The well-being of autistic occupants should be carefully considered in the design of indoor environments. Autism spectrum condition (ASC) is often associated with different perceptions of environmental stimuli and synesthesia (cross-over of senses), with implications for multi-domain perceptions. This study investigated the differences in indoor environmental quality (IEQ) perception between autistic and non-autistic individuals, focusing on multi-domain aspects. The research involved 17 autistic and 20 control participants exposed to six different thermal and visual conditions in an office-like living lab. Environmental measurements were coupled with subjective questionnaires in the four comfort domains (thermal, visual, indoor air quality, and acoustic). The findings revealed that autistic individuals experienced greater discomfort under non-optimal IEQ conditions, particularly in the acoustic domain (large or very large effect sizes, with average satisfaction scores around 2.50 compared to 4.00 in the Control Group). A natural view out mitigated thermal and Indoor Air Quality (IAQ) discomfort for the autistic participants, aligning their perception more with the Control Group, and reducing the difference in satisfaction and acceptability scores between groups by up to 1 scale units. However, acoustic discomfort remained regardless of the view. Thermal-visual, thermal-IAQ, and visual-IAQ correlations were stronger for the autistic group, while visual-acoustic and IAQ-acoustic correlations were stronger for the controls. The results suggest that autistic occupants have more stringent and individually variable IEQ requirements, emphasizing the importance of tailored solutions for indoor design. These findings can contribute to the development of guidelines and standards aimed at enhancing indoor well-being for autistic individuals.

In this simulation study, a cluster of residential and commercial/institutional (CI) buildings undergoes flexibility strategies to reduce electric power during winter demand response (DR) events and the effect of these measures is analysed on the cluster level and on the individual building level. The portfolio used to simulate the flexibility strategies is comprised of 2430 buildings, of which 2400 are modelled as residential and 30 as CI buildings; this ratio roughly represents the real ratio of residential to commercial floor area in the location of study. The residential homes are all-electric, while the CI buildings have heating available through both an electric and a gas boiler, providing a dual-fuel heating system to work with for flexibility measures. The flexibility strategies tested are reactive and predictive, in which reactive measures are considered as not having any preplanning required, while predictive measures include preheating outside of peak hours for residential buildings, and fuel-switching during, before, and/or after DR events for CI buildings. The study investigates and verifies the complimentary nature of a mixed building cluster in order to provide flexibility during DR events, through peak power reductions of 24-37% during demand response events for the aggregated cluster. Compared to the power reductions obtained for only residential building clusters or only CI building clusters, the joining of all buildings to create the mixed cluster presents a greater benefit and confirms the synergy possible between mixed building types in flexibility studies.

This study investigates the influence of waste tire rubber (WTR) on the liquid-phase moisture transport properties in cement mortar under ambient conditions—an area that is still insufficiently explored in current literature. While most studies have focused on mechanical performance, this work integrates capillary absorption testing with a liquid-phase moisture transfer model calibrated using experimental data to quantify sorptivity and water diffusivity. Seven mortar mixtures were prepared: one control (no WTR), three with WTR powder replacing 7%, 14%, and 21% of sand by volume, and three with rubber aggregates at the same replacement levels. The study systematically compares the effects of WTR in both powder and aggregate forms on key parameters such as sorptivity, water diffusivity, accessible porosity, and compressive strength. Capillary absorption tests provided empirical data for model calibration and analysis of liquid-phase moisture transfer behavior. Results showed that WTR substitutions up to 14% reduced accessible porosity by 18.03% and sorptivity by up to 351%, attributed to the hydrophobic nature of rubber, which impedes capillary flow. However, at a 21% replacement level, both porosity and sorptivity increased, while compressive strength declined—indicating that high WTR content compromises mechanical integrity and limits suitability for structural use. Water diffusivity exhibited two regimes: a low-diffusivity phase (<0.01 mm 2 /s) under partial saturation, and a higher-diffusivity phase at near-saturation, consistent with dual-regime moisture transfer models. WTR incorporation led to more tortuous pore structures, which reduced water ingress and improved moisture resistance. WTR powder was more effective than rubber aggregates in reducing sorptivity and porosity, particularly at replacement levels up to 14%. WTR powder at replacement levels up to 14% improved moisture resistance, suggesting its potential use in non-structural cement elements such as renders and plaster coatings. The results clarify how WTR particle morphology influences pores connectivity and liquid-phase moisture transfer, supporting its potential for enhancing durability in moisture-sensitive, non-load-bearing cement applications.

