Search Results (2,542)

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article

This study comparatively examines the effects of different active and passive energy strategies on energy performance, carbon emission reduction, economic feasibility, and thermal comfort potential in a university building in Ankara. This study uses a university building with 8760 h of recorded operational electricity consumption data as a real-world reference case and evaluates different retrofit strategies through dynamic building energy simulations. Simulation results were evaluated not only in terms of total energy consumption but also in terms of operational carbon emissions, levelized cost of energy (LCOE/LCOSE), and the potential for improving indoor temperature stability through passive design strategies. The results show that PV system integration provides the highest energy and carbon reduction performance by reducing the net grid electricity consumption by 89.76%. Among passive systems, the Trombe wall scenario provided the highest energy savings and the lowest LCOSE value. PCM application stood out in terms of indoor temperature stability potential, while the green roof system contributed to temperature control, especially during the summer. In addition, an economic sensitivity analysis based on the discount rate was carried out to reveal the strengths and weaknesses of the proposed strategies in terms of sustainable building design. The study contributes to the comparative analysis of active and passive retrofit strategies in university buildings by offering an integrated and multi-dimensional evaluation approach supported by real operational data. Full article

Decarbonization of existing buildings is obstructed by the performance gap between intended and operational energy consumption. Smart energy management and monitoring of existing buildings through digital twins pose significant attributes towards decarbonization efforts. However, there is limited research that transforms digital twins’ monitored performance into actionable retrofitting strategies. Hence, this research develops a framework that bridges the digital twin concept with standards-based IEQ analytics, guiding retrofit decision-making in existing buildings. The framework offers a low-code workflow that uses Autodesk Tandem to develop a digital twin integrating indoor environmental quality (IEQ) data, including thermal comfort and air quality. IEQ is monitored since inefficient management of its parameters often results in excessive HVAC demand, contributing to the performance gap. The framework structures IEQ parameter evaluations against benchmarks guided by ASHRAE to identify deviations indicative of operational inefficiencies in energy consumption. The digital twin model positions live IEQ tracking and analysis as diagnostic measures, leading to targeted fabric-oriented retrofit prioritization. The framework was tested on a case study in a hot arid climate, where its results indicate that the integration of digital twin-based IEQ analysis with building characteristics effectively identified the need for targeted envelope improvements, including high-performance glazing, external shading elements, and sound isolation, as key factors for eliminating overheating and high noise levels. Validating the proposed retrofits’ effectiveness, energy simulations examines the whole building to find an 11.52% annual reduction in energy use intensity from 145.61 kWh/m²·year to 128.84 kWh/m²·year through shading elements and low-E films for glazing. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

In the context of rural revitalization and carbon neutrality, this study addresses energy inefficiency and thermal discomfort in existing rural housing by optimizing passive design strategies for the “SunnyInside” sunroom model. Using parametric simulation with Ladybug and Honeybee, a dynamic light-thermal coupling model was developed to evaluate climate-adaptive performance in two distinct Chinese climates: the cold climate of Datong and the hot-summer-cold-winter climate of Wuhan. Multi-objective optimization focused on orientation, overhang depth, and photovoltaic (PV) tilt angles to enhance ventilation, shading, and daylighting. Key findings include: (1) Optimal building orientations of 15° west of south (Datong) and 16° east of south (Wuhan); (2) A 1.5m overhang depth in Wuhan improved summer shading efficiency by 28.6% and extended thermal comfort duration by 15%; (3) PV tilt ranges of 29–36° (Datong) and 13–23° (Wuhan) maximized energy performance. These optimizations achieved a 19.3–24.7% improvement in comprehensive performance coefficients and reduced air conditioning energy consumption by 17.8–21.4 kWh/m² (with ≥82% photovoltaic conversion efficiency). The study demonstrates the effectiveness of parametric simulation and intelligent algorithms in refining climate-responsive rural housing renovations, providing quantitative guidelines for PV shading systems across diverse climatic zones. Full article

