Search Results (643)

This study investigates numerically a Coupled Solar Ventilation Evaporative Cooling system for hot and arid climates. The system uses a solar wall chimney to produce natural ventilation and generate hot and dry airflow, which is then directed through a roof-mounted humid hay packed bed to enhance evaporative air conditioning. The resulting cold is transferred via a thermally conductive inner roof plate while a membrane condenser recovers moisture for reusing. A mathematical model was developed to describe heat and mass transfer in the hay packed bed, including solar chimney airflow, pressure drop and the evaporation energy balance. Parametric simulations were carried out for inlet air temperature of 40–60 °C, airflow rates of 0.25–0.45 m³/s, hay moisture contents of 0.006–0.014 kg/kg dry basis and air humidity ratio of 0.002–0.006 kg/kg dry air. Results show that evaporative cooling becomes effective only above certain inlet temperature. Increasing airflow from 0.25 to 0.45 m³/s reduced hay temperature from 30 to 26.8 °C when inlet air temperature exceeded 43.5 °C. Higher hay moisture content enhanced cooling performance, reaching about 26 °C, while higher inlet air humidity reduced evaporation and limited cooling. The operating maps obtained from the numerical simulations provide practical guidance for preliminary system sizing and for optimal operating parameters selection in solar-driven evaporative cooling systems. The mathematical model treats the solar chimney, the evaporative packed bed, the conditioned room and the membrane condenser within the same steady state calculation. The solar energy balance and the pressure balance are used to relate the inlet air temperature and the airflow rate to solar irradiance, ambient temperature and chimney geometry. The model also includes the heat transferred from the room through the roof plate, the sensible heat of the supplied water and the mass transfer and pressure drop effects of the membrane condenser. Full article

Indoor air quality (IAQ) plays a critical role in the health and cognitive performance of students, making its assessment essential for sustainable building design in educational environments. This study evaluates whether the ventilation flow rates prescribed by the Spanish Regulation for Thermal Installations in Buildings (RTIB), together with the occupancy densities defined by the Technical Building Code (TBC), are sufficient to maintain CO₂ concentrations within regulatory limits in classrooms and library reading rooms. A validated three-dimensional CFD model was developed to simulate airflow patterns and CO₂ distribution under typical operating conditions. The model was experimentally validated using measurements from a dedicated test room in the KUBIK experimental building of Tecnalia, demonstrating high predictive accuracy with average relative errors between 14% and 20%. Results indicate that, under current RTIB and TBC design criteria, (modelled for a 36 m² classroom with 24 occupants and a fresh air supply of 1080 m³/h), CO₂ levels frequently exceed the 910 ppm regulatory thresholds established by the RTIB’s direct method, highlighting potential shortcomings in existing standards for educational spaces. Additionally, two mechanical ventilation configurations were analyzed, revealing that floor-supply ventilation promotes more homogeneous pollutant dispersion and lower concentration peaks compared with ceiling-mounted systems. These findings underline the need to reconsider ventilation design strategies in educational buildings and demonstrate the value of CFD modelling as a tool to support evidence-based decisions toward healthier and more sustainable indoor environments. Full article

Residential buildings must now be designed and retrofitted as adaptive climate–health–work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. This review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is a hypothesis-generating, decision-support and benchmark-development framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

This work investigates heat transfer experimentally and numerically within a ventilated cavity, both with and without an internal heat source, simulating a room with a person at the interior at 1:3 scale. This setup has applications in building energy systems, cooling of electronic equipment, solar energy collectors, etc. The experimental configuration consists of a cube in which the left vertical wall is subjected to a uniform heat flux, and the opposing wall is maintained at a constant temperature. A rectangular parallelepiped heat source was placed inside. The remaining walls are thermally insulated, and air is the thermal fluid. Air enters and exits through square ports on the top surface. Experimental temperature profiles were recorded at multiple depths and heights. Corresponding numerical results for temperature fields, flow patterns, turbulent viscosity, and turbulent kinetic energy were generated using the Ansys Fluent 18 CFD software, with six turbulence models assessed against experimental data under steady-state conditions. A key finding is that the Nusselt number and the convective heat transfer coefficients (average) for the hot wall remain negligibly affected by the incorporation or status (on/off) of a heat source at the interior of the cavity, the biggest temperature difference (experimental vs numerical) corresponds to the rkε model with 6.2% when there is no thermal source in the cavity and the lowest difference for the average convective heat transfer coefficient is with the rslrso model with 5.2%. Full article

