Search Results (1,023)

Residential buildings in Mediterranean regions remain major contributors to energy consumption and greenhouse gas emissions. Existing studies often assess renewable energy technologies or innovative building solutions in isolation, with limited attention to their combined performance across different residential typologies. This study evaluates the integrated impact of solar renewable energy systems and smart building technologies on the energy performance of residential buildings in Cyprus. A typology-based methodology is applied to three representative residential building types—detached, semi-detached, and apartment buildings—using dynamic energy simulation and scenario analysis. Results show that solar photovoltaic systems achieve higher standalone reductions than solar thermal systems, while smart building technologies significantly enhance operational efficiency and photovoltaic self-consumption. Integrated solar–smart scenarios achieve up to 58% reductions in primary energy demand and 55% reductions in CO₂ emissions, and 25–30 percentage-point increases in PV self-consumption, enabling detached and semi-detached houses to approach national nearly zero-energy building (nZEB) performance thresholds. The study provides climate-specific, quantitative evidence supporting integrated solar–smart strategies for Mediterranean residential buildings and offers actionable insights for policy-making, design, and sustainable residential development. Full article

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance ($f$) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance $f$ and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R² increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems. Full article

Improving the thermal performance of building envelopes is an effective approach for reducing energy consumption and carbon emissions in cold and heating-dominated regions. This study presents an experimentally calibrated thermal–economic optimization of external wall insulation systems for residential buildings in Northwest China, using Xi’an as a representative cold–dry continental climate. A guarded hot-box apparatus was employed to measure the steady-state thermal transmittance (U-value) of multilayer wall assemblies incorporating expanded polystyrene (EPS), extruded polystyrene (XPS), and rock wool at different insulation thicknesses. The measured U-values were integrated into a dynamic building energy simulation model (DeST-h), and the simulated energy demand was subsequently evaluated through life-cycle cost (LCC) analysis to identify cost-optimal insulation configurations. The results indicate a nonlinear reduction in heating energy demand with increasing insulation thickness, with diminishing marginal returns beyond approximately 50 mm. Among the investigated materials, XPS exhibits the most favorable thermal–economic performance. For the climatic and economic conditions of Xi’an, a 50 mm XPS insulation layer minimizes total life-cycle cost while reducing annual building energy consumption by approximately 23–24% compared with the uninsulated reference case. This experimentally calibrated framework provides practical and policy-relevant guidance for insulation design and retrofit strategies in cold and dry regions. Full article

Building energy renovation planning should be based on a multi-criteria evaluation that targets both reduced energy consumption and a high-quality indoor thermal environment. The present study investigates the building energy retrofit technologies of thermal insulation, highly insulative windows, mechanical ventilation for cooling purposes, and shading, aiming to identify the optimum energy retrofit strategy for different building typologies. Indoor thermal comfort is evaluated with the thermal comfort indexes of the predicted mean vote (PMV) and the Predicted Percentage of Dissatisfied (PPD). Each renovation scenario is evaluated in terms of thermal performance and thermal comfort, while an optimum retrofit scenario is defined as the one that simultaneously achieves the maximum decrease in the yearly energy demand and the greatest decrease in the building’s indoor thermal discomfort. The multi-objective analysis is performed using the EnergyPlus simulation engine, which is used to perform yearly dynamic simulations and provide accurate results. This study considers a typical one-story apartment building located in the city of Athens, Greece. According to the calculations, the retrofit strategy that combines all four examined interventions results in an 11.8% and 56.1% decrease in the building’s heating and cooling energy demand, respectively, while an annual enhancement of 16.6% in the building’s thermal comfort PPD index is calculated. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

