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1. An Application of the Autogyro Theory to Airborne Wind Energy Extraction

   https://doi.org/10.1115/DSCC2013-3840  

   Rimkus, Sigitas ; Das, Tuhin ( October 2013 , Proceedings of the 2013 ASME Dynamic Systems and Control Conference)

   null (Ed.)

   Auto-rotation or autogyro is a well-known phenomenon where a rotor in a wind field generates significant lift while the wind induces considerable aerodynamic torque on the rotor. The principle has been studied extensively for applications in aviation. However, with recent works indicating immense, persistent, and pervasive, available wind energy at high altitudes, the principle of autogyro could potentially be exploited for energy harvesting. In this paper, we carry out a preliminary investigation on the viability of using autogyros for energy extraction. We mainly focus on one of the earliest documented works on modeling of autogyro and extend its use to explore energy harvesting. The model is based on blade element theory. We provide simulation results of the concept. Although the results are encouraging, there are various practical aspects that need to be investigated to build confidence on this approach of energy harvesting. This work aims to build a framework upon which more comprehensive research can be conducted. 

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2. Review on Deep Learning Research and Applications in Wind and Wave Energy

   https://doi.org/10.3390/en15041510  

   Gu, Chengcheng ; Li, Hua ( February 2022 , Energies)

   Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification. 

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3. Wave Power Density Hotspot Distribution and Correlation Pattern Exploration in the Gulf of Mexico

   https://doi.org/10.3390/su14031158  

   Gu, Chengcheng ; Li, Hua ( February 2022 , Sustainability)

   Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns. 

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4. Piezo-Pyro-Photo-electric Energy Harvesting with Smart Materials based Flag

   Ashok BATRA, James SAMPSON ( May 2023 , Sensors transducers)

   Abstract: This paper aims to develop a novel concept for energy harvesting via flexible inverted flags combining photovoltaic cells with piezoelectric flexible films. Using technology currently available, we have designed and fabricated piezo-pyro-photo-electric harvesters made of polyvinylidene fluoride (PVDF) piezoelectric elements combined with mini solar panels made of silicon. Experimental measurements of the motion dynamics and power generation were collected for the flags when subjected to wind, heat, and light sources simultaneously and individually. Results indicate a significant improvement in energy harvesting capability compared to isolated single piezoelectric devices. Thus, we anticipate a substantial impact of piezo- pyro-photo-electric energy harvesting device applications where remote power generation is needed. The Flag uses flexible piezoelectric and pyroelectric strips and flexible photovoltaic cells panel. The piezo-pyro- simultaneously generates power through movement and heat, respectively, while the photovoltaic cells harvest solar energy to produce electric power. The beauty of this Flag is to develop power day and night depending on the energy sources available. The basic concept is presented and validated by laboratory experiments with controlled airflow, light, and infrared heat. The maximum voltage generated was 60 mV when the Flag was simultaneously exposed to low-level wind, thermal and light energies. 

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5. Topological wave energy harvesting in bistable lattices

   https://doi.org/10.1088/1361-665X/ac37ff  

   Hwang, Myungwon ; Arrieta, Andres F ( November 2021 , Smart Materials and Structures)

   Abstract In this paper, we present an input-independent energy harvesting mechanism exploiting topological solitary waves. This class of medium transforming solitons, or transition waves, entails energy radiation in the form of trailing phonons in discrete bistable lattices. We observe numerically and experimentally that the most dominant frequencies of these phonons are invariant to the input excitations as long as transition waves are generated. The phonon energy at each unit cell is clustered around a single invariant frequency, enabling input-independent resonant harvesting with conventional energy transduction mechanisms. The presented mechanism fundamentally breaks the link between the unit cell size and the metamaterial’s operating frequencies, offering a broadband solution to energy harvesting that is particularly robust for low-frequency input sources. We further investigate the effect of lattice discreteness on the energy harvesting potential, observing two performance gaps and a topological wave harvesting pass band where the potential for energy conversion increases almost monotonically. The observed frequency-invariant phonons are intrinsic to the discrete bistable lattices, enabling broadband energy harvesting to be an inherent metamaterial property. 

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