Airflow Spinning Simulation: Current State, Advanced Techniques, and Future Directions

Abstract

Airflow spinning technologies represent a cornerstone of modern textile manufacturing, offering high production rates and diverse yarn properties. The intricate interplay between airflow dynamics and fiber behavior is paramount to optimizing these processes and ensuring superior yarn quality. This review provides a comprehensive overview of the current landscape of airflow spinning simulation, focusing on the application of Computational Fluid Dynamics (CFD) and advanced modeling techniques. We detail the capabilities of leading CFD software, their specific applications across various spinning methods—including compact, airjet, rotor, and vortex spinning—and the emerging role of machine learning in enhancing simulation accuracy and design efficiency. Furthermore, we delve into sophisticated modeling approaches such as CFD-Discrete Element Method (DEM) coupling, multi-scale modeling, and specialized fiber constitutive laws, which are crucial for predicting critical yarn quality parameters like tenacity, evenness, and hairiness. Validation methodologies, including experimental measurements and visualization techniques, are discussed alongside reported accuracies and inherent limitations. Finally, we explore future directions, highlighting advancements in simulation software, the integration of digital technologies, and the potential for virtual spinning environments, underscoring the transformative impact of simulation on the textile industry.