Sinusitis, a common disease of the maxillary sinus, is initially managed with saline solution and medication, resulting in the resolution of symptoms within a few days in most cases. However, Functional Endoscopic Sinus Surgeries are recommended if pharmacological treatments prove ineffective. This research aims to investigate the effects of maxillary sinus surgery on the airflow field, pressure distribution within the nasal cavity, and overall ventilation. This study utilized a three-dimensional realistic nasal cavity model constructed from CT images of a healthy adult. Virtual surgery including uncinectomy with Middle Meatal Antrostomy, two standard procedures performed during such surgeries, was performed on the model under the supervision of a clinical specialist. Two replicas representing pre- and post-operative cases were created using 3D printing for experimental purposes. Various breathing rates ranging from 3.8 to 42.6 L/min were examined through experimental and numerical simulations. To ensure the accuracy of the numerical simulations, the results were compared to measured pressure data, showing a reasonable agreement between the two. The findings demonstrate that uncinectomy and Middle Meatal Antrostomy significantly enhance the ventilation of the maxillary sinuses. Furthermore, increasing inspiratory rates leads to further improvements in ventilation. The static pressure distribution within the maxillary sinuses remains relatively uniform, except in regions close to the sinus ostium, even after surgical intervention.
This work proposes a novel yet practical dragonfly optimization algorithm that addresses four competing objectives simultaneously. The proposed algorithm is applied to a hybrid system driven by the solid oxide fuel cell (SOFC) integrated with waste heat recovery units. A function-fitting neural network is developed to combine the thermodynamic model of the system with the dragonfly algorithm to mitigate the calculation time. According to the optimization outcomes, the optimum parameters create significantly more power and have a greater exergy efficiency and reduced product costs and CO2 emissions compared to the design condition. The sensitivity analysis reveals that while the turbine inlet temperatures of power cycles are ineffective, the fuel utilization factor and the current density significantly impact performance indicators. The scatter distribution indicates that the fuel cell temperature and steam-to-carbon ratio should be kept at their lowest bound. The Sankey graph shows that the fuel cell and afterburner are the main sources of irreversibility. According to the chord diagram, the SOFC unit with a cost rate of 13.2 $/h accounts for more than 29% of the overall cost. Finally, under ideal conditions, the flue gas condensation process produces an additional 94.22 kW of power and 760,056 L/day of drinkable water.
Treatment of sinusitis by surgical procedures is recommended only when medication therapies fail to relieve sinusitis symptoms. In this study, a realistic 3D model of the human upper airway system was constructed based on CT images of an adult male and three different virtual functional endoscopic sinus surgeries (FESS), including only uncinectomy and uncinectomy with two different sizes of Middle Meatal Antrostomy (MMA) performed on that model. Airflow and deposition of micro-particles in the range of 1-30 mu m were numerically simulated in the postoperative cases for rest and moderate activity breathing conditions. The results showed that the uncinate process alone protects the maxillary sinus well against the entry of micro-particles, and its removal by uncinectomy allows particles to deposit on the sinus wall easily. Generally, uncinectomy with a degree of MMA increases the number of deposited particles in the maxillary sinuses compared to uncinectomy surgery alone. In the studied models, the highest particle deposition in the maxillary sinuses occurred among particles with a diameter of 10-20 mu m. Also, if a person inhales particles during rest breathing conditions at a low respiratory rate, the number of particles deposited in the sinuses increases.
The present study proposes and thoroughly examines a novel approach for the effective hybridization of solar and wind sources based on hydrogen storage to increase grid stability and lower peak load. The parabolic trough collector, vanadium chloride thermochemical cycle, hydrogen storage tank, alkaline fuel cells, thermal energy storage, and absorption chiller make up the suggested smart system. Additionally, the proposed system includes a wind turbine to power the electrolyzer unit and minimize the size of the solar system. A rule-based control technique establishes an intelligent two-way connection with energy networks to compensate for the energy expenses throughout the year. The transient system simulation (TRNSYS) tool and the engineering equation solver program are used to conduct a comprehensive techno-economic-environmental assessment of a Swedish residential building. A four-objective optimization utilizing MATLAB based on the grey wolf algorithm coupled with an artificial neural network is used to determine the best trade-off between the indicators. According to the results, the primary energy saving, carbon dioxide reduction rate, overall cost, and purchased energy are 80.6 %, 219 %, 14.8 $/h, and 24.9 MWh at optimal conditions. From the scatter distribution, it can be concluded that fuel cell voltage and collector length should be maintained at their lowest domain and the electrode area is an ineffective parameter. The suggested renewable-driven smart system can provide for the building's needs for 70 % of the year and sell excess production to the local energy network, making it a feasible alternative. Solar energy is far less effective in storing hydrogen over the winter than wind energy, demonstrating the benefits of combining renewable energy sources to fulfill demand. By lowering CO2 emissions by 61,758 kg, it is predicted that the recommended smart renewable system might save 7719 $ in environmental costs, equivalent to 6.9 ha of new reforestation.
