Review on Metrics and Prediction Methods of Civil Aviation Noise

Abstract

Civil aviation noise is one of the main factors hindering the growth of the civil aviation industry. With the increase in global air traffic demand, the problem of aviation noise pollution will become more and more serious. It is of great significance to carry out research in aviation noise. First, by summarizing the characteristics of aviation noise metrics, this paper divides them into three categories: single event noise metrics, cumulative exposure metrics, and daily metrics. Representative metrics of each category are selected for explanation and in-depth analysis. Second, according to the principles of aviation noise prediction models, this paper classifies these existing models into three categories: best practice models, scientific models, and machine learning models. Relevant academic research results are summarized. The best practice model regards the aircraft as noise point source, and its specialty is to predict noise under complex air traffic conditions. The scientific model considers the noise from the level of aircraft components and reflects the underlying physical effects. Based on data, the machine learning model uses algorithms to mine the hidden relationship between various factors and noise to achieve the purpose of noise prediction. Then, this paper introduces two kinds of aviation noise simulation software based on the best practice and scientific models, and lists their access addresses. Finally, challenges and prospects are presented from three aspects: metrics, prediction models and simulation software.