Earth-based materials are promising candidates for balancing thermal performance, hygrothermal regulation, and environmental sustainability. The objective of this study is to evaluate and compare the hygrothermal behavior of two earthen materials, structural cob and lightweight insulating earth, against conventional reference concrete, taking into account not only their insulating properties but also their ability to regulate coupled heat and moisture transfers. Experimental tests show a significantly higher hygroscopic buffering capacity for earth-based materials, with an MBV of 2.23 g/(m²∙%RH) for the structural material and 1.21 g/(m²∙%RH) for the insulation material, compared to less than 0.5 g/(m²∙%RH) for concrete. The sorption isotherms confirm distinct water storage behaviors, with an average sensitivity to relative humidity of 10.47% for the insulation material, compared to 3.8% for concrete and 2.25% for the structural material, in addition to an average reduction of 26% in the adsorption capacity between 23 °C and 45 °C for both earthen materials. Coupled heat–moisture simulations in COMSOL quantitatively demonstrate the hygrothermal superiority of bio-based materials over conventional concrete, as concrete promotes interstitial moisture accumulation due to its low vapor permeability. The parametric sensitivity analysis highlights the effect of hygrothermal properties, where diffusivity controls transport kinetics and sorption governs water storage, while thermal conductivity modulates the spatial redistribution of thermo-hygric fields. The next and final step made it possible to link the phenomena observed at the material scale to the actual energy performance of the building, confirming the potential of the double-wall cob + lightweight earth system to reduce heating and cooling requirements and maintain stable indoor comfort, where the annual heating demand is reduced by approximately 24% compared to the conventional prototype. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Internal combustion engines are a well-established, efficient and dispatchable solution for distributed power generation and they are widely used in various sectors including grid balancing, data centers and combined heat and power systems. Current research efforts focus on further increasing efficiency, enabling decarbonization through renewable fuels and improving responsiveness to electricity demand in the presence of variable renewable energy sources. In this context, the free-piston linear generator (FPLG) stands out as a highly promising technology, as it directly converts piston motion into electricity, offering high efficiency, reduced mechanical complexity and seamless grid integration. Initially explored for its high-efficiency potential with homogeneous charge compression ignition combustion at extreme compression ratios, opposed-piston FPLGs are now commercially available for distributed power generation, delivering global efficiencies exceeding 45%, near-zero emissions and multi-fuel capability. Building on the detailed studies conducted by Svrcek and co-authors, this work investigates the power-generation potential of low-temperature homogeneous combustion using CFD simulations with detailed chemical kinetics. First, rapid compression machine (RCM) experiments with methane were reproduced in simulations to validate the proposed methodology and to consolidate experimental findings on the maximum achievable efficiency. Subsequently, an extensive RCM simulation campaign supported the identification of optimal operating conditions in terms of air–fuel ratio using methane as fuel. The RCM results enabled the definition of a preliminary methane-fueled opposed-piston FPLG configuration. Full-cycle simulations including gas exchange, mixing and combustion demonstrated an indicated efficiency of 58% at an equivalence ratio $\varphi =0.5$ and a compression ratio of 50. The key novelties of this study are the development of a novel RCM-2 configuration that more closely reproduces the dynamic behavior of an opposed-piston FPLG including air-spring effects and the introduction of a divided intake port strategy to simultaneously reduce fuel slip and mitigate knocking behaviour through charge stratification. The simulation results for the proposed configuration confirm the potential of opposed-piston FPLGs for high-efficiency power generation and highlight key parameters affecting performance and emissions formation. Full article

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

Climate change is a global pressing concern that has affected all sectors, including the operations of Small and Medium Entreprises (SMEs) in developing countries, including Malawi. This has negatively affected their economies of scale and exacerbated the SMEs’ growth constraints. Nonetheless, renewable efficient energy (REE) systems, including solar and biogas, could help in building resilience to sustain their performance. In line with this, the study examined the factors that enhance the adoption of renewable efficient energies and constructed their resilience indices. Our study was grounded in the Diffusion of Innovation Theory and the Sustainable Livelihoods Framework. These theories contextualised the study and guided the selection of variables to estimate an Endogenous Switching Regression (ESR) econometric model, alongside estimating the absorptive, adaptive and transformative individual indices for 699 SMEs, using the 2019 Malawi Household Integrated Survey data. The results initially suggests that factors such as access to credit, being male, access to education, access to capital sources, a large profit share, bridging social capital and location among others, have a positive effect in influencing the adoption of renewable efficient systems. We simulated the adoption results and found that SMEs that adopts REE increase their resilience with an Average Treatment Effect of 0.117 and through the subsidy policy effect vulnerable SMEs that later adopt REE would shift their resilience by 0.169. Furthermore, the study found that transformative capacity plays the most important role in building long-term resilience for the SMEs. The study calls for policies, including establishing urban centres where SMEs can access information regarding REE and improving access to formal safety nets and capital sources beyond loan provisions. Full article

This study investigates the quasi-static mechanical behavior of a flexible anti-collision ring (FACR) for bridge protection through axial tests and finite element (FE) simulations. The FACR features a multi-layer steel wire rope coil (SWRC) encased in a chloroprene rubber matrix. Quasi-static tensile and compressive tests (80 mm/s) were conducted on both the SWRC and the FACR, with full-field strain distributions captured via digital image correlation (DIC). The results demonstrate that the rubber matrix significantly enhances load-bearing capacity (by 200% in compression and 337% in tension) and energy dissipation (by 403% and 620%, respectively), with bending identified as the dominant deformation mode. An FE model was developed and validated against experimental data, then employed for parametric analysis. The cross-sectional ratio, governed by the number of SWRC layers, exhibits a strong nonlinear influence on the tensile response, and a three-layer configuration is identified as optimal, achieving the highest energy absorption without compromising compressive performance. A layer-dependent mechanism analysis reveals that excessive layers lead to a drastic stiffness reduction in outer coils, impeding coordinated load sharing. Building upon this mechanism, an optimized two-layer arrangement maximizing the inner-layer SWRC proportion is proposed, achieving 2.0× and 1.9× improvements in peak tensile force and energy dissipation, respectively, while using fewer steel wires. This work provides a fundamental understanding and an efficient optimization strategy for FACRs. 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

Wind turbine blade (WTB) end-of-life waste is projected to increase significantly, yet no sustainable recycling solution with a clear energy, economic, and environmental (3E) assessment exists. This paper presents a validated 3E model of a WTB thermal recycling pilot (1 t/day) to benchmark recycled glass fibre (rGF) against virgin glass fibre (vGF) and identifies the throughput at which rGF becomes competitive. This subsequently leads to a projection of 3E performance at 5000 t/y plant capacity, at which rGF achieves approximately 46% lower specific primary thermal energy, 92% of the CO₂ emissions of vGF, and a selling price of 80% of vGF for a financial break-even. Building on this baseline, a novel combined material, heat, and power system is proposed and simulated, integrating the WTB recycling pilot with a 20 kWₑₗ/130 kWₜₕ organic Rankine cycle to serve residential buildings. Results show that coupling the pilot with 3000 m² of apartments yields a near net-zero CO₂ and energy-cost residential complex, with overall CO₂ emissions falling below those of standalone residential buildings combined with vGF production when more than 25 apartments are integrated. Full article