HAM models have proven their value for analyzing moisture-related pathologies by assessing hygrothermal responses to environmental exposure. In this study, a coupling between CFD and HAM is developed to capture spatial wetting and drying effects, which allows to identify façade degradation patterns. Wind-driven rain (WDR) and evaporation significantly influence the hygrothermal performance and durability of façades, particularly in heritage and renovation. In traditional HAM modeling, WDR and convective heat transfer coefficients (CHTC) are often simplified by applying generic, uniform values across the façade. However, this approximation fails to account for the spatial and temporal variations in WDR and neglects the significant variations in CHTC due to the surrounding velocity flow field. These oversimplifications can limit the validity and accuracy of hygrothermal predictions. This study presents a novel approach by externally coupling CFD simulations with HAM modeling to capture the spatial distribution of WDR and CHTC on building façades. Steady Reynolds-averaged Navier-Stokes equations and Eulerian-multiphase simulations are performed to calculate the wind flow and the rain trajectories with turbulent dispersion of raindrops. Using a cubic low-rise building as a case, the coupled model evaluates the effects of wind and rain exposure on façade deterioration, including frost degradation, salt crystallization, and algae growth. Results indicate that conventional approaches tend to underestimate critical rain loads, while drying potential is overestimated. It highlights how spatial variations in WDR and drying influence degradation mechanisms, emphasizing the need for more detailed spatial analyses. This integrated method provides valuable insights into risks posed by moisture exposure and drying dynamics, offering practical applications for targeted renovation strategies and improved preservation of building materials.

In this study, the effect of fins integration on the operating of a Building integrated photovoltaic (BIPV) system is investigated using CFD simulations. Various designs of the cooling system, directly attached to the rear side of the PV cells, are proposed and analyzed. The considered extended heat transfer surfaces are straight and wavy fins of different shift angles and the corresponding effect on this system operating is evaluated. Results revealed that the proposed cooling techniques have proven effective in reducing the solar cell average temperature, thereby increasing the electrical conversion efficiency for the corresponding system. Concerning the best improvement, it corresponds to wavy fins with a shift angle of 180° where a temperature reduction of about 15°C was noted ( T * = 1.8), which allowed an increase of the electrical efficiency achieving 8.2%. In this wavy fins equipped system, curved inserts allowing the flow to be directed between each two successive fins, are introduced. The proposed system provides better heat exchange, as shown in the Nusselt number analysis; the corresponding average Nusselt number is increased up to 81% compared to the BIPV without fins and up to 16% compared to the system with only wavy fins. The resulting improvement of heat dissipation affected the PV temperature’s levels and consequently allowed an increase in the BIPV system electrical efficiency of about 11% compared to the system without fins. Similarly, a more uniform temperature distribution is noted, which directly affect the BIPV system life time.

UV germicidal irradiance has gained significant popularity as a disinfection technology since the COVID-19 pandemic. Challenges remain in accurately characterizing the in-duct irradiance distribution of UVC lamps under varying air parameters, introducing uncertainty in exposure and efficiency estimations. This study experimentally characterizes irradiance of 222 and 254 nm UV lamps in a conduit using a 9-point average method. Irradiance measurements are recorded from five sides at each point, and a correction factor is proposed to account for spectrometer detection angle overlaps. Results indicate that the 254 nm lamp output increases by 18% with an air temperature rise from 25°C to 35°C but decreases by 21% as air velocity rises from 0.5 to 2 m/s. In contrast, the 222 nm lamp shows negligible sensitivity to changes in air temperature or velocity. Relative humidity variations (25%-60%) did not significantly affect either lamp's output. In addition, the 222 nm lamp generates minimal ozone and total volatile organic compounds (TVOCs) during operation in stagnant air. The particle tracking simulation results revealed higher velocity fluctuations in proximity to the installed 222 nm lamp than the 254 nm lamp due to the larger lamp geometries. Experimental UVGI efficacy tests targeting airborne E. coli inactivation, combined with comprehensive life cycle analysis, demonstrated that the 254 nm lamp offers superior cost-effectiveness and sustainability for continuous in-duct air disinfection. These findings provide practical insights for optimizing UVC systems for air disinfection applications.

