Experience of multi-objective optimization of axial compressor stage

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Abstract

BACKGROUND: Developing a universal optimization approach can reduce the time needed to improve compressor geometry. Therefore, the issue of implementing this approach when solving similar optimization problems is a relevant one.

AIM: Development and testing of the approach of setting up spatial multi-objective optimization problem for the compressor stage.

MATERIALS AND METHODS: The formation of the approach to optimization tasks is based on the experience of research organizations and the methods used in compressor engineering. To test this approach, the IOSO algorithm is used in conjunction with the AutoGrid5 mesh generator and the Ansys CFX solver.

RESULTS: At this study, a general approach was developed to formulate a multi-objective optimization problem, which serves as the basis for this entire project. A complete cycle of verification and validation was performed for the mathematical model of the studied object, which was built in the ANSYS CFX system. A method for creating a parametric model of vanes and flow parts of a stage is described. Two approaches of the optimization problem are presented: using low-Reynolds (SST) and high-Reynolds (k-ε) turbulence models, in order to assess the qualitative impact of these models on the results. For the convenience of data processing, a program was written in Python. A complete list of the object functions, optimization parameters, constraints, and assumptions used in the study is provided. In total, six different geometries of the study object were considered. For each variant, a sample analysis was performed in each of the five design sections. The detailed description of these analyses is omitted from this work. Integral characteristics of each proposed variant were built. Based on the results of the analysis, the most suitable variant was selected, both in terms of geometry and problem formulation.

CONCLUSION: As the study result, the developed approach has been tested. The disadvantages of the used method of setting up multi-objective optimization problem and methods for their solution in subsequent works are noted.

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About the authors

Anton S. Zolotukhin

Peter the Great St. Petersburg Polytechnic University; Power Machines

Author for correspondence.
Email: zolotuhinant@yandex.ru
ORCID iD: 0009-0009-3028-8512
SPIN-code: 7756-7369

Postgraduate of the Higher School of Power Engineering, 3rd cat. design engineer of the Simulations Sector of the Compressor Department

Russian Federation, Saint Petersburg; Saint Petersburg

Lyubov N. Marenina

Peter the Great St. Petersburg Polytechnic University

Email: marenina_ln@mail.ru
ORCID iD: 0000-0001-9380-9754
SPIN-code: 5842-1771

Cand. Sci. (Engineering), Assistant professor of the Higher School of Power Engineering

Russian Federation, Saint Petersburg

Aleksander A. Drozdov

Peter the Great St. Petersburg Polytechnic University

Email: a_drozdi@mail.ru
ORCID iD: 0000-0002-3808-7098
SPIN-code: 6030-5685

Dr. Sci. (Engineering), Professor of the Higher School of Power Engineering

Russian Federation, Saint Petersburg

Aleksander G. Nikiforov

Smolensk State Agricultural Academy

Email: nikiforof@mail.ru
ORCID iD: 0009-0006-6890-2889
SPIN-code: 9236-5572

Dr. Sci. (Engineering), Professor of the Mechanization Department

Russian Federation, Smolensk

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Supplementary files

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2. Fig. 1. The proposed diagram of the approach to setting up multi-objective optimization problem.

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3. Fig. 2. Parametric model of the axial compressor stage.

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4. Fig. 3. Comparison of the characteristics of the NASA Stage 37: initial (а) and parametric (b) models in two versions.

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5. Fig. 4. The diagram of the proposed formulation of the multi-criteria optimization problem for the NASA Stage 37.

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6. Fig. 5. A point cloud of alternative options obtained as a result of the NASA Stage 37 optimization: using the SST turbulence model: а, in  coordinates; b, in  coordinates; and using k-ε turbulence models: c, in  coordinates; d, in  coordinates.

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7. Fig. 6. Pareto frontiers for formulation with the SST (a) and the k-ε (b) turbulence models

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8. Fig. 7. Solid-state models of working and guiding vanes, optimized for the SST and the k-ε turbulence models.

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9. Fig. 8. Post-processing of rotor vane (a), (b) and stator vane (c), (d) for the k–ε turbulence model.

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10. Fig. 9. Post-processing of stator blade at specific section drel = 0,25: a: comparison of alternative profiles: b: in the form ω = f(CL,2); c: in the form ω = f(CL,m); d: in the form ω = f(Cs); e: in the form ω = f(Г).

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11. Fig. 10. Comparison of the integral characteristics of alternative options for two approaches: the SST: (a) и (b); the k–ε: (с) и (d).

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