Regulation Characteristics and Load Optimization of Pump-Turbine in Variable-Speed Operation

In order to improve the operating and regulation characteristics of the hydropower unit and to stabilize the load fluctuations, variable-speed pumped storage technology based on converters has been proposed and given more attention recently. However, different from the conventional units, due to the variability of operation conditions, variable-speed units need to develop a load optimization strategy in terms of operating parameter identification to ensure state matching for operation. Therefore, this paper proposes an optimization search step based on the model test curve, and the process of parameter optimization search is elaborated and calculated in the turbine operating condition and pump operating condition, respectively. A mathematical model of the turbine regulation system is established to analyze the influence of speed and guide vane related parameters on the regulation characteristics, and the achievable operating range and regulation capacity in the variable-speed condition is pointed out based on pump-turbine model test, as well as the advantages over the fixed-speed operation. The results show that by applying the load optimization method, the variable-speed unit can be significantly improved in terms of operating efficiency, especially at low head and low power conditions. Meanwhile, a certain range of active power regulation can be realized by the decoupling control of the converter and measuring the guide vane opening in both modes. The analysis of the model test verifies the effectiveness of the variable-speed regulation operation of pump-turbine and provides a reference for the design and operation of the variable-speed hydropower units.

Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Numerical computations are usually associated with the High Performance Computing. Nevertheless, both industry and science tend to involve devices with lower power in computations. This is especially true when the data collecting devices are able to partially process them at place, thus increasing the system reliability. This paradigm is known as Edge Computing. In this paper, we propose the use of devices at the edge, with lower computing power, for multi-scale modelling calculations. A system was created, consisting of a high-power device—a two-processor workstation, 8 RaspberryPi 4B microcomputers and 8 NVidia Jetson Nano units, equipped with GPU processor. As a part of this research, benchmarking was performed, on the basis of which the computational capabilities of the devices were classified. Two parameters were considered: the number and performance of computing units (CPUs and GPUs) and the energy consumption of the loaded machines. Then, using the calculated weak scalability and energy consumption, a min–max-based load optimization algorithm was proposed. The system was tested in laboratory conditions, giving similar computation time with same power consumption for 24 physical workstation cores vs. 8x RaspberryPi 4B and 8x Jetson Nano. The work ends with a proposal to use this solution in industrial processes on example of hot rolling of flat products.

Benchmarking educational quality – an independent analysis and alternative approach

In a ten-year study over thirty STEM units in seven nationally accredited institutions (two colleges, five universities, including a five-star teaching university) in two different countries were analyzed to evaluate their educational quality using a range of criteria and benchmarked against the finalists of the 2010 IEEE global award for academics. Unit content and teaching were found to be almost exclusively based on Constructivist based principles. However, Constructivism provides subjective guidelines open to different interpretations. The analyzed units demonstrated considerable variation in pass rates and educational standards. One unit consistently achieved circa 100% pass rates but at the expense of the standard of learning outcomes – far below any reasonable expectations. At the other extreme one unit achieved a higher standard of learning but with pass rates below 30%. This problem can potentially be addressed by using the new quantitative Cognitive Load Optimization learning theory and technology.

Electric Two-Wheeler Vehicle Integration into Rural Off-Grid Photovoltaic System in Kenya

In both rural and urban areas, two-wheeler vehicles are the most common means of transportation, contributing to local air pollution and greenhouse gas emissions (GHG). Transitioning to electric two-wheeler vehicles can help reduce GHG emissions while also increasing the socioeconomic status of people in rural Kenya. Renewable energy systems can play a significant role in charging electric two-wheeled vehicles, resulting in lower carbon emissions and increased renewable energy penetration in rural Kenya. As a result, using the Conventional and Renewable Energy Optimization (CARNOT) Toolbox in the MATLAB/Simulink environment, this paper focuses on integrating and modeling electric two-wheeled vehicles (e-bikes) into an off-grid photovoltaic Water-Energy Hub located in the Lake Victoria Region of Western Kenya. Electricity demand data obtained from the Water-Energy Hub was investigated and analyzed. Potential solar energy surplus was identified and the surplus was used to incorporate the electric two-wheeler vehicles. The energy consumption of the electric two-wheeler vehicles was also measured in the field based on the rider’s driving behavior. The modeling results revealed an annual power consumption of 27,267 kWh, a photovoltaic (PV) electricity production of 37,785 kWh, and an electricity deficit of 370 kWh. The annual results show that PV generation exceeds power consumption, implying that there should be no electricity deficit. The results, however, do not represent the results in hourly resolution, ignoring the impact of weather fluctuation on PV production. As a result, in order to comprehend the electricity deficit, hourly resolution results are shown. A load optimization method was designed to efficiently integrate the electric 2-wheeler vehicle into the Water-Energy Hub in order to alleviate the electricity deficit. The yearly electricity deficit was decreased to 1 kWh and the annual electricity consumption was raised by 11% (i.e., 30,767 kWh), which is enough to charge four more electric two-wheeler batteries daily using the load optimization technique.

A Two Stage Task Scheduler for Effective Load Optimization in Cloud – FoG Architectures

In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.

Analysis of Load Optimization in Solid Rocket Motor Propellant Grain with Pressure Cure

At present, the casting of large-size motors often adopts pressure cure. This technology can effectively reduce the risk of damage to the structural integrity of the grain in the case-bonded casting solid rocket motor. In this paper, ABAQUS is used to establish a finite element model of star-shaped grains. The whole process of pressure cure was simulated and modeled, and the Python script was redeveloped. The Evol evolutionary algorithm was used in ISIGHT to optimize the load parameters such as pressure value, attenuation coefficient of the relief curve, and the attenuation coefficient of the cooling curve. The effects of different pressure values and different cooling and depressurizing rates on the residual stress and strain were analyzed. The optimization results show that the closer the pressure value is to the theoretical pressure, the more significant the effect of pressure cure. However, the effect of stress and strain reduction in different directions is slightly different. The different cooling and pressure relief rates have a great influence on the process quantity. Pressure cure works best when the pressure attenuation coefficient is equal to 6850, and the temperature attenuation coefficient is equal to 8650. The optimization analysis of pressure curing provides a reference for engineering practice.