Many gas turbine and rocket engines exhibit unwanted combustion instability at the
experimental testing phase. Instability leads to large amplitude pressure oscillations and …

Structural-response reconstruction is of great importance to enrich monitoring data for better
understanding of the structural operation status. In this paper, a data-driven based structural …

In this study, we propose an innovative latent space-based machine learning model to
predict coupled flame-flow fields across various combustion states. This model consists of …

Self-sustained thermoacoustic oscillations as observed in low-emission combustion-
involved gas turbines and aero-engines involve complicated thermal fluid–acoustics …

Hopf bifurcation, a prevalent phenomenon in solid rocket motors (SRMs), signifies a critical
transition from a fixed point to a limit cycle. The detection of early warning signals (EWSs) for …

Fluid prediction is important in exploration work, helping to determine the location of
exploration targets and the reserve potential of the estimated area. Machine learning …

Traditional numerical simulation methods require substantial computational resources to
accurately determine the complete nonlinear thermoacoustic response of flames to various …

A hybrid deep learning model combining Convolutional Neural Networks (CNNs) with a
Transformer Encoder was proposed to investigate the nonlinear dynamics of a laminar …