Browse Topic: Off-board energy sources
Penn Engineers have developed a novel design for solar-powered data centers that will orbit the Earth and could realistically scale to meet the growing demand for AI computing while reducing the environmental impact of data centers.
Led by Dr. Marina Freitag, a research group from the School of Natural and Environmental Sciences created dye-sensitized photovoltaic cells based on a copper (II/I) electrolyte, achieving an unprecedented power conversion efficiency of 38 percent and 1.0V open-circuit voltage at 1,000 lux (fluorescent lamp). The cells are non-toxic and environmentally friendly — setting a new standard for sustainable energy sources in ambient environments.
Researchers have developed a solar-powered reactor to break down hard-to-recycle forms of plastic waste – such as drinks bottles, nylon textiles and polyurethane foams – using acid recovered from old car batteries, and converting it into clean hydrogen fuel and valuable industrial chemicals.
Autonomous Inspection via small Unmanned Aircraft Systems (sUAS) is increasingly utilized across industrial use cases such as inspection of bridges, buildings, construction sites, roadways, transmission lines, pipes, wind turbines and power systems (1). In principle, the system workflow of inspection; identification and characterization of defects; and mapping in space is very similar across industries. Boeing and Proxim (A Near Earth Autonomy Company) have partnered to pursue this technology in the Aerospace and Defense industry for General Visual Inspection (GVI) of airframes predominantly in a maintenance setting. This activity began by deploying Proxim’s Autonomous Aircraft Inspection (AAI) technology and Boeing's Automated Damage Detection Software (ADDS) on Boeing C-17 Globemaster III at Joint Base Pearl Harbor-Hickam. It has expanded to offer U.S. Department of War (DoW) and Commercial customers aircraft-agnostic enhanced exterior GVI capability at point of need by leveraging unique ADDS AI algorithm in support of both home station and deployed operations. This paper gives an overview to industry developments in Autonomous Inspection, and the development AAI/ADDS technologies.
The present work focuses on the sizing and analysis of a parallel hybrid propulsion architecture for a conventional rotary light Unmanned Aerial Vehicle (UAV) in the 200kg class. First, the design methodology is outlined, with an emphasis on the optimization of the battery pack, which is one of the most crucial component of the whole powertrain. The sizing approach is applied to a wide range of thermal and electric power ratios, as well as two distinct hybridization strategies, to investigate the broad design space and discover possible sweet spots. For this aim, the various design points are then evaluated in terms of impact on aircraft capabilities, considering both extensive and intensive performance. Hence, the results provide the main advantages and disadvantages, performance wise, of the hybrid propulsion in comparison to a conventional full thermal solution.
Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.
The world is hurtling rapidly toward a developed future, and carbon fiber-reinforced polymers (CFRPs) play a key role in enabling technological and industrial progress. These composite materials are lightweight and highly strong, making them desirable for applications in various fields, including aviation, aerospace, automotive, wind power generation, and sports equipment.
Propeller driven rotors utilize propellers on the main rotor blade to spin the rotor. Past research efforts have highlighted dynamic issues that arise from the rotor-propeller Coriolis interaction. For this paper, a comprehensive multi-body analysis methodology, called Elastic Rotorcraft Analysis (ERA), was applied to various propeller driven rotor datasets. The focus of the modeling effort was on propeller driven rotor twirl phenomenon, which arises from rotor-propeller inertial couplings interacting with rotor blade modes. After describing the phenomenon, the paper is split into two parts: validations and predictions. In Part I of the paper, the ERA propeller driven rotor model was validated using three datasets: (i) a propeller flapping vacuum chamber experiment, (ii) a propeller/rotor loads vacuum chamber experiment, and (iii) a propeller driven rotor hover experiment. The ERA model showed good agreement with the data, and captured the important rotor-propeller Coriolis interaction. In Part II of the paper, predictions for several propeller driven rotor configurations were generated and analyzed. Loads were computed for an isolated propeller and are compared to propeller loads during propeller driven rotor operation. The analysis showed that operating the propeller on the rotor blade introduces significant inertial loads on the propeller. Finally, propeller placement along the main rotor blade span was investigated. The results of the present study agree with earlier research, which showed placing the propeller at the midspan location reduced the electrical power coefficient by nearly half compared to a tip mounted propeller.
In the context of increasing global energy demand and growing concerns about climate change, the integration of renewable energy sources with advanced modelling technologies has become essential for achieving sustainable and efficient energy systems. Solar energy, despite its considerable potential, continues to face challenges related to performance variability, limited real-time insights, and the need for reactive maintenance. To overcome these barriers, this work presents a Digital Twin framework aimed at optimizing solar-integrated energy systems through real-time monitoring, predictive analytics, and adaptive control. This work presents a Digital Twin framework designed to address the challenges of designing, operating, maintaining, and estimating renewable energy systems, specifically solar power, based on dynamic load demand. The framework enables real-time forecasting and prediction of energy outputs, ensuring systems operate efficiently and maintain peak performance across diverse conditions. The proposed methodology mirrors the physical system using real-time data inputs, environmental conditions, and physics-based models to create a high-fidelity virtual replica. This allows for dynamic analysis of energy flows, load forecasting, system performance prediction, and scenario testing to optimize design and operational strategies. By integrating predictive analytics, Digital Twin adapts to changing conditions, enabling proactive maintenance, fault detection, and system calibration to meet future load demands. Experimental validation demonstrates that the framework improves system efficiency, adaptability, and reliability, with scalable applications for both centralized and decentralized energy systems. Additionally, its integration with cloud-based platforms and IoT technologies enables real-time monitoring, facilitating continuous optimization and data-driven decision-making. This Digital Twin approach provides an intelligent, data-driven solution for the renewable energy sector, facilitating sustainable, resilient, and efficient energy infrastructures that can reliably meet evolving load demands while optimizing performance throughout their lifecycle.
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