Yanting Zhang

To enhance the load-carrying capacity and operational stability of dry gas seal gas films while reducing gas leakage, and to provide a theoretical basis for structural optimization and innovation of face seals, a numerical model of gas film lubrication in face dry gas seals considering the geometric effects of combined micro-textures is developed based on the governing equations of gas lubrication. The finite difference method is employed to numerically solve the gas film pressure distribution. With the objectives of maximizing the opening force and minimizing the leakage rate, the influences of combined micro-texture structural parameters on gas film performance under typical operating conditions are systematically investigated, and favorable parameter ranges are identified. The results show that the proposed model exhibits high accuracy and reliability, with good agreement with published data. Different combined groove textures significantly affect the gas film thickness and pressure distributions, leading to distinct bearing and stability characteristics. When opening force and leakage are jointly considered, the sealing performance ranks as triangular composite texture, semicircular composite texture, rectangular composite texture, and trapezoidal composite texture. Quantitatively, the trapezoidal texture exhibits the largest increase in the opening force–leakage ratio of approximately 0.29%, whereas the triangular texture shows the smallest increase of about 0.19%. Reasonable design of combined micro-textures can effectively improve the comprehensive gas film performance of face dry gas seals, achieving a coordinated enhancement of opening force and reduction in leakage. The present study provides theoretical guidance for the structural design and engineering application of high-performance dry gas seals.

In deepwater riserless drilling, inefficient cuttings transport remains a critical challenge, leading to incidents such as stuck pipes and lost circulation. While existing studies have primarily focused on liquid–solid two-phase flow, they largely overlook the nonlinear coupling effects of bubble dynamics (coalescence and breakup) in gas–liquid–solid three-phase systems, resulting in poor predictive accuracy under high-pressure, high-viscosity conditions. This study introduces a systematically integrated framework that presents a novel integration of visualization experiments, multiscale numerical simulation (CFD-DEM coupled with the population balance model, PBM), and machine learning. This methodology provides an in-depth understanding of the bubble coalescence and breakup mechanisms in cuttings bed formation and transport efficiency under riserless drilling conditions. The coupled CFD-DEM-PBM model provides high-fidelity quantification of bubble dynamics and its cross-scale interaction with cuttings. Subsequently, high-resolution, experimentally validated data are utilized to train both backpropagation (BP) and radial basis function (RBF) neural networks to achieve physics-informed, intelligent prediction of cuttings concentration and transport rate. The results demonstrate that bubble coalescence reorganizes local flow fields to suppress sedimentation, whereas bubble breakup induces microscale turbulence that promotes particle resuspension; their synergy critically governs the overall transport efficiency. Notably, the RBF neural network achieved superior predictive accuracy (R ² = 0.91182), significantly outperforming both the BP network and traditional empirical models. This work not only offers a reliable basis for real-time parameter optimization and decision-making but also establishes a new paradigm for understanding complex multiphase transport phenomena through the seamless integration of high-fidelity simulation, systematic experimentation, and explainable machine learning.

Concentrating on the problem of massive energy loss in the compressor, expansion valve, and the other components present in the high-temperature heat pump system under extensive temperature lift, the dual-flash compound circulation system is proposed and the thermodynamic model of the dual-flash compound circulation system was established. The article combines the multivariate simulated annealing algorithm, utilizes the system COP as the optimization goal, and completes the calculation of the thermodynamic parameters in the steady-state of the system that is based on satisfying the conditions of the system process. Using R245fa as the refrigerant, the condensation temperature is set within the range of 110°C–140°C for the model calculation. The results show that, compared to the traditional two-stage compression system under the same environment, the COP of the dual-flash compound circulation system can be increased up to 5.71%–12.13%, and the exergy efficiency can be increased by 5.11%–10.71%, respectively. Besides, steam production per unit refrigerant is also increased by 3.79%–5.14%. Finally, the feasibility of the theoretical model is verified by simulation, and it is concluded that the dual-flash compound circulation system has better steam production performance at the extensive temperature lift and the elevated condensation temperature.

In this paper, a composite of tin diselenide (SnSe₂) functionalized by graphite-phase carbon nitride (g-C₃N₄) was successfully prepared by a hydrothermal method, and was characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). These microstructure characterization results verified the successful synthesis of a multilayer g-C₃N₄/rod-shaped SnSe₂ composite. The gas sensitivity results showed that when the g-C₃N₄ ratio was 30%, the g-C₃N₄/SnSe₂ composite sensor had the highest response (28.9%) at 200 °C to 20 ppm sulfur dioxide (SO₂) gas, which was much higher than those of pristine g-C₃N₄ and SnSe₂ sensors at the optimum temperature. A series of comparative experiments proved that the g-C₃N₄/SnSe₂ composite sensor demonstrated an excellent response, strong reversibility and good selectivity for ppm-level SO₂ gas detection. The possible SO₂ sensing mechanism was ascribed to the heterostructure between the n-type SnSe₂ and n-type g-C₃N₄ nanomaterials. Furthermore, we also proposed the influence of the special structure of the g-C₃N₄ functionalized SnSe₂ composite on the gas-sensing characteristics.