Abstract:To address the capacity mismatch between heat injection and thermal diffusion in thermally activated walls, this study proposes an enhanced thermally activated wall (ETAW) design to improve energy-storage efficiency and energy-saving potential. A dynamic heat transfer model is developed to compare the thermal performance of ETAW with that of conventional thermally activated walls (CTAW) and conventional energy-saving walls (CW). Local sensitivity analysis is conducted to investigate the economic impacts of fin parameters, climate conditions, and insulation thickness. Results demonstrate that ETAW exhibits significantly superior dynamic thermal performance relative to CTAW and CW, although the degree of improvement depends on the heat injection mode. Increases in trunk fin size and branch fin size both effectively reduce total operating energy consumption and costs, with the branch fin size exhibiting a more pronounced influence. Adopting a smaller branch-fin inclination angle (e.g. 60°) and a left-oriented installation can reduce operating costs and energy consumption by approximately 10.9% and 10.7% respectively. Insulation thickness shows strong correlations with energy efficiency and economic performance; recommended reduction rates should not exceed 40% in severe cold zones and may be extended to up to 60% in hot-summer zones.

Abstract:To evaluate the contribution of a built-in fin-type Trombe wall to reducing indoor heating energy consumption, this study takes a typical residential building in Lanzhou as the research object. Fins are installed on the heat-absorbing surface of the Trombe wall to enhance heat transfer and thereby improve the indoor thermal environment. The results show that the isosceles right-triangle fins with a height of 20 mm, a transverse spacing of 0.20 m, a longitudinal spacing of 0.533 m, and an in-line configuration provide the greatest improvement in heat transfer performance and indoor thermal conditions. Over the entire simulation period, the average Nusselt number (Nuˉ) of the fin-type Trombe wall is 21.28 higher than that of the finless wall. Compared with a conventional room, the energy-saving rates of the finless Trombe wall and fin-type Trombe wall rooms are 36.38% and 44.63% respectively, meaning the fin-type Trombe wall yields an additional 8.25% energy-saving improvement.

Abstract:Accurate forecasting of building energy consumption is crucial for optimizing energy management, reducing operational costs, and achieving carbon neutrality goals. This study proposes a multi-scale interpretable temporal prediction network model (ITSFN), which enhances prediction accuracy and reliability through the collaborative optimization of long short-term temporal (LSTM) networks and Kolmogorov-Arnold networks (KAN). The model integrates temporal-environmental feature decoupling with a dynamic attention mechanism, explicitly decomposing time-series data into seasonal, trend, and residual components to construct a structured feature space. It employs a parallel architecture of gated recurrent units (GRU) and multi-head attention to model multi-scale features. Tested on an energy consumption dataset from a university building in a hot-summer/cold-winter region, ITSFN outperforms traditional models: it reduces the root mean square error (RMSE) of total energy consumption prediction by 13.9% compared to LSTM and decreases the RMSE of sub-item energy consumption prediction by 31.1% compared to Transformer. Additionally, ITSFN enhances the noise suppression coefficient to 0.89 through feature decoupling, achieves a local attention angle of 0.92 in mutation regions, and reduces over-smoothing by 29.6% compared to traditional methods. By quantifying feature contributions, the model reveals the evolutionary patterns of component weights, further validating its effectiveness and practical applicability.

Abstract:To address the issues of computational redundancy and communication burden in model predictive control (MPC) of indoor thermal environments, this study proposes an integral-type event-triggered control strategy. First, a simplified building resistant-capacitance (RC) thermal network model is established using an equivalent circuit method, incorporating the influence of adjacent thermal zones, and its accuracy is verified. Then an integral-type event-triggered mechanism (ITETM) based on state errors is introduced. Building on this mechanism, an integral-type event-triggered MPC method grounded in the RC thermal network model is formulated. Finally, the performance of the proposed control method is verified by co-simulation experiments using EnergyPlus and MATLAB. The results show that the proposed control strategy effectively reduces computational effort and communication frequency in the optimization process, while lowering building energy consumption and maintaining indoor thermal comfort.

