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This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

The automated berthing of vessels constitutes a high-risk operation. Equipping ships with azimuth and lateral thrusters enhances manoeuvrability, although it poses challenges in achieving optimal thrust allocation (TA) efficiency. This study introduces an improved adaptive non-dominated sorting genetic algorithm III (ANSGAIII) specifically designed to address the TA problem in autonomous ship berthing. The algorithm enhances the objective function by accounting for the nonlinear relationship between actual power consumption and thruster speed, along with considering the treatment of forbidden sectors while maintaining a high level of fidelity. Additionally, the algorithm considers equality constraints, actuator rate and position constraints, and other operational constraints. A modification to the non-dominated sorting method is implemented to improve the processing speed of the optimization algorithm. Significantly, this algorithm surpasses NSGAII, NSGAIII, and CMOEA\D in meeting ship safety and stability requirements while concurrently reducing energy consumption. • An ANSGAIII TA scheme is proposed to enhance berthing safety and energy efficiency. • Cost function and forbidden sector strategy are improved. • The non-dominated sorting method is refined to expedite the TA process. [ABSTRACT FROM AUTHOR]

• An energy model is set up based on energy consumption analyses of machining processes; • NC codes are structurally evaluated and then popularised into the energy model; • A hybrid GA-ACA algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Machining processes comprise numerous energy consumption activities. Given the significance of the circular economy and manufacturing sustainability to modern societies, it is paramount to design effective methodologies to accomplish energy-efficient machining processes. With this aim, this research presents a new approach of energy consumption optimisation for machining processes based on numerical control (NC) programs. In the approach, the following innovative characteristics are exhibited: (i) An energy model is systematically established based on a detailed analysis of energy consumption activities in machining processes; (ii) NC programs for specific machining processes are assessed in detail and popularised into the energy model for instantiation; (iii) An optimisation algorithm hybridising the genetic algorithm and the ant colony algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Two case studies were conducted to validate the presented approach. The case studies revealed that the accuracy of the energy model was 95.3% of the actual energy consumption. The studies also showed that, based on the optimised energy model, the total length of air-cutting toolpaths was reduced by 43.8%, and the total machining time was diminished by 25.8%. It can be concluded that the developed approach can achieve substantial energy savings, and therefore it is highly promising to support machining industries to meet their sustainable targets. [ABSTRACT FROM AUTHOR]