Search Results (265)

This study selects typical existing office buildings in Zhengzhou, a region with a cold climate, as the research object and conducts a comparative analysis of the influencing factors of cooling energy consumption in west-facing and south-facing office spaces. A multi-stage sensitivity analysis methodology integrating global and local sensitivity methods is systematically applied to evaluate 13 key parameters across four categories: building morphology, envelope structure, shading measures, and active design strategies. Five parameters are consistently ranked among the top seven most sensitive parameters for both west- and south-facing orientations: the infiltration rate, the window-to-wall ratio, the cooling setpoint temperature, the number of shading boards, and building width. Two parameters exhibit orientation-specific differences, namely lighting power density and the external wall heat transfer coefficient in west-facing spaces, whereas shading board width and the external window heat transfer coefficient play a greater role in south-facing spaces. Local sensitivity analysis further reveals that within the parameter variation range, the five parameters with higher energy-saving rates for both orientations are air tightness, the window-to-wall ratio, the cooling setpoint temperature, the number of horizontal shading boards, and horizontal shading board width. By increasing the cooling setpoint temperature, south-facing spaces can achieve an energy-saving rate of 25.32%, which is significantly higher than the 21.77% achieved by west-facing spaces. Horizontal shading board width exhibits the most pronounced orientation difference, with south-facing spaces achieving an energy-saving rate of 16.69%, while west-facing spaces only reach 2.97%. The research findings offer quantitative scientific evidence for formulating targeted energy-saving retrofit strategies for existing office buildings in cold climate regions, thereby contributing to the meticulous development of building energy efficiency technologies. Full article

To address the poor thermal insulation and freeze resistance of rural outdoor toilets in cold regions—key obstacles to achieving the UN Sustainable Development Goal (SDG) 6.2 and popularizing rural sanitary toilets—this study fills the literature gap of insufficient research on passive solar heating systems tailored for rural toilets in cold climates. Using TRNSYS simulation, Plackett–Burman key factor screening, single-factor experiments, and Box–Behnken response surface methodology, we optimized the system with building envelope thermal parameters and Beijing’s typical meteorological year data as inputs, taking January’s average indoor temperature as the core evaluation index. Results indicated six parameters (solar wall area, air cavity thickness, vent area ratio, vent spacing, exterior wall insulation thickness, and heat-gain window-to-wall ratio) significantly influence indoor temperature (p < 0.05). The optimal configuration was as follows: solar wall area 3.45 m², window-to-wall ratio 30%, exterior wall insulation thickness 200 mm, vent spacing 1800 mm, air cavity thickness 43 mm, and vent area ratio 5.7%. Post-optimization, the average temperature during the heating season reached 10.81 °C (79.5% higher than baseline), with January’s average, maximum, and minimum temperatures at 7.95 °C, 20.47 °C, and −1.42 °C, respectively. This solution effectively prevents freezing of flushing fixtures due to prolonged low temperatures, providing scientific support for the application of passive rural toilets in China’s cold regions. Full article

Traditional dwellings in southern Xinjiang, exemplified by the Suohema House, have evolved as adaptive responses to the region’s cold and arid climatic conditions, providing thermally comfortable living environments with relatively low energy consumption. Learning from these climate-responsive design strategies offers an effective approach to reconciling the conflict between energy efficiency and indoor comfort. Such exploration is of great significance for preserving regional architectural identity and promoting the development of low-carbon buildings. This study establishes a performance-driven morphological multi-objective optimization framework for traditional dwellings, taking building energy consumption, thermal comfort, and indoor temperature as the primary optimization objectives. The framework integrates parametric modeling, performance simulation, and multi-objective optimization within the Rhino & Grasshopper platform, employing a genetic algorithm to achieve performance-oriented design exploration. Key design variables were identified through data analysis, and the influence weights and prioritization of morphological parameters were quantified. The results reveal that the room depth in residential dwellings (4.57–4.73 m), room width (3.97–6.75 m), room clear height (2.33–2.42 m), wall thickness (lower wall thickness ranging from 1.14 to 1.22 m, upper wall thickness at 0.76 m), and building orientation (true south) have significant impacts on both energy consumption and indoor thermal performance. Based on these findings, adaptive optimization strategies were proposed from three perspectives: scale optimization, spatial hierarchy refinement, and enhancing the performance of building envelopes. The proposed framework provides methodological guidance for the conservation and adaptive renewal of traditional dwellings, as well as for the design of new, green, and low-carbon residential buildings suited to the climatic conditions of southern Xinjiang. Full article

