由于加压工作条件下雾化粒径数据的稀缺以及基于物理机制粒径预测模型的缺乏,本文开展了加压工作条件下预膜空气雾化喷嘴燃油粒径预测模型研究。通过试验设计(Design of Experiment,DOE),采用激光粒度仪测试了空气压力、空气温度、空气...由于加压工作条件下雾化粒径数据的稀缺以及基于物理机制粒径预测模型的缺乏,本文开展了加压工作条件下预膜空气雾化喷嘴燃油粒径预测模型研究。通过试验设计(Design of Experiment,DOE),采用激光粒度仪测试了空气压力、空气温度、空气压降、燃油温度、油气比等多参数交叉影响下的雾化粒径数据,基于表面波不稳定理论构建了包含韦伯数、雷诺数、奥内佐格数等无量纲参数的预测模型。通过试验数据的验证,发现预测模型最大误差为14.1%,平均误差为5.2%,且残差符合正态分布。敏感性分析表明,预测模型准确捕捉了无量纲参数以及试验工况参数对粒径的影响。展开更多
Hyperspectral images(HSIs)are susceptible to various noise interferences during the imaging process,leading to degraded image quality and affecting the accuracy of information extraction.Efficient denoising methods ar...Hyperspectral images(HSIs)are susceptible to various noise interferences during the imaging process,leading to degraded image quality and affecting the accuracy of information extraction.Efficient denoising methods are crucial for ensuring the accuracy of subsequent remote sensing analysis and applications.In view of the characteristics of hyperspectral image data,such as high dimensionality,strong spectral correlation,and high computational complexity,a threedimensional visual state space U-Net(VSSU3D)was proposed in this paper.By introducing a visual state space module into the traditional U-Net,and combining the spatial-spectral characteristics of hyperspectral images with the core idea of the Mamba model,targeted optimizations wereachieved to effectively model global information dependencies while reducing computational complexity.Additionally,a simplified channel attention module was embedded between the encoder and decoder to enhance cross-scale feature fusion capabilities.Experimental results on multiple publicly available hyperspectral image datasets demonstrated that VSSU3D achieved denoising performance comparable to or superior to existing advanced methods,which verified its effectiveness.展开更多
文摘由于加压工作条件下雾化粒径数据的稀缺以及基于物理机制粒径预测模型的缺乏,本文开展了加压工作条件下预膜空气雾化喷嘴燃油粒径预测模型研究。通过试验设计(Design of Experiment,DOE),采用激光粒度仪测试了空气压力、空气温度、空气压降、燃油温度、油气比等多参数交叉影响下的雾化粒径数据,基于表面波不稳定理论构建了包含韦伯数、雷诺数、奥内佐格数等无量纲参数的预测模型。通过试验数据的验证,发现预测模型最大误差为14.1%,平均误差为5.2%,且残差符合正态分布。敏感性分析表明,预测模型准确捕捉了无量纲参数以及试验工况参数对粒径的影响。
文摘Hyperspectral images(HSIs)are susceptible to various noise interferences during the imaging process,leading to degraded image quality and affecting the accuracy of information extraction.Efficient denoising methods are crucial for ensuring the accuracy of subsequent remote sensing analysis and applications.In view of the characteristics of hyperspectral image data,such as high dimensionality,strong spectral correlation,and high computational complexity,a threedimensional visual state space U-Net(VSSU3D)was proposed in this paper.By introducing a visual state space module into the traditional U-Net,and combining the spatial-spectral characteristics of hyperspectral images with the core idea of the Mamba model,targeted optimizations wereachieved to effectively model global information dependencies while reducing computational complexity.Additionally,a simplified channel attention module was embedded between the encoder and decoder to enhance cross-scale feature fusion capabilities.Experimental results on multiple publicly available hyperspectral image datasets demonstrated that VSSU3D achieved denoising performance comparable to or superior to existing advanced methods,which verified its effectiveness.