The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.
This article aims to support the targeted worldwide green transition process by introducing and thoroughly analyzing a low-temperature heating and high-temperature cooling, smart building system. This concept allows for greater use of renewable energy while utilizing less input energy than conventional heating and cooling techniques. The proposed system consists of a reversible water-to-water heat pump driven by low-temperature geothermal energy. A rule-based control strategy is developed to establish an intelligent connection with the regional energy grids for peak shaving and compensating for the building's energy costs over the year. The dynamic simulation is carried out for a multi-family building complex in Stockholm, Sweden, using TRNSYS. The most favorable operating condition is determined via an artificial neural network-assisted tri-objective optimizer based on the grey wolf algorithm in MATLAB. The comparison of the proposed smart model with the conventional system in Sweden results in 332%, 203%, and 190% primary energy reduction, cost saving, and carbon dioxide emission mitigation, respectively. As indicated by the parametric results, the conflicting fluctuation between desirable and unfavorable indicators highlights the importance of multi-objective optimization. The grey wolf optimizer obtains 12% higher efficiency, 1.2 MWh lower annual bought energy, 24 $/MWh lower unit cost, and 5.1 MWh more yearly sold energy than the design condition. The scattered distribution reveals that tank volume and subcooling degree are sensitive parameters. According to the transient results, the suggested smart system can independently satisfy the building's heating, cooling, and electricity demands for more than 81% of the year, thanks to the two-way connection with the electricity and heating networks via the rule-based controller.
The present research introduces an innovative zero-energy building complex equipped with a rule-based control approach for higher integration of renewable resources in the local energy network while bringing down energy costs. The idea centers on establishing several smart controllers to achieve a bidirectional interaction with the heating/electricity network for peak demand shaving and mitigate energy costs. The proposed system comprises Alkaline fuel cells integrated with a hydrogen storage tank driven by either a vanadium chloride cycle or an electrolyzer unit. The system also has an absorption chiller and smart thermal energy storage to supply the heating and cooling demands. TRNSYS-MATLAB developed code is applied to assess the system's indicators from techno-economic standpoints for a residential building complex in the Scandinavian climate. Also, the parametric investigation and time-dependent analysis are carried out to examine the impact of decision parameters and the ambient condition. According to the results, the solar system's physical appearance is very important since it significantly affects performance efficiency and total cost. The results further reveal that picking up the cells' current from 300 A to 500 A improves the performance efficiency by around 12% while lowering the total cost, illustrating the importance of optimization. The results highlight the importance of smart controllers by showing that over 70% of the year's net energy values are positive, indicating that the proposed system may meet demand and sell excess electricity+heating productions to regional networks. The results further demonstrate that since the net energy values are positive for the majority of days in the spring and summer, the system might operate more independently from the local energy networks on warmer days. Eventually, the higher share of solar in summer and wind energy in colder days for hydrogen production shows that the renewable resources combination results in a secure energy supply to obtain the highest independence from the local grid throughout the year.
The present paper introduces a new smart building system driven by photovoltaic thermal panels. The concept is to improve the contribution of renewable energy in the local matrix for peak load shaving by having a two-way connection with the local electricity network via a rule-based energy monitoring control design. Besides, the feasibility of removing the electrical storage unit with high investment cost is studied by establishing a dynamic interaction between the energy production and usage components to reduce the energy costs over the year. The system has intelligent thermal energy storage integrated with an electrically-driven coil, heat exchanger, pumps, and several smart valves and control units. The transient system simulation (TRNSYS) package is implemented to assess the practicality of the suggested intelligent model for a building complex in Malmo, Sweden. According to the parametric outcomes, by raising the panel area, while the generated electricity increases, the solar utilization factor falls, indicating conflictive changes among performance metrics. The results also show that the renewable resource covers the building's heating and electricity demands for the majority of the year and that a significant amount of energy is sold to the neighbourhood electricity grid, demonstrating the viability of the introduced intelligent model.