Building-integrated photovoltaics (BIPVs) represent a pivotal technology for enhancing the utilization of renewable energy in buildings. However, challenges persist, including the lack of integrated design models, limited analytical dimensions, and insufficient consideration of climate change impacts. This study proposes a comprehensive performance assessment framework for BIPV that incorporates global climate change factors. An integrated simulation model is developed using EnergyPlus8.9.0, Optics6, and WINDOW7.7 to evaluate BIPV configurations such as photovoltaic facades, shading systems, and roofs. A multi-criteria evaluation system is established, encompassing global warming potential (GWP), power generation, energy flexibility, and economic cost. Future hourly weather data for the 2050s and 2080s are generated using CCWorldWeatherGen under representative climate scenarios. Monte Carlo simulations are conducted to assess performance across variable combinations, supplemented by sensitivity and uncertainty analyses to identify key influencing factors. Results indicate (1) critical design parameters—including building orientation, wall thermal absorptance, window-to-wall ratios, PV shading angle, glazing optical properties, equipment and lighting power density, and occupancy—significantly affect overall performance. Equipment and lighting densities most influence carbon emissions and flexibility, whereas envelope thermal properties dominate cost impacts. PV shading outperforms other forms in power generation. (2) Under intensified climate change, GWP and life cycle costs increase, while energy flexibility declines, imposing growing pressure on system performance. However, under certain mid-century climate conditions, BIPV power generation potential improves due to altered solar radiation. The study recommends integrating climate-adaptive design strategies with energy systems such as PEDF (photovoltaic, energy storage, direct current, and flexibility), refining policy mechanisms, and advancing BIPV deployment with climate-resilient approaches to support building decarbonization and enhance adaptive capacity. Full article

Atrium spaces are widely applied in university buildings. However, achieving effective energy reduction while maintaining adequate daylighting and indoor comfort remains a major challenge at the early design stage. This study identifies key building form design variables significantly influencing atrium daylighting, energy use, and thermal comfort, including building orientation, atrium width-to-depth ratio, atrium aspect ratio, atrium bottom area ratio, and skylight–roof ratio. A multi-objective optimization (MOO) framework is proposed to balance daylight performance, energy consumption, and thermal comfort under fixed envelope parameters. Using typical single- and double-atrium teaching buildings in cold regions as case studies, this research adopts Useful Daylight Illuminance (UDI), Energy Use Intensity (EUI), and Discomfort Time Percentage (DTP) as key indicators to evaluate the interactions between design parameters and building performance. Based on the Pareto-optimal results for the studied prototypes, a south-by-west orientation, moderately slender atrium proportions, relatively compact atrium bottom areas, and medium skylight–roof ratios together yield a balanced performance. Compared with the reference to the initial solution, the optimized solutions reduce EUI by up to 5.66% while also improving UDI and DTP. These results are intended as quantitative references and optimization for early-stage geometric forms design of atrium teaching buildings in cold regions. Full article

Achieving the decarbonization of the building stock by 2050 requires not only technological innovation but also strategies capable of addressing energy poverty, which threatens to exclude millions of households from a fair transition. Measuring this phenomenon remains challenging: at the European level, monitoring systems rely mainly on aggregated statistics, useful for territorial comparisons but often too approximate to describe the conditions of individual households and dwellings. This paper proposes a building-scale methodology that integrates socio-economic and technical data collected directly through surveys, interviews, and utility bills. The approach was applied to a private multi-family building built in the early twentieth century in Turin (Italy), involving 16 households. Results indicate that 31% of households exceed the 10% energy expenditure threshold, with heating emerging as the main cost driver. Correlation analyses suggest that single parameters such as income or dwelling size are not sufficient on their own to explain vulnerability, whereas the integration of socio-technical factors provides a more detailed picture of household conditions. Based on this evidence, four intervention strategies were developed, ranging from the insulation of the envelope to the installation of photovoltaics, conceived to be implemented progressively according to real technical and economic constraints. The novelty of this study lies in linking building-scale evidence with concrete design solutions, bridging the gap between measurement and action. Full article