A low-power IPv6 mesh standard, Thread, is gaining traction in smart-home, building-automation, and industrial IoT deployments. It extends mesh connectivity with the help of Router-Eligible End Devices (REEDs), which can be promoted to, or demoted from, the router status. Promotion and demotion hinge on two tunable parameters, the router upgrade and the router downgrade thresholds. Yet the OpenThread reference stack ships with fixed values (16/23) for these thresholds. This paper presents a systematic study of how these thresholds shape router-election dynamics across diverse traffic loads and network topologies. Leveraging an extended OpenThread Network Simulator, a sweep through both router upgrade and router downgrade thresholds with different gaps was performed. Results reveal that the default settings may over-provision routing capacity and may result in increased frame retransmissions, wasting airtime and reducing energy efficiency. Full article

The growing depletion of fossil fuel resources and rising energy costs underscore the need for efficient renewable energy technologies, such as unglazed transpired solar collectors (UTSCs). UTSCs harness solar energy to preheat outdoor air, thereby improving building energy efficiency and reducing reliance on conventional heating systems. This study presents a computational fluid dynamics (CFD) analysis of UTSC performance under Lithuanian winter conditions (ambient air temperature −2.64 °C, solar irradiance 733.45 W/m², wind speed 1.93 m/s) using two- and three-dimensional models developed in ANSYS FLUENT. The 3D model simulates a realistic wall fragment with multiple repeating sheet metal profiles and an air gap, while the 2D model represents a longitudinal section applicable to generic UTSC configurations. Both models were validated against experimental data and used to evaluate airflow velocity, pressure distribution, and air temperature rise. The results indicate overall thermal efficiencies of 54.32% for the 3D model and 54.07% for the 2D model, demonstrating that simplified 2D models can achieve comparable accuracy while significantly reducing computational cost. These findings highlight the potential of high-resolution CFD modelling for optimizing UTSC design and enabling faster, more reliable assessments for integration in industrial and commercial building applications. Full article

The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing network (MCFFQN) model that incorporates state-dependent road capacity and congestion propagation mechanisms to accurately capture the stochastic and dynamic nature of mixed traffic flows. An evaluation framework for intersection performance is established based on key indicators such as vehicle delay, the energy consumption of new energy vehicles, and the fuel consumption and emissions of conventional vehicles. A recursive solution algorithm is developed and validated through simulations under various traffic demand scenarios. Building on this model, a signal timing optimization model aimed at minimizing total costs—including delay and environmental impacts—is formulated and solved using the Mesh Adaptive Direct Search (MADS) algorithm. A case study demonstrates that the optimized signal timing scheme significantly enhances intersection performance, reducing vehicle delay, energy consumption, fuel consumption, and emissions by over 20%. The proposed methodology provides a theoretical foundation for sustainable traffic management under mixed traffic conditions. Full article

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article

The accurate calculation of gear time-varying mesh stiffness is of significant importance for the dynamic modeling of gear systems. Currently, research on calculation methods for helical gear mesh stiffness is relatively limited, with the primary approaches being finite element methods and analytical methods. This paper proposes an optimized helical gear meshing stiffness model. Building upon the slice potential method, this approach comprehensively accounts for the effects of tooth-surface friction and local contact deformation. Results indicate that tooth surface friction causes abrupt changes in meshing stiffness values, while local contact deformation leads to an overall decrease in meshing stiffness values. To validate the application value of the optimized calculation method, the contact line method was replaced with the optimized slice potential energy method for simulating the external sound field of locomotive traction transmission systems. Comparisons with actual measurement data revealed that the sound pressure level data from this study’s meshing stiffness model align more closely with experimental results than those from the contact line method model, with the maximum error decreasing from 5% to 2.2%, effectively enhancing the accuracy of the rapid modeling method. Full article

