Sparse Reconstruction of Pressure Field for Wedge Passive Fluidic Thrust Vectoring Nozzle

Area of Science:

• Aerospace Engineering
• Fluid Dynamics
• Control Systems

Background:

• Fluidic thrust vectoring control (FTVC) offers enhanced aircraft agility without mechanical complexity.
• Accurate onboard identification of jet deflection state is crucial but challenging with limited measurements.

Purpose of the Study:

• To develop and validate a sparse reconstruction method for estimating the pressure field in a passive FTVC nozzle.
• To enable robust jet deflection state identification using minimal sensor data.

Main Methods:

• Utilized Proper Orthogonal Decomposition (POD) combined with l1-regularized compressed sensing for pressure field reconstruction.
• Employed a genetic algorithm to optimize the placement of sparse pressure sensors.
• Experimentally validated the method on a low-speed jet platform.

Main Results:

• Reconstructed the full Coanda wall pressure distribution from sparse measurements with high accuracy.
• Achieved low local pressure coefficient errors (|ΔCp| < 0.02) using only four pressure taps.
• Outperformed conventional Kriging interpolation, requiring fewer sensors for comparable or superior reconstruction fidelity.

Conclusions:

• The proposed POD-compressed sensing approach offers an efficient and interpretable strategy for pressure field estimation in FTVC.
• This method supports the development of lightweight, low-maintenance, and precise fluidic thrust vectoring systems.