Application of the Improved Grey Wolf Algorithm in Spacecraft Maneuvering Path Planning

In many space missions, spacecraft are required to have the ability to avoid various obstacles and finally reach the target point. In this paper, the path planning of spacecraft attitude maneuver under boundary constraints and pointing constraints is studied. The boundary constraints and orientation constraints are constructed as finite functions of path evaluation. From the point of view of optimal time and shortest path, the constrained attitude maneuver problem is reduced to optimal time and path solving problem. To address this problem, a metaheuristic maneuver path planning method is proposed (cross-mutation grey wolf algorithm (CMGWO)). In the CMGWO method, we use angular velocity and control torque coding to model attitude maneuver, which increases the difficulty of solving the problem. In order to deal with this problem, the grey wolf algorithm is used for mutation and evolution, so as to reduce the difficulty of solving the problem and shorten the convergence time. Finally, simulation analysis is carried out under different conditions, and the feasibility and effectiveness of the method are verified by numerical simulation.

Fluid-Structure Interaction Analysis of Aircraft Hydraulic Pipe with Complex Constraints Based on Discrete Time Transfer Matrix Method

Fluid-structure interaction (FSI) is prevalent in aircraft hydraulic pipes due to high-pressure fluid pulsation, complex pipe path routing and boundary constraints, which pose a serious threat to the safety and reliability of the aircraft hydraulic system. This paper focuses on the FSI response of aircraft hydraulic pipes with complex constraints. A comprehensive fourteen-equation model for describing the FSI of pipe conveying fluid with wide pressure and Reynolds number range is proposed. The excitation models and complex boundary constraints of liquid-filled pipes are established. Moreover, based on the transfer matrix method (TMM), combined with the time discreteness and analytical integral method, a discrete time transfer matrix method (DTTMM) for solving the FSI fourteen-equation model in time domain is presented. Then, the numerical solution and experiment of an ARJ21-700 aircraft hydraulic pipe with complex constraints is carried out with four working conditions. The obtained results verify the correctness of the proposed model and solution method, and reveal the universal laws of the FSI response about aircraft hydraulic pipes, which can also provide theoretical and experimental references for modeling, solutions and verification in the FSI analysis of pipe conveying fluid.

A Significant Solar Energy Note on Powell-Eyring Nanofluid with Thermal Jump Conditions: Implementing Cattaneo-Christov Heat Flux Model

PTSCs (parabolic trough solar collectors) are widely employed in solar-thermal applications to attain high temperatures. The purpose of this study is to determine how much entropy is created when Powell-Eyring nanofluid (P-ENF) flows across porous media on a horizontal plane under thermal jump circumstances. The flow in PTSC was generated by nonlinear surface stretching, thermal radiation, and Cattaneo-Christov heat flux, which was utilized to compute heat flux in the thermal boundary layer. Using a similarity transformation approach, partial differential equations were converted into ordinary differential equations with boundary constraints. Then, the boundary restrictions and partial differential equations were merged to form a single set of nonlinear ordinary differential equations. To obtain approximate solutions to ordinary differential equations, the Keller-Box approach is utilized. Nanofluids derived from silver- and copper-based engine oil (EO) has been employed as working fluids. The researchers observed that changing the permeability parameter reduced the Nusselt number while increasing the skin frictional coefficient. Total entropy variation was also calculated using the Brinkman number for flow rates with Reynolds number and viscosity changes. The key result is that thermal efficiency is inversely proportional to particular entropy production. For example, using Cu-EO nanofluid instead of Ag-EO nanofluid increased the heat transport rate efficiency to 15–36%.

Numerical investigation into thermal buckling of friction pairs in hydro-viscous drive under nonlinear radial temperature distribution

Thermal buckling deformation can significantly impact the operating performance of hydro-viscous drive. A thermal buckling finite element shell model was established with the nonlinear radial temperature as the thermal loading condition. The thermal buckling behavior of friction pairs was investigated under three different boundary constraints. Moreover, the influence of thickness and material parameters on the critical buckling temperature was discussed. The simulation results coincide with the failure modes of friction pairs in practice, and the most common ones are the coning mode and the potato chip mode. The ability to resist thermal buckling deformation can be improved as the thickness increases. In addition, the steel disc with outer edge simply supported is more prone to thermal buckling, because the critical temperature is minimum. The thermal expansion coefficient is the primary factor in thermal buckling study, which is inversely proportional to the critical temperature. These provide a theoretical basis for avoiding thermal failure of friction pairs in a hydro-viscous drive.

MSBC-Net: Automatic Rectal Cancer Segmentation from MR Scans

<div>Accurate segmentation of rectal cancer and rectal wall based on high-resolution T2-weighted magnetic resonance imaging (MRI-HRT2) is the basis of rectal cancer staging. However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. In this study, a multi-task learning network, namely mask segmentation with boundary constraints (MSBC-Net), is proposed to overcome these limitations and to obtain accurate segmentation results by locating and segmenting rectal cancer and rectal wall automatically. Specifically, at first, a region of interest (RoI)-based segmentation strategy is designed to enable end-to-end multi-task training, where a sparse object detection module is used to automatically localize and classify rectal cancer and rectal wall to mitigate the problem of background interference, and a mask and boundary segmentation block is used to finely segment the RoIs; second, a modulated deformable backbone is introduced to handle the variable features of rectal cancer, which effectively improves the detection performance of small objects and adaptability of the proposed model. Moreover, the boundary head is fused into the mask head to segment the ambiguous boundary of the target and constrain the mask head to obtain more refined segmentation results. In total, 592 annotated rectal cancer patients in MRI-HRT2 are enrolled, and the comprehensive results show that the proposed MSBC-Net outperforms state-of-the-art methods with a dice similarity coefficient (DSC) of 0.801 (95\% CI, 0.791-0.811), which can be well extended to other medical image segmentation tasks with high potential clinical applicability.</div>

MSBC-Net: Automatic Rectal Cancer Segmentation from MR Scans

<div>Accurate segmentation of rectal cancer and rectal wall based on high-resolution T2-weighted magnetic resonance imaging (MRI-HRT2) is the basis of rectal cancer staging. However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. In this study, a multi-task learning network, namely mask segmentation with boundary constraints (MSBC-Net), is proposed to overcome these limitations and to obtain accurate segmentation results by locating and segmenting rectal cancer and rectal wall automatically. Specifically, at first, a region of interest (RoI)-based segmentation strategy is designed to enable end-to-end multi-task training, where a sparse object detection module is used to automatically localize and classify rectal cancer and rectal wall to mitigate the problem of background interference, and a mask and boundary segmentation block is used to finely segment the RoIs; second, a modulated deformable backbone is introduced to handle the variable features of rectal cancer, which effectively improves the detection performance of small objects and adaptability of the proposed model. Moreover, the boundary head is fused into the mask head to segment the ambiguous boundary of the target and constrain the mask head to obtain more refined segmentation results. In total, 592 annotated rectal cancer patients in MRI-HRT2 are enrolled, and the comprehensive results show that the proposed MSBC-Net outperforms state-of-the-art methods with a dice similarity coefficient (DSC) of 0.801 (95\% CI, 0.791-0.811), which can be well extended to other medical image segmentation tasks with high potential clinical applicability.</div>