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In today's AI-driven era, computer vision, including autonomous driving, robotics, and healthcare, is prevalent. How-ever, acquiring ample data while managing resources and privacy constraints is challenging. This article proposes a solution: synthetic data generation. We use CAD software to craft intricate 3D models, process them in Blender, and evaluate quality using metrics like Structural Similarity and PSNR (Peak Signal to Noise Ratio). Synthetic data achieves up to 90% similarity with real data and an average PSNR of 21dB. Our method offers a streamlined, dependable ap-proach for enhancing computer vision, especially in object detection, addressing data acquisition challenges.
The vortex tube can separate a mass flow into a hot and cold mass flow. In this paper, the energy balance in the boundary layer of the vortex tube is analyzed with respect to a possible effect of temperature separation in the boundary layer by the viscous term of the enthalpy balance equation. A Large Eddy Simulation is used to generate the velocity profiles used for the computation of the viscous source terms. The dominant contributions of the source terms in the boundary layer of the vortex tube are identified and computed from the velocity fields. It is demonstrated how the strong velocity gradients in the boundary layer create a viscous flux of energy. An implementation of balance equations both with and without source term show the effect of energy separation in the boundary layer of the vortex tube.