Accurately modeling particle movement through fluids is crucial in fields ranging from chemical engineering to aerospace. The drag coefficient, which influences how particles settle and move in fluid environments, is a core factor in these calculations. While the behavior of spherical particles is well understood, predicting the drag coefficient of irregularly shaped particles has long been a challenge. These complexities have highlighted the need for a more sophisticated approach to model particle-fluid interactions, especially for non-spherical particles. Accurately modeling particle movement through fluids is crucial in fields ranging from chemical engineering to aerospace. The drag coefficient, which influences how particles settle and move in fluid environments, is a core factor in these calculations. While the behavior of spherical particles is well understood, predicting the drag coefficient of irregularly shaped particles has long been a challenge. These complexities have highlighted the need for a more sophisticated approach to model particle-fluid interactions, especially for non-spherical particles. General Physics Phys.org – latest science and technology news stories
Flow of the future: AI models tackle complex particle drag coefficients
