Comparison of Semi-Empirical Single Point Wall Pressure Spectrum Models with Experimental Data

Thomson, Nicholas; Rocha, Joana

doi:10.3390/fluids6080270

Cited by 13 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To ensure consistency from experiment to experiment, and to make up for missing data, Thomson and Rocha used only the listed Mach number, stream-wise location, pressure, and temperature from the original studies. All other values were calculated using consistent formulas (please refer to Appendix B in [39] for more information). Thomson and Rocha reported more values than were present in the original studies [39].…”

Section: (18b)mentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: (18b)mentioning

confidence: 99%

“…All other values were calculated using consistent formulas (please refer to Appendix B in [39] for more information). Thomson and Rocha reported more values than were present in the original studies [39]. The values from Thomson and Rocha are reported in Table 5.…”

Section: (18b)mentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The model proposed by Goody is largely agreed to be the best model for ZPG wind tunnel experiments [39,72,73,84]. As a result, when researchers decided to expand to non ZPG flows they used the Goody model as a baseline and then modified the coefficients of the model to better fit with the experimental data that they collected.…”

Section: Pressure Gradient Modelsmentioning

confidence: 99%

“…This section discusses the differences between wind tunnel and flight test single point wall pressure spectrum measurements as discussed by Thomson and Rocha [84].…”

Section: Comparison Of Wind Tunnel To Flight Test Datamentioning

confidence: 99%

Modelling of Wall Pressure Fluctuations Induced by Turbulent Boundary Layer Flow

Thomson¹

View full text Add to dashboard Cite

Semi-empirical models are used to predict the power spectral density of wall pressure fluctuations in the turbulent boundary layer. From the analysis of current semiempirical models and a large survey of experimental data, it was found that the models proposed by Goody and Smol'yakov have the lowest mean squared error when predicting the power spectral density for wind tunnel experiments. The Rackl and Weston model has the lowest mean squared error when predicting the power spectral density for flight test data. In addition, although current studies of the power spectra obtained in the wind tunnel are similar, they are not generally an accurate representation of flight test experiments. Current advancements in power spectral density wall pressure fluctuation prediction have focused on expanding the range of experiments that can be predicted to include adverse pressure gradient flows, however, favourable pressure gradient flows have not received much attention. An experiment was performed to capture the effects of the favourable pressure gradient on the power spectral density. A model was then created to improve upon some of the limitations of existing models. The proposed model improves upon the prediction of the high-frequency roll-off location, incorporates improvements made by past models, and captures the effects of favourable pressure gradients.

show abstract

“…In practice, the limited availability of outside data made this difficult. Thomson (2021) was the only available source, but did not include any supersonic cases and the only high subsonic cases did not include enough data to be used [73]. Nonetheless, the three best available datasets cover a wide range of Re and U ∞ values: Gravante, Salze, and Goody.…”

Section: Model Evaluation and Validationmentioning

confidence: 99%

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

Huffman

View full text Add to dashboard Cite

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.

show abstract

Comparison of Semi-Empirical Single Point Wall Pressure Spectrum Models with Experimental Data

Cited by 13 publications

References 29 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

Modelling of Wall Pressure Fluctuations Induced by Turbulent Boundary Layer Flow

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

Contact Info

Product

Resources

About