Search Results (3,180)

The article presents a numerical model of a high-temperature superconducting (HTS) transformer rated at 13.8 kVA, equipped with windings made of 2G ReBCO tapes. The model was developed to analyze the coupled electromagnetic and thermal phenomena occurring during the inrush current period of transformer energization. It describes the dynamic processes of critical current exceedance, resistive zone formation, and local temperature rise within the superconducting tape structure under realistic operating conditions. The geometry of the ReBCO tape is represented with its active superconducting layer and metallic stabilizer layers. Temperature-dependent material properties of each layer, such as electrical resistivity, thermal conductivity, and specific heat capacity, are incorporated into the model. This approach enables a detailed analysis of the temperature distribution across all layers of the superconducting tape. The results indicate that the highest thermal stress occurs during the first inrush current peak, whose amplitude exceeds the critical current of the winding. At this stage, a distinct temperature rise is observed in the stabilizer layers, followed by gradual cooling in subsequent cycles of operation. The simulated current and temperature waveforms show good agreement with experimental measurements performed on a liquid-nitrogen-cooled transformer prototype. The developed model enables quantitative evaluation of local overheating risks, analysis of Joule loss distribution, and assessment of the influence of supply parameters and circuit impedance on the thermal stability of the system. Its application supports the optimization of HTS transformer design and provides valuable insight into the reliability of superconducting windings under transient inrush current conditions. Full article

It is still very challenging to design office buildings to be comfortable in hyper-arid conditions. In this paper, computational fluid dynamics (CFD) has been employed to investigate and improve the thermal performance of an office building in Béchar, Algeria, with ambient temperatures exceeding 40 °C. The scenario was analyzed using a complete methodology that integrated field measurements, questionnaires from the occupants, and CFD simulations. The investigation covered two cases: the reference case (Building 1) and a CFD-optimized building envelope (Building 2). The baseline simulation showed that the people were highly dissatisfied with the temperature, with 2.33 PMV and over 65% PPD values for the summer season. The new building envelope, with new insulation and aluminum cladding systems, showed much better improvement in the thermal comfort level. The outcome showed that PMV values were within tolerance (0.5 to +0.5), PPD levels decreased between 30% to 57%, and temperature decreased by about 6 °C. High correlation between CFD prediction and field measurement (r = 0.94) shows that the method is reliable. This study proves that CFD is a useful tool to forecast how to design for the climate. It gives evidence-based solutions for keeping individuals more comfortable and using less energy on cooling under weather extremes. The results make a contribution to sustainable building practice in very dry climates and offer a paradigm that can be used repeatedly for improving thermal comfort in poor environmental conditions. Full article

This study investigates the influence of urban greenery on microclimate conditions in Novi Sad, a city characterized by a temperate oceanic climate, by integrating high-resolution remote sensing data with in situ measurements from 12 urban climate stations. Sentinel-2 imagery was used to capture vegetation patterns, including tree lines and small green patches, while air temperature data were collected across two climatically contrasting years. Vegetation extent and structural characteristics were quantified using NDVI thresholds (0.6–0.8), capturing variability in vegetation activity and canopy density. Results indicate that high-activity vegetation, particularly dense tree canopies, exerts the strongest cooling effects, significantly influencing air temperatures up to 750 m from measurement sites, whereas total green area alone showed no significant effect. Cooling effects were most pronounced during summer and autumn, with temperature reductions of up to 2 °C in areas dominated by mature trees. Diurnal–nocturnal analyses revealed consistent spatial cooling patterns, while seasonal variability highlighted the role of evergreen and deciduous composition. Findings underscore that urban heat mitigation is driven more by vegetation structure and composition than by green area size, emphasizing the importance of preserving high-canopy trees in urban planning. This multidimensional approach provides actionable insights for optimizing urban greenery to enhance microclimate resilience. Full article

This study presents a monitoring-calibrated, systems-level retrofit assessment for a 15-year-old Grade-A office building in Beijing, China (temperate monsoon climate). One year of continuous monitoring (2023–2024) was combined with calibrated multi-physics simulations (EnergyPlus/DesignBuilder, Radiance, representative CFD) to evaluate retrofit scenarios for lighting, envelope and HVAC systems. Baseline EUI = 108 kWh·m⁻²·yr⁻¹ (total site electricity ≈ 3,088,893 kWh·yr⁻¹). HVAC accounted for ≈48% of site electricity. Key findings: (1) LED lighting retrofit delivered measured lighting savings of ~26.7% (simulated potential up to ~32.7%) but may increase cooling loads in some operating regimes (simulated +8.3%) if not coordinated with HVAC and envelope measures; (2) glazing upgrades and airtightness improvements materially increase HVAC savings; (3) a prioritized, phased retrofit (lighting → envelope → HVAC) can capture ~80–85% of integrated carbon reductions while lowering immediate CAPEX and business disruption; (4) scheduling major HVAC upgrades before the cooling season and envelope works during transitional months improves operational and economic outcomes. Calibration and uncertainty metrics are reported (annual energy error < 5%). Full article

