Design and Characterization of a High-Speed Subsonic Aeroacoustic Wind Tunnel

Giardino, Frank; Rocha, Joana

doi:10.2514/1.c035113

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Lowson: Is inaccurate under most frequencies. At low frequencies, it tended to underestimate PSD, while it overestimated PSD at high frequencies [7,26]. 2.…”

Section: Models and Methodsmentioning

confidence: 93%

“…Goody: At subsonic speeds, this model is accurate at both high and low Reynolds numbers [21,26]. At supersonic airspeeds, the model is less accurate [27].…”

mentioning

confidence: 97%

“…Efimtsov: This model tends to be accurate if the Reynolds number is high, though it does become inaccurate at extremely high airspeeds (Mach 4 and 8) [17,21,27]. It also tends to be inaccurate at low Reynolds numbers [26]. 5.…”

mentioning

confidence: 99%

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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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“…Lowson: Is inaccurate under most frequencies. At low frequencies, it tended to underestimate PSD, while it overestimated PSD at high frequencies [7,26]. 2.…”

Section: Models and Methodsmentioning

confidence: 93%

“…Goody: At subsonic speeds, this model is accurate at both high and low Reynolds numbers [21,26]. At supersonic airspeeds, the model is less accurate [27].…”

mentioning

confidence: 97%

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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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“…Lowson: Produces the most inconsistent results of all models discussed in Subsection 3.1.1. At low frequencies, regardless of Mach number and Reynolds number, the Lowson model tends to underestimate PSD, while overestimating it at high frequencies [6,59].…”

Section: Accuracy and Shortcomings Of Existing Modelsmentioning

confidence: 94%

Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Flow Wall Pressure Fluctuations Using Machine Learning Regression Techniques

Huffman

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Aircraft cabin noise is a significant contributor to health risks in regular air travellers and crew, being associated with an elevated risk of cardiovascular disease, hearing loss, and sleep deprivation. At cruise conditions, the noise is primarily caused by random pressure fluctuations in the aircraft turbulent boundary layer, and as such the search for an accurate empirical model to predict these fluctuations is an important ongoing research topic. The earliest models by Lowson and Robertson were derived by simplifying and solving the governing Reynolds-averaged Navier-Stokes equations for the fluctuating pressure term, while subsequent models were usually derived via the application of statistical and mathematical techniques to simplify earlier models, or by making appropriate modifications to address apparent shortcomings. However, past research has yet to yield a universally applicable model, with most only being accurate near the Mach and Reynolds numbers they were designed for. However, more recent work by Dominique demonstrated that artificial neural networking, a type of machine learning technique, could potentially produce a model that was accurate under most flight conditions. This thesis extends Dominique's research by creating a new equation via the application of a different machine learning technique (nonlinear least squares regression analysis) and a novel iterative process to develop the model form. The resulting equation was accurate at most Reynolds numbers and low airspeeds (approximately 11 m/s), though more outside data will be needed to fully understand its accuracy and shortcomings. i First, I'd like to express gratitude to my thesis supervisor, Professor Joana Rocha. Thank you for the support, guidance, invaluable advice, and opportunity you have provided me over the last two years. I'd also like to thank my colleagues, Nicholas Thomson and Justin Denne, for their practical advice and support, particularly for helping me overcome the challenges of sifting through and understanding the immense amount of wind tunnel data I worked with. Above all else, I dedicate this thesis to my wonderful parents, Pat and Geoff Huffman, and grandparents, Edward and Doris Baryluk, for their unwavering love and support. Thank you for encouraging me to go on this incredible journey, thank you for believing in me, and thank you for all the help you've provided along the way. I couldn't have done it without you.To all those who helped me, no matter how big or small, thank you. You made it possible to create a thesis I'm proud of.

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“…Such is evident in an open-loop, open-jet, aeroacoustic wind tunnel at Carleton University. The compressor-driven facility is capable of producing a maximum velocity of Mach 0.7 through a 15.0 cm by 6.1 cm nozzle for 30 seconds before an insufficient pressure differential is reached [21].…”

Section: Flow-driving Devicesmentioning

confidence: 99%

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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Design and Characterization of a High-Speed Subsonic Aeroacoustic Wind Tunnel

Cited by 6 publications

References 25 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

Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Flow Wall Pressure Fluctuations Using Machine Learning Regression Techniques

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

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