Increasing demand for energy due to comfort requirements in the built environment coupled with development of road networks and amplifying heat island effect call for a comprehensive approach that can answer both issues. The lifespan of an asphalt layer is affected by surface temperature. In this paper, we aim to study the feasibility of heat recovery and its effects in terms of energy harvesting efficiency and asphalt surface temperature by creating a numerical model and validating the model based on onsite measurements at laboratory scale. The experimental setup was developed at Technical University of Civil Engineering in Bucharest, and measurements were monitored during the summer. The heat recovery system used for this study was made of copper pipes, and material cost and layout optimization need to be addressed in future studies. The numerical model was validated using measured data. During this study, we obtained favorable results in terms of heat recovery, reducing surface temperature and selection of system materials. Further research is required for heat recovery system and pump automation (based on the surface temperature), in order to optimize energy consumption and improve overall efficiency.

The market for smart greenhouses has been valued at USD 1.77 billion in 2022 and is expected to grow to 3.39 billion by 2030. In order to make this more efficient, with the help of Internet of Things (IoT) technology, it is desired to eliminate the problem of traditional agriculture, which has poor monitoring and accuracy control of the parameters of a culture. Climate control decisions in a greenhouse are made based on parameter monitoring systems, which can be remotely controlled. Instead of this adjustment of the measured parameters, it would be preferable from the point of view of energy consumption that they should be calculated at optimal values from the design phase of the greenhouse. For this reason, it would be better to perform an energy simulation of the greenhouse first. For the study carried out in this work, a small greenhouse (mini-greenhouse) was built. It was equipped with an IoT sensor system, which measured indoor climate parameters and could send data to the cloud for future recording and processing. A simplified mathematical model of the heat balance was established, and the measured internal parameters of the mini-greenhouse were compared with those obtained from the simulation. After validating the mathematical model of the mini-greenhouse, this paper aimed to find the optimal position for placing a normal-sized greenhouse. For this, several possible locations and orientations of the greenhouse were compared by running the mathematical model, with which the most unfavorable positions could be eliminated. Then, some considerably cheaper “mini-greenhouses” were made and placed in the locations with the desired orientations. Using sensor systems and technologies similar to those presented in this work, the parameters from all mini-greenhouses can be monitored in real time. This real-time monitoring allows for the simultaneous analysis of all greenhouses, without the disadvantages of data collection directly in the field, with all data being recorded in the cloud and other IoT-specific advantages being made use of. In the end, we can choose the optimal solution for the location of a real-size greenhouse.

Sewerage systems are a main element of a city’s infrastructure. Roughness coefficients are fundamental parameters for sewage system operation. The intermittent nature of the flow leads to the appearance of deposits that become an integral part of the sewerage systems. Deposited material not only leads to the loss of hydraulic capacity and decreases the concentration of dissolved oxygen (which is found in direct relation to all quality parameters), but it also results in more transported particles being intercepted. In the design calculations, the roughness coefficient is estimated rather than calculated. It has been demonstrated that the estimation of stress within and above roughness elements improves the predictive capability for the concentration of suspended sediment. In this study, we focused on a local evaluation of the roughness coefficient when the flow velocity is below the minimum self-cleansing velocity. Some authors consider the selection of the most reliable method for estimating bed shear stress to be the main challenge. Other authors have suggested that all possible methods should be applied simultaneously to achieve a reliable bed shear stress estimation, knowing that the roughness coefficient can be determined through the shear boundary stress. We calculate the local roughness coefficient in Manning’s equation using a laboratory model, considering clear water flowing over a solid boundary with consolidated deposits, represented by artificial roughness elements (calibrated hemispheres). The European standard EN 752:2017 specifies a minimum average cross-sectional velocity of 0.7 m/s for pipe self-cleansing. This study established the range of possible roughness coefficient values when the minimum velocity design criterion is not met. The second criterion was to consider acceptable a sediment deposit occupying between 1% and 2% of the collector diameter. Velocity distributions around artificial roughness and statistical parameters of the turbulent flow were obtained using a PIV system. Five methods were implemented and the range of roughness coefficient values varied between 0.007 and 0.023. This variation is closely related to sewer performance. We selected the dissipation method as the primary reference for this study, as it is most closely aligned with the underlying physics of flow over roughness elements. This approach allows for robust validation by correlating multiple characteristic mechanisms of the turbulent cascade.