Stabilization of molten salt materials using metal chlorides for solar thermal storage

The effect of a variety of metal-chlorides additions on the melting behavior and thermal stability of commercially available salts was investigated. Ternary salts comprised of KNO3, NaNO2, and NaNO3 were produced with additions of a variety of chlorides (KCl, LiCl, CaCl2, ZnCl2, NaCl and MgCl2). Thermogravimetric analysis and weight loss experiments showed that the quaternary salt containing a 5 wt% addition of LiCl and KCl led to an increase in short term thermal stability compared to the ternary control salts. These additions allowed the salts to remain stable up to a temperature of 630 °C. Long term weight loss experiments showed an upper stability increase of 50 °C. A 5 wt% LiCl addition resulted in a weight loss of only 25% after 30 hours in comparison to a 61% loss for control ternary salts. Calorimetry showed that LiCl additions allow partial melting at 80 °C, in comparison to the 142 °C of ternary salts. This drop in melting point, combined with increased stability, provided a molten working range increase of almost 100 °C in total, in comparison to the control ternary salts. XRD analysis showed the oxidation effect of decomposing salts and the additional phase created with LiCl additions to allow melting point changes to occur

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product

Spin and orbital valence bond solids in a one-dimensional spin-orbital system: Schwinger boson mean field theory

A generalized one-dimensional $SU(2)\times SU(2)$ spin-orbital model is studied by Schwinger boson mean-field theory (SBMFT). We explore mainly the dimer phases and clarify how to capture properly the low temperature properties of such a system by SBMFT. The phase diagrams are exemplified. The three dimer phases, orbital valence bond solid (OVB) state, spin valence bond solid (SVB) state and spin-orbital valence bond solid (SOVB) state, are found to be favored in respectively proper parameter regions, and they can be characterized by the static spin and pseudospin susceptibilities calculated in SBMFT scheme. The result reveals that the spin-orbit coupling of $SU(2)\times SU(2)$ type serves as both the spin-Peierls and orbital-Peierles mechanisms that responsible for the spin-singlet and orbital-singlet formations respectively.Comment: 6 pages, 3 figure

Hawking Fluxes, Back reaction and Covariant Anomalies

Starting from the chiral covariant effective action approach of Banerjee and Kulkarni [Phys. Lett. B 659, 827(2008)], we provide a derivation of the Hawking radiation from a charged black hole in the presence of gravitational back reaction. The modified expressions for charge and energy flux, due to effect of one loop back reaction are obtained.Comment: 6 pages, no figures, minor changes and references added, to appear in Classical and Quantum Gravit

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

Visualization of facet-dependent pseudo-photocatalytic behavior of TiO2 nanorods for water splitting using In situ liquid cell TEM

We report an investigation of the pseudo-photocatalytic behavior of rutile TiO2 nanorods for water splitting observed with liquid cell transmission electron microscopy (TEM). The electron beam serves as a “light” source to initiate the catalytic reaction and a “water-in-salt” aqueous solution is employed as the electrolyte. The observation reveals that bubbles are generated preferentially residing near the {110} facet of a rutile TiO2 nanorod under a low electron dose rate (9.3–18.6 e-/Å2·s). These bubbles are ascribed to hydrogen gas generated from the pseudo-photocatalytic water splitting. As the electron beam current density increases to 93 e-/Å2 ·s, bubbles are also found at the {001} and {111} facets as well as in the bulk liquid solution, demonstrating the dominant effects of water electrolysis by electron beam under higher dose rates. The facet-dependent pseudo-photocatalytic behavior of rutile TiO2 nanorods is further validated using density functional theory (DFT)calculation. Our work establishes a facile liquid cell TEM setup for the study of pseudo-photocatalytic water splitting and it may also be applied to investigation of other photo-activated phenomena occurring at the solid-liquid interfaces

Photon Momentum Transfer in Single-Photon Double Ionization of Helium

We theoretically and experimentally investigate the photon momentum transfer in single-photon double ionization of helium at various large photon energies. We find that the forward shifts of the momenta along the light propagation of the two photoelectrons are roughly proportional to their fraction of the excess energy. The mean value of the forward momentum is about 8/5 of the electron energy divided by the speed of light. This holds for fast and slow electrons despite the fact that the energy sharing is highly asymmetric and the slow electron is known to be ejected by secondary processes of shake off and knockout rather than directly taking its energy from the photon. The biggest deviations from this rule are found for the region of equal energy sharing where the quasifree mechanism dominates double ionization