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Decarbonization in the aeronautical industry is a daunting challenge due to the lack of sustainable aviation fuel. Hydrogen offers a potential option for this problem, given that it is an energy-dense fuel capable of reducing climate impact. Along with it, the liquid hydrogen preheating and thermal protection bring inevitable safety issues. Currently, how to construct the optimal thermal management system (TMS) architecture and further clarify its principles is still unclear. Thus, this study compares different TMS architectures through optimized genetic algorithms (GA), and firstly points out the excellent architecture as well as its operating parameters. Strikingly, our results reveal the optimized algorithm can significantly improve efficiency and precision of the optimizing processes. Benefiting from adaptive population strategy and elite preservation strategy, optimized GA saves half the time compared to conventional GA, and the variance is 60 % smaller than that of conventional GA. Using optimized GA, the series-parallel scheme is determined as the best solution and it exhibits better advantages on lightweight. In addition, results also show the leading factor is air/helium heat exchanger area. In conclusion, we hope this work can provide guidance for the optimal GA, contribute to decarbonization, and provide more ideas for the optimization of hydrogen TMS in the future. • A systematic optimization model of TMS is developed by considering multi-factors. • Dynamic self-adaptation of population strategy is proposed in improved GA. • The optimal framework and controlling parameters of TMS is determined. • The mechanism of how the variety of factors synergetic evolve is found out. [ABSTRACT FROM AUTHOR]

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development. • Digital infrastructure construction markedly enhances industrial energy efficiency. • There is a non-linear picture of the impact of digital infrastructure construction on industrial energy efficiency. • The effects of green technology innovation and advanced industrial structure are crucial channels. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

To address the drawbacks of low energy utilization and high cost in traditional photovoltaic (PV) vehicle energy management systems, a hybrid energy management system for PV vehicles is proposed, which can automatically manage energy under complex conditions. An improved PV model and the solar irradiation S – temperature T transfer function are used to construct Simulink simulation models. The simulation compares the charging effects of a PV cell and a charging station, as well as the performance of four energy management schemes. Finally, validation experiments are conducted for the improved PV model and the PV charging model. The simulation results show that: (1) The charging speed of the PV cell can reach 3.37 times that of the charging station, highlighting the practicality of PV power supply research. (2) In complex environments, Energy Management Scheme 2 has a more effective energy management capability compared to other schemes. The experimental results show that: (1) Compared to the original PV model, the improved PV model provides simulated data that are closer to the experimental data of the PV cell. (2) The accuracy of simulating the charging voltage and current in the PV charging model has significantly improved, reaching over 94.2% and 89.5%, respectively. • An improved PV model and irradiance – temperature transfer function are proposed. • The implementation of using photovoltaics for powering electric vehicles. • A hybrid energy management system for photovoltaic (PV) vehicles is proposed. • The system ensures normal operation of PV vehicles under complex conditions. • The system can improve the energy efficiency of photovoltaic vehicles. [ABSTRACT FROM AUTHOR]