To address the challenge of laser beam distortion induced by thermal effects in high-power slab laser amplifiers, a coupled thermal–mechanical–optical model for a face-pumped Yb:YAG multi-pass amplifier was developed. The thermal effects under different thermal management strategies were systematically investigated using the finite element method. Firstly, the temperature distribution, thermal stress, and deformation within the Yb:YAG crystal were analyzed and compared under both room-temperature (293 K) and cryogenic (150 K) cooling conditions using a microchannel cooling structure. The results demonstrate that under a pump power of 100 W and room-temperature cooling, the peak temperature of the gain medium reaches 363 K, with a peak thermal stress of 1.04 MPa and a maximum thermal deformation of 1.44 μm. In contrast, under cryogenic cooling at 150 K, the maximum temperature is reduced to 188 K, and both thermal stress and deformation exhibit a more uniform distribution within the pumped region. Subsequently, the thermal lensing of bonded and non-bonded Yb:YAG crystals was compared and analyzed by ray-tracing. It was found that under a pump power of 100 W, the thermal focal lengths of non-bonded Yb:YAG are 1112 mm and 2559 mm at cooling temperatures of 293 K and 150 K, respectively. For bonded crystals with a 3 mm undoped YAG thickness under identical pumping and cooling conditions, the corresponding thermal focal lengths measure 1508 mm and 3044 mm. When the undoped YAG thickness increases to 6 mm, the thermal focal lengths further extend to 1789 mm and 4206 mm, respectively. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

Deep oil and gas reservoirs exist under high-temperature and high-pressure (HTHP) conditions. Conventional coring without thermal preservation during retrieval induces thermal imbalance, biasing petrophysical and phase measurements and distorting resource evaluation. Internationally, most temperature-preserved corers are designed for low-temperature conditions and rely on passive insulation, whereas existing HTHP simulators can reproduce pressure and temperature but lack the capability to evaluate active thermal retention throughout coring and retrieval. Here, we develop and validate a full-scale testing platform for active in situ temperature-preserved coring (active ITP-coring), consisting of a simulated HTHP core chamber, a through-chamber conductive module, a high-pressure simulation module, an ambient-temperature simulation module, and a data acquisition and control module. The system operates stably at 150 °C and 140 MPa, reproduces realistic ambient cooling histories (with maximum and average rates of 11.22 and 5.11 °C/min), and demonstrates that, under HTHP conditions, active preservation limits the internal temperature drop to 4.2 °C over 40.5 min (temperature retention of 98.93%), markedly outperforming the 13.1 °C decrease within 14.9 min without active preservation. These results verify the system’s reliability and, at the laboratory scale, demonstrate the feasibility of active ITP-coring, providing a reproducible methodology and quantitative evidence for engineering deployment in deep reservoirs. Full article

Assessing preferences for heating, ventilation, and air conditioning (HVAC) sounds is important for improving comfort in living spaces. Recently, preference assessments using neurophysiological measurements have gained attention. However, associations between HVAC sound preferences and cortical activity remain insufficiently understood to establish neurophysiological indices. In this study, we developed machine learning models that estimate preference scores from magnetoencephalographic (MEG) signals recorded during HVAC sound presentation. We also developed spatial filters based on the common spatial pattern to extract MEG signals associated with the preferences. Both were trained for each participant using MEG signal pairs and participant’s paired-comparison judgments of HVAC sounds based on either coolness or preference. The preference scores estimated from the training data were strongly correlated with the average preference scores obtained through a psychological paired-comparison method (r > 0.98). Analysis of trained linear models revealed that the spatial filters primarily contributing to score estimation extracted theta (4–8 Hz) and alpha (8–13 Hz) oscillations. These suggest that the signals extracted by the spatial filters may reflect cortical activity associated with the coolness and preference of HVAC sounds, and that the preference estimation models may capture the relationship between cortical activity and psychological scales of HVAC sound preferences. 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

In this paper, we present a water mist dehazing network to improve the accuracy of radiation temperature measurements of the billet surface in the secondary cooling zone of continuous casting. First, we develop a billet radiation attenuation model that accounts for the wavelength-dependent attenuation coefficient of water mist in the secondary cooling zone. Using this model and the corresponding dataset, the water mist transmittance is calculated. Furthermore, the water mist dehazing network—which is distinct from conventional dehazing networks designed for natural environments—comprises three key components: water mist feature extraction based on a combined Unet and Transformer structure; fusion of prior water mist transmittance data using an attention mechanism; and composite transmittance estimation via a multi-path dense network. The experimental results demonstrate that the proposed network effectively reduces water mist’s interference with billet surface temperature measurements in both the spatial and temporal dimensions. Compared with the standalone Unet and Unet + Transformer network architectures, the proposed network achieves a significantly improved dehazing performance, thus verifying its practical value and reliability for billet surface temperature measurement tasks. Full article

