[A-CG47] 有人・無人航空機による気候・地球システム科学研究の推進

[ACG47-P03] 航空機搭載型ライダーと機械学習を用いた気流推定

*菊地 亮太¹、佐藤 昌之²、生地 将英¹、濵田 吉郎¹、高橋 暢宏³ (1.JAXA 航空技術部門、2.熊本大学、3.名古屋大学)

キーワード：航空安全、乱気流、航空機搭載型ドップラーライダー

Gust-alleviation systems that use airborne Doppler lidar technology are expected to enhance aviation safety by significantly reducing the risk of turbulence-related accidents. Accurate measurement and estimation of vertical wind velocity are crucial for the successful implementation of such systems. In this study, an airflow vector estimation algorithm based on data from airborne lidars is proposed and investigated for preview control to prevent turbulence-induced aircraft accidents in flight. Existing techniques assume that the wind field between lidars is homogeneous or can be functionally approximated. However, this assumption leads to performance degradation when dealing with complex turbulence fields. To address the issues, a machine learning-based method for estimating airflow, specifically a neural network-based method, is proposed, utilizing a methodology for generating turbulent flow data as the learning data. With the proposed method, airflow estimation can be extended to complex turbulent flow fields that were previously problematic for existing methods. This paper presents case studies and statistical evaluations of the proposed machine learning-based airflow estimation method and an existing method, with a focus on differences in predicted airflow field characteristics and their statistical prediction performance.