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The theoretical correlations have been employed so far in 1D models to estimate train drag inside the tunnel, but verifying their accuracy is essential. This study uses 3D numerical models to compare drag coefficients inside a tunnel with theoretical predictions. Two train models (cubic and realistic) are examined with two uniform and accelerated velocity profiles. Findings demonstrate that the train drag significantly increases at the tunnel entrance. However, the nose and side drag will gradually decrease in the middle and exit of the tunnel, while base drag remains almost constant. Results also show that theoretical model predictions for side drag are acceptable, but there are significant discrepancies in estimating local nose and base drag. The model underestimates the nose drag at the tunnel entrance and overestimates it in the middle and exit, resulting in a lower mean error. For base drag, the model significantly underestimates values during train movement inside the tunnel.

Thermal energy systems in buildings play a central role in global decarbonization efforts, accounting for a significant share of energy use and carbon emissions. This study addresses a key research question: how can advanced control strategies further enhance the performance of already energy-efficient, low-exergy thermal systems in low-energy buildings? To address this, a model predictive control (MPC) framework is designed to optimize the operation of an advanced thermal system based on modern concepts of low-temperature heating and high-temperature cooling, including ground-source heat pumps, borehole thermal storage, and modern air handling units. This approach employs a multi-layered MPC cost function, considering both immediate operational costs (electricity and heating) as well as system impact penalties, such as CO₂ emissions, thermal energy storage preservation, comfort violations, and peak load shaving, in response to fluctuating market cost signals, outdoor temperature, and thermal storage limitations. Applied to a validated, ultra-efficient commercial building, the MPC framework achieves a 13 % reduction in annual market-responsive operational costs, a 20 % improvement in long-term savings, and a four-year shorter payback period compared to existing well-established rule-based control. The results further confirm the robustness of predictive control under realistic forecast errors, as demonstrated by Monte Carlo simulations. From an environmental perspective, the CO₂ emission index stays below both Swedish electricity and district heating baselines, demonstrating the environmental benefits of predictive control through strategic sector coupling. Beyond the case study, the proposed method provides a scalable pathway for integrating predictive control into next-generation smart buildings. It highlights the potential of MPC as the final optimization layer in advanced thermal systems, aligning with global objectives for cost-promising and carbon-neutral building operations.

The present study introduces and thoroughly investigates a novel smart heating, ventilation, and air conditioning system with thermal storage in a newly built commercial building in Uppsala, Sweden. The system combines 25 double U-tube borehole thermal energy storage, district heating, and intelligent control strategies to effectively manage heating and cooling demands for offices and restaurants. A novel optimal adaptive control framework dynamically adjusts the radiator supply temperature by accounting for solar radiation, ventilation flow rate, occupancy gains, and outdoor temperature. These modifications are optimized using the particle swarm method to enhance thermal comfort and energy efficiency. The proposed framework is compared with the existing control system based solely on outdoor temperature from techno-economic, environmental, and comfort aspects. According to the results, the outdoor temperature history and wind velocity have minimal effects on heating demand deviations, while solar radiation, occupancy gains, and ventilation performance play significant roles. The results further indicfate that solar radiation is the most influential factor in warmer months, whereas occupancy and ventilation gain are more important in colder months. Results demonstrate substantial enhancements in thermal comfort, with the weighted temperature deviation index reduced by 72.7% and the comfort consistency ratio increased by 54.4%. The designed adaptive controller reduces the annual heating supplied to radiators and the payback period by 13.2% and 9.0%, respectively, and decreases CO2 emissions and the index by 9.4% and 2.6%, respectively. After 20 years, the adaptive controller outperforms the basic model in terms of profit, increasing it by 20.4% to 190,260 USD, proving its economic superiority in the long run. In transitional months like April (14.9 MWh, 56.3% of the total) and May (15.9 MWh, 69.9%), when efficient solar gains reduce heating demands, the suggested adaptive controller also has substantial monthly energy savings.

