RBF-POD reduced-order modeling of flow field in the curved shock compression inlet

Sun, Fei; Su, Weiyi; Wang, Mou-Yuan; Wang, Ren‐Jie

doi:10.1016/j.actaastro.2021.04.039

Cited by 27 publications

(7 citation statements)

References 20 publications

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“…38 Therefore, it is widely used in research on fluid science and other fields. 39,40 Meanwhile, RBF-GA optimization algorithm has been verified and applied to the field of aerodynamics. 41 The honeycomb arrangement angle θ is set as the design variable, and the averaged total pressure loss coefficient ðω) at 130% C ax is designated as the objective function.…”

Section: Mathematical Model and Optimizationmentioning

confidence: 99%

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A study of angle parametric optimization of the composite honeycomb on turbine blade tip

Kong,

Ning,

Sun

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part G:

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To reduce the secondary flow loss of the high-pressure turbine caused by the tip leakage flow, the design of the composite honeycomb tip has been experimentally and numerically examined in this study. The arrangement angle of the composite honeycomb is defined and optimized using the radial basis function and genetic algorithm. The tip flow field of the high-pressure turbine blade with a 2.128 mm clearance is simulated by CFX 18.0, and the arrangement angle of the composite honeycomb is optimized to obtain a lower loss. Moreover, the flat tip, basic honeycomb tip, and optimized honeycomb tip are tested in a low-speed cascade test facility. The results show that the vortices and radial velocity induced by the honeycomb structure cavity increase the kinetic energy loss of the leakage flow and reduce the leakage flow rate. Compared with the basic honeycomb, the optimized honeycomb has a supplemental blocking effect on the tip leakage flow by reducing the crosswise pressure gradient in the blade tip. The optimized honeycomb also changes the outlet angle of the leakage flow in the tip clearance, reduces the leakage vortex, and the total pressure loss is further reduced by 2.5%.

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Section: Mathematical Model and Optimizationmentioning

confidence: 99%

Section: Mathematical Model and Optimizationmentioning

confidence: 99%

A study of angle parametric optimization of the composite honeycomb on turbine blade tip

Kong,

Ning,

Sun

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…The RBF surrogate model, as a robust and accurate interpolation method, is suitable for fitting functions with a high nonlinear degree and is widely used in research on fluids and other fields. [23][24][25][26][27][28][29][30] In this study, the RBF and GA are combined to supplement CFD; the reduced frequency and free-stream turbulence intensity are taken as variables, the profile loss coefficient is taken as the objective function for optimization, and the experimental results are used for verification. This approach overcomes the poor engineering applicability caused by optimizing a single inlet parameter in previous studies or the lack of a globally optimal parameter due to partial traversal optimization.…”

Section: Overviewmentioning

confidence: 99%

“…As mentioned above, the RBF is a widely used alternative model [23][24][25][26][27][28][29][30] with a specific concept similar to minimizing the total surface curvature of a fitted surface passing through sample value points. Combining the RBF with machine learning can be used to predict the extreme value of a reduced-order model.…”

Section: Brief Description Of the Rbfmentioning

confidence: 99%

Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Sun,

Huang,

Kang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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In a high-altitude cruising state, boundary layer separation exists in high-lift low-pressure turbines, and inflow conditions corresponding to different blade designs can directly affect the working efficiency of low-pressure turbines. In particular, the reduced frequency of wake and free-stream turbulence intensity in an inlet flow can greatly influence boundary layer separation and transition development. In this paper, the influence of different inflow turbulence intensities and reduced wake frequencies on the development of suction surface boundary layers in high-lift low-pressure turbines under the influence of upstream wakes is studied by numerical simulations and experiments. Due to the combination of inflow free-stream turbulence intensity and reduced wake frequency, many inflow conditions can be chosen in the design process, and the unsteady influence of upstream wakes complicates the boundary layer flow. In this paper, an RBF (radial basis function)-GA (genetic algorithm) machine learning method is used to explore the optimal inlet conditions corresponding to the minimum profile loss of the Pak-B profile. The search region of the free-stream turbulence intensity is 2%–4%, and the reduced frequency of the wake is changed by changing the flow coefficient, whose variation range is 0.7–1.3. It is found that the RBF-GA machine learning method can attain an inflow condition with a lower profile loss while using the same amount of computation and effort.

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“…By constructing a low-dimensional and parsimonious dynamical system from existing data, the ROM can considerably reduce the complexity of the original system, ultimately resulting in improved computational efficiency . At present, many ROMs have been proposed, among which proper orthogonal decomposition (POD) is the most representative. , − POD originated from the statistical analysis of vector data and has been widely used in data dimensionality reduction, flow field analysis, and other application scenarios . The research on POD-based ROM represents a new and popular trend in utilizing hybrid machine learning methods to reduce the complexity of the network structure and flow.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Reduced-Order Model for Bubbling Fluidized Beds

Li,

Wang,

Kong

et al. 2024

Ind. Eng. Chem. Res.

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Simulation of dense gas−solid flow in fluidized beds is a computationally intensive procedure, and emerging speedup simulation methods are still unsatisfactory. This work developed a pioneering data-driven reduced-order model (ROM) for efficient modeling of dense gas−solid flow in bubbling fluidized beds (BFB) by integrating the proper orthogonal decomposition (POD) and the radial basis function neural network (RBFNN). Specifically, this study extracts the fundamental eigenvectors of the gas−solid flow process and constructs a prediction function for the corresponding eigenvector coefficients. The effectiveness of this ROM is conclusively assessed by comparing it with the full-order model (FOM) in terms of simulated results and performance criteria. The results indicate that the 10-bases-ROM and 64-bases-ROM exhibit 50 and 90% of the energy, respectively, and achieve flow field reconstruction accuracy of 50 and 90%. Moreover, compared to the FOM, the 10-bases-ROM and the 64-bases-ROM demonstrate 700-fold and 120-fold increases in simulation efficiency, respectively. These findings indicate that the proposed model has the potential to be an effective tool for industrial engineering process predictions in real time.

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RBF-POD reduced-order modeling of flow field in the curved shock compression inlet

Cited by 27 publications

References 20 publications

A study of angle parametric optimization of the composite honeycomb on turbine blade tip

A study of angle parametric optimization of the composite honeycomb on turbine blade tip

Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Data-Driven Reduced-Order Model for Bubbling Fluidized Beds

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