A discussion of the scaling effect in numerical simulation of the extrusion process

The main objective of the work of this paper is to study the possibility of using a small scale geometrical model in the numerical simulation of aluminium extrusion. The advantages and shortcomings of the application of the geometrically similar model in FEM simulation are discussed. Thermal – mechanical and metallurgical combined simulations are performed within two tests using geometrically similar models and assessment is made in terms of mechanical and material properties. It was found that small scale simulation could not reproduce most of the important forming parameters of the original process, although it could help to bring about significant savings in computation time

A low-bandgap dimeric porphyrin molecule for 10% efficiency solar cells with small photon energy loss

Dimeric porphyrin molecules have great potential as donor materials for high performance bulk heterojunction organic solar cells (OSCs). Recently reported dimeric porphyrins bridged by ethynylenes showed power conversion efficiencies (PCEs) of more than 8%. In this study, we design and synthesize a new conjugated dimeric D-A porphyrin ZnP2BT-RH, in which the two porphyrin units are linked by an electron accepting benzothiadiazole (BT) unit. The introduction of the BT unit enhances the electron delocalization, resulting in a lower highest occupied molecular orbital (HOMO) energy level and an increased molar extinction coefficient in the near-infrared (NIR) region. The bulk heterojunction solar cells with ZnP2BT-RH as the donor material exhibit a high PCE of up to 10% with a low energy loss (Eloss) of only 0.56 eV. The 10% PCE is the highest for porphyrin-based OSCs with a conventional structure, and this Eloss is also the smallest among those reported for small molecule-based OSCs with a PCE higher than 10% to date

Life cycle greenhouse gas emissions of multi-pathways natural gas vehicles in china considering methane leakage

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordNatural gas has been promoted rapidly recent years to substitute traditional vehicle fuels. However, methane leakages in the natural gas supply chains make it difficult to ascertain whether it can reduce greenhouse gas emissions when used as a transport fuel. This paper characterizes the natural gas supply chains and their segments involved, estimates the venting and fugitive leakages from natural gas supply chains, decides the distribution among segments and further integrates it with life cycle analysis on natural gas fueled vehicles. Domestic natural gas supply chain turns out to be the dominant methane emitter, accounting for 67% of total methane leakages from natural gas supply chains. Transportation segments contribute 42–86% of the total methane leakages in each supply chain, which is the greatest contribution among all the segments. Life cycle analysis on private passenger vehicles, transit buses and heavy-duty trucks show that compressed natural gas and liquefied natural gas bring approximately 11–17% and 9–15% greenhouse gas emission reduction compared to traditional fossil fuels, even considering methane leaks in the natural gas supply chains. Methane leakages from natural gas supply chains account for approximately 2% of the total life cycle greenhouse gas emissions of natural gas vehicles. The results ascertain the low-carbon attribute of natural gas, and greater efforts should be exerted to promote natural gas vehicles to help reduce greenhouse gas emissions from on-road transportation.National Natural Science Foundation of ChinaInternational Science & Technology Cooperation Program of Chin

Recent progress in random metric theory and its applications to conditional risk measures

The purpose of this paper is to give a selective survey on recent progress in random metric theory and its applications to conditional risk measures. This paper includes eight sections. Section 1 is a longer introduction, which gives a brief introduction to random metric theory, risk measures and conditional risk measures. Section 2 gives the central framework in random metric theory, topological structures, important examples, the notions of a random conjugate space and the Hahn-Banach theorems for random linear functionals. Section 3 gives several important representation theorems for random conjugate spaces. Section 4 gives characterizations for a complete random normed module to be random reflexive. Section 5 gives hyperplane separation theorems currently available in random locally convex modules. Section 6 gives the theory of random duality with respect to the locally $L^{0}-$convex topology and in particular a characterization for a locally $L^{0}-$convex module to be $L^{0}-$pre$-$barreled. Section 7 gives some basic results on $L^{0}-$convex analysis together with some applications to conditional risk measures. Finally, Section 8 is devoted to extensions of conditional convex risk measures, which shows that every representable $L^{\infty}-$type of conditional convex risk measure and every continuous $L^{p}-$type of convex conditional risk measure ($1\leq p<+\infty$) can be extended to an $L^{\infty}_{\cal F}({\cal E})-$type of $\sigma_{\epsilon,\lambda}(L^{\infty}_{\cal F}({\cal E}), L^{1}_{\cal F}({\cal E}))-$lower semicontinuous conditional convex risk measure and an $L^{p}_{\cal F}({\cal E})-$type of ${\cal T}_{\epsilon,\lambda}-$continuous conditional convex risk measure ($1\leq p<+\infty$), respectively.Comment: 37 page

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

Fine-Scale Variations in Eucritic Pyroxene FeO/MnO: Process vs. Provenance.

Most asteroidal igneous rocks are eucrite-like basalts and gabbros, composed mostly of ferroan low- and high-Ca pyroxenes and calcic plagioclase, plus smaller amounts of silica (most commonly tridymite), ilmenite, chromite, troilite, Ca-phosphate, metal and sometimes ferroan olivine. Eucrite-like mafic rocks are fragments of the crusts of differentiated asteroids, and most are likely from 4 Vesta

Effects of using different plasmonic metals in metal/dielectric/metal subwavelength waveguides on guided dispersion characteristics

The fundamental guided dispersion characteristics of guided light in a subwavelength dielectric slit channel embedded by two different plasmonic metals are investigated when varying the gap width. As a result, an overall and salient picture of the guided dispersion characteristics is obtained over a wide spectrum range below and above the plasma frequencies of the two different plasmonic metals, which is important preliminary information for analyzing this type of subwavelength waveguide. In particular, the effects of using two different metals on the guided mode dispersions are emphasized in comparison with the effects of using the same plasmonic metal cladding.Comment: 13 pages, 3 figures, typos corrected, reference added, text modifie

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