Multi-input model uncertainty analysis for long-range wind farm noise predictions
Date
2023
Authors
Nguyen, P.D.
Hansen, K.L.
Zajamsek, B.
Catcheside, P.
Hansen, C.H.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Applied Acoustics, 2023; 205:109276-1-109276-10
Statement of Responsibility
Phuc D. Nguyen, Kristy L. Hansen, Branko Zajamsek, Peter Catcheside, Colin H. Hansen
Conference Name
Abstract
One of the major sources of uncertainty in predictions of wind farm noise (WFN) reflect parametric and model structure uncertainty. The model structure uncertainty is a systematic uncertainty, which relates to uncertainty about the appropriate mathematical structure of the models. Here we quantified the model structure uncertainty in predicting WFN arising from multi-input models, including nine ground impedance and four wind speed profile models. We used a numerical ray tracing sound propagation model for predicting the noise level at different receivers. We found that variations between different ground impedance models and wind speed profile models were significant sources of uncertainty, and that these sources contributed to predicted noise level differences in excess of 10 dBA at distances greater than 3.5 km. We also found that differences between atmospheric vertical wind speed profile models were the main source of uncertainty in predicting WFN at long-range distances. When predicting WFN, it is important to acknowledge variability associated with different models as this contributes to the uncertainty of the predicted values.
School/Discipline
Dissertation Note
Provenance
Description
Available online 15 February 2023
Access Status
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© 2023 Elsevier Ltd. All rights reserved.