Turbine blade cooling has been a topic of significant interest, as increasing turbine entry temperatures result in higher cooling requirements. The present numerical method divides the blade into a finite number of elements in the span and peripheral directions and solves the heat transfer fundamental equations for convection and conduction in both directions. As inputs, the span and chord gas temperature and heat transfer coefficient distributions are required. The results include high resolution temperature prediction for the blade and coolant, at all span and chord positions. The advantages of the method include the capturing of blade temperature variation in all directions, while considering the thermal diffusion due to conduction. Mach number effects to the resulted blade and coolant temperature are highlighted, as local distribution of the gas static temperature can have a dominant role. The effect of averaging the input parameters to the predicted blade temperature is discussed and finally, different values for the material conductivity are simulated and the results are analysed.
MULTIFILE
Understanding sludge rheology and optimizing equipment performance is crucial for energy efficiency in wastewater treatment plants (WWTPs). This study examined sludge rheology after thermal hydrolysis pretreatment (THP) at 60, 80, and 120 ◦C for 2 h, followed by anaerobic digestion (AD) at 37 ◦C for 20 days, and assessed impacts on pump and agitator performance. Post-treatment, sludge showed reduced viscosity and improved flowability, indicated by changes in Herschel-Bulkley parameters, enhancing pump and agitator efficiency, particularly at 120 ◦C. These rheological improvements were correlated to the solubilization of sludge components after THP and solids reduction after AD, highlighting the interconnectedness of rheology and treatment outcomes. Despite high heat demands, an energy balance showed that THP scenarios, especially at 120 ◦C, had lower energy requirements for pumps and agitators, leading to energy savings without increased heat consumption. These findings underscore the influence of rheological changes in improving energy efficiency in WWTPs.
As electric loads in residential areas increase as a result of developments in the areas of electric vehicles, heat pumps and solar panels, among others, it is becoming increasingly likely that problems will develop in the electricity distribution grid. This research will analyse different solutions to such problems to determine Using a model developed as part of this project, we will simulate various cases to determine under which circumstances load balancing at a community-level is more (cost) effective than alternative solutions (e.g. grid reinforcement and/or household batteries).
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
