Search Results (2,660)

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

This study presents a comparative analysis of six DFS implementations representing different maturity levels and investigates the systemic gap between technological capabilities and regulatory approaches. A structured narrative review with case-based analysis was conducted using the Scopus database (2015–2026) with six targeted queries. The case selection followed the PICo protocol. An original ten-criterion DFS maturity assessment rubric—grounded in the Technology Readiness Level (TRL), Integration Readiness Level (IRL), and Digital Twin Maturity Model frameworks—was applied to all six cases. Inter-rater validation yielded substantial agreement (κw = 0.797; unweighted κ = 0.674 [95% CI: 0.509, 0.839]). The results indicate a clear maturity gradient (Dimension X: 4–9 points; Dimension Y: 2–8 points). Benefits reported in the analysed primary studies include up to a 55 s reduction in evacuation time, a 72% improvement compared with static signage, and a 34-percentage-point increase in evacuation success rate under simulation-based conditions. Five normative recommendations are proposed to address the structural regulatory gap between current prescriptive frameworks and DFS deployment in Poland and the EU. This study argues that prescriptive rules should remain the baseline, whereas complex facilities may adopt performance-based DFS solutions, provided that equivalence to conventional protection levels is rigorously demonstrated. From a sustainability perspective, the study frames DFS as a dynamic safety layer that supports occupant protection, operational resilience, and lifecycle adaptability in complex buildings exposed to uncertain fire and crowd conditions. Full article

Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems. Yet today, dynamic bandwidth allocation (DBA) in the PON and physical resource block (PRB) scheduling in the mmWave RAN operate independently, a critical design flaw that causes severe latency accumulation, resource fragmentation, and consistent failure to meet the divergent quality-of-service requirements of network slices. This paper breaks that deadlock by introducing the first slice-aware, computationally efficient orchestration framework that jointly optimises DBA and PRB allocation in a converged mmWave-PON O-RAN. We formulate the problem as a constrained Markov decision process (CMDP) with explicit latency, reliability, and throughput constraints for URLLC, eMBB, and mMTC slices. The core technical advance is a reward-shaped proximal policy optimisation (RS-PPO) algorithm whose potential-based shaping function directly penalises DBA–PRB misalignment and dense feedback on queue build-up, accelerating learning without compromising optimality. To make this work in near-real time on the O-RAN RIC, we embed three complementary efficiency engines: graph convolutional network (GCN) state abstraction, action masking, and prioritised N-step replay. Extensive 3GPP-compliant simulations show that RS-PPO slashes URLLC end-to-end latency by 37% (from 1.38 ms to 0.87 ms), boosts PRB utilisation by 28% (from 68% to 87%), and delivers 99.999% reliability, all while converging 45% faster and cutting inference time by 45% (to just 2.3 ms). The result is a sub-5 ms control cycle, compatible with O-RAN specifications and deployable as an xApp on the near-RT RIC. Our framework closes a long-standing coordination gap left unresolved by prior art, enabling true slice-aware convergence between the optical and wireless domains. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

Maintaining good indoor air quality (IAQ) is essential for student health, cognitive performance, and overall well-being. Traditional ventilation strategies, particularly constant air volume systems and manual window operation, often fail to maintain optimal IAQ while simultaneously increasing building energy consumption. In response, smart ventilation systems have emerged as a promising alternative capable of dynamically modulating airflow based on occupancy patterns and real-time pollutant levels. This study presents a systematic review of fourteen carefully selected peer-reviewed studies (2015–2025) that represent the most recent and methodologically robust research on smart ventilation applications in school environments across diverse climatic conditions. The selected studies encompass experimental, simulation-based, and hybrid methodologies, and classify control strategies into demand-controlled, temperature-adaptive, occupancy-based, AI-enhanced, and building management system (BMS)-integrated approaches. Collectively, the findings demonstrate measurable improvements in IAQ indicators (e.g., carbon dioxide (CO₂), particulate matter (PM_2.5), ozone (O₃), and volatile organic compounds (VOCs)) and significant energy savings, in some cases exceeding 60%, while also identifying system vulnerabilities such as fault sensitivity, short monitoring durations, and limited long-term validation. Importantly, the review reveals critical geographic and climatic research gaps, particularly in hot–arid regions where ventilation-related cooling demand is substantial, as well as limited long-term assessments in cold climates. Furthermore, although smart ventilation systems perform effectively under controlled conditions, insufficient real-world verification, user interaction analysis, and climate-specific optimization constrain broader implementation. Addressing these gaps through climate-dependent performance evaluation and long-term operational studies is essential to unlocking the full potential of smart ventilation systems in delivering healthier, energy-efficient classrooms. Full article

