The study employs statistical dynamics and structural equation modeling to analyze the impact of economic policies on industrial intelligent green transition, focusing on technology fusion development as a key mediating mechanism. Using stratified sampling of 100 enterprises across steel, chemical, and high-tech sectors, we construct a structural equation modeling framework to examine causal pathways from exogenous factors (policy, market environment, technological innovation) to endogenous outcomes (industrial structure optimization, performance, sustainability, and green development). Empirical results reveal that policy support significantly enhances technology fusion, which in turn drives industrial upgrading and sustainable performance, with notable regional and industrial disparities. These findings provide empirical evidence for optimizing policy design to accelerate China?s industrial green transformation.

Bioretentions represent a prevalent stormwater runoff management technique in urban areas, frequently employed to address the treatment of pollutants in runoff. However, the efficacy of nitrate-nitrogen removal by bioretention systems is subject to variation due to a multitude of environmental factors. In this study, three bioretentions (B1, B2, and B3) were constructed to investigate the migration and transformation characteristics of nitrate nitrogen in bioretentions under different soil mediums (sandy soil, sandy soil + newspaper, and sandy soil + wood chips). The findings indicated that the efficacy of nitrate nitrogen removal varied with different media types, with sandy soil augmented by wood chips demonstrating the optimal removal performance, followed by sandy soil combined with newspaper, and sandy soil alone exhibiting the least efficient removal. The B3 plant retention system demonstrated the optimal removal efficacy among the three mediums examined. The B2 medium exhibited a secondary level of effectiveness, while the B1 medium exhibited the least efficient removal capacity. With an increase in hydraulic retention time, there was a corresponding increase in the removal rate of the three bioretentions, but a concomitant decrease in the removal rate. The addition of carbon source, whether it be newspaper or wood chips, led to the swift activation of the phytoretention system, thereby enhancing the rate of nitrate nitrogen removal over a brief period.

With the acceleration of global energy transformation, thermal risk issues in power grid project clusters under the context of Energy Internet have become prominent. This paper addresses this issue by constructing a multi-source data fusion thermal risk monitoring model. Using the COMSOL Multiphysics 6.0 platform, four 72 hours simulation scenarios were designed for a 110 kV power grid cluster in an industrial park, consisting of two main substations and eight 10 kV feeders. Simulation results show that in the extreme high temperature scenario (Scenario 3), the top oil temperature of the main transformer reached a peak of 89.3?C, a 22.7?C increase compared to the normal operating condition (Scenario 1), and the average cluster thermal risk index (TRIcluster) was 0.68. In the load sudden change scenario (Scenario 2), the cable joint temperature rose by 18.5?C within 15 minutes, with the maximum single-device thermal risk index (TRImax) being 0.91. In the equipment aging scenario (Scenario 4), the TRIcluster fluctuation increased by 42% compared to Scenario 1. The calculated values and simulation results agree 91.3%, validating the model?s effectiveness. Finally, a cluster collaborative dynamic optimization strategy is proposed to enhance the thermal security defense capabilities of the Energy Internet.

In the context of global device miniaturization trend, there is an urgent need for heat dissipation in electronic devices, and microchannel heat sinks have attracted much attention due to their advantages of small size and high heat transfer capacity. This paper firstly introduces the application of Computational fluid dynamics simulation methods in microchannel research and various design cases, then elaborates the knowledge related to Machine Learning, including its development history, main tasks and common algorithms, and analyzes its performance in predicting the performance coefficients of microchannels. Finally, we discuss the feature importance analysis technique, point out that there are differences in the evaluation of feature importance in different models, and introduce the new SHapley Additive exPlanations technique and its limitations, and finally conclude: Computational fluid dynamics simulation is an important tool for microchannel simulation research, and then when predicting the performance parameters based on the obtained data, it is necessary to select appropriate Machine learning algorithms and extend the data set to improve the prediction accuracy, while the feature importance analysis helps to understand the input-output relationship, which should be combined with physics and ML to improve the analysis method for the optimal design of microchannel heat sinks.

The dynamics of two-phase flow and the thermal distribution characteristics within an epidemic prevention surveillance vehicle exert a profound influence on the risk of cross-infection among occupants and their comfort levels. In this study, numerical simulation techniques are employed to comprehensively analyze the two-phase flow motion and thermal distribution characteristics inside the vehicle. Specifically, the research explores the impact of modular temperature and humidity control on two-phase flow behavior and spatial thermal distribution. The findings indicate that the maximum aerosol concentration recorded was 7.2?10-17 ?g/m3 at a height of 1.5 m above the vehicle floor. Additionally, as the temperature rises, the intensity of the two-phase flow motion concomitantly increases, while the level of humidity exhibits an inverse trend. From the perspective of in-vehicle comfort, the modular temperature and humidity zoning control approach contributes to a more homogeneous distribution of air velocity and temperature within the epidemic prevention surveillance vehicle. Consequently, this configuration offers an augmented level of thermal comfort for both medical personnel and patients

The objective of this paper is to present novel sufficient conditions for the oscillation of all solutions of a class of second-order neutral type damped non-linear differential equations by using the Riccati transformation and integral inequality technique. The merits of the obtained results are illustrated by the following ex-ample.