Thermal energy demand in buildings represents one of the largest contributors to global energy use and CO₂ emissions. Advanced thermal energy systems, including borehole thermal energy storage (BTES) integrated with highly intelligent air handling units, offer promising solutions to reduce emissions while ensuring affordable and reliable comfort. This study examines a state-of-the-art commercial building in Uppsala, Sweden, that already employs a fossil-free and highly efficient BTES–district heating configuration. Although this system is well-designed and operates intelligently, it still has important limitations, including underutilization of borehole potential, limited thermodynamic efficiency from direct-use exchange, and a lack of flexibility under varying energy tariffs. Therefore, this work aims to make an already smart and efficient system even smarter by integrating a ground source heat pump with adaptive seasonal energy management. A comparative benchmarking analysis is carried out using validated TRNSYS simulations and real operational data to evaluate performance, economic viability, and environmental outcomes. The results show that integrating a clever heat pump system enhances the annual heat extraction from the ground by approximately 27 %, resulting in a 29 % decrease in overall heating costs, and improves long-term savings by around 20 %, despite an 11 % rise in upfront investment. Environmentally, the enhanced system substantially reduces CO₂ emissions, cutting the annual impact by more than 90 % compared to the current configuration, aligning with the Swedish zero-emission targets. However, the operational cost savings strongly depend on peak heat (power) costs, which are expected to rise under policymakers' frameworks. This indicates that the long-term viability of adding heat pumps in Sweden is shaped not only by technical performance and CO<inf>2</inf> savings but also by evolving local energy price structures. Yet, the considerable CO₂ savings helped by Sweden's green electricity mix and the opportunity to enjoy hourly spot-price variability through advanced controllers make heat pump integration a compelling option for future-proofing ultra-efficient buildings.

Nosocomial transmission of airborne pathogens remains a persistent threat in multi-bed hospital wards. This study quantifies how personalized exhaust devices, combined with the main two-bed patient room ventilation, can suppress cross-contamination and contributes a configuration-spanning assessment that links personalized exhaust operating set-points to removal efficacy across six layouts, providing design guidance absent from prior single-layout studies. A three-dimensional hospital ward was solved with the Reynolds-Averaged Navier–Stokes (RNG k-ε). At the same time, particle trajectories representing pathogen-laden aerosols were computed by one-way-coupled Discrete Phase Modeling augmented with a Discrete Random Walk stochastic dispersion scheme. Six different ventilation layouts were combined with personalized exhaust flow rates of 0 (off),10, 20, and 40 L/s. Two infection scenarios were simulated: (i) one infectious patient served as the particle source, while a second patient and a healthcare worker were modeled as susceptible targets; (ii) both patients were infectious, with the healthcare worker being the target for exposure. Ventilation geometry strongly governed room airflow and particle transport; without personalized exhaust, inhalation fractions differed by an order of magnitude between layouts. Activating personalized exhaust at 20 L/s reduced inhalation fractions by ≥ 80% in half of the layouts, while 40 L/s achieved complete particle removal in all configurations. In the dual-infection scenario, simultaneous operation of both personalized exhaust units at 20 L/s diminished healthcare-worker exposure by 75%. These results demonstrate that personalized exhaust devices provide robust, configuration-independent mitigation of aerosol transmission and should be considered a complementary strategy to conventional ward ventilation. 

Despite the exciting potential of microgrids in future smart energy systems, they encounter significant challenges, including fluctuations in energy demand and output, as well as the unpredictable behavior of electric vehicles. This article examines the ability of microgrids to enhance the integration of renewable energy sources to achieve Zero-Energy Buildings (ZEBs) and facilitate the deployment of Vehicle-to-Grid (V2G) technologies. The designed microgrid comprises vehicles utilizing V2G technology for daily energy storage and a hydrogen cycle featuring electrolyzers and fuel cells for seasonal storage. Probability functions based on uncertainty for distance, arrival, and departure periods from charging stations are formulated to mitigate uncertainties associated with electric vehicles (EVs). A genetic algorithm is employed to optimally regulate EVs' charging and discharging range and the hydrogen cycle's dynamic configuration. The system's feasibility is evaluated for a district in Tehran, characterized by a hot semi-arid climate per the Köppen climate classification, comprising 600 EVs and 3000 residential and 55 commercial buildings. The performance of the suggested smart system is compared with traditional scenarios from techno-ecological, economic, and environmental perspectives. The findings indicate that 62.6 % of the overall energy demand is met by renewable sources (wind and solar), and the microgrid can independently fulfill the need for over 50 % of the year, owing to the implemented hybrid optimum controllers. The findings indicate that 41 % and 16 % of total renewable electricity generation are stored in hydrogen systems and electric vehicles, respectively, highlighting their significant potential for both short-term and long-term storage. Compared to the same traditional scenarios, the suggested system, with an annual energy gain of 8.9 GWh, exhibits superior performance due to its little reliance on the grid while simultaneously ensuring the happiness of electric vehicle owners and the stability of energy storage systems. The intelligent microgrid demonstrates significant efficiency, conserving over 12,600 MWh of energy and decreasing more than 8800 tons of CO2 emissions. Furthermore, this system generates a substantial financial benefit of approximately USD 468,000, highlighting its notable environmental and economic merits.

