An overall blockage attenuation-based aerodynamic performance and stability design optimization method for transonic axial flow compressors

Wang, Xuesong; Sun, Jinju; Benini, Ernesto; Song, Peng; He, Youwei

doi:10.1108/hff-07-2022-0437

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…The working fluid is perfect air. For turbulence model, the one-equation SA turbulence model (Wang et al , 2023; Spalart and Allmaras, 1992) is used. Furthermore, the multigrid technique is also used to accelerate to a converged flow-field solution.…”

Section: Study Case and Numerical Approachmentioning

confidence: 99%

A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

Shi,

Lu,

Song

et al. 2023

HFF

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Purpose In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions. Design/methodology/approach This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss. Findings The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%. Originality/value The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

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Section: Study Case and Numerical Approachmentioning

confidence: 99%

A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

Shi,

Lu,

Song

et al. 2023

HFF

View full text Add to dashboard Cite

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Performance Optimization of an Axial Compressor Using a Novel Multifidelity Surrogate Model Based on Flow Field Extraction

Liu,

Gong,

et al. 2024

Journal of Engineering for Gas Turbines and Power

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During the utilization of efficient optimization algorithms for axial compressors, the construction of a precise performance prediction surrogate model stands as a pivotal step. To reduce the cost of constructing the surrogate model while ensuring prediction accuracy, a novel multi-fidelity surrogate model based on flow field extraction (FFMFS) is proposed in this paper. In constructing FFMFS, two sets of samples with different fidelity are employed for model training, and six important flow field variables in axial compressors are extracted to modify the performance deviation between low-fidelity and high-fidelity results. Based on the proposed FFMFS, the aerodynamic performance of a 1.5-stage subsonic axial compressor is optimized, and the numerical method used in the optimization is validated on a 3.5-stage axial compressor test bench. During optimization, adjustments are made to the rotor blade profile, taking into account a total of 28 design variables and 6 objective functions. The FFMFS constructed for this compressor demonstrates a high prediction accuracy with a value of 0.96, while also significantly reducing the sample generation cost. The optimization results show that the compressor efficiency and pressure ratio are significantly improved across the entire operating range. As a result of adjusting the rotor blade profile, the flow loss inside the compressor is evidently reduced. This work provides a new framework for constructing MFS with flow field information of axial compressors.

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An overall blockage attenuation-based aerodynamic performance and stability design optimization method for transonic axial flow compressors

Cited by 2 publications

References 52 publications

A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

A data-driven flow loss prediction model for the blade hub region of a boundary layer ingestion fan rotor

Performance Optimization of an Axial Compressor Using a Novel Multifidelity Surrogate Model Based on Flow Field Extraction

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