Based on comprehensive experimental datasets—proximate/ultimate analyses, XPS, solid-state ¹³C NMR, and Raman spectroscopy—we constructed and optimized a compositionally faithful macromolecular model of SG coking coal. Using density-functional theory (DFT) calculations, we simulated electrostatic-potential (ESP) fields and frontier molecular orbitals (FMO) to probe elementary oxidation steps relevant to combustion, and focused on how heteroatom speciation and carbon ordering govern site-selective reactivity. Employing multi-peak deconvolution and parameter synthesis, we obtained an aromatic fraction f_a = 76.56%, a bridgehead-to-periphery ratio X_BP = 0.215, and Raman indices I_D1/I_G ≈ 1.45 (area) with FWHM(G) ≈ 86.7 cm⁻¹; the model composition C₁₉₀H₁₄₄N₂O₂₁S and its predicted ¹³C NMR envelope validated the structural assignment against experiment. ESP–FMO synergy revealed electron-rich hotspots at phenolic/ether/carboxyl and thiophenic domains and electron-poor belts at H-terminated edges/aliphatic bridges, rationalizing carbon-end oxidation of CO, weak electrostatic steering by O₂/CO₂, and a benzylic H-abstraction → edge addition → O-insertion/charge-transfer sequence toward CO₂/H₂O, with thiophenic sulfur comparatively robust. We quantified surface functionalities (C–O 65.46%, O–C=O 24.51%, C=O 10.03%; pyrrolic/pyridinic N dominant; thiophenic-S with minor oxidized S) and determined a naphthalene-dominant, stacked-polyaromatic architecture with sparse alkyl side chains after Materials Studio optimization. The findings are significant for mechanistic understanding and control of coking-coal oxidation, providing actionable hotspots and a reproducible workflow (multi-probe constraints → model building/optimization → DFT reactivity mapping → spectral back-validation) for blend design and targeted oxidation-inhibition strategies. Full article

The widespread use of transparent building envelope structures satisfies people’s needs for architectural esthetics and daylighting. However, they also feature notable drawbacks such as high energy consumption, poor thermal insulation performance of traditional glass curtain walls, significant solar heat gain in summer and heat loss in winter, which lead to “cold in winter and hot in summer” indoors, reliance on high-power air conditioning, and energy consumption far exceeding that of opaque walls. Even when coated or insulated glazing is adopted, improper design can still fail to effectively reduce the overall heat transfer coefficient, placing higher demands on the daylighting performance and solar radiation control of transparent envelopes in existing buildings. Through experiments and numerical simulations, this study systematically analyzes the performance of different types of glass used in transparent building envelope structures and their impacts on building energy consumption. Based on the climatic characteristics of central-southern Anhui, measured data were compared between a Low E-glass sunroom and a conventional tempered glass sunroom. The results show that the solar radiation transmittance of the Low-e glass is only 45.31% of that of ordinary glass, the peak indoor temperature is reduced by 6–7 °C, and nighttime temperature fluctuations are smaller, verifying its excellent thermal insulation performance and thermal stability. To further investigate, the Ecotect software 2011 was used to simulate the daylighting performance of 12 types of glazing and the radiation transmittance under 19 conditions. The results indicate: triple-glazed vacuum composite silver-coated glass exhibits excellent shading performance suitable for summer; single-silver-coated glass has the best daylighting performance, and Triple-Silver coatings combined with high-transmission substrates can improve the daylight factor by 10.55%; argon-filled insulated glazing reduces radiation by 6.5% compared with ordinary IGUs, making it more suitable for the climate of central-southern Anhui. The study shows that optimization of transparent envelopes must be predicated on regional climate, combining experimentally validated glazing thermal parameters with simulation-based design optimization to provide theoretical support and technical references for glass selection and transparent envelope design in near-zero energy buildings in central-southern Anhui. Full article

This paper presents a multiscale monitoring and management framework designed to enhance energy and indoor environmental performance in retrofitted public schools. The proposed system comprises three layers: (i) a cost-effective sensor network deployed at building, room, and device levels; (ii) a data processing layer supporting redundancy, fault detection, and consistency scoring; and (iii) a role-adaptive interface providing customized dashboards for managers, educators, and students. The framework was deployed in two Mediterranean schools undergoing photovoltaic (PV) integration and envelope rehabilitation. The monitoring layer captures key parameters including temperature, humidity, CO₂, PM2.5, occupancy, and circuit-level energy use, enabling multiscale analysis of demand-side behavior and local PV utilization. Data from a full academic year demonstrate a reduction in lighting energy use of up to 22%, classroom-level savings of 10–15%, and an increase in PV self-consumption from 60% to 75%. These improvements were achieved without compromising indoor comfort, as validated by stable environmental conditions aligned with recognized thresholds. The synchronized collection of energy and environmental data allows transparent evaluation of behavioral engagement, operating patterns, and system effectiveness. This research shows that cost-effective, role-adaptive monitoring platforms can support resilience and decarbonization goals in public-sector buildings, particularly where commercial building management systems are financially or technically unfeasible. Full article

