A Simplified Method for Predicting Tunneling-Induced Ground Movement considering Nonuniform Deformation Boundary

Stochastic medium (SM) theory is a practical method in ground settlement prediction, while its nonintegrable double integral form makes the solution process complicated. A simplified analytical solution based on the SM theory is developed to predict the ground movement in tunneling excavation. With the simplified solution, the ground movement for single tunnel and twin tunnels could be predicted based on the gap parameter G and influence angle β. A feasible approach is developed to estimate these two parameters using the maximum ground settlement Smax and tunnel design parameters, including tunnel depth H and diameter R. The proposed approach can be used to predict the ground movement curve for both circular and noncircular cross section tunnels. To validate its accuracy, the results predicted by the simplified procedure are compared with those obtained by the SM theory and measured in situ. The comparisons show that the current results agree well with those obtained by the SM theory and measured in situ. The comparison of five tunnels in literature illustrates that the simplified method can provide a more reasonable prediction for the ground movement induced by tunneling.

The Effect of Solution Time on the Microstructure of A356.2 Aluminum Alloy Provided by Rheocasting and Squeeze Casting

The evolution of the microstructure of A356.2 alloys prepared by the rheocasting and squeeze casting during solution heat treatment was investigated. In contrast with the conventional solution heat treatment process (3 hours at 540oC), a short time solution treatment process (less than 1 hour at 540oC) is applied in this paper. The results show that the rheocastings require a shorter solution time than the squeeze-castings to obtain spheroidized Si particles. After solution for 10 min, the X-ray diffraction inspection results show that the Mg2Si phase completely is dissolved in both rheocastings and squeeze-castings. However, a small amount of Mg2Si is found at the edge of the Si particle by scanning electron microscope observation. After solution for more than 20 min, the Mg2Si phase is completely dissolved. Fe-rich phases, including AlSiFeMg and AlFeSi, exist throughout the solution process. The developed T6 heat treatment with a short solution time can effectively improve production efficiency and decrease process cost for the rheocasting process. Key words: A356.2 alloy, microstructure, short solution time, rheocasting, squeeze casting

A novel algorithm to allocate customers and retailers in a closed-loop supply chain under probabilistic demand

In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1- the type of expected movement 2- expected coverage (distance and time) for minimizing the costs and maximizing the profit by considering the probabilistic customer and uncertainly demand. First of all, the expected distances between customers and retailers are calculated per movement method. These values are compared with the Maximum expected coverage distance of retailers, which is displayed in algorithm 1 heuristically, and the minimum value is picked. Also, to allocate customers to retailers, considering the customer's movement methods and comparing it with Maximum expected coverage time, which is presented in Algorithm 2 heuristically, the minimum value is chosen to this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.

Method to Predict the Non-Uniform Potential Distribution in Random Electrical Machine Windings under Pulse Voltage Stress

This article describes a practical method for predicting the distribution of electric potential inside an electrical machine’s winding based on design data. It broadens the understanding of winding impedance in terms of inter-winding behavior and allows to properly design an electrical machine’s insulation system during the development phase. The predictions are made based on an frequency-dependent equivalent circuit of the electrical machine which is validated by measurements in the time domain and the frequency domain. Element parameters for the equivalent circuit are derived from two-dimensional field simulations. The results demonstrate a non-uniform potential distribution and demonstrate that the potential difference between individual turns and between turns and the stator core exceeds the expected values. The findings also show a link between winding impedance and potential oscillations inside the winding. Additionally, the article provides an overview of the chronological progression of turn-based models and shows how asynchronous multiprocessing is used to accelerate the solution process of the equivalent circuit.

Global optimization for the multilevel European gas market system with nonlinear flow models on trees

The European gas market is implemented as an entry-exit system, which aims to decouple transport and trading of gas. It has been modeled in the literature as a multilevel problem, which contains a nonlinear flow model of gas physics. Besides the multilevel structure and the nonlinear flow model, the computation of so-called technical capacities is another major challenge. These lead to nonlinear adjustable robust constraints that are computationally intractable in general. We provide techniques to equivalently reformulate these nonlinear adjustable constraints as finitely many convex constraints including integer variables in the case that the underlying network is tree-shaped. We further derive additional combinatorial constraints that significantly speed up the solution process. Using our results, we can recast the multilevel model as a single-level nonconvex mixed-integer nonlinear problem, which we then solve on a real-world network, namely the Greek gas network, to global optimality. Overall, this is the first time that the considered multilevel entry-exit system can be solved for a real-world sized network and a nonlinear flow model.