With the integrated development of high-speed railways and urban underground rail transit, large high-speed railway station buildings are often seamlessly connected or even co-constructed with subway structures, forming a complex structural system that integrates high-speed rail, subway, and station buildings. To investigate the dynamic performance of such “ integrated station-bridge” station buildings constructed with subways, this paper takes Yichang North Station as an engineering case study and examines its vertical dynamic characteristics under multi-source train-induced loads. The station adopts a structural configuration where the station tracks are fully integrated with the station building, while the main lines are separated from it. To accurately simulate the entire process of train operation, this study established a refined “train-track-station” spatially coupled dynamics model that incorporates high-speed and subway trains, tracks, and the station structure. Based on this model, various operational scenarios were systematically analyzed, including high-speed trains passing at different speeds, parallel operation of multiple train lines, and combined operation of high-speed and subway trains. The results demonstrate that, when single or multiple high-speed train lines pass through the station at the design entry speed of 80 km/h, the vertical vibration acceleration of the elevated waiting level meets human comfort standards. The train-induced vibration response is transmitted and superimposed along the “column–beam–slab” path, resulting in localized acceleration peaks at the mid-span regions of beams and slabs directly above the tracks. Second, the impact of subway train operation alone on the vibration of the elevated level is significantly weaker than that of high-speed trains. Furthermore, under combined high-speed and subway train operations, the additional vibration contribution from subway trains shows a decreasing trend as the number of simultaneously operating high-speed train lines increases. The findings of this study validate the effectiveness of the structural design of Yichang North Station in terms of train operational safety and passenger waiting comfort. The revealed patterns of multi-source vibration transmission and superposition can provide important theoretical and numerical references for the dynamic optimization design and vibration control of similar integrated transportation hub structures. Full article

In sustainability and environmental performance assessment, as in many other multidimensional policy contexts, Benefit of the Doubt (BoD) composite indicators are widely used to compare units across multiple dimensions. However, their interpretation remains limited when panel data are available. In these settings, observed changes over time may reflect two distinct sources: a unit may move closer to cumulative best practices, thereby generating a catch-up effect, or the best-practice frontier itself may evolve, generating a benchmark shift effect. The present paper proposes a dynamic decomposition framework that separates these two components. Building on the reference technology approach pioneered by Tulkens and Vanden Eeckaut, the BoD setting is adapted to include contemporaneous, sequential, and intertemporal frontiers. This yields three indices that satisfy an exact multiplicative decomposition at the unit level. The framework is fully non-parametric, producing unit-specific measures of catch-up alongside a cross-sectional summary measure of benchmark shift. The results of simulation experiments conducted under pure benchmark shift, pure catch-up, and mixed dynamics demonstrate the efficacy of the method in accurately recovering the underlying data-generating processes. As an empirical illustration, the framework is applied to renewable energy performance across European Union countries over the period 2015–2024, using Eurostat SHARES (Short assessment of renewable energy sources) data. The empirical results indicate an average annual benchmark expansion of approximately 3.3%, together with heterogeneous catch-up dynamics across country groups. Full article

Phase change materials (PCMs) have been widely investigated for latent thermal energy storage in building envelopes; however, conventional single-melting-point PCMs often exhibit limited adaptability under dynamically varying thermal conditions. This study investigates the thermodynamic feasibility of hybrid-melting-point PCMs to improve transient thermal regulation in multilayer building wall systems. A transient numerical model was developed to evaluate wall assemblies incorporating single and hybrid PCM configurations under structured dynamic thermal loading conditions representing mild, hot, and cold regimes. To isolate the influence of melting-point distribution, hybrid systems containing multiple phase-transition temperatures were compared against conventional single-transition PCM systems with identical total latent heat capacities. The results demonstrate that distributing melting thresholds broadens the effective activation temperature range and enhances attenuation of indoor temperature fluctuations under varying thermal loads. Compared with the conventional single-melting-point system, the proposed hybrid configuration reduced peak indoor temperature by up to 18.5% and increased the minimum indoor temperature by up to 51.9%. Additional material-level simulations revealed that staged phase transitions promote sequential latent heat activation and prolong thermal buffering behavior. The findings suggest that hybrid-melting-point PCMs can improve the transient thermal adaptability of PCM-integrated building envelopes without increasing total latent heat storage capacity. The present study is intended as an exploratory thermodynamic feasibility assessment rather than a climate-specific annual building-energy prediction framework. Full article

