Semi-empirical wall pressure spectral modeling for zero and favorable pressure gradient flows

Thomson, Nicholas; Rocha, Joana

doi:10.1121/10.0012188

Cited by 3 publications

(4 citation statements)

References 35 publications

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“…Dimensional parameters [7,11,[14][15][16][17][18][19]22]: Additionally, datasets were sourced from experimental wind tunnel data previously collected by J. B. Blitterswyk [21] and N. Thompson [30] at Carleton University, resulting in a total of 14 unique wind tunnel datasets (which were divided into 12 for training and 2 for testing). Measurements were collected via a microphone array that stored wall pressure fluctuations, along with any data necessary to evaluate several key flow parameters (for example, U∞ and Re) [22].…”

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“…Experimental testing was performed in a closed-loop wind tunnel at Carleton University, fitted with an adjustable roof to ensure flow remained at a zero pressure gradient. The effects of background and machinery noise were minimized by installing acoustic foam in the test section of the wind tunnel [21,30]. Key parameters for each dataset are summarized below in Tables 1 and 2, but in general, they can be characterized as low subsonic speed and low Reynolds number.…”

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“…The resulting training dataset was of the size n = 23,372, while the testing dataset was n = 3718. [30]. † = From [21].…”

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The Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Boundary Layer Wall Pressure in Aircraft Using Machine Learning Regression Techniques

Huffman,

Rocha

2024

Aerospace

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Aircraft cabin noise poses a health risk for regular passengers and crew, being connected to a heightened risk of cardiovascular disease, hearing loss, and sleep deprivation. At cruise conditions, its most significant cause is random pressure fluctuations in the turbulent boundary layer of aircraft, and as such the derivation of an accurate model to predict the power spectral density of these fluctuations remains an important ongoing research topic. Early models (such as those by Lowson and Robertson) were derived by simplifying the governing equations, the Reynolds-averaged Navier Stokes equations, and solving for fluctuating pressure. Most subsequent equations were derived either by applying statistical and mathematical techniques to simplify the Robertson and Lowson models or by making modifications to address apparent shortcomings. Overall, these models have had varying success—most are accurate near the Mach and Reynolds numbers they were designed for, but less accurate under other conditions. In response to this shortcoming, Dominique demonstrated that a novel technique (machine learning, specifically artificial neural networking) could produce a model that is accurate under most flight conditions. This paper extends this research further by applying a different machine learning technique (nonlinear least squares regression analysis) and dimensional analysis to produce a new model. The resulting equation proved accurate under its design conditions of low airspeed (approximately 11 m/s) and low turbulent Reynolds number (approximately 850,000). However, a larger dataset with more diverse flight conditions would be required to make the model more generally applicable.

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The Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Boundary Layer Wall Pressure in Aircraft Using Machine Learning Regression Techniques

Huffman,

Rocha

2024

Aerospace

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“…In closed-jet wind tunnels, pressure gradients can be quickly modified using converging, diverging, or morphing walls. In 2022, Thomson used an eight-point adjustable ceiling in the test section of a closed-loop wind tunnel at Carleton University to achieve zero and favourable pressure gradients [31]. Likewise, Parthasarathy designed an electro-pneumatic actuation mechanism that could rapidly deform a test-section ceiling in the subsonic boundary layer wind tunnel at the University of Illinois to sequentially create favourable and adverse pressure gradients [32].…”

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Aerodynamic Characterization of a Closed-Loop, Semi-Open Jet Wind Tunnel using Experimental and Computational Methods

Denne

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Aerodynamic characterization literature generally resolves streamwise pressure gradients, velocity distributions, non-uniformity, and turbulence intensity; yet, evaluation of turbulence in the frequency domain and comparison of experimental measurements with transient computational results are less common. Thus, an in-depth aerodynamic characterization of a closed-loop, semi-open jet wind tunnel was completed using experimental and computational methods. The work aims to improve the characterization process by evaluating turbulence scales and comparing experimental and transient computational results. Velocity profiles were experimentally measured and compared with computational results. The facilities' dormant heat exchanger was also evaluated using simplified models. Agreement was observed between experimental and computational velocity profiles and turbulence intensity distributions. A 400 mm long, 140 mm high, and 300 mm wide testing envelope was revealed with competitive non-uniformities and turbulence intensities less than 1%. Boundary layer growth occurred at an approximate rate of 3 mm every 200 mm streamwise for all velocities along with a near-zero pressure gradient. Agreement was also observed between heat exchanger models, indicating adequate cooling capabilities. Results conclude that the test section's flow field is comparable to existing facilities.iii Acknowledgements I would like to express my gratitude for the supervision and guidance that I have received from my supervisor Dr. Joana Rocha. In times of frustration, you always encouraged me to persevere and had faith in my abilities. Thank you for your dedication. I thank all of the Mechanical and Aerospace Department staff for their technical and administrative aid. A special thank you to James Cann, Alex Proctor, and Aric Adcock for providing technical assistance, manufacturing, and an overall welcoming atmosphere. I would also like to thank Neil McFadyen for his support with the Clusters, remote access capabilities, and general information technology needs. I am grateful to have been given the opportunity to work with members of the

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Artificial neural networks and guided gene expression programming to predict wall pressure spectra beneath turbulent boundary layers

Kurhade,

Vadlamani,

Haridas

2023

Physics of Fluids

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This study evaluates the efficacy of two machine learning (ML) techniques, namely, artificial neural networks (ANNs) and gene expression programing (GEP), that use data-driven modeling to predict wall pressure spectra (WPS) underneath turbulent boundary layers. Different datasets of WPS from experiments and high-fidelity numerical simulations covering a wide range of pressure gradients and Reynolds numbers are considered. For both ML methods, an optimal hyperparameter environment is identified that yields accurate predictions. Despite a higher memory consumption, ANN models are faster to train and are much more accurate than the GEP models, yielding an order of magnitude lower logarithmic Mean Squared Error (lMSE) than GEP. Novel training schemes are devised to address the shortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise in the training data, (b) exploiting the low- and high-frequency trends to guide the GEP search, and (c) a stepped training strategy where the chromosomes are first trained on the canonical datasets, followed by the datasets with complex features. When compared to the baseline scheme, these training strategies accelerated convergence and resulted in models with superior accuracy (≈30% reduction in the median lMSE) and higher reliability (≈75% reduction in the spread of lMSE in the interquartile range). The final GEP models captured the complex trends of WPS across varying flow conditions and pressure gradients, surpassing the accuracy of Goody's model.

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Semi-empirical wall pressure spectral modeling for zero and favorable pressure gradient flows

Cited by 3 publications

References 35 publications

The Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Boundary Layer Wall Pressure in Aircraft Using Machine Learning Regression Techniques

The Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Boundary Layer Wall Pressure in Aircraft Using Machine Learning Regression Techniques

Aerodynamic Characterization of a Closed-Loop, Semi-Open Jet Wind Tunnel using Experimental and Computational Methods

Artificial neural networks and guided gene expression programming to predict wall pressure spectra beneath turbulent boundary layers

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