Numerical noise prediction and source identification of a realistic landing gear

Ricciardi, Túlio Rodarte; Wolf, William; Moffitt, Nicholas J.; Kreitzman, Jordan R.; Bent, Paul

doi:10.1016/j.jsv.2021.115933

Cited by 16 publications

(6 citation statements)

References 18 publications

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“…The grid shown in Fig. 4(a) has a more pronounced stretching towards the farfield boundaries, which is typical of airframe noise predictions of complex configurations (see [15], for example). On the other hand, Fig.…”

Section: B Airfoil Trailing Edge Noise Due To Vortex Sheddingmentioning

confidence: 78%

“…This work is motivated by CFD simulations from a realistic landing gear performed modeling only the half bottom of the aircraft fuselage [15]. Hence, in this previous analysis, the aeroacoustic predictions had to employ incomplete FWH surfaces.…”

Section: Discussionmentioning

confidence: 99%

“…The motivation for the current study regards incomplete FWH surfaces, where a point of concern has been raised during the analysis of a realistic nose landing gear where only the fuselage half bottom was simulated in order to save computational resources [15]. In this case, the solid FWH surface was not entirely closed due to geometrical constraints.…”

mentioning

confidence: 99%

See 2 more Smart Citations

On the application of incomplete FWH surfaces for aeroacoustic predictions

Ricciardi¹,

Wolf²,

Spalart³

2021

Preprint

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Section: B Airfoil Trailing Edge Noise Due To Vortex Sheddingmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

On the application of incomplete FWH surfaces for aeroacoustic predictions

Ricciardi¹,

Wolf²,

Spalart³

2021

Preprint

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“…We approximate the Euler equations using a finite volume method on a cartesian mesh. We employ a second-order monotonic upstream schemes for conservation laws (MUSCL) 42 approach with Roe flux 43 and minmod limiter to spatially semi-discretize Equation (1). Afterwards, we integrate the resulting system of ODEs using a second-order BDF scheme defined by the coefficients a 0 = 1∕3, a 1 = −4∕3, a 2 = 1 and 𝛽 = 2∕3.…”

Section: The Euler Equations Of Gas Dynamicsmentioning

confidence: 99%

An adaptive, training‐free reduced‐order model for convection‐dominated problems based on hybrid snapshots

Zucatti,

Zahr

2023

Numerical Methods in Fluids

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The vast majority of reduced‐order models (ROMs) first obtain a low dimensional representation of the problem from high‐dimensional model (HDM) training data which is afterwards used to obtain a system of reduced complexity. Unfortunately, convection‐dominated problems generally have a slowly decaying Kolmogorov ‐width, which makes obtaining an accurate ROM built solely from training data very challenging. The accuracy of a ROM can be improved through enrichment with HDM solutions; however, due to the large computational expense of HDM evaluations for complex problems, they can only be used parsimoniously to obtain relevant computational savings. In this work, we exploit the local spatial coherence often exhibited by these problems to derive an accurate, cost‐efficient approach that repeatedly combines HDM and ROM evaluations without a separate training phase. Our approach obtains solutions at a given time step by either fully solving the HDM or by combining partial HDM and ROM solves. A dynamic sampling procedure identifies regions that require the HDM solution for global accuracy and the reminder of the flow is reconstructed using the ROM. Moreover, solutions combining both HDM and ROM solves use spatial filtering to eliminate potential spurious oscillations that may develop. We test the proposed method on inviscid compressible flow problems and demonstrate speedups up to a factor of five.

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“…Consequently, the conventional band-pass filter cannot handle noises in this condition for refining signal [18][19][20]. In view of this, the Kalman filter is employed to refine signals for dealing with noise interference due to its robust performance for the speed fluctuation and characteristic of excellent tracking signal [21][22][23].…”

Section: Refining Signal By Kalman Filtermentioning

confidence: 99%

Optimal Placement of Sensors Based on Data Fusion for Condition Monitoring of Pulley Group under Speed Variation Condition

et al. 2022

Machines

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Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal structure, sensor placement for acquiring optimal monitoring points is still a challenging task. Therefore, a novel sensor optimization method based on data fusion is proposed. In this method, the Kalman filter is firstly used to refine the collected signal for dealing with the variable noises. Subsequently, the variable periodicity strength of the signal is calculated to recognize the non-stationary characteristics of the measured signal. A data fusion technique based on maximum likelihood estimation (MLE) is then introduced to estimate sensitive components from the multi-source sensor signals for finding out optimal sensor placement points. The method is validated experimentally on a test rig of the pulley group with variable speed conditions. Analysis results show that the proposed method can recognize the optimal sensor placement points for the pulley group.

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Numerical noise prediction and source identification of a realistic landing gear

Cited by 16 publications

References 18 publications

On the application of incomplete FWH surfaces for aeroacoustic predictions

On the application of incomplete FWH surfaces for aeroacoustic predictions

An adaptive, training‐free reduced‐order model for convection‐dominated problems based on hybrid snapshots

Optimal Placement of Sensors Based on Data Fusion for Condition Monitoring of Pulley Group under Speed Variation Condition

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