论文标题
多输入模型的不确定性分析，用于远程风电场噪声预测
Multi-input model uncertainty analysis for long-range wind farm noise predictions
论文作者
论文摘要
风电噪声预测（WFN）的不确定性的主要来源之一反映了参数和模型结构的不确定性。模型结构不确定性是系统的不确定性，它涉及有关模型适当数学结构的不确定性。在这里，我们量化了预测由多输入模型引起的WFN的模型结构不确定性，包括九种地面阻抗和四个风速轮廓模型。我们使用数值射线追踪声音传播模型来预测不同接收器的噪声水平。我们发现，不同地面阻抗模型和风速轮廓模型之间的变化是不确定性的重要来源，并且这些来源在大于3.5 km的距离下导致预测的噪声水平差异超过10 dBA。我们还发现，大气垂直风速模型之间的差异是预测长期距离的WFN的主要不确定性来源。预测WFN时，重要的是要确认与不同模型相关的可变性，因为这有助于预测值的不确定性。
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.
