Bottoming cycles are drawing a real interest in a world where resources are becoming scarcer and the environmental footprint of power plants is becoming more controlled. Reduction of flue gas temperature, power generation boost without burning more fuel and even production of heat for cogeneration applications are very attractive and it becomes necessary to quantify how much can really be extracted from a simple cycle to be converted to a combined configuration. As supercritical CO2 is becoming an emerging working fluid [2, 3, 5, 7 and 8] due not only to the fact that turbomachines are being designed significantly more compact, but also because of the fluid’s high thermal efficiency in cycles, it raises an increased interest in its various applications. Evaluating the option of combined gas and supercritical CO2 cycles for different gas turbine sizes, gas turbine exhaust gas temperatures and configurations of bottoming cycle type becomes an essential step toward creating guidelines for the question, “how much more can I get with what I have?”. Using conceptual design tools for the cycle system generates fast and reliable results to draw this type of conclusion. This paper presents both the qualitative and quantitative advantages of combined cycles for scalability using machines ranging from small to several hundred MW gas turbines to determine which configurations of S-CO2 bottoming cycles are best for pure electricity production.
The paper describes the study performed by SoftInWay in the scope of the Phase I SBIR project funded by NASA. The project was dedicated to a study of optimization of the variable geometry reset angle schedules with the use of innovative autonomous AI technology. In the scope of the project, an automated compressor performance data generation workflow was developed. Three highly loaded multistage axial compressors were designed. The developed workflow was used to generate the training, validation, and test data sets for all three compressors. Multiple different architectures of artificial neural networks were studied, and parametric models for the representation of performance speedlines were developed. Utilizing the developed approaches, the artificial neural networks were trained for all three compressors to predict their performance with a relative error below 3 %. The trained neural networks were successfully used in the optimization of the variable inlet guide vanes and variable stator vanes reset angle schedules with a relative error of total-to-total pressure ratio prediction below 2 % for most of the points and relative error of total-to-total efficiency prediction below 1 % for all the points of the operational line. The capability of the developed AI models to accurately predict the optimal combination of reset angles and efficiency of the axial compressor with multiple vanes controlled independently allowed doing quick evaluations of efficiency and stability margins. The availability of such information enables the opportunity to make technical-economical decisions about the reasonability of implementation of independent variable vanes and their number during engine system analysis.
Single-stage axial pressure compound and two-row velocity compound supersonic turbines have a very high specific work, which allows for high power levels at high-pressure ratios and small flow rates in a very compact dimensional envelope. The latter is extremely desirable in certain types of Liquid Rocket Engine (LRE) turbopumps. Besides, due to their compactness, mechanical simplicity, and low cost, such turbines are favorable in high-pressure ratio Organic Rankine Cycles (ORC), gas expanders in chemical and technological processes, various mechanical drive applications, etc. However, despite their mechanical simplicity, the supersonic aerodynamic effects in such turbines are very complex. There are a number of design and analysis methodologies for such turbines, which are currently used in the industry. Nevertheless, some important nozzles arrangement parameters are still insufficiently investigated. It is known that there is an influence of the principle of drilled nozzles arrangement in a cascade and (round cross-section) convergent-divergent nozzles overlapping on overall turbine efficiency. Though, the data were either not formalized in some sort of loss model or formalized in a simplified formula that does not take into account meridional nozzle angle and the diameter where the nozzles are arranged (turbine mean diameter). The paper describes the study of the qualitative and quantitative influence of the mentioned above parameters on aerodynamic losses in drilled convergent-divergent nozzles and overall supersonic turbine performance. Supersonic aerodynamic effects and their influence on turbine performance were determined by employing the state-of-the-art commercial CFD code. Respective mesh sensitivity studies and domains architectures selection considerations are presented. The formalization of the studied nozzle parameters is discussed. The methods used for the determination of the parameters dependencies minimizing the number of CFD calculations are explained. The figures with visualizations of the critical results and numerical values are provided. The obtained loss models are described. The obtained loss models were incorporated into a turbine design and analysis program and then compared with experimental data. The utilization of the improved loss model provides the opportunity to design high-efficient supersonic turbines with conical drilled nozzles and satisfy specific design constraints with the use of nozzle arrangement parameters.
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