Blue-green roofs have been utilized and studied for their enhanced water storage capacity compared to conventional roofs or extensive green roofs. Nonetheless, research about the thermal effect of blue-green roofs is lacking. The goal of this research is to study the thermal effect of blue-green roofs in order to assess their potential for shielding the indoor environment from outdoor temperature extremes (cold- and heat-waves). In this field study, we examined the differences between blue-green roofs and conventional gravel roofs from the perspective of the roof surface temperatures and the indoor temperatures in the city of Amsterdam for late 20th century buildings. Temperature sensor (iButtons) values indicate that outside surface temperatures for blue-green roofs are lower in summer and fluctuate less during the whole year than temperatures of conventional roofs. Results show that for three warm periods during summer in 2021 surface substrate temperatures peaked on average 5°C higher for gravel roofs than for blue-green roofs. Second, during both warm and cold periods, the temperature inside the water crate layer was more stable than the roof surface temperatures. During a cold period in winter, minimum water crate layer temperatures remained 3.0 o C higher than other outdoor surface temperatures. Finally, also the variation of the indoor temperature fluctuations of locations with and without blue-green roofs have been studied. Locations with blue-green roofs are less sensitive to outside air temperature changes, as daily temperature fluctuations (standard deviations) were systematically lower compared to conventional roofs for both warm and cold periods.
Semi-closed greenhouses have been developed in which window ventilation is minimized due to active cooling, enabling enhanced CO2 concentrations at high irradiance. Cooled and dehumidified air is blown into the greenhouse from below or above the canopy. Cooling below the canopy may induce vertical temperature gradients along the length of the plants. Our first aim was to analyze the effect of the positioning of the inlet of cooled and dehumidified air on the magnitudes of vertical temperature and VPD gradients in the semi-closed greenhouses. The second aim was to investigate the effects of vertical temperature gradients on assimilate production, partitioning, and fruit growth. Tomato crops were grown year-round in four semiclosed greenhouses with cooled and dehumidified air blown into the greenhouses from below or above the crop. Cooling below the canopy induced vertical temperature and VPD gradients. The temperature at the top of the canopy was over 5°C higher than at the bottom, when outside solar radiation was high (solar radiation >250 J cm-2 h-1). Total dry matter production was not affected by the location of the cooling (4.64 and 4.80 kg m-2 with cooling from above and from below, respectively). Percentage dry matter partitioning to the fruits was 74% in both treatments. Average over the whole growing season the fresh fruit weight of the harvested fruits was not affected by the location of cooling (118 vs 112 g fruit-1). However, during summer period the average fresh fruit weight of the harvested fruits in the greenhouse with cooling from below was higher than in the greenhouse with cooling from above (124 vs 115 g fruit-1).
Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