This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a state-of-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 °C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.
This paper introduces an innovative and cost-effective multi-generation plant, driven by the central receiverbased concentrated solar systems, to facilitate the desired global green-transition process. The vanadium chlorine thermochemical cycle, which uses hydrogen instead of natural gas in the combustion chamber, is used as an innovative approach for reducing greenhouse gas emissions. The proposed system also includes a thermoelectric generator (TEG) for excess power generation and a multi-effect desalination (MED) unit to reduce exergy loss. The suggested system's technological, economic, and environmental metrics are analyzed and compared to a similar system that stores the created hydrogen rather than burning it in the combustion chamber. Furthermore, the viability of the studied model is investigated under the optimal operating condition, using the example of Sevilla in order to make the conclusions more reliable. According to the findings, the suggested novel configuration is a better alternative in terms of cost and environmental impact owing to decreased product energy costs and CO2 emissions. The outcomes further indicate that the substitution of the condenser with TEG leads to considerably higher power production. According to the optimization findings, the multi-objective grey wolf algorithm is the best optimization strategy compared to the non-dominated genetic and particle swarm approaches. At the best optimization point, 2.5% higher exergy efficiency, 1 $/GJ cheaper product energy cost, and 0.12 kg/kWh lower levelized CO2 emission are achieved compared to the operating condition. The Sankey diagram indicates that the solar heliostat system has the highest irreversibility. The exergy analysis results further reveal that the flue gas condensation process through the Rankine cycle and MED unit lead to a 53.2% reduction in exergy loss. Finally, considerable CO2 emission reductions show that the suggested new method is an effective solution for cleaner energy production in warmer climate countries.
This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.
This article proposes a cutting-edge smart building design that contributes to sustainable development objectives by fostering clean energy, facilitating sustainable cities and communities, and promoting responsible consumption and production. The main goal is to create a clever rule-based framework that will boost the penetration of renewable energy in local grids, reduce the size of the components and, consequently, investment costs, and promote the shift towards a more environmentally friendly future. The system is driven by photovoltaic thermal panels, a novel biomass heater scheme, and a scaled-down heat pump to supply the entire energy demands of multi-family houses. The grey wolf optimizer and a cascade forward neural network model achieve the most optimal condition. According to the results, the suggested smart model outperforms the conventional Swedish system, with an energy cost of 121.2 euro/MWh and a low emission index of 11.2 kg/MWh. The results show that knowing how biomass price changes affect the heat pump's operational mode is crucial to ensuring the system's economic viability. In comparison to the design condition, the optimized model increased efficiency by 3.8% while decreasing overall cost (2.1 euro/h), emission index (4.4 kg/MWh), and energy costs (29.9 $/MWh). The results further demonstrate that the heat pump meets the vast majority of the year's heating needs, but as electricity prices rise in December, the biomass heater becomes the principal energy provider. May is the month with the lowest average monthly cost, while December and July stand out as the most expensive months of the year due to a dramatic increase in demand. Eventually, the results show that the system runs without external energy sources through the designed optimal control framework and generates excess electricity for around half the year.
This article investigates the efficacy of temperature-controlled airflow systems in modern operating rooms for contaminant control, a critical factor in preventing surgical site infections. We have conducted experimental measurements in an operating room equipped with temperature-controlled ventilation to map the airflow field and contaminant dispersion (airborne particles with diameters ranging from 0.5 to 1 μm). The results were used to validate the computational fluid dynamics code, which was then employed to simulate and examine different conditions, including contaminant release locations and air supply rates. Realizable k-epsilon and passive scalar models were utilized to simulate airflow and airborne particle phases. We assessed the airflow distribution and contaminant dispersion, utilizing indices such as ventilation and air change efficiency scales. The analysis provided quantitative insights into the distribution and removal of contaminants, as well as the speed at which the room air was replaced. Contamination was found to be effectively reduced when contaminants were released near exhaust outlets or under central unidirectional inlets. The presence of the operating table caused a big distortion of the central downward airflow, forming a horizontal air barrier at the periphery. Under this unique interior configuration, an appropriate air supply ratio between central and periphery zones was required to achieve optimal overall ventilation performance.
