Min Zeng

The spiral-wound heat exchanger is a key equipment in the liquefied natural gas application, but the flow and heat transfer mechanisms remain unclarified. In this study, a three-dimensional numerical model is created, focusing on exploring the impact of four crucial structural parameters on the flow and heat transfer performance of natural gas, including the external diameter of tubes, the diameter of the core cylinder, the longitudinal pitch of tubes in the same layer, and the radial pitch of tube bundles between the adjacent layer. It was found that the tube diameter, core cylinder diameter, and radial pitch had significant effects on Nu and Δp _m. The optimal Nu on the shell side was obtained at medium core cylinder size. The longitudinal pitch had a weak effect on the performance of both sides, and the longitudinal pitch corresponding to the maximum values of Nu and Δp _m on both sides increased with the increase in the inlet Reynolds number. Under the effect of centrifugal force, a shifted tendency was shown by the velocity and temperature fields.

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The “LFWA+FD” focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.

This article presents a fully three-dimensional numerical study on the process of melt pool evolution. To overcome the simplifications used in many existing studies, an enthalpy method is developed for the phase change, and an accurate interface capturing method, i.e., the coupled volume-of-fluid and level set (VOSET) method, is employed to track the moving gas–liquid interface. Meanwhile, corresponding experimental studies are carried out for validation. The obtained numerical results show the formed interface morphology during the process of melt pool with its typical sizes and are quantitatively consistent with those data measured in experiments. Based on the numerical results, the thermodynamic phenomena, induced by the interaction between heat and momentum exchange, occurring in the formation of melt pool are presented and discussed. Mechanisms of the melt pool evolution revealed in the present study provide a useful guidance for better controlling the process of additive manufacturing.