Abstract:As a major temporary facility in railway construction, the carbon emissions generated by track-laying bases constitute a significant source of embodied carbon during the materialization stage. In this study, a carbon emission measurement model for the life cycle of railway track-laying bases is established using the carbon emission factor method. The characteristic emission contributors are then extracted as potential influencing factors, and key factors are identified by feature-importance ranking. Furthermore, an interpretable machine learning model is used to visualize the contribution of these key factors and to analyze their impact mechanisms on carbon emissions. The results show that the total life cycle carbon emissions of a track-laying base range from 4 825.134 t to 15 122.059 t. Carbon emissions from building materials in the production stage account for the largest share (72% to 86%). According to the importance ranking, the five key influencing factors are base area, foundation treatment method, road hardening method, mechanical track length, and stock track length. The influence of these key factors on carbon emissions are further analyzed by SHAP (Shapley additive explanations) summary plots and dependency scatter plots. The findings provide a theoretical basis for promoting carbon reduction strategies in the construction and operation of railway track-laying bases.

Abstract:Under the background of the “dual carbon” goals, exploring new pathways for industrial restructuring, reducing carbon emissions, improving energy efficiency, and developing new high-quality productivity for CO₂ high-energy utilization has become a research focus. To determine the mechanism of CO₂ phase-change-induced cracking and expand its engineering applications, this study investigates the safety and environmental advantages of CO₂ based on its multiphase characteristics and energy-utilization potential. A numerical gas-water coupling model is established. Using CO₂ at 25 ℃ as the research object, a simulation scheme is designed to examine different water temperatures as heat-carrying fluids, thereby revealing the CO₂-water heat-transfer mechanism under hydrothermal-CO₂ coupling. Furthermore, the heat and mass transfer characteristics of two-phase flow are analyzed by a CO₂-water enhanced tube-type heat-exchanger test. The results show that as the hydrothermal-fluid temperature increases, the CO₂ heating rate increases proportionally: for every 1 ℃ increase in water temperature, CO₂ temperature increases by 0.9 ℃. The water-stream temperature gradually decreases as CO₂ absorbs heat, and the dissipation temperature of the hydrothermal fluid is proportional to the CO₂ absorption temperature. The heat-transfer coefficient of the hydrothermal fluid increases from 1 790 W/(m²·K) to 2 090 W/(m²·K) and continues to rise with higher initial water temperatures. The heat transfer coefficient of CO₂ is positively correlated with that of the hydrothermal fluids. The CO₂ phase-change heat-absorption temperature shows an exponential growth trend with increasing water temperature. The maximum internal CO₂ pressure increases from 131 MPa to 199 MPa, undergoing sequential stages: thermal expansion of liquid CO₂, thermal expansion of gaseous CO₂, phase-change energization, and pressure stabilization. CO₂ heat absorption is positively correlated with the initial water temperature, and the thermal power of the water-flow heat source increases with rising water temperature. The effectiveness of supercritical CO₂-water convective heat transfer is verified through the establishment of correlation equations and experimental analysis. This research provides significance theoretical and engineering insights for energy-conversion applications under non-uniform heat-flow conditions, such as photothermal systems, boilers, and CO₂ fracturing and storage.

Abstract:Aiming at the problems of complex installation, high power supply and maintenance costs of existing transmission line wind deflection/galloping monitoring terminals, this paper proposes a wireless intelligent monitoring scheme based on spacer dampers: MEMS(micro-electro-mechanical-system)three-axis inertial sensors are embedded in the spacer dampers to obtain the three-directional vibration and torsional responses of the conductors; combined with the compressed sensing framework and the orthogonal matching pursuit (orthogonal matching pursuit) reconstruction algorithm, the sparsity of data is utilized and a Gaussian random sensing matrix is adopted to realize the reconstruction of the full-line galloping curves. The simulation and experimental results show that: under the given span condition, the sparsity can be adaptively set with the sampling length; when the observation ratio is 10% to 20%, high reconstruction accuracy can be achieved, which meets the online monitoring needs of long-span lines. This wireless intelligent spacer damper monitoring scheme provides an engineering feasible path for the real-time monitoring and reconstruction of transmission line wind deflection/galloping.

