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.

Human nasal hair has a potential capability to maintain respiratory health, yet its functional significance is frequently underappreciated due to cosmetic preferences. Despite its physiological relevance, the mechanical influence of nasal hair on airflow dynamics and particle deposition remains inadequately understood. This study used computational fluid-particle dynamics and fluid-structure interaction (CFPD-FSI) simulations to analyze three distinct nasal hair configurations in a realistic human nasal airway model under steady laminar flow conditions (5 and 10 L/min per cavity). The Discrete Phase Model (DPM) was utilized to predict the transport and deposition of microparticles in the nasal cavity with and without nasal hairs. The one-way coupled CFD-FSI study showed that the investigated airflow rates lead to a negligible deformation in hairs (in the worst-case scenario, 7.8 mu m at the hair tip), which implies that the rigid hair assumption can be confidently used for the nasal hairs in the laminar regime. The findings also revealed that the presence of nasal hair can increase nasal resistance by approximately 20-80% while enhancing micro-particle filtration efficiency by 12-38%, depending on the morphological configuration. Furthermore, the results demonstrated that nasal hair alters local airflow patterns in the anterior part of the upper airways, specifically by enhancing swirling flows in the nasal valve region, thereby increasing deposition fraction in this zone. These findings provide quantitative evidence of the physiological role of nasal hair as a protective filtration mechanism and its simultaneous unfavorable impact on nasal resistance and intranasal drug delivery.

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. 

Indoor air quality (IAQ) plays a critical role in human health, yet indoor pollutants frequently accumulate despite improvements in ventilation and building design. Air cleaners are increasingly used as a complementary mitigation strategy, but their real-world effectiveness and clinical relevance remain heterogeneous. This review synthesizes evidence from 36 peer-reviewed studies to evaluate the pollutants targeted by air-cleaning technologies, their removal efficiency under realistic conditions, and their associations with respiratory, cardiovascular and other health outcomes. HEPA-based systems consistently reduce indoor particulate matter (PM), often by 20–80%, and show moderate evidence for short-term cardiopulmonary benefits, including reductions in blood pressure and inflammatory biomarkers. In contrast, evidence for gaseous pollutant removal, long-term health outcomes, cognitive effects and economic impacts remains limited, and some technologies may generate harmful byproducts such as ozone. The review also considers implications for vulnerable populations and energy-efficient buildings where ventilation may be constrained. By critically evaluating multidisciplinary evidence, this work bridges the gap between air-cleaning performance metrics and public-health relevance, supporting informed decision-making for practitioners, policymakers and building professionals.

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.

Background: During the COVID-19 pandemic, the elderly population experienced disproportionately high mortality rates, with care homes reporting significantly elevated casualties. Care homes constitute a unique built environment with combined elements of healthcare and residential facilities, yet they remain underexplored in ventilation and infection risk research. Methods: The present study employed computational fluid dynamics (CFD) simulation to investigate the performance of portable air cleaner (PAC) on infection risk and impact on draught rate. Different placements of PAC were investigated to evaluate the impact on infection risk and draught rate. A modified Wells-Riley model was used to evaluate infection risk, and the draught rate was evaluated by using Fanger's draught rate model. Results: The findings reveal that PAC can reduce infection risk (reduction of 75 %-86 %) and achieve an acceptable draught rate (< 10 %). However, three cases showed that improper PAC placement failed to significantly reduce infection risk. Different placements of PAC resulted in distinct airflow patterns and led to 74 % variation of infection risk, highlighting the importance of strategic deployment. Placing PAC closer to susceptible individuals, such as the elderly, resulted in a low infection risk. Placing PAC in the centre of the room led to the lowest infection risk but impractical due to tripping hazards. Conclusion: The present study demonstrates that the deployment of PAC in care homes can deliver a promising reduction in infection risk with an acceptable draught rate. Placement of PAC requires thorough consideration such as guidance by numerical simulations to deliver intended infection risk reduction without compromising draught rate and practicability.

Thermal energy demand in buildings represents one of the largest contributors to global energy use and COQ 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 welldesigned 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 COQ 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 CO2 savings but also by evolving local energy price structures. Yet, the considerable COQ savings helped by Sweden's green electricity mix and the opportunity to enjoy hourly spotprice variability through advanced controllers make heat pump integration a compelling option for futureproofing 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.

Hybrid concentrating solar power systems integrated with high-temperature steam electrolysis provide the concurrent generation of electricity, heat, and hydrogen with elevated overall efficiency. The real-time simulation and supervisory control of integrated thermal-hydraulic networks are limited by the lack of fluid-flexible solvers and hierarchical control frameworks that can manage multi-regime thermodynamic behavior and mode switching. This study introduces a GPU-accelerated, generic, object-oriented solver-controller framework that integrates modular component representations with a hierarchical supervisory control architecture, facilitating precise dynamic modeling and stable control of hybrid thermal-hydraulic systems. A backward-differentiation thermal-hydraulic network solver is integrated with modular component representations and a three-layer hierarchical control structure, which features a classifier-based supervisory layer for steam-source selection and adaptive regulators for load distribution, thermal storage interaction, and electrolysis thermal management. The suggested algorithm enables district heating network simulations to achieve a 40-50 % reduction in execution time under worst-case scenarios when executed on a GPU, facilitating real-time performance. The framework replicates industrial reference results with deviations under sub-percent, ensures stable operation during 45 % load ramps, restricts solar steam generator pressure variations to +/- 2 bar, maintains electrolysis thermal gradients below 10 K/cm, and produces hydrogen with a mole fraction of 0.82 at 80 % steam utilization. The suggested modeling and control framework offers a transferable foundation for the real-time development of digital twins and supervisory control of hybrid thermal-hydraulic energy systems, establishing a platform conducive to the future incorporation of optimization strategies and artificial intelligence-driven acceleration.

Controlling respiratory contaminants in airborne infection isolation rooms (AIIRs) is essential to minimize transmission risk and protect healthcare workers. While background ventilation is a widely used and effective engineering strategy for infection control, source-oriented local ventilation offers a more targeted alternative. This study explores a ceiling-mounted local exhaust concept for AIIRs, designed to capture coughing particles in a practical and flexible way. We employed the Eulerian-Lagrangian framework to simulate transient airflow and particle dispersion in AIIRs. Four exhaust configurations were tested under different coughing behaviors. Our findings reveal that the capture efficiency of the above-chest exhaust drops from 97% to 7% with a change in coughing angle, while the ceiling-mounted exhaust maintains an efficiency of 20–40%. The ceiling-mounted solution exhibits higher capture stability and greater resilience across different conditions. Subsequent trajectory analysis of 16 variants identifies the influence of heat-source arrangement and ventilation flow direction on particle transport. A real-ward case study further indicates that the ceiling-mounted local exhaust system can suppress the initial concentration peak in the breathing zone, demonstrating short-term exposure reduction under a representative clinical layout. Overall, this work presents an adaptive, source-oriented ceiling-mounted local exhaust approach for effective short-term removal of coughing particles in AIIRs.