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With the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems. [ABSTRACT FROM AUTHOR]

We develop a deep learning-based algorithm, called DeepForce, to link ab initio physics with the continuum theory to predict concentration profiles of confined water. We show that the deep-learned forces can be used to predict the structural properties of water confined in a nanochannel with quantum scale accuracy by solving the continuum theory given by Nernst–Planck equation. The DeepForce model has an excellent predictive performance with a relative error less than 7.6% not only for confined water in small channel systems (L < 6 nm) but also for confined water in large channel systems (L = 20 nm) which are computationally inaccessible through the high accuracy ab initio molecular dynamics simulations. Finally, we note that classical Molecular dynamics simulations can be inaccurate in capturing the interfacial physics of water in confinement (L < 4.0 nm) when quantum scale physics are neglected. [ABSTRACT FROM AUTHOR]

In this paper, we show some nonexistence results of radial solutions for the following Minkowski curvature problems in an exterior domain: $$ \begin{cases} \ -\text{div} \big(\phi(\nabla v(x))\big)=k(x)f(v(x)), \quad\quad x\in\Omega,\\ \ v=0\ \text{on} \ \partial\Omega, \qquad\lim\limits_{x\rightarrow\infty}v(x)=0\\ \end{cases} $$ for R sufficiently large, where |$\phi(s)=\frac{s}{\sqrt{1-s^{2}}}$| for |$s\in{\mathbb R}$| with |$s^2\lt1,$|   |$\Omega=\{x\in{{\mathbb R}^{N}}:\ |x| \gt R\}$|⁠ , |$N\geq3$| is an integer, |$|\cdot|$| denotes the Euclidean norm on |$\mathbb{R}^{N}$|⁠ , R is a positive parameter, |$f:\mathbb{R}\rightarrow\mathbb{R}$|  is an odd and locally Lipschitz continuous function and |$k \in C^{1}(\mathbb{R}^{+},\ \mathbb{R}^{+})$| with |$\mathbb{R}^{+}=(0, +\infty)$|⁠. We also apply the fixed-point index theory to establish the existence of positive radial solutions of the above problems for R sufficiently small. [ABSTRACT FROM AUTHOR]

Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder–decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid–fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. We show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model. [ABSTRACT FROM AUTHOR]

Amongst thermoelectrics, Bi2Te3 is special owing to its peak performance near room temperature, which enables it to be used for both energy harvesting and cooling. Despite extensive studies on this compound, Bi2Te3-based bulk materials are usually prepared by field-assisted sintering and hot pressing. This necessitates sophisticated equipment and tedious compositional control, both of which are undesirable for large scale applications. In this work, a low-temperature liquid phase sintering (LPS) method was employed to prepare p-type Bi0.5Sb1.5Te3/SiC composites with enhanced thermoelectric properties. In addition, a nearly two-fold increase in the power factor was observed post heat treatment at 350 °C. This can be ascribed to carrier concentration modulation due to porosity induced by heat treatment. Further addition of 0.6 vol% SiC results in a low lattice thermal conductivity of 0.33 W m−1 K−1, which can be ascribed to phonon scattering due to various defects induced by the SiC inclusion. Ultimately, a figure of merit ZT of 1.05 at 340 K was achieved for Bi0.5Sb1.5Te3/0.6 vol% SiC, 20% higher than that of the pristine sample. Moreover, an average ZT of 0.87 at 300–500 K was attained, comparable to state-of-the-art values via high-temperature processing. This work showcases the promising application of the low-temperature LPS technique for energy-efficient processing of thermoelectrics. [ABSTRACT FROM AUTHOR]