Abstract:Battery energy storage systems (BESS) feature rapid response and strong regulation capabilities, making them effective in handling the intermittency of renewable energy. However, as the penetration of BESS increases, their failure characteristics pose a growing influence on power system reliability. To address this issue, this paper proposes a reliability assessment method for power system operation that explicitly accounts for BESS failures. First, a multi-dimensional performance degradation probability model of BESS is established using the generalized generating function. By integrating this model with the probability of thermal runaway failure, an available output power model that comprehensively considers BESS failure is established. Then, a load curtailment optimization model considering BESS is constructed. Furthermore, a Monte Carlo simulation-based reliability assessment method is proposed to capture both thermal runaway failures and multi-dimensional performance degradation of BESS. Finally, the effectiveness of the proposed method is verified through case studies in the IEEE RTS-79 bus system.

Abstract:Driven by the “dual-carbon” goals, this study proposes a two-layer capacity optimization method for compressed air energy storage (CAES) in abandoned mines, addressing both the challenges of renewable energy integration and the resource utilization of abandoned mine spaces. Unlike traditional static capacity designs that rely solely on roadway volume, this approach balances economic performance with renewable energy integration, providing a practical framework for planning energy storage systems in abandoned mines. The proposed model consists of a planning layer and an operation layer: the planning layer seeks overall economic optimality, while the operation layer aims to maximize renewable energy utilization. These layers interact iteratively, and the model is solved using an improved particle swarm optimization algorithm to determine the optimal configuration. Multi-scenario simulations based on the modified IEEE 33-node system show that, compared with traditional fixed-capacity configurations, the proposed model increases renewable energy absorption by an average of 6.53% and reduces total costs by 45.45% across four typical scenarios. The results verify the model’s effectiveness in improving both renewable energy utilization and economic performance.

Abstract:The active neutral point clamped (ANPC) three-level topology with hybrid SiC and Si power module is widely used in photovoltaic and energy storage systems due to its high efficiency. However, in conventional hybrid SiC/Si power modules, the use of Si devices constrains efficiency improvements, while the introduction of SiC devices can lead to challenges such as uneven heat distribution, voltage overshoot, and oscillation. To address these issues, this paper proposes a comprehensive design method that incorporates both loss equalization of power devices and parasitic inductance optimization of the module layout to improve the performance of hybrid SiC/Si ANPC modules. First, a loss model of the power module is established, and thermal performance is optimized to reduce junction temperatures and chip-to-chip temperature differences. Second, a parasitic inductance model is constructed, and inductance is minimized through optimized layout design. Finally, a hybrid SiC/Si power module based on the ANPC topology is developed and experimentally evaluated. The results demonstrate significant improvements in terms of power loss, parasitic inductance, and thermal distribution, verifying the effectiveness of the proposed electro-thermal optimization design.

Abstract:A simulation model of a homogeneous charge compression ignition (HCCI) internal combustion engine was developed using the CHEMKIN software to investigate the effects of key parameters, including hydrogen doping ratio (α), inlet temperature, and equivalence ratio (φ), on combustion performance. The study focused on in-cylinder temperature and pressure, heat release rate and NO emissions. Results reveal that increases in α and inlet temperature lead to higher in-cylinder temperature, pressure, and heat release rate, as well as earlier ignition timing. When φ approaches 1, peak values of in-cylinder temperature, pressure, and heat release rate are maximized, while the ignition timing advances as φ decreases. In-cylinder NO formation is only slightly affected by α; however, the peak mole fraction of NO rises with increasing α. As the in-cylinder combustion concludes, NO emissions decrease significantly. Increasing α from 0 to 0.2 considerably reduces NO emission. Although the main NO formation pathways remains unchanged, the total reaction rate increases. NO in the cylinder primarily originates from HNO, while NH primarily consumes NO. These findings provide feasibility evidence for improving combustion efficiency and emission performance in future ammonia-hydrogen HCCI engines.

Abstract:The comprehensive performance of a lithium battery thermal management system (BTMS) is critical to battery capacity and service life. To improve the system performance after module packaging, this study proposes a novel liquid cooling plate structure incorporating a double-channel Tesla valve. First, numerical simulations were conducted to compare the cooling performance of same-side versus opposite-side outlets, as well as to evaluate the double-channel Tesla valve against the original Tesla valve and a straight channel design. Then, an orthogonal experimental design was used to identify four key parameters with significant impact on overall performance. A Kriging response surface model was then established to describe the relationship between design variables and objective functions, followed by multi-objective optimization using the non-dominated sorting genetic algorithm (NSGA-Ⅱ). Results show that the opposite-side inlet-outlet configuration provides superior cooling performance. Under counterflow conditions, the double-channel Tesla valve reduced the maximum battery temperature ( T _max) by 0.67 ℃ compared with the straight channel, while the pressure drop (Δ p) was 117.67 Pa and 437.39 Pa lower than those of the original Tesla valve and the straight channel, respectively. After optimization, the improved Tesla valve channels reduced Δ T and Δ p by 1.52% and 11.16%, respectively, while increasing the cooling plate thermal performance factor (CTPF) by 4.81%. These findings provide a valuable reference for the structural design and optimization of liquid cooling systems for power batteries.

