Comparison of three-dimensional density distribution of numerical and experimental analysis for twin jets

Lee, Chungil; Ozawa, Yuta; Haga, T.; Nonomura, Taku; Asai, Keisuke

doi:10.1007/s12650-021-00765-z

Cited by 15 publications

(5 citation statements)

References 33 publications

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“…In the field of experimental aerodynamics, several methods can be used for fluid field visualization, including a method based on a pressure-sensitive paint for visualizing the pressure on the surface of an object [31], a backgroundoriented schlieren method for quantitatively visualizing the gradient of density fields, and particle image velocimetry for visualizing velocity fields [32]. Generally, the obtained data have high spatial resolution and low temporal resolution because of the limitations of high-speed cameras.…”

Section: ) Analysis Proceduresmentioning

confidence: 99%

“…Based on these eigenvalue pairs, the state matrix A d is constructed. Here, the initial value of the state variable x d,1 is set to a random number, and the time development of the state variable x d,t is calculated using (32). The unitary matrix, which is calculated using the QR decomposition of a matrix of random numbers, is used as the output matrix C d .…”

Section: B Noise Robustness Evaluation Of Sparsity-promoting Dynamic ...mentioning

confidence: 99%

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Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition

et al. 2022

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In the present study, we organize the existing sparsity-promoting dynamic mode decomposition (DMDsp) in terms of noise robustness, propose faster optimization algorithm for DMDsp, and evaluate its characteristics. Two kinds of DMDsp, namely system-based DMDsp (sDMDsp) and observationbased DMDsp (oDMDsp), combined with three kinds of optimization algorithm, namely the fast iterative shrinkage thresholding algorithm (FISTA), the alternating direction method of multipliers (ADMM), and a greedy algorithm, are investigated. For both sDMDsp and oDMDsp, FISTA yields the shortest processing time. The processing time for sDMDsp with FISTA is shorter than that for oDMDsp with FISTA. The original data reconstruction errors for sDMDsp and oDMDsp are similar among the three optimization algorithms. The noise robustness for sDMDsp and oDMDsp is evaluated. sDMDsp and oDMDsp have similar robustness to observation noise, except for a system with large system and observation noise, for which oDMDsp outperforms sDMDsp.INDEX TERMS Data analysis, Fluid dynamics, Modal analysis, Optimization method, System dynamics.

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Section: ) Analysis Proceduresmentioning

confidence: 99%

Section: B Noise Robustness Evaluation Of Sparsity-promoting Dynamic ...mentioning

confidence: 99%

Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition

et al. 2022

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“…Therefore, there are no high-speed cameras that can visualize the supersonic flow with a sufficiently high sampling rate with maintaining the full image resolution, though the corresponding PIV spatial resolution is acceptable but not significantly high due to its interrogation window. Here, it should be noted that although the spatial resolution substantially decreases due to the interrogation window when applying the image correlation such as the particle image velocimetry (PIV) or background oriented schlieren (BOS) (Lee et al, 2021;Tan et al, 2019;Edgington-Mitchell et al, 2018), the improvement of the spatial resolution for the image correlation is out of scope in the present study while there are some studies regarding this topic. For example, one of the methods for the improvement of spatial resolution is the single-pixel ensemble correlation method, but its application is limited to the time-averaged velocity field (Westerweel et al, 2004;Scharnowski et al, 2012;Ozawa et al, 2020a;Nonomura et al, 2021a).…”

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confidence: 92%

Spatiotemporal Superresolution Measurement based on POD and Sparse Regression applied to a Supersonic Jet measured by PIV and Near-field Microphone

Ozawa,

Nagata,

Nonomura

2022

Preprint

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The present study proposed the framework of the spatiotemporal superresolution measurement based on the and sparse regression with dimensionality reduction using the proper orthogonal decomposition (POD). The non-time-resolved particle image velocimetry (PIV) and the time-resolved near-field acoustic measurements using microphones were simultaneously performed for a Mach 1.35 supersonic jet. POD is applied to PIV and microphone data matrices and the sparse linear regression model of the reduced-order data is calculated using the least absolute shrinkage and selection operator regression. The effects of the hyperparameters of the superresolution measurement were quantitatively evaluated through randomized cross-validation. The superresolved velocity field indicated the smooth convection of the velocity fluctuations associated with the screech tone, while the convection of the large-scale structures at the downstream side was not observed. The proposed framework can reconstruct the unsteady fluctuation with multiple frequency phenomena, although the reconstruction is limited to the phenomena that is associated with the microphone output.

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“…The experimental techniques in the aeroacoustic field have evolved alongside improvements in the specifications of measurement devices and the development of superior analytic algorithms. For example, schlieren, particle image velocimetry (PIV), and pressure-sensitive paint (PSP) methods can visualize density gradient fields [21][22][23][24], velocity fields [25][26][27][28][29], and surface pressure fields [30], respectively. These experimental techniques are productive and enable us to easily conduct parametric studies.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of Ideally Expanded Supersonic Jet Using RANS Simulation for High-Resolution PIV Data

Ozawa,

Nonomura

2024

Aerospace

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Data assimilation using particle image velocimetry (PIV) and Reynolds-averaged Navier–Stokes (RANS) simulation was performed for an ideally expanded supersonic jet flying at a Mach number of 2.0. The present study aims to efficiently reconstruct all the physical quantities in the aeroacoustic fields that match well with a realistic, experimentally obtained flow field. The two-dimensional, two-component PIV measurement was applied to the jet axis plane, and the time-averaged velocity field was obtained using single-pixel ensemble correlation. Two-dimensional axisymmetric RANS simulation using the Menter shear stress transport (SST) model was also performed, and the parameters of the SST model were optimized via data assimilation using the ensemble Kalman filter. The standard deviation of the observation noise σ, which is a parameter of the ensemble Kalman filter, is estimated by the previously proposed method (Nakamura et al., Low-Grid-Resolution-RANS-Based Data Assimilation of Time-Averaged Separated Flow Obtained by LES. Int. J. Comp. Fluid. Dyn., 2022), and its effectiveness was investigated for the first time. This method effectively estimated the magnitude of σ at each generation without tuning the hyperparameters. The assimilated flow fields exhibited similar flow structures observed in PIV such as the potential core length or shear layer. Therefore, the present framework can be used to estimate time-averaged full flow fields that match well with experimentally observed flow fields, and has the potential to construct a database for the Navier-Stokes-based stability analysis that requires a full flow field.

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Comparison of three-dimensional density distribution of numerical and experimental analysis for twin jets

Cited by 15 publications

References 33 publications

Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition

Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition

Spatiotemporal Superresolution Measurement based on POD and Sparse Regression applied to a Supersonic Jet measured by PIV and Near-field Microphone

Data Assimilation of Ideally Expanded Supersonic Jet Using RANS Simulation for High-Resolution PIV Data

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