Sparse-sensor reconstruction of oblique detonation-wave temperature fields using a diffusion-guided residual coordinate-attention U-shaped network

Oblique detonation engines (ODEs) hold significant promise for hypersonic propulsion, where accurate sensing of the internal flow field is critical for achieving optimal performance. However, reconstructing the full flow field from sparse, incomplete, and irregular wall temperature measurements remains a formidable challenge. To address this, we propose the diffusion-guided residual coordinate-attention U-shaped network (DRC-UNet), which is designed to recover the global combustion chamber temperature field. DRC-UNet integrates three key components: a score-based diffusion model (SDM) for robust data completion, residual connections to enhance deep feature propagation, and coordinate attention for improved spatial localization. Using only 36 uniformly distributed wall-temperature sensors, DRC-UNet outperforms the baseline U-shaped network (U-Net) by improving the average structural similarity index measure (SSIM) from 0.95 to 0.99 and increasing the average peak signal-to-noise ratio (PSNR) from 28.31 to 41.81 dB, while better recovering shock fronts. Furthermore, under extreme data sparsity—where only 12 randomly distributed wall sensors (approximately 2% of the total measurement points) are available—DRC-UNet still achieves high-fidelity reconstructions, maintaining a mean absolute percentage error (MAPE) below 1.5%. In contrast, conventional end-to-end networks often struggle under such conditions, as they rely on fixed sensor layouts and require retraining to adapt to varying data sparsity. These results demonstrate that the proposed framework provides a robust and generalizable solution for intelligent sensing, capable of accurately reconstructing complex flow fields from incomplete or irregular data. This study provides a foundation for advanced diagnostics and control in oblique detonation engines by enabling temperature field reconstruction from sparse sensors.