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

## TL;DR

This paper introduces a new method to estimate pressure fields in fluidic thrust vectoring nozzles using sparse measurements and advanced algorithms.

## Contribution

The novel approach combines proper orthogonal decomposition and compressed sensing for efficient pressure field reconstruction.

## Key findings

- Using only four pressure taps, the method achieves pressure coefficient errors within |ΔCp| < 0.02.
- The proposed method outperforms Kriging interpolation in capturing flow structure characteristics with fewer sensors.

## Abstract

Fluidic thrust vectoring control (FTVC) enables highly agile flight without the mechanical complexity of traditional vectoring nozzles. However, a robust onboard identification of the jet deflection state remains challenging when only limited measurements are available. This study proposes a sparse reconstruction of the pressure field method for a wedge passive FTVC nozzle and validates the approach experimentally on a low-speed jet platform. By combining the proper orthogonal decomposition (POD) algorithm with an l1-regularized compressed sensing method, a full Coanda wall pressure distribution is reconstructed from the sparse measurements. A genetic algorithm is then employed to optimize the wall pressure tap locations, identifying an optimal layout. With only four pressure taps, the local pressure coefficient errors were maintained within |ΔCp| < 0.02. In contrast, conventional Kriging interpolation requires increasing the sensor count to 13 to approach the reconstruction level of the proposed POD–compressed sensing method using 4 sensors, yet still exhibits a reduced fidelity in capturing key flow structure characteristics. Overall, the proposed approach provides an efficient and physically interpretable strategy for pressure field estimation, supporting lightweight, low-maintenance, and precise fluidic thrust vectoring control.