Abstract:As one of the mainstream direction finding schemes, correlative interferometer has many extraordinary advantages, such as low complexity computation, high accuracy, strong anti-interference etc. Besides, the uniform circular array can measure the azimuth angle and elevation angle simultaneously, and has the advantages of uniform direction finding accuracy and compact structure. Motivated by the above, this paper investigates the correlative interferometer direction finding using a uniform circular array. Since traditional electrical phase detectors are faced with some bottlenecks, such as bandwidth limitation, low frequency, electromagnetic interference etc., a photonic phase detector scheme has been presented to measure the phase difference. A dual-parallel Mach-Zehnder modulator (DPMZM) is adopted to construct an optical parallel interference structure which can estimate the phase differences by optical power measurement. The phase shift in DPMZM can be set to 0° and 180° by adjusting the DC bias. Then, the power of the output optical signal can be measured in both two cases. Besides, a Phase difference mapping function based on the ratio of optical power measurements has been proposed to improve the resolution of the power measurement and the estimation accuracy of the phase difference. Finally, the availability of the proposed scheme is investigated via experiments which shows that measurement error of phase difference is less than ±1° from 0° to 360° and the angle-of-arrival estimation accuracy is less than 1.008 2°.

Abstract:Metal parts are widely used in various fields, and their surface defects usually distribute unevenly and some characteristics are weak, which often causes missing and false detection. To solve this problem, a YOLOv5s-MD algorithm is proposed. Aiming at the problem of complex features of metal surface defects, an improved spatial pyramid pooling module is introduced to improve the deep feature extraction for small targets of different sizes. To address the problem of feature dispersion and calculation increase, a lightweight attention mechanism and the GSConv module are added to improve the model’s ability to effectively extract defect features at different sizes. For the boundary regression mismatch caused by irregular size information of metal surface defects, a loss function considering vector angle is adopted. The results show that the YOLOv5s-MD algorithm has an average accuracy of 75.3% in metal surface defect detection, which can effectively increase the detection accuracy and reduce the false detection rate for metal surface defects.

Abstract:Multimodal lip recognition aims to enhance speech recognition accuracy and robustness by integrating lip movements and speech information, while also aiding specific user groups in communication. However, existing lip-speaking models predominantly focus on English datasets, leaving research on Chinese lip recognition in its nascent stage. Addressing challenges in handling data features across different modalities, integrating these features, and achieving comprehensive fusion of multimodal features, we propose a multimodal split attention fusion audio visual recognition (MSAFVR) model. Through experiments utilizing a Chinese Mandarin lip reading (CMLR) dataset, our model, MSAFVR, demonstrates significant advancements, achieving a remarkable 92.95% accuracy in Chinese lip reading, surpassing state-of-the-art Mandarin lip reading models.

Abstract:Cycle mining can help people deeply understand the structure and function of complex networks, which is of great significance for practical application fields such as road traffic networks, bioprotein networks, financial and economic networks, etc. However, the massive data in the information age makes cycle mining extremely challenging. In response to the problem of large data volumes but relatively limited available data that cannot mine complete cycles, the concept of approximate cycle (AC) is defined, and the approximate cycle detection algorithm (ACD) and its optimization algorithm (IACD) are proposed. Both algorithms are divided into three stages: first, calculate hotpoints through vertex degree calculation; secondly, perform forward and backward searches on the dataset based on hotpoints to obtain hotpoints and their neighbors, and use this to construct an index (H-Index); finally, calculate the tightness coefficient and average tightness coefficient between different vertices based on H-Index, the path between vertex pairs with a tightness coefficient greater than the average tightness coefficient is an approximate cycle. The IACD algorithm has been optimized in two aspects based on the ACD algorithm. On the one hand, it increases the deduplication of vertices in the acquisition of hotpoints and their neighbors, while reducing the number of searches for data. On the other hand, it uses function vectorization instead of cyclic modification in the construction of indexes. The experimental data used are all real datasets of SNAP public website. The experimental results show that both algorithms can run smoothly on larger datasets and have good scalability and efficiency. The efficiency of the IACD algorithm is about 25% higher than that of the ACD algorithm.