Double-skin façade (DSF) systems are important for energy efficiency because they effectively utilize natural ventilation and daylight. However, the uninterrupted vertical gaps in these systems may pose safety risks in the event of a fire by causing the rapid spread of smoke and hot gases. This study presents a hybrid approach that combines computational fluid dynamics (CFD)-based simulations and machine learning (ML) techniques to predict heat flow and fire-room control-volume heat release rate (FR-HRR). Within the scope of the study, 400 different scenarios were modeled with different combinations of basic natural ventilation design parameters consisting of gap width, gap height, window opening area, and air inlet and outlet area. The data obtained were evaluated with different ML models, including Fine Tree, Bagged Tree, Support Vector Machine, and Artificial Neural Network models; in particular, the Fine Tree model gave the most successful results with high accuracy rates (R² = 0.99 for FR-HRR; R² = 0.91 for heat flow). The analysis showed that DSF gap width provided a dominant model-based contribution within the investigated CFD-generated dataset. This approach provides a preliminary CFD-informed ML framework for the rapid comparative assessment of fire-related responses in open-boundary naturally ventilated DSF configurations during the early design stage. Full article

Accurate and stable indoor occupancy information is essential for occupant-based intelligent ventilation control. Under a single-camera setting, existing indoor occupancy detection methods commonly suffer from missed detections caused by occlusion and blind zones, false detections caused by people outside the room, and cumulative entry–exit errors that are difficult to correct. These problems lead to false fluctuations in detected occupancy, affect control performance, and may further reduce indoor comfort or cause unnecessary energy use. To address the practical situation in which indoor spaces are commonly equipped with a single security camera, this study proposes an indoor occupancy detection method by fusing field-of-view information and entry–exit events with a single camera. The study covers method development, multi-scenario validation, parameter analysis, and a ventilation control application. The proposed method uses YOLOv8x and DeepSORT as front-end models and performs post-processing on their outputs to extract field-of-view occupancy information, entry–exit events, and blind-zone events. An occupancy confirmation and correction module is then constructed. The blind-zone event mechanism reduces the influence of missed entry–exit events and camera blind zones on occupancy judgment. The correction module integrates frame-by-frame ID counts, historical outputs, and multiple event signals to verify and suppress false occupancy changes caused by false detections, missed detections, and blind zones, thereby producing more stable indoor occupancy results. Experimental results show that the proposed method outperforms the baseline methods based on front-end object detection and tracking in terms of score, RMSE, and F1 score in three typical scenarios: an office, a home, and a classroom. In the office scenario, the proposed method achieved a score of 99.36%, an RMSE of 0.081, and an F1 score of 0.781. The detection stability was also improved in the home and classroom scenarios. In the high-density and strongly occluded classroom scenario, the absolute detection performance of the fusion-based detection method was limited by the front-end models, indicating that the method still has certain applicability boundaries in complex high-density scenes. Parameter sensitivity analysis shows that key parameters, including the entry–exit area depth, confidence threshold, and time threshold, affect the detection results of the fusion-based detection method. Under the test conditions of this study, the method performs well when the entry–exit area depth is approximately 1.5d, the YOLOv8x confidence threshold is 40%, and the time threshold is 5 × FPS. These results can provide a reference for initial parameter setting and on-site calibration in similar scenarios. Using the office scenario as a case study, the method was further applied to occupant-based ventilation control. The average CO₂ concentration during occupied periods under the proposed method was 622.43 ppm, which was closest to the result under ground-truth occupancy control, with a deviation of only 0.9 ppm. This indicates that the method can help improve indoor air quality. Compared with conventional schedule-based control, occupant-based ventilation control driven by the proposed fusion method reduced cumulative fan energy consumption by approximately 65.2%, showing good energy-saving potential at the ventilation-control level. In summary, the proposed method can effectively improve the accuracy and stability of indoor occupancy detection under a single-camera setting and provide more reliable input for occupant-based ventilation control. The framework is modular, and the front-end object detection and tracking models can be replaced according to actual deployment needs. However, the validation in this study is still mainly based on scenarios where existing security cameras can cover the main activity areas and all entry–exit passages. The applicability of the method under more complex camera arrangements, lighting variations, and automatic region configuration requires further investigation. Full article

