 o C Neural network surrogate models were used in [18] to optimize the main design parameters of a radial turbine and showed high accuracy in learning the nonlinear physical model objects These models that learn from data alone can be prone to errors in some predictions that require the knowledge of the physics involved. The subsequent models can be appropriately utilized for optimizing problems offering an inherently continuous and differentiable correlation function that makes available the usage of analytical gradient methods for its optimization [22] In this context, the current study involves a deep neural network (DNN) in designing and optimizing the radial turbine system for the sCO2 -BC for the first time, to the author’s best knowledge. The off-design performance of the optimized model is computed
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