Machines

Promoting the green transformation of traditional diesel-powered ships is crucial for achieving carbon peaking and carbon neutrality goals. This study focuses on diesel-engine ships operating in the inland river areas of Anhui Province, China. It proposes two electrification retrofit schemes based mainly on lithium iron phosphate (LIP) batteries: full electrification and diesel-engine redundancy. The economic and environmental impacts of these schemes are analyzed and compared with those of conventional diesel-powered ships. A cost prediction algorithm based on model prediction is proposed, supported by a mathematical model for cost analysis. Results indicate that for electric tankers to become economically viable, battery costs must decrease through yearly improvements in energy density and reduced degradation rates. Additionally, government support is essential, such as raising carbon prices and providing subsidies—either an annual operational subsidy of CNY 80,000 or an initial construction subsidy of CNY 500,000. The study concludes that continued advances in battery technology, together with policy and financial support, will accelerate the large-scale electrification of ships.

A wheeled–legged robot has the advantage of stable and agile movement on flat ground and an excellent ability to overcome obstacles. However, when faced with a narrow footprint, there is a limit to its ability to move. We developed the control moment gyroscope (CMG) unicycle–legged robot to solve this problem. A scissored pair of CMGs was applied to control the roll balance, and the pitch balance was modeled as a double-inverted pendulum. We performed Linear Quadratic Regulator (LQR) control and model predictive control (MPC) in a system in which the control systems in the roll and pitch directions were separated. We also devised a method for controlling the rotation of the robot in the yaw direction using torque generated by the CMG, and the performance of these controllers was verified in the Gazebo simulator. In addition, forward driving control was performed to verify mobility, which is the main advantage of the wheeled–legged robot; it was confirmed that this control enabled the robot to pass through a narrow space of 0.15 m. Before implementing the verified controllers in the real world, we built a CMG test platform and confirmed that balancing control was maintained within $\pm 1^{\circ }$.

The interference fit connection with slug rivets is widely used in aircraft assembly, and an appropriate interference value is vital for aircraft structural integrity. This study proposed a prediction–optimization framework that a deep neural network (DNN) surrogate was trained on a parametric finite element dataset to regress four interference measurements (G1–G4), and the trained DNN was embedded into a genetic algorithm (GA) to search process parameters that meet prescribed target interference. An orthogonal design with range analysis was employed to rank factor importance and provide interpretable trends, while finite element model (FEM) re-runs were used for validation. Compared with support vector regression, random-forest regression, and Bayesian regression, the DNN demonstrated superior fitting accuracy and a more favorable error distribution on held-out data. GA solutions obtained using the DNN surrogate achieved the target interference with a maximum relative deviation of 9.75%, confirming the effectiveness of the proposed workflow for rapid, physics-consistent interference control. The contributions of the study were as follows: (i) an end-to-end, quick-response, reproducible FEM→DNN→GA pipeline for slug-rivet interference; (ii) quantitative factor ranking with mechanistic interpretation; and (iii) minute-scale parameter optimization suitable for engineering deployment.