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.

Using nasal sprays as a drug delivery method to the nasal cavity is widespread due to their convenience and effectiveness in treating various conditions. The high velocity of droplets exiting the nozzle can significantly impact the flow field, leading to changes in deposition patterns. Therefore, it is crucial to understand the interactions between the droplets and the fluid. In this research, we propose an innovative and cost-effective approach to investigate the two-way interactions between droplets and the fluid in numerical simulations of nasal sprays. We employ ultra-high-speed photography using an infinitesimal light pulse to examine spray puffs and extract droplet characteristics. We aim to determine whether the two-way interaction assumption produces significant differences in a numerical model. We first measured the droplet size distribution and spray cone angle in unconfined ambient conditions to achieve the objective. We then extended the measurement to real-sized 3D printed models of the nasal passage truncated in various sections to analyze how droplet deposition occurs in different nasal locations. We also conducted transient numerical simulations based on the measured data to investigate the importance of two-way interactions assumption. The results of the numerical simulations were then compared to the experimental results. Comparing the experimental and numerical results demonstrated that the two-way interaction assumption produced significant differences, indicating that it must be considered while modeling the nasal spray. Overall, this research's findings can significantly contribute to optimizing the design of nasal sprays and enhancing the effectiveness of drug delivery to the targeted location.

Accurate modeling of exhalation dynamics is essential in estimating infection rates. In this study, we analyzed the predictive capabilities of three Unsteady Reynolds-Averaged Navier-Stokes (URANS)-based turbulence models: Realizable k-epsilon, renormalization group (RNG) k-epsilon, and shear-stress transport (SST) k-omega for sinusoidal exhalation. The exhaled jet flow extends over a distance from the exhalation source, normalized by the exhalation source diameter, and was analyzed across the jet region. Furthermore, this region was divided into three sub-regions: near-field, transitional, and fully developed field for turbulence evaluation. These models were validated against time-resolved particle image velocimetry data and empirical measurements under quiescent ventilation conditions. Results from the centerline velocity decay profiles demonstrated that each model exhibited performance across the sub-regions of the exhaled jet. Using three performance metrics for quantitative validation, the RNG k-epsilon model demonstrated superior performance overall across the jet flow region. When sectioned into sub-regions, its performance is better in transitional and fully developed regions due to its enhanced strain-term formulation. Meanwhile, the SST k-omega model provides superior accuracy in near-wall shear and boundary-layer interactions. The Realizable k-epsilon model performs well in the transitional region but underperforms in the near-field and fully developed regions. These results advance the characterization of breath-generated flows, providing insights into airborne transmission dynamics that can inform the optimization of ventilation strategies and mitigation measures in indoor environments. Semi-empirical equations, derived using the best-performing region-specific URANS models, estimate centerline velocities during exhalation (0 < t < 2 s) in developed field regions.

The prevailing scarcity of accurate lung models poses challenges to predicting airborne particle deposition across genders. The present work demonstrates the details of the geometrical specifications of central airways for ten healthy humans (male and female). The data were extracted from HRCT scan images with a minimum resolution of 1 mm. The images cover the trachea to all branches of the G6-G8 generations. The presented data include airway segment diameters, lengths, branching angles, and angles of inclination to gravity, in addition to their average and standard deviation. Our first goal in this study is to generate an average lung model exclusively for humans in laboratory and 1D numerical inhalation investigations. Thus, our primary emphasis in this work is to find the average suitable inclination angle in all generations of central airways for men and women by comparing the available data from previous studies. In the second part of the paper, we have also investigated the particle deposition efficiency in these ten models using the Mimetikos PreludiumTM software package. We compared the regional deposition between males and females and the available respiratory system models.