In recent years, pandemic outbreaks have raised concerns about the spread of respiratory infections and their impact on public health. Since the pathogen emission during human respiration is recognized as the primary source, characterizing the physical properties of exhaled particles and airflow has become a crucial focus of attention. This article critically reviews experimental studies in exhaled particles and airflow, examines the uncertainty introduced by different measurement methods, analyzes how it is reflected in measurement outcomes, and provides an in-depth understanding of particle size distribution and airflow behaviors of human respiration. The measurement techniques assessment highlights the variability among particle sizing techniques in detection size range, collection efficiency, hydration status of captured particles, and experimental protocols. A combination of sampling-based instruments and laser imaging systems is recommended for particle sizing to cover a wider detection range, with refined setups in thermal conditions, sampling distance, volume, and duration. Meanwhile, it identifies the complementary nature of qualitative and quantitative measurements of airflow characterization techniques. Image recording systems plus data reconstruction programs are suggested to capture dynamic airflow features while accuracy validation by other techniques is required at the same time. Subsequent analysis of the measurement data showed that the various experimental measurements provided substantial information, but they also revealed disagreements and challenges in quantification. The dominance of submicron aerosols in exhaled particles and jet-like transport in exhaled airflow is obvious. More efforts should be made to measure particles larger than 20 μm, capture airflow dynamics in a high temporal and spatial resolution, and quantify the impact of face coverings to improve the understanding of human respiratory emissions.
Thermal comfort has been the main target of the ventilation in subway systems. However, pollutant concentration and aerosol dispersion could be the leading health issues in underground metro stations. This study numerically simulated a train movement inside a subway system using the Dynamic Mesh Technique for a 3-D computational domain consisting of four stations and connecting tunnels. The effects of both the ventilation system and the train-induced fluid flow inside the subway system were investigated. Then, the particle generation and dispersion due to train braking are considered, and the impact of the ventilation system on reducing the particle concentration inside the station was investigated. It is shown that the airflow inside the subway system is entirely affected by the piston effect. The airflow generated by the train movement is much higher than that generated by the operation of the ventilation system when only one train passes through the tunnel. The results show that the ventilation system, consisting of the supply and exhaust fans inside the tunnel and supply grilles inside the platform, can reduce the particle concentration by half, except for the platform beside the stopped train when the train enters the station and during half of the train stop time. The other design concept demonstrates that the under-platform exhaust system considerably reduces the concentration of the particles released by the train braking system on the trackside platform.
This study introduces a novel energy conversion and management framework to reduce carbon emissions in the energy sector and expedite the global shift towards sustainable practices. The system is driven by biomass-based solid oxide fuel cells for efficient power generation. Central to this approach lies the integration of additional hydrogen injection provided by a thermally-driven vanadium chloride cycle, aiming to enhance the quality of the syngas entering the fuel cells. The system is also combined with a super-critical CO2 cycle that generates power by passively enhancing performance through flue gas condensation. The proposed model's feasibility is evaluated in depth, techno-economically, considering thermodynamics and specific cost theories. As part of artificial intelligence, a neural network model is coupled with the genetic algorithm to determine the best operating status while minimizing computation time. According to the results, the suggested new integration results in higher efficiency and lower cost than a similar system without hydrogen injection. The results further show that the triple-objective optimization achieves output power, second-law efficiency, and overall system cost of 3425 kW, 48.5 %, and 2.3 M$/year, respectively. Eventually, the gasifier is the main contributor to the highest level of exergy destruction, and fuel utilization and current density are the most important parameters in modeling.
The airflow and micro-particle dispersion in a 3-D ventilated scaled room has been simulated numerically. The flow field was studied by the Eulerian method using a Reynolds Averaged Navier-Stokes model, and we used the Lagrangian approach to solve the equations of particle motion. The purpose is to evaluate and compare various discrete random walk methods (DRW) and continuous random walk methods (CRW) to evaluate particle concentration distribution in indoor environments. The isotropic DRW method's performance has been compared with models in which anisotropy of turbulence is applied, including CRW and modified DRW models based on near-wall direct numerical simulation results, near-wall kinetic energy, and the helicity of the flow. The results reveal that the isotropic DRW method can predict particle concentration in the indoor environment, and using a modified DRW model is not necessary.