This paper proposes a decentralized data trading approach based on the Automated Market Maker (AMM) mechanism, aiming to address the bottlenecks in data trading efficiency and fairness via the collaborative innovation of market-oriented pricing mechanisms and automated trading processes. We focus on constructing an automatic pricing and matching mechanism based on liquidity pools. Subsequently, mathematical modeling and simulations reveal the slippage generation mechanism in data liquidity pools under trading shocks and imbalances. To address these issues, a novel dual slippage optimization mechanism integrating dynamic trade splitting and alternating order sorting is proposed, which decomposes orders into sub-orders and reorganizes their timing, establishing a dynamic equilibrium model. Experiments show that the method reduces the average slippage amplitude from 2.1% to 0.5%, representing a 76.2% reduction, and significantly enhances price stability and market efficiency. This research explores the mechanism of applying AMM to data asset trading and mitigates the limitations of AMM in this scenario, providing a theoretical and empirical foundation for building low-cost, high-fairness data trading systems through mechanism innovation and technical optimization. Full article

Earth–Air Heat Exchangers (EAHEs) are passive systems that use the thermal interaction between air and soil along buried ducts to moderate supply air temperature, thereby lowering building energy consumption and improving indoor comfort conditions. This device has been employed in several countries and under diverse climatic characteristics. The integration of EAHE systems with bioclimatic design strategies contributes to improved building energy performance and more efficient use of thermal resources. This study aims to computationally investigate the thermoenergetic performance of EAHE system, for both cooling and heating purposes, installed in Social Housing (SH) across different Brazilian bioclimatic zones, and to propose strategies that improve the energy efficiency of these built environments. The study involves the validation and verification of a computational model and the thermoenergetic assessments of an SH unit, investigating different solar orientations and the installation of EAHE. These evaluations are performed via dynamic simulations conducted with the EnergyPlus software. The results show that the installation of the EAHE system coupled to the SH improves the thermoenergetic performance of the indoor environment, mainly by enhancing thermal comfort across different Brazilian bioclimatic zones (BZ). In BZ2R, the EAHE increased the annual PHFT by 4.5%, corresponding to seventeen additional days per year within the acceptable operative temperature range. The highest monthly improvement was observed in BZ1M, where the PHFT increased by 14.3% in January, equivalent to more than four additional days of thermal comfort in that month. The system proved to be more effective in zones 1M, 2R, 3B, and 4B, particularly in climates with lower annual average dry-bulb temperatures. Regarding energy performance, the EAHE showed benefits in specific months and conditions, indicating that its feasibility should be assessed through monthly thermoenergetic analyses rather than only annual indicators. This work provides validated and verified references and parameters for future projects and contributes to the state of the art in this field, as there are still few studies evaluating EAHE systems integrated into buildings using this software, despite its widespread use in building performance analysis. Full article

This study proposes a reproducible methodology for optimizing occupancy sensor placement and assessing the sustainable energy performance of smart lighting systems in university classrooms. The research was conducted in Block H of the South Campus of the Universidad Politécnica Salesiana, Quito, using one representative classroom for detailed geometric analysis and extending the optimization to eight classrooms with different dimensions, areas, and installed lighting configurations. The proposed framework integrates Voronoi-based spatial analysis, genetic algorithm optimization, and dynamic occupancy-based lighting control simulation as a retrofit-oriented strategy for existing educational buildings. For the representative classroom, the optimized sensor position was located near the geometric center of the room and achieved an estimated spatial coverage of 94.7% under the adopted sampling-based geometric model and an effective detection radius of 6 m. The multi-classroom analysis showed that the required number of sensors depends on classroom geometry and the adopted sensing radius; at R = 6 m, most classrooms satisfied the 90% coverage criterion with one sensor, while the largest classroom required two sensors. Based on occupancy schedules and automatic control rules, the dynamic simulation showed reductions in lighting operating time of 48% and 52% for 10 h and 12 h daily scenarios, respectively. These reductions were translated into lower daily and monthly energy consumption across different lighting configurations. The results indicate that optimized occupancy-based control can support sustainability-oriented energy management in university buildings by reducing unnecessary electricity use while preserving the existing lighting infrastructure. However, the results are limited to occupancy-based control and do not include daylight harvesting, photometric validation, or a complete economic payback assessment. Full article

