Investigation on the Flow Field Entropy Structure of Non-Synchronous Blade Vibration in an Axial Turbocompressor

Zhang, Mingming; Hou, Anping

doi:10.3390/e22121372

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…The highly accurate data is the basis for the reduction modeling of the aerodynamic force on the blade. In this paper, the reduction approach is mainly driven by the data achieved from the fluid-structure coupling computation [21]. The research target is a 1.5- Compared to the proposed FDDMD, the improvement of flow reduction is very limited by the amplitude optimization process of SPDMD.…”

Section: Data Collectionmentioning

confidence: 99%

“…The highly accurate data is the basis for the reduction modeling of the aerodynamic force on the blade. In this paper, the reduction approach is mainly driven by the data achieved from the fluid-structure coupling computation [21]. The research target is a 1.5-stage axial compressor displayed in Figure 14 with part of the computation domain, including struts, inlet guide vanes, rotor, and stator.…”

Section: Application Of Fractal Dimension On Aerodynamic Force Reduct...mentioning

confidence: 99%

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Reduced Order Modeling of System by Dynamic Modal Decom-Position with Fractal Dimension Feature Embedding

Zhang,

Bai,

Xia

et al. 2024

Fractal Fract

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The balance between accuracy and computational complexity is currently a focal point of research in dynamical system modeling. From the perspective of model reduction, this paper addresses the mode selection strategy in Dynamic Mode Decomposition (DMD) by integrating an embedded fractal theory based on fractal dimension (FD). The existing model selection methods lack interpretability and exhibit arbitrariness in choosing mode dimension truncation levels. To address these issues, this paper analyzes the geometric features of modes for the dimensional characteristics of dynamical systems. By calculating the box counting dimension (BCD) of modes and the correlation dimension (CD) and embedding dimension (ED) of the original dynamical system, it achieves guidance on the importance ranking of modes and the truncation order of modes in DMD. To validate the practicality of this method, it is applied to the reduction applications on the reconstruction of the velocity field of cylinder wake flow and the force field of compressor blades. Theoretical results demonstrate that the proposed selection technique can effectively characterize the primary dynamic features of the original dynamical systems. By employing a loss function to measure the accuracy of the reconstruction models, the computed results show that the overall errors of the reconstruction models are below 5%. These results indicate that this method, based on fractal theory, ensures the model’s accuracy and significantly reduces the complexity of subsequent computations, exhibiting strong interpretability and practicality.

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Section: Data Collectionmentioning

confidence: 99%

“…The highly accurate data is the basis for the reduction modeling of the aerodynamic force on the blade. In this paper, the reduction approach is mainly driven by the data achieved from the fluid-structure coupling computation [21]. The research target is a 1.5-stage axial compressor displayed in Figure 14 with part of the computation domain, including struts, inlet guide vanes, rotor, and stator.…”

Section: Application Of Fractal Dimension On Aerodynamic Force Reduct...mentioning

confidence: 99%

Reduced Order Modeling of System by Dynamic Modal Decom-Position with Fractal Dimension Feature Embedding

Zhang,

Bai,

Xia

et al. 2024

Fractal Fract

Self Cite

View full text Add to dashboard Cite

show abstract

“…The research object was a 1.5 stage axial compressor, including struts, inlet guide vanes, first stage rotor and stator. Detailed introductions for the rig are presented in Zhang [34].…”

Section: Data Collection For Machine Learningmentioning

confidence: 99%

“…While considering the fluid-structure interaction, the blade vibration was computed under the response to the flow. The detail simulation process of the compressor is described in reference [34]. The structural equations for mechanical blade were solved by the finite element method.…”

Section: Data Collection For Machine Learningmentioning

confidence: 99%

Study on the Intelligent Modeling of the Blade Aerodynamic Force in Compressors Based on Machine Learning

2021

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In order to obtain the aerodynamic loads of the vibrating blades efficiently, the eXterme Gradient Boosting (XGBoost) algorithm in machine learning was adopted to establish a three-dimensional unsteady aerodynamic force reduction model. First, the database for the unsteady aerodynamic response during the blade vibration was acquired through the numerical simulation of flow field. Then the obtained data set was trained by the XGBoost algorithm to set up the intelligent model of unsteady aerodynamic force for the three-dimensional blade. Afterwards, the aerodynamic load could be gained at any spatial location during blade vibration. To evaluate and verify the reliability of the intelligent model for the blade aerodynamic load, the prediction results of the machine learning model were compared with the results of Computation Fluid Dynamics (CFD). The determination coefficient R2 and the Root Mean Square Error (RMSE) were introduced as the model evaluation indicators. The results show that the prediction results based on the machine learning model are in good agreement with the CFD results, and the calculation efficiency is significantly improved. The results also indicate that the aerodynamic intelligent model based on the machine learning method is worthy of further study in evaluating the blade vibration stability.

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Investigation on the Mechanism and Parametric Description of Non-Synchronous Blade Vibration

Zhang

Hou

Han

2021

Entropy

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In order to explore the mechanism during the process of the non-synchronous vibration (NSV), the flow field formation development is investigated in this paper. Based on the fluid–structure interaction method, the vibration of rotor blades is found to be in the first bending mode with a non-integral order (4.6) of the rotation speed. Referring to the constant inter blade phase angle (IBPA), the appearances of frequency-locking and phase-locking can be identified for the NSV. A periodical instability flow emerges in the tip region with the mixture of separation vortex and tip leakage flow. Due to the nonlinearities of fluid and structure, the blade vibration exhibits a limit cycle oscillation (LCO) response. The separation vortex presenting a spiral structure propagates in the annulus, indicating a pattern as modal oscillation. A flow induced vibration is initiated by the spiral vortex in the tip. The large pressure oscillation caused by the movement of the spiral vortex is regarded as a main factor for the presented NSV. As the oscillation of blade loading occurs with blade rotating pass the disturbances, the intensity of the reverse leakage flow in adjacent channels also plays a crucial role in the blade vibration.

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Investigation on the Flow Field Entropy Structure of Non-Synchronous Blade Vibration in an Axial Turbocompressor

Cited by 3 publications

References 30 publications

Reduced Order Modeling of System by Dynamic Modal Decom-Position with Fractal Dimension Feature Embedding

Reduced Order Modeling of System by Dynamic Modal Decom-Position with Fractal Dimension Feature Embedding

Study on the Intelligent Modeling of the Blade Aerodynamic Force in Compressors Based on Machine Learning

Investigation on the Mechanism and Parametric Description of Non-Synchronous Blade Vibration

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