Development of Numerical Model and Design Framework for Mixed-Mode Ventilation in the Tropics

In Singapore, the building sector is responsible for consuming about one-third of the country’s total energy consumption (BCA, 2020). This is because more buildings are being built over the years to cater to the increasing population size and economic activities. To mitigate climate change, Singapore has unveiled a green plan where there is a target by the Government to green 80% of Singapore’s buildings by the year 2030 (MND, 2021). To improve energy efficiency and reduce energy consumption in building, alternative cooling systems such as mixed-mode ventilation is recommended in this paper. Mixed-mode ventilation for building concepts have been explored and used in other countries (Etheridge, D., & Ford, B. (2008); Ezzeldin, S., & Rees, S. J. (2013); Keep, T., Lavedrine, I., Wood, J., & Arup. (2008).). Literature study shows that by using mixed-mode ventilation system, there is potential to save about half of the energy consumed as compared to conventional mechanical ventilation system (Ezzeldin, S., & Rees, S. J. (2013)). Furthermore, mixed-mode cooling strategies should be able to provide a satisfactory indoor environment (Ezzeldin, S., & Rees, S. J. (2013)). For this paper, the use of simulation techniques such as Computational Fluid Dynamics (CFD) is adopted to accurately predict both spatial and temporal field solutions as well as the overall system evaluation and design.</p

Implementation of Numerical Techniques for Estimation and Investigation of Photovoltaic Heat Island (PVHI)

Rapid urbanization has had a large role in contributing to the Urban Heat Island (UHI) which translates to warmer air temperatures. Solar energy or photovoltaics (PV) is one of the most widely used renewable sources of electricity. With the introduction of PV systems in the urban landscape, little is known of the extent to which such systems contribute to the UHI. This study aims to investigate the Photovoltaics Heat Island (PVHI) effect through numerical analysis and derive a simplified model to estimate the temperature due to the PVHI. A numerical analysis was conducted to comprehend the extent of the PVHI on a 16kWp PV system in the SIT@Dover campus, located in Singapore. Three MX2302A temperature sensors were installed directly above three surfaces: PV system, rooftop, and grass field to capture temperature data at a logging interval of 5 min. A HOBO U30 USB Weather Station Data Logger in the vicinity was set at 5 min logging interval to record ambient temperature, irradiance and windspeed data. A clear day 24-h mean temperature data revealed that the PV system elevated the air temperature directly above it by 2.97 °C in comparison to the ambient temperature. On the same day with the overhead sun between 1200 – 1400hrs, the PVHI effects were accentuated 13°C above the ambient temperature. A linear regression technique known as Analysis of Variance (ANOVA) was implemented to derive a multi-variate linear equation to predict the temperature resulting from the PVHI. The statistical significance indicators of each of the three independent variables (ambient temperature, irradiance and windspeed) were p. The equation was tested and validated on seven days of actual data. The coefficient of determination of the calculated and actual PVHI temperature was found to be r2 = 0.917, and the mean variation between the actual and calculated PVHI temperature was found to be 1.67°C, both indicative of a reliable model for estimation.</p

Impact of Crops on the Microclimate and PV System Performance

Faced by land constraints, Singapore needs to look for viable options to bolster renewable energy consumption whilst trying to prop up local food production. Agrivoltaics is seen as a feasible method where hydroponic farming is integrated underneath solar photovoltaics (PV) systems on a common land. This study aims to investigate the impact crops have on the microclimate and whether it contributes to enhanced PV performance. Situated in the SIT@Dover campus in Singapore, the agrivoltaics setup consists of two identical plots, Plot A and Plot B. Each plot consists of twenty solar panels, erected at a height of 2.5m. Beneath the panels contain two hydroponics growth tables, each capable of housing 180 pots of crops. A HOBO External Temperature/RH Sensor Data Loggers were installed 30 cm above each of the growth tables to record data at 5 min logging intervals. A HOBO U30 USB Weather Station Data Logger records meteorological data at intervals of 5 min. Plot A (control) was intentionally left without crops while Plot B was sowed with 90 Brassica oleracea var. sabellica pots (50% capacity). The Brassica oleracea var. sabellica was left to grow in the hydroponics system and harvested on the 32nd day of the crop cycle. Microclimate and energy yield data from the 31st day of the crop cycle were analysed. The 12-h mean temperature from 0700hrs to 1900hrs revealed that the Brassica oleracea var. sabellica on Plot B reduced the temperature above it by 0.97°C as compared to Plot A. The 12-h mean relative humidity tabulated for Plot A and B was 67.45% and 72.30% respectively. During overhead conditions from 1100 hrs to 1300 hrs, Plot B saw the microclimate temperature lowered by 1.33°C as compared to Plot A while the relative humidity elevated by 6.81% as compared to Plot A. The increase in relative humidity is attributed to the transpiration experienced by the Brassica oleracea var. sabellica during photosynthesis which shows that plants have the potential to induce cooler microclimatic conditions. The solar PV on Plot B generated 4.36% lesser energy yield as compared to Plot A. The difference is attributed to uneven irradiance captured by the plots due to building shading caused by the morning sun. Cooler microclimate temperature on Plot B does not contribute to improved PV performance primarily due to the large gap between the growth tables and the underside of the solar panels which encourages sufficient convection to take place.</p

Isolation and identification of plant probiotics for leafy greens.

Plant probiotic bacteria are plant-associated microorganisms that, when applied in a specific amount, improve the growth and yield of the host plants while also suppressing diseases. These plant probiotics have been shown to improve the production of phytohormones, antibiotics, and lytic enzymes; fix atmospheric nitrogen; and even help in the solubilization of soil mineral nutrients (Rahman et al., 2018). Major general of probiotic bacteria that promote plant growth include Bacillus, Pseudomonas, Acinetobacter, Arthrobacter and Serratia (Rahman et al., 2018).This study determined the reproducibility of using three plant probiotics strains (Isolate 4 (Acinetobacter pittii), Isolate 9 (Bacillus altitudinis), and Isolate 8 (Bacillus licheniformis)) on the plant growth of curly kale, Jericho lettuce and Thai basil seeds.Plant probiotic bacteria are plant-associated microorganisms that, when applied in a specific amount, improve the growth and yield of the host plants while also suppressing diseases. These plant probiotics have been shown to improve the production of phytohormones, antibiotics, and lytic enzymes; fix atmospheric nitrogen; and even help in the solubilization of soil mineral nutrients (Rahman et al., 2018). Major general of probiotic bacteria that promote plant growth include Bacillus, Pseudomonas, Acinetobacter, Arthrobacter and Serratia (Rahman et al., 2018).In addition, some PGPR strains are effective in suppressing pest, pathogen and effective as biocontrol agents. Study reported that various Bacillus strains are important in controlling pathogens and improving plant growth due to antagonistic relationships (Rahman et al., 2018). A previous study also showed that PGPR is involved in the production of plant hormones such as indole acetic acid (IAA). The bacteria species most studied and likely to produce this compound are Bradyrhizobium japonicum SB-1 and Bradyrhizobium thuringiensis (Asghari et al., 2020). This study determined the reproducibility of using three plant probiotics strains (Isolate 4 (Acinetobacter pittii), Isolate 9 (Bacillus altitudinis), and Isolate 8 (Bacillus licheniformis)) on the plant growth of curly kale, Jericho lettuce and Thai basil seeds.</p