To address the lack of quantitative comparative studies on the coupled effects of geometric parameters on phase-change heat transfer performance in eccentric elliptical cross-sectional radial gravity heat pipes, this work systematically investigates the effects of eccentricity and elliptical cross-sectional aspect ratio on the internal temperature field distribution, flow characteristics, and total thermal resistance based on a three-dimensional two-phase flow numerical model. The results indicate that the total thermal resistance exhibits a nonlinear trend, first decreasing and then increasing with increasing eccentricity. For the investigated EE-RHP geometry with an outer tube of 60 ? 40 mm and an inner tube diameter of 25 mm, an eccentricity of 10 mm yields lower wall temperatures and the lowest total thermal resistance among the investigated cases under all examined heating power conditions. Further analysis reveals that the aspect ratio influences vapor transport paths and temperature field uniformity by modifying the annular flow channel geometry; as a/b increases, the flow restriction along the minor axis becomes more pronounced, leading to a slight increase in total thermal resistance. A comprehensive comparison demonstrates that a/b = 1.25 achieves a more favorable balance between flow passage continuity and heat transfer area. These results provide quantitative guidance for the selection of structural parameters and the optimal design of eccentric elliptical cross-sectional radial gravity heat pipes.

Sports stadiums, as high-energy-consuming public buildings, face challenges such as high energy consumption and low waste heat utilization. This paper constructs an optimization model for a heat recovery and storage system coupled with the building?s thermal environment. Through key component selection, layout optimization, and multi-operating-condition simulation analysis, the system?s energy-saving performance is improved. A 1:1 stadium model was constructed using the TRNSYS and FLUENT co-simulation platform. Verification results show that the deviation between simulated and measured energy consumption is ?5%, and the temperature deviation is ?1.2?C. Simulation data demonstrates that the system?s heat recovery efficiency reaches 75.4%-78.6% under normal operating conditions, increasing to 79.3%-81.2% under event conditions and remaining above 72.5% under extreme conditions. Daily fluctuations in energy storage are controlled within 5.8%-7.6% under normal operating conditions and approach 8% under extreme conditions. The heat pump COP reaches 4.5 at 25?C, but significantly decreases to 2.3 at -5?C. This efficiency exceeds traditional rotary heat exchangers (?60%) by 15%-20%, demonstrating significant improvement. The research achieved the goals of heat recovery efficiency ?75% and energy storage fluctuation ?8%, providing support for upgrading energy-saving technologies in sports stadiums.

Icing and ice accumulation on blades or cooling apertures in aero-engines along with other mechanical systems present significant hazards to operating efficiency and structural integrity. Porous structures enhance fluid flow and modify thermal conductivity. Therefore, this study examines droplet spreading and heat transfer on subcooled porous surfaces, modelling the interactions between droplets and porous structures based on the multiphase flow-phase field method coupled with a heat transfer module. The model's accuracy and reliability were validated by comparing experimental results with numerical simulations. The dynamics of droplets on porous surfaces are examined in detail, focusing on the influence of Weber number, while emphasizing its effect on the coupled heat transfer during impact on subcooled porous structures. It is found that increasing the Weber number markedly enhances droplet dynamics, resulting in a larger spreading factor and an increased tendency for icing, accompanied by pronounced secondary heat transfer effects, which modify the local heat flux distribution during the impact and solidification processes. When the Weber number increases from 60 to 160, the peak heat flux is observed to increase by approximately 90%. In addition, the reduction in surface wettability leads to distinct penetration and heat transfer modes of the droplet. Specifically, the peak heat flux is lowered by roughly 3% compared with the normal wettability conditions, and no pronounced secondary heat transfer behavior is observed. These results provide theoretical insights into droplet icing behavior and support the development of more effective anti-icing strategies.

High temperature thermal storage systems are a key enabler of energy transition. China has over 50 MW-scale projects, with an average annual capacity expansion of 25%. However, operating temperatures of 300-1000?C are prone to failures such as molten salt freezing, pipe-line corrosion, and leakage, resulting in a 15%-20% unplanned system downtime rate and annual economic losses exceeding 10 million Yuan for a single 100 MW-scale system. Existing diagnostic methods suffer from bottlenecks such as long modelling cycles, strong sample dependency, and slow deployment. The low code platform cuts deployment from six months to 45 days. A 10 MW ANSYS FLUENT platform (4M grids) tested six scenarios (three freezing, the leakage, and ten reps each), proving the method?s superiority for efficient operation. A fault characteristic model is constructed, including the molten salt freezing resistance coefficient and corrosion rate formula. Wavelet denoising and spatiotemporal interpolation are used to preprocess data. An algorithm is developed that integrates an improved RBF neural network (12 nodes in the input layer and 18 nodes in the hidden layer) with a fault knowledge base. A 10 MW system simulation platform (4 million structured grids) was built using ANSYS FLUENT. Six fault scenarios (three types of freezing and three types of leakage) were designed, with each scenario tested ten times. Results showed that this method achieved a diagnostic accuracy of 97.2%, an improvement of 18.7 and 7.9 percentage points, respectively, compared to the traditional threshold method (78.5%) and a single BP network (89.3%). The identification rate for minor faults was 92.3%, with a false alarm rate of 2.8%. The average diagnostic time was 1.8 seconds, supporting efficient system operation and maintenance.