Computational Fluid Dynamics (CFD) simulations are extensively used to model indoor environments, including airflow patterns, temperature distribution, and contaminant dispersion. These simulations provide valuable insights for improving indoor air quality, enhancing thermal comfort, optimizing energy efficiency, and informing design decisions. The recent global pandemic has emphasized the importance of understanding airflow patterns and particle dispersion in indoor spaces, highlighting the potential of CFD simulations to guide strategies for improving indoor air quality and public health. Consequently, there has been a significant increase in research focused on studying the transport and dispersion of pollutants in indoor environments using CFD techniques. These simulations are vital in advancing engineers' understanding of indoor environments; however, achieving accurate results requires careful method selection and proper implementation of each step. This paper aims to review the state-of-the-art CFD simulations of indoor environments, specifically focusing on strategies employed for three main simulation components: geometry and grid generation, ventilation strategies, and turbulence model selection. Researchers can select suitable techniques for their specific applications by comparing different indoor airflow simulation strategies.
The recent epidemic of the coronavirus disease showed the increased importance of controlling the transmission of contamination in the ward areas more than before. The performance of the ventilation systems in healthcare facilities can significantly impact the overall healthcare quality. This paper aims to compare two ventilation designs in an isolation room of a hospital and study the indoor airflow pattern. Computational fluid dynamics using ANSYS Fluent software was employed for the numerical simulation of the fluid flow. The simulation included the prediction of flow patterns and particle trajectories with the additional investigation into the impact of considering human thermal plume and modeling particle trajectories considering the turbulent fluctuations using the discrete random walk method in the simulation.
This study investigates the performance of integrated personal exhaust ventilation and physical barriers in mitigating airborne transmission, addressing the critical need for effective infection control in indoor environments. Using computational fluid dynamics, we modeled aerosol dispersion in a test room and validated these results with experimental data. Experimental validation strengthened the computational findings by providing empirical evidence for system efficacy under varying airflow conditions. We examined various prevention levels, including no prevention measures, only physical barriers, and physical barriers integrated with personal exhaust ventilation. The designed system with a barrier height of 65 cm and a personal exhaust flow rate of 9 L/s per person demonstrated strong efficacy in mitigating airborne transmission. Further numerical analysis was conducted to evaluate the impact of critical parameters, including barrier height and exhaust flow rate, on the aerosol removal efficiency of the integrated system. Results indicate that reducing the barrier height to 45 cm and the exhaust flow rate to 6 L/s per person retains 95% of aerosol removal efficiency, offering the most cost-effective and sustainable design without compromising system's performance in limiting airborne transmission. These findings suggest that moderate adjustments can enhance system sustainability by enabling significant material and energy savings.
Natural ventilation has the potential to enhance indoor air quality in classrooms with elevated CO2 levels, although it may introduce outdoor pollutants. This study introduces a novel controller for automatic windows that simultaneously monitors outdoor air pollution and temperature, synchronizing window openings with mechanical ventilation system to create a comfortable, healthy, and energy-efficient indoor environment. The practicality of the proposed controller is assessed for a classroom in Delhi, Warsaw, and Stockholm, each with contrasting climates and outdoor pollution levels, specifically PM2.5 and NO2. The controller parameters are optimized for each city using a non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between thermal comfort, CO2 levels, and energy consumption. The results show that the controller successfully met the indoor air quality standards in all cities; however, its operation was significantly influenced by the climate and pollution levels. While natural ventilation was utilized for 44% and 31% of the year in Warsaw and Stockholm, respectively, it was used for only 11% of the year in Delhi, the most polluted city. The optimization process significantly reduced energy use across all cities while also successfully reducing indoor CO2 concentrations. Although thermal comfort decreased slightly, it remained within acceptable thermal comfort conditions.
