Energy converters based on vortex-induced vibrations (VIV) have shown great potential for harvesting energy from low-velocity flows, which constitute a significant portion of ocean energy. However, solid-solid triboelectric nanogenerators (TENG) are not wear-resistant in corrosive environments. Therefore, to effectively harvest ocean energy over the long term, a novel solid-liquid triboelectric nanogenerator based on vortex-induced resonance (VIV-SL-TENG) is presented. The energy is harvested through the resonance between VIV of a cylinder and the relative motions of solid-liquid friction pairs inside the cylinder. The factors that affect the output performance of the system, including the liquid mass ratio and the deflection angle of the friction plates, are studied and optimized by establishing mathematical models and conducting computational fluid dynamics simulations. Furthermore, an experimental platform for the VIV-SL-TENG system is constructed to test and validate the performance of the harvester under different conditions. The experiments demonstrate that the energy harvester can successfully convert VIV energy into electrical energy and reach maximum output voltage in the resonance state. As a new type of energy harvester, the presented design shows a promising potential in the field of ‘blue energy’ harvesting.

The accurate prediction of sea surface temperature (SST) is essential for studying marine phenomena, understanding climate dynamics, and forecasting environmental changes. However, developing a general SST prediction model is challenging due to significant regional variations and the impacts of diverse climate phenomena. To improve the performance of SST predictions, we propose a hybrid framework that effectively models the spatial and temporal dependencies of SST data with a Gaussian process-enhanced Long Short-Term Memory network. The LSTM module adaptively captures both long and short-term temporal trends in SST variation, while the Gaussian process incorporates the spatial dependency of neighboring data to further refine the predictions. Furthermore, our proposed framework estimates the uncertainty associated with SST predictions, providing crucial information for practical applications. Comprehensive experiments are conducted on the OISST dataset, with a focus on the Bohai Sea and the South China Sea. The results of our framework outperform state-of-the-art methods, validating its superiority in SST prediction.

Mesoscale eddies play a critical role in sea navigation and route planning, yet traditional prediction methods have often overlooked their spatial relationships, relying on indirect approaches to capture their distribution across extensive maps. To address this limitation, we present BiST-SA-LSTM, an end-to-end prediction framework that combines Bidirectional Spatial Temporal LSTM and Self-Attention mechanisms. Utilizing data sourced from the South China Sea and its surrounding regions, which are renowned for their intricate maritime dynamics, our methodology outperforms similar models across a range of evaluation metrics and visual assessments. This is particularly evident in our ability to provide accurate long-term forecasts that extend for up to 10 days. Furthermore, integrating sea surface variables enhances forecasting accuracy, contributing to advancements in oceanic physics.

In this paper, an axial flux electromagnetic energy harvester driven by a Stirling engine (AFEEH-SE) is presented for recovering waste heat above 200 °C. A gamma-type Stirling engine with a slider-crank drive mechanism serves as the power unit to convert thermal energy into rotational mechanical energy. The harvester comprises a rotating magnet array and a stationary coil array. Finite element simulations were conducted to analyze and compare the voltage output under different magnet and coil parameter configurations. Subsequently, a prototype utilizing mineral oil combustion as the heat source was designed, achieving a rotational speed of 950 rpm under open-circuit conditions. Through systematic adjustments to the magnet and coil parameters, the optimal performance configuration was determined to maximize the output power of the harvester. Under this optimized configuration, the AFEEH-SE achieved an effective power output of 57.13 mW, capable of charging a 2.2 mF capacitor to 28 V in 49 s. This study demonstrates the feasibility of the AFEEH-SE in practical applications and provides a solid foundation for the future field of waste heat recovery.