The improvement of outdoor comfort has received extensive research attention due to extreme summer weather. Trees (an important street landscaping element) have been broadly proven to affect the outdoor environment quality and pedestrian comfort, primarily through canopying and biological effects. These processes affect the light and thermal conditions, influencing subjective comfort levels. This study explores the effects of trees on various climatic indices in the pavements of Qingbaijiang (QBJ), a district of Chengdu, China. Through the analysis of field measurement in nine pavements beside avenues (Jul. 2023), multiple linear regression showed that local residents’ climatic neutral points are 24.52°C (physiologically equivalent temperature (PET)) and 0.952 (illumination, LUX) klx, respectively. Further, tree crown diameter (TCD) plays a significant role in climate adjustment by reducing PET by 8°C (from 40°C to 32°C). In addition, pedestrians had different thermal responses to different species of trees. Finally, Cinnamomum camphora could create a microclimate partially aligned with people’s neutral temperature (around 24°C), although it is hard to achieve a comprehensive comfort improvement relying on trees only. These findings can then direct further the management of urban landscaping and forestry for climate improvements. Urban designers and planners could select tree species that better meet people’s demands during urban construction.

Ventilation plays a crucial role in influencing airflow and particle dispersion, highlighting the importance of effective systems to mitigate the spread of viruses in indoor spaces. While most research has concentrated on non-residential buildings, less attention has been given to optimizing ventilation in residential settings. This study examines a typical ventilation system in a detached house in British Columbia, Canada, using Computational Fluid Dynamics simulations to evaluate its effectiveness in reducing airborne infection risks. The study investigates eight different scenarios, varying diffuser locations (near the ceiling and floor), two types of ventilation rates based on standards, and the positioning of the infected individual while considering flow, heat, and particle dynamics. The concentration of injected and distributed particles is used to assess infection probabilities as an additional risk indicator. In addition, the Wells-Riley model is applied to quantify the infection probability. The results indicate that the location of the infected person and diffuser placement significantly impact particle dispersion and infection risk, with near-ceiling outlets generally reducing concentrations more effectively than near-floor outlets. Higher ventilation rates decrease particle concentration when diffusers are near the ceiling but can increase concentrations with near-floor diffusers due to turbulence. Optimizing diffuser placement and ventilation rates is crucial for minimizing airborne transmission in residential settings.

Research and development have demonstrated that effective building energy prediction is significant for enhancing energy efficiency and ensuring grid reliability. Many machine learning (ML) models, particularly deep learning (DL) approaches, are widely used for power or peak demand forecasting. However, evaluating prediction models solely based on accuracy is insufficient, as complex models often suffer from low interpretability and high computational costs, making them difficult to implement in real-world applications. This study proposes a multi-perspective evaluation analysis that includes prediction accuracy (both overall and at different power levels), interpretability (global/local perspectives and model structure), and computational efficiency. Three popular DL models—recurrent neural network, gated recurrent unit, long short-term memory, and three tree-based models—random forecast, extreme gradient boosting, and light gradient boosting machine—are analyzed due to their popularity and high prediction accuracy in the field of power demand prediction. The comparison reveals the following: (1) The best-performing prediction model changes under different power demand levels. In scenarios with lower power usage patterns, tree-based models achieve an average CV-RMSE of 13.62%, which is comparable to the 12.17% average CV-RMSE of DL models. (2) Global and local interpretations indicate that past power use and time-related features are the most important. Tree-based models excel at identifying which specific lagged features are more significant. (3) The DL model behavior can be interpreted by visualizing the hidden state at each layer to reveal how the model captures temporal dynamics across different time steps. However, tree-based models are more intuitive to interpret using straightforward decision rules and structures. This study provides guidance for applying ML algorithms to load forecasting, offering multiple perspectives on model selection trade-offs.

Hydronic floor heating has been recognized as one of the important systems utilized for warming residential buildings, therefore, the flow field and temperature distribution studies of the floor and locally heated residential spaces are essential. In this work, such a study is performed on a room with different horizontal aspect ratios through three-dimensional numerical simulations using FLUENT software. The governing equations accounting for the continuity, momentum, and energy together with the radiation have been solved using the Boussinesq approximation, the radiation DO model, and the modified k − ε turbulence model, while the SIMPLE method was utilized. Results show that at least 65% of the total heat transfer from the heated floor is due to radiation, while in the locally heated room, the radiation from the radiator was at most 55% of the total. When floor heating was utilized, the uniform temperature is obtained for all of the room aspect ratios, whereas in the case of local heating, thermal stratification occurs along the room height, as the temperature was considerably higher at upper heights. In the floor heating, the air velocity throughout the room was less than 0.2 m/s, whereas, for the local heating, the air velocity was higher than 0.2 m/s close to the radiator. In comparison with the local heating, the floor heating caused at least 20% energy saving and optimum thermal and velocity comfort conditions.