The present article introduces and investigates a new approach for shaving the peak electricity demand and mitigating energy instability. At the heart of this concept is a smart integration for efficient hydrogen production/storage/usage to minimize energy costs and maximize the renewable penetration in the local electricity grid. The system is driven by a wind farm integrated with proton exchange membrane (PEM) electrolyzers and reverse osmosis desalination units for efficient electricity, hydrogen, and freshwater production. It also combines with PEM fuel cells equipped with a hydrogen tank to meet the demand constantly when renewable electricity is unavailable or unstable. The system's practicality is assessed and compared for various Swedish cities with high wind potential from thermodynamic, economic, and environmental aspects to see where it works effectively. The comparative results of various scenarios show that integrating 32 wind turbines, 2 electrolyzers, and 2 reverse osmosis units, with 25% of electricity going to electrolyzers, 20% to reverse osmosis, and 55% to the grid, is the most optimal configuration/allocation. Optimal locations for the power plant are identified in Visby, Halmstad, and Lund due to favorable wind conditions. Setting up the system in Visby could prevent 1878.2 tonnes of CO2 emissions, generate 93,910 MWh of electricity annually, and create 213 ha of green space. The proposed system in Visby could boast the biggest electricity generation capacity, reaching 11,263 MWh, sufficient to power 938 households. Scaling this model to 12 cities in Sweden could provide the electricity needs of 4500 households, demonstrating the potential for widespread impact.
Ventilation systems are a vital component of buildings in order to ensure a healthy and comfortable environment for the occupants. In cold climate regions, ventilation systems are responsible for approximately 30% of building heat losses. In addition to outdoor pollutants (particulate matters, NOX, etc.), indoor emissions from materials in the form of gas pollutants and emissions from occupants are the principal indoor air quality metrics for securing an acceptable indoor concentration level. Therefore, it is of great interest to study the use of gas-phase air cleaning technologies in low-energy centralized air handling units. This study focused on reducing buildings' heating requirements by recirculating indoor air while maintaining an acceptable indoor air quality level. The heating performance of a typical residential and office building in the central Swedish climate was studied by dynamic building simulations. Indoor air recirculation rates and air changes per hour were the key parameters considered during the simulation of the building's heating demand and indoor gaseous air pollution concentration. We found that introducing indoor air recirculation reduces buildings' heating demand depending on the air change rates per hour. The results show that it is possible to reduce the energy use for heating by less than approximaytely 10% and 20% for residential and office buildings, respectively and maintain acceptable indoor air quality by using gas-phase air cleaning.
Despite various preventive interventions, nosocomial cross-infection remains a significant challenge in healthcare facilities worldwide. Consequently, prolonged hospitalization, elevated healthcare costs, and mortality rates are major concerns. Proper ventilation has been identified as one of the possible interventions for reducing the risk of cross-infection between patients and healthcare workers in hospital wards by diluting infectious agents and their carrying particles. The use of air cleaners in conjunction with the ventilation system further reduces the concentration of indoor pathogens. This article presents a systematic review of the ventilation solutions employed in hospital wards where pathogen removal performance can be enhanced using air-cleaning techniques while maintaining the thermal comfort of patients and healthcare staff. We provide a comparative analysis of the performance of different ventilation strategies adopted in one-, two-, or multi-bed hospital wards. Additionally, we discuss the parameters that influence the aerosol removal efficiency of ventilation systems and review various air-cleaning technologies that can further complement the ventilation system to reduce contaminant concentrations. Finally, we review and discuss the impact of different ventilation strategies on the perceived thermal comfort of patients and healthcare workers. This study provides insights into the cross-contamination risks associated with various hospital ward setups and the vital role of the ventilation system in reducing the adverse effects of infection risk. The findings of this review will contribute to the development of effective ventilation solutions that ensure improved patient outcomes and the well-being of healthcare workers.
The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka’s deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka’s model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15 l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract. Graphical Abstract: (Figure presented.)
Thermal comfort conditions profoundly affect the occupants' health and productivity. A diffuse ceiling ventilation system is an air distribution system in which the air is supplied to the occupied zone with relatively a low velocity through the perforated panels installed in the ceiling. The current study evaluated the impact of diffuse ceiling design parameters, i.e. diffuse panel configurations and heat load distributions, on the thermal comfort condition of the occupants. In this regard, the computational fluid dynamics technique was used to evaluate thermal comfort conditions in a waiting room, meeting room and office. The central and dispersal configuration of active diffuse panels was considered. The PMV-PPD model was applied to evaluate the overall occupants' comfort, while the draft rate was considered to assess local thermal comfort. The model validation was performed by comparing the collected laboratory measurement data. Overall, the results indicated that the central active diffuse panel configuration had a better thermal comfort than the dispersed one. The evaluation of dispersed configuration in realist scenarios, including office and waiting room, had the highest dissatisfaction, with a PPD value of 9%. Local thermal comfort assessment revealed that dispersed configuration had the highest draft rate of 14% in the office.
