Collaborative optimization of turbo-expander impellers and guide vanes to mitigate flow-induced vibrations

When subjected to a high-flow gas impact, the impeller and guide vane are prone to vibration, jeopardizing equipment safety and stability. This study presents a collaborative optimization strategy for reducing the flow-induced vibration of the turbo-expander's impeller and guide vane. Parametric modeling of the impeller and guide vane profiles is conducted, followed by dimensionality reduction of parameters based on geometric characteristics. Flow-induced vibration arises from the complex interactions between the impeller and guide vane, where adjustments to one component inevitably influence the other due to intricate coupling. Traditional heuristic algorithms, constrained by numerous interacting design parameters, typically optimize individual structures rather than addressing the overall system performance. To overcome this limitation, this paper integrates tent chaotic mapping into the conventional particle swarm optimization algorithm, leveraging it to initialize the search space. This approach broadens the optimization scope for both components, enhances global search coverage, and improves system-wide performance. Moreover, through extensive optimization comparisons within collaborative optimization, the introduction of an adaptive t-distribution effectively balances the exploration of uncharted domains with the exploitation of known information, enabling more robust solutions to complex coupled problems. The proposed optimization framework allows for direct parameter model updates, minimizing errors associated with surrogate models and significantly improving optimization accuracy. Results demonstrate that the method successfully avoids premature convergence while maintaining efficient execution performance. Notably, the pressure pulsation amplitudes in the impeller and guide vane runners of the turbo expander were reduced by 50.5% and 37.3%, respectively, while the radial vibration acceleration amplitude of the impeller decreased by 74.3%.

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Publication Details

Journal
Physics of Fluids
Published
2025-03-01
DOI
https://doi.org/10.1063/5.0257754
Citations
2
Primary Topic
Turbomachinery Performance and Optimization
Type
article
Field-Weighted Citation Impact
9.99

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article

Collaborative optimization of turbo-expander impellers and guide vanes to mitigate flow-induced vibrations

Zheming Tong, Shuiguang Tong, Keping Chen, Xiangkun Elvis Cao et al.
2 citations
Physics of Fluids
Turbomachinery Performance and Optimization
9.99
article

Collaborative optimization of turbo-expander impellers and guide vanes to mitigate flow-induced vibrations

Zheming Tong, Shuiguang Tong, Keping Chen, Xiangkun Elvis Cao, Sheng He
article en
2 citations

Abstract

When subjected to a high-flow gas impact, the impeller and guide vane are prone to vibration, jeopardizing equipment safety and stability. This study presents a collaborative optimization strategy for reducing the flow-induced vibration of the turbo-expander's impeller and guide vane. Parametric modeling of the impeller and guide vane profiles is conducted, followed by dimensionality reduction of parameters based on geometric characteristics. Flow-induced vibration arises from the complex interactions between the impeller and guide vane, where adjustments to one component inevitably influence the other due to intricate coupling. Traditional heuristic algorithms, constrained by numerous interacting design parameters, typically optimize individual structures rather than addressing the overall system performance. To overcome this limitation, this paper integrates tent chaotic mapping into the conventional particle swarm optimization algorithm, leveraging it to initialize the search space. This approach broadens the optimization scope for both components, enhances global search coverage, and improves system-wide performance. Moreover, through extensive optimization comparisons within collaborative optimization, the introduction of an adaptive t-distribution effectively balances the exploration of uncharted domains with the exploitation of known information, enabling more robust solutions to complex coupled problems. The proposed optimization framework allows for direct parameter model updates, minimizing errors associated with surrogate models and significantly improving optimization accuracy. Results demonstrate that the method successfully avoids premature convergence while maintaining efficient execution performance. Notably, the pressure pulsation amplitudes in the impeller and guide vane runners of the turbo expander were reduced by 50.5% and 37.3%, respectively, while the radial vibration acceleration amplitude of the impeller decreased by 74.3%.

Physics of FluidsVol. 37(3)
Expand (United States) (US), Massachusetts Institute of Technology (US), Zhejiang University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 3%
Turbomachinery Performance and Optimization
9.99
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