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this challenge, this paper proposes that the core solution lies in the deep integration of physical mechanisms and data intelligence. We systematically review and define a new research paradigm characterized by the trinity of digital cores (geometric foundation), physical simulation (mechanism constraints), and artificial intelligence (efficient reasoning). This review clarifies the core technological path: first, AI technologies such as generative adversarial networks and super-resolution empower digital cores to achieve high-fidelity, multiscale geometric characterization; second, cross-scale physical simulations (e.g., molecular dynamics and the lattice Boltzmann method) provide indispensable constraints and high-fidelity training data. Building on this, the methodology evolves from surrogate models to physics-informed neural networks, and ultimately to neural operators that learn the solution operator. The analysis demonstrates that integrating these techniques into an automated “generation–simulation–inversion” closed-loop system effectively overcomes the limitations of isolated data and the lack of physical interpretability. This closed-loop workflow offers innovative solutions to complex engineering problems such as parameter inversion and history matching. In conclusion, this integration paradigm serves not only as a cornerstone for constructing reservoir digital twins and realizing real-time decision-making but also provides robust technical support for emerging energy industries, including carbon capture, utilization, and sequestration (CCUS), geothermal energy, and underground hydrogen storage. Full article

Using Shanghai as a case study, this paper estimates multi-sector urban carbon emissions by integrating multi-source statistical data from 2000 to 2023 with IPCC guidelines. Via rolling-window time-series validation, XGBoost is the most reliable model. To better understand the underlying drivers, explainable machine-learning approaches, including SHAP and the Friedman H-statistic, are applied to examine the nonlinear effects and interactions of population scale, industrial energy efficiency, investment structure, and infrastructure. The results suggest that Shanghai’s emission pattern has gradually shifted from a scale-driven process toward one dominated by structural change and efficiency improvement. Building on an incremental framework, four scenarios, Business-as-Usual, Green Transition, High Investment, and Population Plateau, are designed to simulate emission trajectories from 2024 to 2060. The simulations reveal a two-stage pattern, with a period of rapid growth followed by high-level stabilisation and a weakening path-dependence effect. Population agglomeration, economic growth, and urbanisation remain the main contributors to emission increases, while industrial upgrading and efficiency gains provide sustained mitigation over time. Scenario comparisons further indicate that only the Green Transition pathway supports early peaking, a steady decline, and long-term low-level stabilisation. Overall, this study offers a data-efficient framework for analysing urban carbon-emission dynamics and informing medium- to long-term mitigation strategies in megacities. Full article

Sustainable renovation is a critical aspect for designing energy-efficient buildings with reasonable cost and high indoor living standards. The objective of this paper is to investigate various renovation scenarios for an old, uninsulated building with a floor area of 100 m² located in Athens, aiming to determine the global optimal solution through a multi-criteria analysis. The multi-criteria analysis considers energy, economic, and thermal comfort criteria to perform a multi-lateral approach. Specifically, the criteria are: (i) maximization of the energy savings, (ii) minimization of the life cycle cost (LCC), and (iii) minimization of the mean annual predicted percentage of dissatisfied (PPD). These criteria are combined within a multi-criteria evaluation procedure that employs a global objective function for determining a global optimum solution. The examined retrofitting actions are the addition of external insulation, the replacement of the existing windows with triple-glazed windows, the addition of shading in the openings in the summer, the application of cool roof dyes, the use of a mechanical ventilation system with a heat recovery unit, and the installation of a highly efficient heat pump system. The interventions were examined separately, and the combined renovation scenarios were studied by including them in the external insulation because of their high importance. The present study encompassed the investigation of a baseline scenario and 26 different renovation scenarios, conducted through dynamic simulation on an annual basis. The results of the present analysis indicated that the global optimal renovation scenario, including the addition of external insulation, the installation of highly efficient heat pumps, and the use of shading in the openings in the summer, saved energy by 74% compared to the baseline scenario. The LCC was approximately EUR 33,000, the simple payback period of the renovation process was around 6 years, the annual CO₂ emissions avoidance reached 4.6 tn_CO2, and the PPD was at 9.7%. An additional sensitivity analysis for determining the optimal choice under varying weights assigned to the criteria revealed that this renovation design is the most favorable option in most cases. These results prove that the suggested renovation scenario is a feasible and viable solution that leads to a sustainable design from multiple perspectives. Full article