DARSI: A deep auto-regressive time series inference architecture for forecasting of aerodynamic parameters

Abstract

In the realm of fluid mechanics, where computationally-intensive simulations demand significant time investments, especially in predicting aerodynamic coefficients, the conventional use of time series forecasting techniques becomes paramount. Existing methods prove effective with periodic time series, yet the challenge escalates when faced with aperiodic or chaotic system responses. To address this challenge, we introduce DARSI (Deep Auto-Regressive Time Series Inference), an advanced architecture and an efficient hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) components. Evaluated against established architectures (CNN, DLinear, LSTM, LSTNet, and PatchTST) for forecasting Coefficient of Lift ($C_L$) values corresponding to Angles of Attack (AoAs) across periodic, aperiodic, and chaotic regimes, DARSI demonstrates remarkable performance, showing an average increase of 79.95% in CORR, 76.57% reduction in MAPE, 94.70% reduction in MSE, 76.18% reduction in QL, and 75.21% reduction in RRSE. Particularly adept at predicting chaotic aerodynamic coefficients, DARSI emerges as the best in static scenarios, surpassing DLinear and providing heightened reliability. In dynamic scenarios, DLinear takes the lead, with DARSI securing the second position alongside PatchTST. Furthermore, static AoAs at 24.7 are identified as the most chaotic, surpassing those at 24.9 and the study reveals a potential inflection point at AoA 24.7 in static scenarios for both DLinear and DARSI, warranting further confirmation. This research positions DARSI as an adept alternative to simulations, offering computational efficiency with significant implications for diverse time series forecasting applications across industries, particularly in advancing aerodynamic predictions in chaotic scenarios.