Ensuring the effective removal of airborne particles is essential for maintaining indoor air quality, particularly in environments with limited ventilation. This study examines how ion polarity regime, voltage, and relative humidity influence aerosol particle removal in a controlled, room-sized chamber (35.8 m³) using a custom-built air ionizer. Experiments were conducted under stagnant and ventilated conditions (0.5 h⁻¹) while varying ionizer polarity (positive, negative, bipolar, alternating), voltage (6 kV, 10 kV), humidity (40%, 70%), and aerosol type (incense smoke, nebulized KCl). Positive and negative unipolar ionization achieved over 90% removal within 60 min, with decay rates of 0.04–0.05 min⁻¹, half-lives of 13–17 min, and clean air delivery rates (CADR) of 60–90 m³ h⁻¹. Bipolar ionization was less efficient due to ion-ion recombination, yielding CADR values below 25 m³ h⁻¹, while alternating polarity improved deposition (40–70 m³ h⁻¹) by reducing recombination losses. Relative humidity had a minimal influence on unipolar performance but moderated efficiency in bipolar and alternating modes. Under low ventilation, unipolar negative ionization sustained high removal (96.7%), while ozone remained below the detection limits of the methods used. These findings indicate that ion polarity control and field strength strongly influence particle removal and that unipolar or alternating-polarity operation can provide effective particle removal under controlled chamber conditions, including a low-ventilation case of 0.5 h⁻¹. Full article

Vernacular settlements in hot–humid regions preserve climate-responsive spatial knowledge, yet evidence on how linked outdoor, transitional, and indoor spaces jointly shape microclimate and thermal comfort remains limited. This study investigates a compact Hakka settlement in southern Jiangxi, China, by integrating field measurements, calibrated simulation, PET-based thermal-comfort assessment, and parametric scenario comparison to examine microclimatic differentiation across cold alleys, patios, halls, semi-open interfaces, and interior rooms. The results reveal clear microclimatic gradients across the linked vernacular spatial sequence. During the summer afternoon peak, cold alleys reduced air temperature by approximately 2.5 °C and PET by approximately 8.5 °C relative to ordinary streets, while semi-enclosed spaces adjacent to patios reduced air temperature by approximately 4.0 °C but increased relative humidity by 8–12%, indicating a cooling–moisture trade-off. Measured and simulated air temperature and wind speed showed satisfactory agreement and reproduced the main thermal and ventilation hierarchy across the connected spaces. Parametric comparison further identified case-based geometry-performance tendencies under the tested boundary conditions: within the tested cold-alley scenarios, widths of approximately 0.8–1.4 m combined with an H/W ratio close to 3:1 showed relatively favorable airflow-temperature performance in terms of shading continuity, moderated airflow, and reduced summer thermal exposure. The findings suggest that thermal comfort in compact hot–humid vernacular settlements depends on radiant-load reduction, moderated ventilation, and thermal buffering rather than on ventilation enhancement alone. Beyond the case-specific evidence, this study contributes a sequence-based, locally calibratable approach for preliminary retrofit appraisal in comparable compact hot–humid vernacular settlements. Full article

