The cross-sectional shape profile geometry of blown powder clad deposit is important for overall structural integrity of the clad layer. The penetration zone of the clad deposit is represented within its shape profile geometry. The shape profile geometry of the clad deposit is also important for thermomechanical modelling. Although a rough estimation of blown powder clad deposit shape profile can be made based on the powder feeding parameters using empirical formulae, it may not be sufficiently accurate to be used in a thermomechanical model, as it may lead to inaccuracy in the prediction of temperature distributions, residual stresses, and distortion. The pulsed-laser blown powder deposition process is a highly coupled multivariable problem. Hence the deterministic numerical-methods-based prediction of pulsed-laser powder deposit shape profile geometry is time consuming, costly, and may not be adequate to predict the profile geometry over a wide range of varying process parameters. The present investigation deals with the cross-sectional shape profile geometry modelling of the pulsed-laser assisted superalloy powder deposition (PLPD) process using a soft computing approach. A simple yet effective mapping technique was used in the present work to map the experimentally obtained shape profiles of the powder deposits. The mapped characteristics of the powder deposits' shape profiles were used in the back-propagation artificial neural network (ANN) modelling of the PLPD process. The present modelling technique can be conveniently used to incorporate the PLPD shape profile geometry parameters in thermomechanical analyses for accurate prediction of temperature distributions and residual stresses. Based on the present soft computing modelling methodology, an estimation of top reinforcement and penetration zone shape boundaries of a blown powder clad deposit can also be made.

This paper aims to disclose the law of fish migration trajectories at different water depths. For this purpose, the grass carps in a reservoir in southwestern China were taken as the targets, outdoor experiments were performed to monitor their behaviours and environmental factors in the reservoir. Then, the Hydroacoustic Technology, Inc. (HTI) acoustic tracking system and backpropagation neural network (BPNN) were introduced to simulate and analyse the migration of the fish in the natural state. Meanwhile, the vertical distribution of fish was discussed at different temperatures and dissolved oxygen contents. The results show that the BPNN algorithm has a good fitting effect on the planar migration trajectories of the fish, but fails to achieve a desirable fitting result concerning the migration trajectories in the Z direction. Fortunately, the fitting effect of migration trajectories was greatly enhanced by normalization. The fish were distributed differently in spring and summer across the different water depths, under the influence of water temperature and dissolved oxygen content. Overall, the fish obeyed the normal distribution in the vertical direction, and selected water depth mainly based on dissolved oxygen content. The research findings lay a scientific basis for fish resource protection, river ecology assessment and water environment restoration.