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Abstract As an alternative to thermoelectric generators, heat engines show great interest thanks to their ability to convert temperature spatial gradient into time-domain temperature variations or vibrations. To this end, MultiPhysic Memory Alloys (MPMAs), combining shape memory characteristics with ferromagnetic properties, provide significant attractive characteristics such as sharp transition with reduced hysteresis as well as magnetic properties enabled by heating, thus allowing easier device development and implementation. In this study, we report the development of a heat engine for small-scale energy harvesting where the MPMA transfers its heat to a pyroelectric element that provides thermal to electrical energy conversion, yielding a more direct energy conversion path compared to conventional electromechanical heat engines. Furthermore, thermally decoupling the pyroelectric element from the MPMA allows a faster cooling of the latter, accounting for higher variation frequency. Compared to the use of electromagnetic transduction through a coil attached to the moving MPMA, this approach is shown to provide 3 to 9 times more power density (according to considered volume), with theoretical potential gains from 8 to 25 with the use of nonlinear electrical interfaces.

Abstract This paper introduces a new switching means giving a real time optimum connection mode of domestic appliances on either the electrical grid or a photovoltaic panel (PVP) output. A fuzzy algorithm makes the decision which ensures the optimal energy-management based on PVP generation and appliances states with respect to energy-saving criteria. Validation is driven on a 1 kW peak (kWp) PVP and domestic appliances of different powers installed at the Energy and Thermal Research Centre (CRTEn) in the north of Tunisia. Results confirm that energy saved during daylight is 80–90% of the PVP generated energy.

Abstract Predicting the distribution and resource of gas hydrates and understanding gas hydrate forming mechanisms are critical for assessing natural gas hydrate exploration potential, as well as exploiting hydrates. This study aims to provide a portable solution for evaluating resource of natural gas hydrate and quantifying contribution of methane sources via numerical simulations constrained by site-specific data. To numerically describe the complex process of biogenic methane production, an integrated simulation package, TOUGH + Hydrate + React (TOUGH + HR), was developed by coupling reactive transport, biodegradation and deposition of organic matter with behavior of hydrate-bearing system. Based on observed data from site SH2 in the South China Sea, a growing one-dimensional column model was constructed, and simulated via the developed TOUGH + HR tool. The results showed that when considering biogenic methane was the only source for hydrate, simulated maximum saturation of hydrate reached ~ 0.19, which is much lower than the observed value (~0.46), suggesting that the in-situ biogenic methane is not enough to form the high-saturation hydrate. When the upward flux of methane (considered as thermogenic methane) increased to 1.00 × 10−11 k g · m - 2 · s - 1 , both simulated saturation and distribution of hydrates matched the observed data well, including the profile of remained total organic carbon (TOC), the location of interface between dissolved methane and sulfate (SMI), and the derived chlorinity. Simulation results suggest that the ratio of biogenic methane to thermogenic methane forming hydrates was about 1:3. Predicted amount of methane hydrate using the column model was 3258.33 kg, very close to the estimated based on field observation (3112.82 kg).

Applied Energy, 258 ISSN:0306-2619 ISSN:1872-9118

Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field's characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks to assimilate diverse sensor data types. This includes line-of-sight wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier-Stokes equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model's ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework's source code is openly accessible.

Abstract In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system’s energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole system with the proposed strategy based on the collected real-world driving data. Furthermore, processor-in-the-loop tests verify that the proposed strategy can achieve a much higher convergence rate and a better performance in terms of the minimization of both energy loss and aging cost compared with state-of-the-art learning-based strategies.