This paper addresses the challenge of improving the thermal performance of building envelopes in hot arid climates by identifying optimal configurations for biomimetic opaque ventilated façade (OVF) designs. To overcome the complexity of parameter interactions in such systems, a multi-objective optimization framework is developed using computational fluid dynamics (CFD) simulations integrated with parametric modeling and machine learning surrogate models. A central contribution of this research is the application of machine learning-based surrogate models to predict CFD simulation outcomes with high accuracy. This predictive capability enables the rapid generation and evaluation of thousands of façade design alternatives without the need for full-scale CFD runs, significantly reducing computational effort and time. The proposed workflow establishes a direct connection between parameterized biomimetic geometries and thermal performance indicators, allowing for a comprehensive exploration of the design space through automated optimization. The optimization process relies on response surface modeling to approximate system behavior and evaluate design performance across multiple objectives. The final results reveal that the computationally optimized biomimetic façades achieved superior thermal performance compared to the initial bio-inspired design. To validate and extend the findings, additional simulations were carried out to evaluate the performance of selected designs under varying wind conditions and solar exposures. The larger wide mound configuration consistently performed best, offering a strong balance across the defined objectives. This solution was then applied to three-floor and five-floor commercial buildings in Riyadh, Saudi Arabia, where it showed a clear reduction in the average inner skin surface temperature of the OVF. The design proved suitable for construction with conventional methods and could be integrated into a range of architectural styles without major changes to the façade. These results reinforce the potential of combining biomimetic design strategies with computational optimization to develop high-performance façade systems for hot desert climates. The novelty of this work lies in combining biomimetic design principles with machine learning-driven optimization to systematically explore the design space and identify configurations that balance thermal efficiency with material economy. Full article

The integration of photovoltaic systems with heat pumps can significantly influence primary energy consumption indicators and therefore plays a particularly important role in the low-energy construction sector. This study provides a simulation-based assessment of the impact of selected photovoltaic panel parameters on the primary energy (PE) index in a low-energy building equipped with an air-source heat pump. The building, located in the relatively cold climate of north-eastern Poland, was analyzed in two insulation variants of the building envelope. In each variant and system configuration, the total amount of energy produced by the panels (E_PV) and used by the system (E_used), as well as the degree to which the system’s electricity demand was covered by the photovoltaic panels (η_cov) and their self-consumption degree (η_self), were assessed. The results showed that, in the baseline scenarios, photovoltaic panels were able to generate 5586 kWh of electricity, covering an average of 60–63% of the system’s demand and achieving a self-consumption of approximately 59%. The E_PV, E_used, and η_cov are inversely proportional to the η_self and PE index. The PE index, η_cov, and η_self ranged from 22.6 to 80 kWh/m², 25.3 to 77.5%, and 23.9 to 100%, respectively, depending on the variant and configuration. The wide range of the obtained results confirms that the analyzed factors have a significant impact on the performance of building-integrated photovoltaic panels. In addition, the use of ASHP and PV instead of a gas boiler and grid electricity reduced both the EP index and CO₂ emissions by 59–67%. Full article

The energy-saving performance of the building envelope, which plays a pivotal role in energy conservation and thermal insulation, has been the subject of extensive research. In the context of China’s high-quality green development, this study proposes a building energy-saving strategy based on optimal thermal comfort. It analyzes the impact of factors such as regional dwell time and PMV types on energy-saving effects, summarizes the optimal comfort parameters under the highest energy efficiency rate, and sets relevant parameters in the DeST building energy simulation software to analyze a typical public building. The analysis examined the impact of changing the heat transfer coefficients of exterior walls and windows on the annual cumulative heating and cooling loads. It established the relationship between the thermal transmittance of building envelopes and energy consumption and assessed the carbon emissions during the building’s operation and maintenance phase. The results indicate that as building envelope thermal transmittance coefficient decreases, particularly that of external windows and walls, overall cumulative heating and cooling loads decline accordingly. Notably, the reduction in external windows’ thermal transmittance coefficient has the most significant impact on total building thermal load. Furthermore, as the envelope thermal transmittance coefficient decreases, seasonal heating and cooling demands decline simultaneously, with the most substantial effect on heating load reduction during winter. Total annual building carbon emissions also decrease with the reduction in envelope thermal transmittance coefficient, particularly external wall thermal transmittance coefficient. Based on the findings of this study, the building envelope of the public building was redesigned, taking into account construction costs, the owner’s requirements, and energy efficiency alongside the reduction in carbon emissions. Comparisons of the redesigned building’s envelope thermal performance, experimental testing, and in situ measurements confirmed that it fulfilled the engineering requirements. This study also demonstrates that DeST software provides reliable technological support for low-carbon building design, retrofitting, and operation. Full article