Abstract:Based on the interpretable Machine learning algorithm gradient boosting machine (GBM), this study employs the long-term pavement performance (LTPP) database to predict the rut depth of asphalt pavement by considering various influential factors, including environmental, traffic, structural, and material variables. Compared with artificial neural network (ANN) and support vector machines (SVM), the GBM model provides superior interpretability by explaining the partial dependence of key factors. The results show that, compared with ANN and SVM, the GBM model reduces the RMSE by 0.75 and 0.25, and the MAE by 0.54 and 0.07, respectively, on the test datasets. The main factors affecting rut depth include the initial rutting depth measurement, time elapsed since the first measurement, total asphalt pavement thickness, and cumulative equivalent single axle load (ESAL). The partial dependency analysis helps pavement maintenance departments better understand rutting development under various influential factors, thereby supporting more effective pavement maintenance and management decisions.

Abstract:To improve the elastic shear buckling strength (ESBS) of variable cross-section corrugated steel webs (CSWs) while controlling engineering cost (EC), this study optimizes the web geometry of a continuous rigid frame bridge with CSWs of variable cross-section. First, finite element analysis (FEA) models of CSWs with different geometric parameters were established. The relationships between web dimensions and ESBS were obtained through response surface fitting, while the relationships between geometric parameters and EC were calculated using the slicing method. Second, the Pareto optimal solution set was derived using the NSGA-Ⅱ algorithm. The combined weight of the optimization objectives was determined by integrating the expert scoring method, the entropy weight method, and the minimum deviation principle, and the optimal scheme was selected using the technique for order preference by similarity to ideal solution (TOPSIS). Finally, the impact of parameter variations on the optimization results was analyzed. The results verify that the established FEA model and fitting formula are accurate and effective through buckling mode validation and response surface significance tests. Compared with the original design, the optimal scheme increases the ESBS by 93.5% and the EC by 37.1%. Some Pareto solutions outperform the original design in both indicators, indicating potential for improvement in the original dimensions. According to the response surface fitting formula, ESBS increases with larger wave height, plate thickness, and short-side height, but decreases with larger flat strip width and long-side height. The subjective weighting significantly affects the final scheme selection: as the subjective weight of ESBS increases, both ESBS and EC in the optimal scheme rise accordingly.

Abstract:Accurate water quality prediction is essential for effective ecological water management. However, water quality exhibits complex non-stationary dynamics and multi-dimensional nonlinear relationships driven by temporal evolution and environmental variability. In multi-parameter river water quality prediction, intricate spatial-temporal dependencies make it difficult for traditional models to effectively integrate dynamic topology and long-period features. To address this challenge, we propose a spatial-temporal graph convolutional network (STGCN) model. In the temporal dimension, a masked sub-series transformer module is employed to extract long-term trends through self-supervised pretraining. Combined with dilated causal convolution, it captures cumulative water quality effects and alleviates the response lag common in traditional models when facing abrupt changes. In the spatial dimension, a dynamic graph learning module integrates a predefined station-distance adjacency matrix with a dynamic residual map to generate adaptive graph structures. Experimental results demonstrate that the proposed model outperforms existing methods in water quality prediction, achieving an R2 greater than 0.93 across all water quality indicators.

Abstract:The agglomeration characteristics and radiation effects of urban commercial center nodes make them the “engines” driving urban development. Evaluating and applying the concept of “radiation ability” can deepen the understanding of the underlying mechanisms and provide guidance for urban renewal and planning practices. From the dimensions of “radiation source” and “radiation channel”, this study constructs an evaluation model for assessing the node radiation strength of urban commercial centers by selecting eight primary indicators and twenty-nine secondary indicators. Taking Chongqing’s five main business areas as examples, three principal component factors of radiation ability are extracted through principal components analysis : f₁ (spatial interaction and communication), f₂ (functional layout), and f₃ (spatial structure). The radiation index (R) of the five business areas is then calculated, and the differences in radiation capability among them are analyzed based on R and the scores of each principal component. Finally, guided by the goal of improving the radiation strength of commercial center nodes, this study proposes spatial renewal and optimization pathways focusing on strengthening spatial propagation channels, enhancing functional features, and developing a multi-core spatial structure.

Abstract:The transport and placement of proppant within fractures in the process of hydraulic fracturing significantly impact oilfield productivity. In this study, by using the Cross constitutive equation and the Eulerian-granular multiphase flow model, we conducted numerical simulations of sand-carrying flow within fractures with considering fluid filtration loss along the fracture wall. The model was experimentally validated using a custom-designed fracture test facility. The results indicate that, for Cross fluid, an increase in wall filtration loss rate leads to a gradual reduction in the volume fraction of proppant within the fracture, accompanied by a decrease in sand bank height although the overall difference is small. This study further confirms that considering wall filtration, the use of small-particle-size, low-density proppants significantly enhances their placement efficiency at the distal end of fractures and reduce the risk of sand blockage.