Within the scope of this article is the presentation of a modelling and measurement approach for the effects of roof greenings and the application of the approach to evaluate the influence of roof greenings upon the thermal conditions inside a typical residential building. It is shown that overheating in summer can be reduced, and thermal comfort for inhabitants can be increased. The cooling is caused by the transpiration of plants and by the evaporation of water from the substrate. Other relevant physical effects are the shading of plants and the increase in the heat capacity of the building. In state-of-the-art buildings, a layer with a high insulating effect is incorporated into the envelope. This leads to the effect that a huge fraction of the cooling power is taken from the outside of the building and only a smaller part is taken from the inside. In order to mitigate this decoupling, a hydraulic connection between the greening and the interior of the building is introduced. To evaluate the effect of the inside cooling, the difference in the number of yearly hours with overheating in residential buildings is estimated. In addition, the reduction in energy demand for the climatisation of a typical residential building is calculated. The used methods are as follows: (1) Performance of laboratory and free field measurements. (2) Simulation of a typical residential building, using a validated approach. In summary, it can be said that green roofs, in particular with hydraulic connections, can significantly increase the interior thermal comfort and potentially reduce the energy required for air conditioning. Full article

This article presents an original, multi-depth soil-temperature monitoring system based on TMP117 digital sensors designed for deployment at several depths. The objective was to evaluate the system’s accuracy and applicability for building-energy performance assessment under contemporary climate conditions. Urban measurements at depths between 1.0 and 2.0 m were compared with ground temperatures derived using PN-EN 16798-5-1:2017-07 with Typical Meteorological Year (TMY) inputs and with observations from the Polish Institute of Meteorology and Water Management (IMWM). Standard inputs underestimated soil temperature on average by 1.1–2.3 °C (TMY) and 2.0–2.8 °C (IMWM), with the bias increasing with depth. For a ground-to-air heat-exchanger (GAHE) assessment, energy benefits estimated from standard inputs were lower in measurements by approximately 30–60% for pre-cooling and 70–86% for pre-heating. Measurements also revealed location-dependent differences between boreholes attributable to underground infrastructure. These findings indicate that non-local or outdated climate datasets can materially overestimate GAHE potential and confirm the need for local, multi-depth ground measurements and periodic updates of standard climate inputs to reflect urbanized conditions and climate change. The presented system constitutes a practical, scalable tool for engineers and designers of HVAC systems relying on ground heat exchange. Full article

With the IMO’s 2050 decarbonization target, hydrogen is a key zero-carbon fuel for shipping, but the lack of systematic risk assessment methods for hydrogen-powered marine propulsion systems (under harsh marine conditions) hinders its large-scale application. To address this gap, this study proposes an integrated risk evaluation framework by fusing Failure Mode, Effects, and Criticality Analysis (FMECA) with the Fuzzy Analytic Hierarchy Process (FAHP)—resolving the limitation of traditional single evaluation tools and adapting to the dynamic complexity of marine environments. Scientific findings from this framework confirm that hydrogen leakage, high-pressure storage tank valve leakage, and inverter overload are the three most critical failure modes, with hydrogen leakage being the primary risk source due to its high severity and detection difficulty. Further hazard matrix analysis reveals two key risk mechanisms: one type of failure (e.g., insufficient hydrogen concentration) features “high severity but low detectability,” requiring real-time monitoring; the other (e.g., distribution board tripping) shows “high frequency but controllable impact,” calling for optimized operations. This classification provides a theoretical basis for precise risk prevention. Targeted improvement measures (e.g., dual-sealed valves, redundant cooling circuits, AI-based regulation) are proposed and quantitatively validated, reducing the system’s overall risk value from 4.8 (moderate risk) to 1.8 (low risk). This study’s core contribution lies in developing a universally applicable scientific framework for marine hydrogen propulsion system risk assessment. It not only fills the methodological gap in traditional evaluations but also provides a theoretical basis for the safe promotion of hydrogen shipping, supporting the scientific realization of the IMO’s decarbonization goal. Full article

This study evaluates indoor thermal comfort and the energy performance of HVAC control strategies in the Congo Zone of a zoological facility located in Poland. The main objective in this zone is to maintain adequate relative humidity, which is more critical for plants and animals than the indoor air temperature range. Long-term measurements were carried out to determine the variation of air system heat transfer as a function of outdoor air temperature. To determine the energy demand for heating, cooling, and air transport, eight control algorithms were analysed, each differing in a single detail but potentially affecting overall energy use and thermal comfort. The algorithms combined the following features: maintaining a constant supply or indoor air temperature; operating with a constant or modulated recirculation damper position; maintaining a constant or variable airflow (CAV or VAV); operating within the normal setpoint range or with an extended range of 1 °C; controlling temperature only or both temperature and humidity; and utilising or not utilising free cooling. The control algorithm operating in the facility maintained indoor humidity within acceptable limits for 98% of the year but failed to meet temperature requirements for 28% of the time. Refined strategies achieved energy savings of up to 74% in fan power and 80% in cooling demand, though often at the cost of reduced humidity control. Full article