Machine-learnt topology of complex tip geometries in gas turbine rotors
| Autor: | Alessandro Corsini, Sergio Lavagnoli, Gino Angelini |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Gas turbines
Work (thermodynamics) Computer science 020209 energy Mechanical Engineering Energy Engineering and Power Technology aeroderivative gas turbines gas turbine aero-thermodynamics turbomachinery flow Statistical model Topology (electrical circuits) 02 engineering and technology Topology 01 natural sciences Turbine 010305 fluids & plasmas Data-driven 0103 physical sciences 0202 electrical engineering electronic engineering information engineering |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. 235:383-392 |
| ISSN: | 2041-2967 0957-6509 |
| DOI: | 10.1177/0957650920948413 |
| Popis: | The work presents a data driven based strategy to develop a new statistical model of complex tip shape for high-pressure turbine stages exploiting an existing dataset of optimized squealer-like rotor tips. Using the exploratory data analysis (EDA), a set of statistical methods were used to improve the quality of previous CFD-based optimization dataset, as an aid in reducing outliers, data skewness and avoiding the presence of redundant information. The pre-processed dataset was analyzed by unsupervised learning method in order to gain insight on the correlation between tip geometry and single stage axial turbine performance. Utilizing the Principal Component Analysis (PCA), we developed a new continuous, dimensionality-reduced parametrization which allows overcoming the limitations of discrete topology approaches. The novel statistical shape model, coupled with genetic operators into a NSGA-II optimization strategy, was used to explore the design space of optimal solutions generating new designs to enrich the available Pareto front in terms of aero-thermodynamic performance metrics. Two metamodels for performance prediction, respectively based on Artificial Neural Network (ANN) and Gradient Boosting Regressor (GBR) have been developed in order to guide the Pareto front exploration avoiding the use of computationally intensive CFD simulations. New tip designs were carried out to spread the previous optimal front and, successively, aiming to design individuals able to reduce macroscopic not uniformity of the flow keeping optimal aerodynamic performance. |
| Databáze: | OpenAIRE |
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