RESUMO
The global need for energy is increasing at a high rate and is expected to double or increase by 50%, according to some studies, in 30 years. As a result, it is essential to look into alternative methods of producing power. Solar photovoltaic (PV) power plants utilize the sun's clean energy, but they're not always dependable since they depend on weather patterns and requires vast amount of land. Space-based solar power (SBSP) has emerged as the potential solution to this issue. SBSP can provide 24/7 baseload carbon-free electricity with power density over 10 times greater than terrestrial alternatives while requiring far less land. Solar power is collected and converted in space to be sent back to Earth via Microwave or laser wirelessly and used as electricity. However, harnessing its full potential necessitates tackling substantial technological obstacles in wireless power transmission across extensive distances in order to efficiently send power to receivers on the ground. When it comes to achieving a net-zero goal, the SBSP is becoming more viable option. This paper presents a review of wireless power transmission systems and an overview of SBSP as a comprehensive system. To introduce the state-of-the-art information, the properties of the system and modern SBSP models along with application and spillover effects with regard to different sectors was examined. The challenges and risks are discussed to address the key barriers for successful project implementation. The technological obstacles stem from the fact that although most of the technology is already available none are actually efficient enough for deployment so with, private enterprises entering space race and more efficient system, the cost of the entire system that prevented this notion from happening is also decreasing. With incremental advances in key areas and sustained investment, SBSP integrated with other renewable could contribute significantly to cross-sector decarbonization.
RESUMO
A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.
RESUMO
The drag based Savonius wind turbine (SWT) has shown immense potential for renewable power generation in built-up areas under complex urban wind conditions. While a series of studies have been conducted on improving SWT's efficiency, optimal performance has yet to be achieved using traditional design approaches such as experimental and/or computational fluid dynamics methods. Recently, artificial intelligence and machine learning have been widely used in design optimization. As such, an ANN-based virtual clone can be an alternative to traditional design methods for wind turbine performance determination. Therefore, the main goal of this study is to investigate whether ANN-based virtual clones are capable of determining the performance of SWTs with a shorter timeframe and minimal resources compared to traditional methods. To achieve the objective, an ANN-based virtual clone model is developed. Two sets of data (computational and experimental) are used to validate and determine the efficacy of the proposed ANN-based virtual clone model. Using experimental data, the model's fidelity is over 98%. The proposed model produces results in one-fifth the time of the existing simulation (based on the combined ANN + GA metamodel) method. The model also reveals the location of the dataset's optimized point for augmenting the turbine's performance.