Night ventilation coupled with thermal mass is an effective means of reducing overheating in passive buildings. Successful systems require a high airflow rate coupled with enhanced convective heat transfer to the thermal mass. This work presents results for enhanced convection when the primary thermal mass is in the ceiling. Such mass distribution occurs, for example, in multi-story apartments in developing economies. Experimental results are measured in a scale model of a typical room. The original contribution is the use of upward-directed ventilation at an angle of 30° to 40° from a window located at a typical distance below the ceiling. At scaled air change rates of 4.9 air changes per hour, the measured convective heat transfer coefficient at the ceiling was 7.7 W/m² K. In contrast, when air flowed horizontally from the window, the heat transfer coefficient was 3.5 W/m² K or less, indicating that substantial improvement was gained by directing airflow toward the ceiling. To link the experimental results to an application in a full-size building, an approximate model is presented to estimate the impact of directed night ventilation on the thermal mass (specifically the concrete slab ceiling) and room air temperatures. Coupling angled flow with nighttime ventilation, the ceiling slab and peak daytime air temperature can be reduced by 5 °C compared to horizontal ventilation from a window at conventional height. These results have enabled collaborators in Gujarat, India, to launch tests in a full-scale home serving a low-income community without access to air conditioning. Full article

Operating room ventilation is a key engineering factor in maintaining clean air environments. This study presents an integrated three-part methodology combining Computational Fluid Dynamics parametric analysis, performance assessment with effect size analysis and multi-criteria decision analysis using quantitative engineering metrics, and surrogate modeling for thermal effect propagation in an orthopedic operating room. Simulations were conducted in ANSYS Fluent 2020 R₂, benchmarking an existing local operating room against an ASHRAE 170-2021 compliant model, followed by parametric evaluation of four ceiling inlet configurations. The existing system exhibited critically low velocities (0.05–0.10 m/s) with a coefficient of variation of 0.73, indicating severe flow non-uniformity. The proposed Multi-Velocity Ceiling Diffuser—featuring a high-velocity core (0.40 m/s) over the surgical area and a low-velocity peripheral frame (0.20 m/s)—achieved 85% coverage of the ASHRAE-recommended velocity range (0.20–0.30 m/s), a coefficient of variation of 0.14 (81% improvement), and 62 air changes per hour, representing an 86% reduction in supply airflow compared to a full-ceiling system. Effect size analysis confirmed that MVCD performance shows large practical differences from smaller inlet designs (Cohen’s d ≥ 0.41) and negligible difference from full-ceiling systems (Cohen’s d = 0.05). Multi-criteria decision analysis—with feasibility and cost quantified using engineering estimates (ductwork area, downtime days, standardized cost data)—ranked MVCD as optimal under the modeled assumptions (composite score = 0.84), outperforming the existing system (0.59) and full-ceiling design (0.51). To address the isothermal assumption limitation, a Random Forest surrogate model was implemented as a differentiable approximation strategy for parametric uncertainty propagation. Under non-isothermal conditions, the MVCD is predicted to maintain a spatial median velocity of 0.19 m/s (5th–95th percentile range: 0.17–0.21 m/s) and 71% ASHRAE compliance (parameter sampling range across literature-derived distributions: 63–78%). Achieving ASHRAE velocity criteria is an engineering surrogate for ventilation effectiveness; the relationship between these metrics and clinical infection outcomes depends on multiple factors beyond airflow, including surgical technique, patient factors, and antimicrobial prophylaxis. No clinical inference is permitted from the present results. Experimental measurement in a physical MVCD-equipped operating room is required to validate these predictions prior to clinical implementation. Full article