Several research studies have ranked indoor pollution among the top environmental risks to public health in recent years. Good indoor air quality is an essential component of a healthy indoor environment and significantly affects human health and well-being. Poor air quality in such environments may cause respiratory disease for millions of pupils around the globe and, in the current pandemic-dominated era, require ever more urgent actions to tackle the burden of its impacts. The poor indoor quality in such environments could result from poor management, operation, maintenance, and cleaning. Pupils are a different segment of the population from adults in many ways, and they are more exposed to the poor indoor environment: They breathe in more air per unit weight and are more sensitive to heat/cold and moisture. Thus, their vulnerability is higher than adults, and poor conditions may affect proper development. However, a healthy learning environment can reduce the absence rate, improves test scores, and enhances pupil/teacher learning/teaching productivity. In this article, we analyzed recent literature on indoor air quality and health in schools, with the primary focus on ventilation, thermal comfort, productivity, and exposure risk. This study conducts a comprehensive review to summarizes the existing knowledge to highlight the latest research and solutions and proposes a roadmap for the future school environment. In conclusion, we summarize the critical limitations of the existing studies, reveal insights for future research directions, and propose a roadmap for further improvements in school air quality. More parameters and specific data should be obtained from in-site measurements to get a more in-depth understanding at contaminant characteristics. Meanwhile, site-specific strategies for different school locations, such as proximity to transportation routes and industrial areas, should be developed to suit the characteristics of schools in different regions. The socio-economic consequences of health and performance effects on children in classrooms should be considered. There is a great need for more comprehensive studies with larger sample sizes to study on environmental health exposure, student performance, and indoor satisfaction. More complex mitigation measures should be evaluated by considering energy efficiency, IAQ and health effects.
In this study three-dimensional computational model of a segment of bronchial airway surface liquid has been investigated to study the effect of various cilia abnormalities on mucociliary clearance (MCC), which was reported in common respiratory diseases. Numerical simulations have been devoted to studying a two-layer fluid model of the airway surface liquid (ASL) consisting of a Newtonian lower periciliary liquid (PCL) layer and a nonlinear viscoelastic upper mucus layer. The time-dependent governing and constitutive equations have been discretized and solved by a finite difference projection method on a staggered grid. The immersed boundary method has also been employed to study the effect of cilia propulsive effect on ASL. Numerical results have been devoted to investigating the influence of various cilia abnormalities, such as phase difference between cilia, cilia beat pattern, cilia beat frequency, cilia lattice geometry, missing cilia regions, and cilia density on MCC. The mucus was modeled as a nonlinear viscoelastic fluid in 3D geometry. Numerical results show that some cilia abnormalities such as cilia density, cilia beat pattern, and cilia beat frequency have a dominant effect on MCC and some abnormalities such as missing cilia regions and phase differences between cilia have a moderate influence on that. Results also show the negligible impact of cilia lattice geometry on mucus flow.
This work presents novel energy production/storage/usage systems to reduce energy use and environmental effects, in order to address concerns about excessive heating demand/emissions in buildings. This focus is the design, control, and comparison of a biomass-fired model with a novel heater type and a solar-driven system integrated with photovoltaic thermal (PVT) panels and a heat pump. The heater has an external boiler and shell and tube heat exchanger, providing enhanced control over the combustion process and increased efficiency. Another feature of the present work is establishing a rule-based automation framework to manage the energy storage/flow among the components/grid/building. This smart integration reduces the size of the components, eliminates the need for a battery, and allows the system to interact in both directions with the electricity grid. The practicality of both systems is assessed and compared via a code developed in TRNSYS-MATLAB, considering the specific conditions of Toronto, Canada, characterized by high heat demand in winter. According to the results, the proposed solar-based system has an acceptable energy cost (78.9 USD per MWh of heating and electricity) attributable to the developed controllers applied to thermal energy storage. The results show that the PVT-based system integrated with a heat pump is environmentally superior, with a reduction in CO2 emission of 7.2 tonnes over a year. However, the biomass-fired system is an excellent option from the aspect of efficiency, with a relatively high energy efficiency of 69 %. Also, it is observed that the night set-back of the supply temperature can reduce the annual primary energy use and emission up to 60.3 MWh and 21.1 t, respectively. While the system relies more on the heat pump in cold months, the solar energy system supplies the entire demand in summer, demonstrating the significance of PVT and heat pump integration to increase energy reliability throughout the year.