#thermal-soaring.
Posts on the Alpisto engineering blog filed under#thermal-soaring.
- 01#flybeeper#fanet-vario#fanet
The lift does not fade: where 11,046 competition thermals end and how to see it coming
In 11,046 competition thermals the lift hardly fades: pilots climb at full rate almost to the top, so your own vario cannot warn you. The pilots who left before you and the day's earlier tops can, about a minute ahead.
11 min read - 02#flybeeper#fanet-vario#fanet
The thermal leans, then it drifts: 8,409 thermals rebuilt from competition tracks
From the tracks of 28 competitions I rebuilt 8,409 thermals. Near mountain terrain the column stays put and leans at 45°; higher up and over flat land it stands nearly straight and drifts at two thirds to three quarters of the wind.
22 min read - 03#flybeeper#machine-learning#reinforcement-learning
Machine Learning for Thermal-Soaring Optimisation: a Survey of Approaches, Architectures and Open Problems
A survey of deep learning and reinforcement learning for autonomous thermal soaring — LSTM/GRU/TCN/Transformer architectures, POMDP and PPO/TD3 formulations, Kalman + ML hybrid filtering, and a concrete five-stage plan for an on-board AI co-pilot for paragliders.
17 min read