Traditional Chinese sticky rice–lime mortar, a key material for restoring historic masonry buildings, suffers significant degradation under combined salt erosion and freeze–thaw cycling. This study experimentally investigated the coupled effects of chloride, sulfate, and freeze–thaw action on sticky rice–lime mortar under simulated service conditions. Specimens prepared using traditional methods were subjected to freeze–thaw cycling in pure water, 5% Na₂SO₄ solution, 5% NaCl solution, and 5% NaCl + 5% Na₂SO₄ solution. Their mechanical properties, phase compositions, and pore structures were characterized through compressive, dynamic elastic modulus, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP) tests. After six freeze–thaw cycles, the relative dynamic elastic modulus (0.72, 0.58, 0.57, 0.55), mass loss (1.7%, 3.69%, 4.82%, 5.60%), and compressive strength loss (30.05%, 43.90%, 47.56%, 52.43%) progressively worsened from pure water to Na₂SO₄ to NaCl to compound salt conditions, indicating that under the same concentration, the deterioration induced by sodium chloride freeze–thaw is more severe than that caused by sodium sulfate, while the compound salt freeze–thaw condition leads to the most severe deterioration. Under compound salt freeze–thaw, the deterioration mechanisms include expansion due to gypsum formation, salt crystallization, ice formation, and the dissolution of cementitious phases driven by CaCl₂ attack. Furthermore, clear correlations are observed among the mass loss rate, compressive strength loss rate, and relative dynamic elastic modulus, as well as between the peak strain and secant modulus. These findings provide valuable insights for improving the durability of historic restoration mortars. Full article

In this study, we present a discrete switching host–parasitoid model that incorporates biological and chemical control interventions within the integrated pest management (IPM) measures. The coupling of multi-tactic control measures induces rich and complex dynamical behaviors in the proposed system. We begin by systematically characterizing the existence and stability of fixed points in the control subsystem. The analysis then proceeds to demonstrate how the system undergoes multiple bifurcation routes, including period-doubling, transcritical, and Neimark–Sacker bifurcations. Building on this theoretical foundation, extensive numerical simulations are conducted, not only corroborating our analytical predictions but also revealing emergent phenomena such as cascading period-doubling routes and chaotic regimes. Finally, high-resolution two-parameter stability diagrams are employed to identify the critical dynamical transition boundaries, and the corresponding ecological implications for practical pest management decision-making are elaborated in depth. Full article

In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China’s traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing’s computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China’s 14th Five-Year Plan. The “Eastern Data, Western Computing” strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of “construction–application–regulation” in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. Full article

Toluene, as a common organic solvent in academic laboratories in university campuses, poses potential exposure concerns to students and staff in university campuses. Hence, by using a computational fluid dynamics simulation, we investigated the dispersion characteristics of toluene at a campus in Guangzhou under meteorological conditions and the impact of newly constructed buildings on toluene concentrations. The numerical simulation results reveal that toluene is readily accumulated in the free movement area under the prevailing east wind, in the administrative area under the prevailing north-northeast wind, and in the teaching area under the prevailing south wind. Therein, the teaching buildings (TB3–TB6) possess the highest average concentration of toluene compared with other functional areas. In the presence of newly constructed buildings, the toluene concentrations are decreased under the south-southeast wind but are aggravated under the southeast wind. As the height increases, under south-southeast winds, the merging of vortex structures continuously reduces toluene concentrations at TB3 and TB4 and the expansion of the wake region rebounds the toluene pollution at TB5 and TB6; under southeast winds, the expanding vertical vortex structures aggravate toluene pollution at TB3 and TB5 but attenuate toluene pollution at TB4 and TB6. Our results reveal that the teaching areas of the target campus represent a critical zone for potential student exposure during summer and require particular attention. This study provides new insights into the coupled effects of prevailing wind conditions and campus morphology on VOC dispersion characteristics and improves the understanding of airflow pollutant interactions in complex campus environments. Full article