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. 

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 CO<inf>2</inf> emissions. Furthermore, this system generates a substantial financial benefit of approximately USD 468,000, highlighting its notable environmental and economic merits.

During the COVID-19 pandemic, the elderly (65+ years) experienced disproportionately high mortality rates, with care homes reporting significantly elevated casualties. Care homes constitute a unique built environment, functioning as both healthcare and residential facilities, yet they remain underexplored in ventilation research today. This study evaluates the performance of portable air cleaners (PAC) to reduce infection risk by using computational fluid dynamics (CFD) simulations. The draught rate (DR) was evaluated to analyse the potential local discomfort resulting from PAC. The findings reveal that PAC can reduce infection risk moderately (~20%) and achieve acceptable DR (< 10%). Given the growing ageing population, further research into alternative engineering controls (PAC), is essential to enhance infection risk mitigation and enhance occupant satisfaction in care home environments.

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–ε, renormalization group (RNG) k–ε, and shear-stress transport (SST) k–ω 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–ε 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–ω model provides superior accuracy in near-wall shear and boundary–layer interactions. The Realizable k–ε 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.

Nasal hairs, often overlooked in human respiratory system studies, can be a decisive factor in maintaining respiratory health. Vibrissae can capture a certain range of particle sizes due to their filtering function, while they may also contribute to more breathing resistance. In this study, the role of nasal hairs in particle filtration and pressure drop within the nasal vestibule was investigated using computational fluid dynamics (CFD) simulations. Seven nasal hair specifications were examined in simplified human nasal vestibule models under steady laminar flow conditions at two airflow rates of 10 and 15 L/min. The deposition of microparticles in the simulated geometries was also numerically studied. The simulation results showed that the investigated nasal hairs lead to about a 2-20 Pa increase in the pressure drop, depending on the hair specifications and airflow rates. The associated growth in nasal resistance could potentially influence breathing comfort. Additionally, nasal hair was shown to enhance particle filtration, with the deposition fraction of particles correlating with the projected area of the hairs on a normal plane to the flow direction, which goes up by an increase in the number of hairs or their length. These findings clarify the significance of nasal hairs in the respiratory system and aim to balance the trade-off between improved particle filtration and increased breathing resistance due to nasal hairs. The acquired knowledge can be used in recommendations to different individuals regarding nasal hair trimming based on their health conditions.

Laminar airflow (LAF) is essential for maintaining a sterile environment in operating rooms, but its rapid unidirectional flow decay leads to low airflow efficiency and increases energy consumption. The objective of this study is to investigate the energy-saving and air quality benefits of using a low-turbulence air curtain around laminar airflow, which is referred to as protective laminar airflow (PLAF). Numerical simulations were used to model airflow and particle transport, and a series of experiments were conducted in a real operating room at St. Olavs Hospital, Norway, to validate the simulation results. The findings indicate that when the unidirectional airflow supply velocity is maintained at 0.25 m/s, combined with an air curtain that has the width of 2 cm and the velocity of 1.5 m/s, the PLAF system outperforms the conventional LAF system operating at a unidirectional airflow supply velocity of 0.30 m/s. This configuration results in a 17.3% energy saving, showing the potential of this airflow distribution strategy to enhance both cleanliness and energy efficiency.

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.