Ammonia (NH₃) from pig houses contributes to air-quality degradation and odor, yet farm-level emissions are highly sensitive to housing design, slurry chemistry and management. This study developed and validated a minute-resolution dynamic model for indoor NH₃ concentration and house-level emission in a mechanically ventilated finishing pig house. Volatilization from the slurry surface was computed from total ammonia nitrogen (TAN), pH and temperature using established mass-transfer formulations, and coupled between two zones (pit headspace and room airspace) via advection and diffusion across the slatted-floor open area. Over one production cycle, key drivers and indoor NH₃ were monitored; discrete TAN observations were upsampled to minute resolution by linear interpolation. Model coefficients were optimized by a genetic algorithm with chronological 70/30 splits for calibration and validation in the grower and finisher phases, respectively. The calibrated model reproduced minute-scale dynamics (validation RMSE 1.53–1.76 ppm, R² 0.87–0.88; MAPE 9.95–10.87%). Sobol’s global sensitivity analysis identified ventilation rate as the dominant driver of indoor concentration, and TAN and slurry pH as the principal drivers of emissions. The model provides decision support for minute-scale monitoring and management, and can be integrated with factor-control methods and ICT-based supervisory systems. Full article

Background/Objectives: Intraoperative methadone has emerged as a significant pharmacological strategy in cardiac surgery to improve postoperative analgesic outcomes and reduce the reliance on rescue short-action opioids. This review aims to synthesize evidence regarding the safety and efficacy of intravenous methadone compared to other strategies for postoperative pain control in adult and pediatric cardiac surgeries. Methods: This narrative review relied on electronic searches in PubMed, Web of Science, Cochrane Library, and EMBASE up to January 2026. From 199 articles retrieved, 41 were included, focusing on analgesic efficacy, safety, pharmacokinetic variations during cardiopulmonary bypass (CPB), and cost-effectiveness. Results: The implementation of methadone results in up to 70% reduction in postoperative opioid requirements. Patients experience significantly lower pain scores from 24 to 72 h and improvement in satisfaction regarding pain management. In pediatric populations (neonates and children), the use of methadone leads to a significant reduction in opioid needs and a high rate of extubation in the operating room. Pharmacokinetically, a 48% drop in methadone concentration occurs during CPB due to hemodilution and sequestration. Safety data confirms that intraoperative use does not prolong mechanical ventilation; however, doses exceeding 0.25 mg/kg are linked to an increased incidence of delirium. Economically, methadone can be cost-effective, resulting in savings of up to $6355 per patient. Conclusions: Intraoperative methadone improves postoperative analgesia, opioid consumption, patient satisfaction, and costs after cardiac surgery. Its opioid-sparing effects make it particularly attractive for ERAS protocols, although vigilance against dose-related delirium and QT prolongation remains essential. Further research, especially in pediatrics, is needed to refine dosages and safety protocols. Full article

Formaldehyde (HCHO) is a typical volatile organic compound (VOC) that poses significant risks to human health. Long-term exposure, even at low concentrations, has been associated with various malignant diseases, including nasopharyngeal, colon, and brain cancers. Common technologies for HCHO abatement include ventilation, adsorption, photocatalysis, and catalytic oxidation. Among these methods, catalytic oxidation is regarded as the most promising due to its high removal efficiency, low cost, minimal energy consumption, and no toxic by-products. In recent years, supported catalysts with excellent room-temperature activity and high dispersibility have attracted considerable attention. These catalysts can usually be divided into two categories: noble metal catalysts and non-noble metal catalysts. Zirconia (ZrO₂) has become an ideal support owing to its advantages of high specific surface area, abundant and tunable acid–base sites, and strong metal–support interaction (SMSI). Various modification strategies have been developed to improve the catalytic performance of ZrO₂-based systems, such as the construction of phase interfaces and the stabilization of single-atom species. This review summarizes the recent research progress of ZrO₂-based systems for the catalytic oxidation of formaldehyde. It provides a detailed discussion of the physicochemical properties of ZrO₂ supports and the reaction mechanisms involved, and highlights achievements in crystal phase regulation, elemental doping, metal–support interaction, and composite modification. Finally, future challenges and development directions for these catalysts are also outlined. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article