This study investigates the thermal resilience of naturally ventilated Lebanese residential buildings in the context of future climates, based on four climate zones: coastal (moderate and humid), low mountain (cool and seasonally variable), inland plateau (semi-arid with high summer heat), and high mountain (cold, with significant winter conditions). The aim of the study is to evaluate how passive envelope interventions can enhance indoor thermal resilience under five present and future work scenarios: TMY, SSP1-2.6 (2050 and 2080), and SSP5-8.5 (2050 and 2080). A baseline model was developed for typical building stock in each climate using EnergyPlus-23.2.0. The passive design parameters of window type, shading depth, and building orientation were systematically altered to analyze their effect on thermal comfort and building thermal resilience. Unlike previous studies that assessed either individual passive strategies or a single climate condition, this research combines multi-objective optimizations with overheating resilience metrics, by optimizing passive interventions using the GenOpt-3.1.0 and BESOS (Python-3.7.3 packages to minimize indoor overheating degree (IOD) and maximize climate change overheating resistivity (CCOR) index. Our findings indicate that optimized passive interventions, such as deep shading (0.6–1.0 m), low-e or bronze glazing, and southern orientations, can reduce overheating in all climate zones, reflecting a substantial improvement in thermal resilience. The novelty of this work lies in combining passive envelope optimization with future climate situations and a long-term overheating resilience index (CCOR) in the Mediterranean region. The results provide actionable suggestions for enhancing buildings’ resilience to climate change in Lebanon, thus informing sustainable design practice within the Eastern Mediterranean climate. Full article

The fact that the Ising model in higher dimensions than 1D features a phase transition at the critical temperature $T_c$ despite its apparent simplicity is one of the main reasons why it has lost none of its fascination and remains a central benchmark in modeling physical systems. Building on our previous work, where an approximative analytic free-energy expression for finite 2D-Ising lattices was introduced, we investigate different extrapolation strategies for estimating $T_c$ of the infinite system from exactly solvable small lattices. Finite square lattices of linear dimension N with free and periodic boundary conditions were analyzed, exploiting their exactly accessible density of states to compute the heat capacity profiles $C\left(T\right)$. Different approaches were compared, including scaling models for the peak temperature $T_{\max }\left(N\right)$ and an envelope construction across the set of $C\left(T\right)$-profiles. We find that both approaches converge to the same asymptotic value and compare favorably to the established Binder cumulant method. Remarkably, a model for $T_{\max }$ with a single model parameter following an $N/(N+1)$-law provides robust convergence, with a physical analogy motivating this proportionality. Our findings highlight that surprisingly few, but highly accurate, finite-size results are sufficient to obtain a precise extrapolation. Full article

Airplane cabin temperature is a critical environmental factor governing the safety and reliability of airborne equipment. Compared with measuring temperature, predicting temperature is more cost- and time-saving and can cover an extreme flight envelope. Physics-informed neural networks (PINNs) offer a promising prediction solution whose performance hinges on the availability of precise governing differential equations. However, building governing differential equations between flight parameters and cabin temperature is a great challenge, as it is comprehensively influenced by aerodynamic heat, avionic heat, and internal flow. To solve this, a new PINN framework based on “a priori monotonicity” is proposed. Underlying physical trends (monotonicity) from flight data are extracted to construct the loss function as a data-driven constraint, thus eliminating the need for any governing equations. The new PINN is developed to estimate the seven cabin temperatures of an unmanned aerial vehicle. The model was trained on data from four flight sorties and validated on another four independent sorties. Results demonstrate that the proposed PINN achieves a mean absolute error of 1.9 and a root mean square error of 2.6, outperforming a conventional neural network by approximately 35%. The core value of this work is a new PINN framework that bypasses the development of complex governing equations, which enhances its practicality for engineering applications. Full article