Numerical investigation of gaseous pollutant cross-transmission for single-sided natural ventilation driven by buoyancy and wind

Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology

The urban form and extreme microclimate events can have an important impact on the energy performance of buildings, urban comfort and human health. State-of-the-art building energy simulations require information on the urban microclimate, but typically rely on ad-hoc numerical simulations, expensive in-situ measurements, or data from nearby weather stations. As such, they do not account for the full range of possible urban microclimate variability and findings cannot be generalized across urban morphologies. To bridge this knowledge gap, this study proposes two data-driven models to downscale climate variables from the meso to the micro scale in arbitrary urban morphologies, with a focus on extreme climate conditions. The models are based on a feedforward and a deep neural network (NN) architecture, and are trained using results from computational fluid dynamics (CFD) simulations of flow over a series of idealized but representative urban environments, spanning a realistic range of urban morphologies. Both models feature a relatively good agreement with corresponding CFD training data, with a coefficient of determination R² = 0.91 (R² = 0.89) and R² = 0.94 (R² = 0.92) for spatially-distributed wind magnitude and air temperature for the deep NN (feedforward NN). The models generalize well for unseen urban morphologies and mesoscale input data that are within the training bounds in the parameter space, with a R² = 0.74 (R² = 0.69) and R² = 0.81 (R² = 0.74) for wind magnitude and air temperature for the deep NN (feedforward NN). The accuracy and efficiency of the proposed CFD-NN models makes them well suited for the design of climate-resilient buildings at the early design stage.

Resuspension and deposition of PM2.5 and PM10 containing radiocesium during and after indoor cleaning of uninhabited houses in Fukushima, Japan

Radiocesium contamination in homes could be a serious concern following Japan's 2011 Fukushima Daiichi Nuclear Power Plant accident, including exposure to radiocesium during cleaning when residents return home after the lifting of evacuation orders. This study measured PM_2.5 and PM₁₀ concentrations containing radiocesium during cleaning (dusting, vacuuming with a cordless cyclone unit, and vacuuming with a corded paper-pack unit), as well as air exchange rates, in 12 residential houses in Fukushima. Surface dusting of walls, shelves, and furniture significantly increased concentrations of PM_2.5 and PM₁₀ by up to 6.3 and 16 times the background (outdoor) level, respectively. Vacuuming with a paper-pack unit increased levels by 2.2 and 3.3 times, while vacuuming with a cordless cyclone unit increased these by 1.3 and 1.5 times, respectively. Measurements in 11 houses revealed an average air exchange rate of 0.22/h and dry deposition rates for PM_2.5 and PM₁₀ of 0.13/h and 0.32/h, respectively. Dry deposition rates were not correlated with building age, although the air exchange rates showed statistically significant increases with increasing building age. Dry deposition rates of PM_2.5 significantly decreased with increasing air exchange rates.

Numerical Study on Performance Optimization of an Energy-Saving Insulated Window

Window energy consumption has become a key factor in designing buildings with optimal energy efficiency. To that end, herein, the use of an energy-saving insulated window (ESIW) is proposed, particularly for winter heat conservation. DeST software was used to evaluate the energy consumption properties of a house with an ESIW-structure window, as well as that of six other window structures currently on the market. The results were subsequently compared. Furthermore, a series of numerical simulations were carried out using Airpak software to investigate the insulation performance of four ESIW models (A, B, C, and D) under different influencing factors. Finally, the response surface method (RSM) was used to obtain the optimal ESIW structure installation conditions and the weight of each factor. The data shows that houses with ESIW-structure windows exhibit a more suitable indoor natural temperature; less heating load, cooling load, and cumulative annual load; and a more feasible price–load ratio than other energy-saving windows. Furthermore, the average temperature gradually decreased in response to decreasing the electric heater power and energy-saving standard, and increasing the heat transfer coefficient (HTC) and window-to-wall ratio (WWR). Thus, as the energy-saving standard (ESS) increases, the importance of the WWR increases in parallel. This study puts forward an HTC prediction formula that is applicable to different conditions. The optimal thermal efficiency conditions consisted of HTC = 1.07 W/m2 × K, WWR = 0.26, and an ESS of 75%. This study demonstrates that the ESIW system has optimal energy-saving properties and broad adaptability and operability, which can be applied in building insulation as a key insulation component.

Socio-physical liveability through socio-spatiality in low-income resettlement archetypes - A case of slum rehabilitation housing in Mumbai, India

This study looks into the socio-physical liveability through socio-spatiality in low-income settlement archetypes. Paradoxically, recently mushrooming slum rehabilitation housing which have delivered secured tenure to its inhabitants, face threats of being deserted from lack of socio-physical liveability. Recurring of informality issues has advocated to investigate the reasons behind the 'rebound' phenomenon. This study explores the efficacy of socio-spatiality and its linkages with socio-physical liveability, taking Mumbai slum rehabs as case study. A comparative analysis of the current built-environment indicators and liveability status of major informal archetypes was performed, followed by analyses of the socio-physical problems associated with it. A critical evaluation of the rehabilitation housing of Mumbai highlights the problems caused by the current dense built-environment design. Reflecting on global instances, this article demonstrates the significance of socio-spatiality and suggests environmentally sustainable indicator-based built-environment recommendations, which if implemented in the forthcoming slum rehab housing planning, would enhance well-being and liveability among the low-income sector in future. While analysing the 'rebound' phenomenon, this study delivered a heuristics of socio-physical liveability, built-environment and their respective indicators. This method would aid the architects, planners and policymakers in reshaping the forth-coming built-environment while safeguarding the socio-physical liveability of the low-income sector.

Numerical investigation of gaseous pollutant cross-transmission for single-sided natural ventilation driven by buoyancy and wind

Single-sided natural ventilation was numerically investigated to determine the impact of buoyancy and wind on the cross-transmission of pollution by considering six window types commonly found in multistory buildings. The goal of this study was to predict the gaseous pollutant transmission using computational fluid dynamics based on the Reynolds-averaged Navier-Stokes equations and baseline k-ω turbulence equations. The results indicated that ventilation rates generally increased with increasing wind speeds if the effects of buoyancy and wind were not suppressed; however, the re-entry ratio representing the proportion of expelled air re-entering other floors and the corresponding risk of infection decreased. If the source of the virus was on a central floor, the risk of infection was the highest on the floors closest to the source. Different window types were also considered for determining their effectiveness in controlling cross-transmission and infection risk, depending on the source location and driving force (e.g., buoyancy and wind).