High Performance P-Type Perovskite Lagao3 Thin Film Field Effect Transistor Prepared by Solution-Processed

Metal oxide semiconductor (MOS) is essential to compose high-performance electronic devices, however, the investigation on p-type MOS is relatively rare compared with its n-type counterpart. In this work, LaGaO3 thin films with superior p-type conductivity have been prepared via a facile solution process. Moreover, we have implemented Al2O3 and SiO2 as the dielectric of the p-channel LaGaO3 thin film transistors (TFTs) annealed at different temperatures. Particularly, the LaGaO3/Al2O3 TFTs annealed at 700 °C exhibit an ultrahigh hole mobility of 12.4 cm2V-1s-1, Under the same conditions, LaGaO3/Al2O3 thin film transistor is two orders of magnitude higher than LaGaO3/SiO2 thin film transistor. The advanced p-type characteristics of the LaGaO3 thin film, along with its facile low-cost fabrication process can shed new light on future design of high-performance complementary MOS circuit with other optimized facile-integrated dielectrics.

Numerical differentiation for two-dimensional scattered data on arbitrary domain base on Hermite extension with an implicit iteration process

<abstract><p>In this paper, we develop a method for numerical differentiation of two-dimensional scattered input data on arbitrary domain. A Hermite extension approach is used to realize the approximation and a modified implicit iteration method is proposed to stabilize the approximation process. For functions with various smooth conditions, the numerical solution process of the method is uniform. The error estimates are obtained and numerical results show that the new method is effective. The advantage of the method is that it can solve the problem in any domain.</p></abstract>

USING OF MULTILAYER NEURAL NETWORKS FOR THE SOLVING SYSTEMS OF DIFFERENTIAL EQUATIONS

The article considers the study of methods for numerical solution of systems of differential equations using neural networks. To achieve this goal, thefollowing interdependent tasks were solved: an overview of industries that need to solve systems of differential equations, as well as implemented amethod of solving systems of differential equations using neural networks. It is shown that different types of systems of differential equations can besolved by a single method, which requires only the problem of loss function for optimization, which is directly created from differential equations anddoes not require solving equations for the highest derivative. The solution of differential equations’ system using a multilayer neural networks is thefunctions given in analytical form, which can be differentiated or integrated analytically. In the course of this work, an improved form of constructionof a test solution of systems of differential equations was found, which satisfies the initial conditions for construction, but has less impact on thesolution error at a distance from the initial conditions compared to the form of such solution. The way has also been found to modify the calculation ofthe loss function for cases when the solution process stops at the local minimum, which will be caused by the high dependence of the subsequentvalues of the functions on the accuracy of finding the previous values. Among the results, it can be noted that the solution of differential equations’system using artificial neural networks may be more accurate than classical numerical methods for solving differential equations, but usually takesmuch longer to achieve similar results on small problems. The main advantage of using neural networks to solve differential equations` system is thatthe solution is in analytical form and can be found not only for individual values of parameters of equations, but also for all values of parameters in alimited range of values.

Development of SnO2 Composites as Electron Transport Layer in Unencapsulated CH3NH3PbI3 Solar Cells

Improving morphological and electronic properties of the electron transport layer (ETL) is a critical issue to fabricate highly efficient perovskite solar cells. Tin dioxide is used as an ETL for its peculiarities such as low-temperature solution-process and high electron mobility and several handlings have been tested to increase its performances. Herein, SnO2:ZnO and SnO2:In2O3 composites are studied as ETL in planar n-i-p CH3NH3PbI3 solar cells fabricated in ambient air, starting from glass/ITO substrates. Morphological, electrical and optical properties of zinc- and indium-oxide nanoparticles (NPs) are investigated. First-principle calculations are also reported and help to further explain the experimental evidences. Photovoltaic performances of full devices show an improvement in efficiency for SnO2:In2O3–based solar cells with respect to pristine SnO2, probably due to a suppression of interfacial charge recombination between ITO/ETL and ETL/perovskite. Moreover, a better homogeneity of SnO2:In2O3 deposition with respect to SnO2:ZnO composites, conducts an increase in perovskite grain size and, consequently, the device performances.