井下车用零件多裂纹扩展的时变可靠性分析

针对工程中井下车用零件的多裂纹损伤问题，通过扭转疲劳试验模拟了车用零件中传动轴的疲劳损伤，并应用长焦显微镜与照相机相结合的测量法对零件一定疲劳循环次数后的表面裂纹进行了测量统计，同时以Paris模型及剩余强度估算模型对统计数据进行处理，得到了剩余强度.再应用所建立的时变可靠性数学模型，求取了零件时变强度的漂移率及波动率，并最终得出了该零件在一定疲劳循环后的可靠度.该方法从实际应用上解决了时变理论对零部件时变强度的动态检测问题，将时变理论向工程化应用推进一步，具有良好的应用前景.     

Hydrogen Spillover Enhanced Hydroxyl Formation and Catalytic Activity Toward CO Oxidation at the Metal/Oxide Interface

H₂‐promoted catalytic activity of oxide‐supported metal catalysts in low‐temperature CO oxidation is of great interest but its origin remains unknown. Employing an FeO(111)/Pt(111) inverse model catalyst, we herewith report direct experimental evidence for the spillover of H(a) adatoms on the Pt surface formed by H₂ dissociation to the Pt?FeO interface to form hydroxyl groups that facilely oxidize CO(a) on the neighboring Pt surface to produce CO₂. Hydroxyl groups and coadsorbed water play a crucial role in the occurrence of hydrogen spillover. These results unambiguously identify the occurrence of hydrogen spillover from the metal surface to the noble metal/metal oxide interface and the resultant enhanced catalytic activity of the metal/oxide interface in low‐temperature CO oxidation, which provides a molecular‐level understanding of both H₂‐promoted catalytic activity of metal/oxide ensembles in low‐temperature CO oxidation and hydrogen spillover.     

水动力作用对污染物在河流水沙两相中分配的影响研究进展

通过总结近年来有关水动力作用对河流中泥沙吸附/释放重金属、磷等污染物影响研究成果,分析了水流紊动强度和流速对污染物在河流水沙两相中分配的影响规律。当水流紊动强度较低时,泥沙主要以床沙形式存在,泥沙对污染物的吸附/释放速率和强度较低;随着紊动强度增大,水沙界面切应力随之增大并促使泥沙悬浮,污染物与悬浮泥沙颗粒接触面积增大,使得泥沙对污染物的吸附/释放速率和强度显著增加。流速对污染物在水沙两相间分配的影响较为复杂。泥沙静止时,流速的增大会减小边界层厚度,增大水体溶解氧含量、氧化还原电位等参数,并使污染物在水体里的扩散由分子扩散转变为以紊动扩散为主,增强污染物在水-沙界面的交换通量,对污染物在水沙两相间的分配产生影响;随着流速进一步增大,床沙逐渐起动、悬浮,此时除上述因素外,泥沙运动状态、悬浮颗粒间碰撞强度、悬浮物的絮凝等均会对泥沙吸附/释放污染物产生影响,由于影响因素较多,作用机制复杂,目前关于泥沙运动对污染物在水沙两相间分配的影响所得结论仍存在分歧。相应的机理研究及数学模型的建立均做了较大简化。综观当前的研究成果,水沙运动及其化学生物过程等对污染物在河流水沙两相中的分配作用机理和耗氧有机物、有毒有机物等污染物与泥沙的作用机理是这一领域的未来研究重点。     

Ln0.5A0.5MnO3 (Ln=Lanthanide,A= Ca,Sr) Perovskites Exhibiting Remarkable Performance in the Thermochemical Generation of CO and H2 from CO2 and H2O

Perovskite oxides of the Ln_0.5A_0.5MnO₃ (Ln=lanthanide, A=Sr, Ca) family have been investigated for the thermochemical splitting of H₂O and CO₂ to produce H₂ and CO respectively. The amounts of O₂ and CO produced strongly depend on the size of the rare earth ions and alkaline earth ions. The manganite with the smallest rare earth possessing the highest distortion and size disorder as well as the smallest tolerance factor, gives out the maximum amount of O₂, and, hence, the maximum amount of CO. Thus, the best results are found with Y_0.5Sr_0.5MnO₃, which possesses the highest distortion and size disorder. Y_0.5Sr_0.5MnO₃ shows remarkable fuel production activity even at the reduction and oxidation temperatures as low as 1200 °C and 900 °C, respectively.     

An improved algorithm for the shallow water equations model reduction: Dynamic Mode Decomposition vs POD

We propose an improved framework for dynamic mode decomposition (DMD) of 2‐D flows for problems originating from meteorology when a large time step acts like a filter in obtaining the significant Koopman modes, therefore, the classic DMD method is not effective. This study is motivated by the need to further clarify the connection between Koopman modes and proper orthogonal decomposition (POD) dynamic modes. We apply DMD and POD to derive reduced order models (ROM) of the shallow water equations. Key innovations for the DMD‐based ROM introduced in this paper are the use of the Moore–Penrose pseudoinverse in the DMD computation that produced an accurate result and a novel selection method for the DMD modes and associated amplitudes and Ritz values. A quantitative comparison of the spatial modes computed from the two decompositions is performed, and a rigorous error analysis for the ROM models obtained is presented. Copyright © 2015 John Wiley & Sons, Ltd.     

Numerical modeling of chlorine concentration in water storage tanks

In this paper, we describe a numerical model to simulate the evolution in time of the hydrodynamics of water storage tanks, with particular emphasis on the time evolution of chlorine concentration. The mathematical model contains several ingredients particularly designed for this problem, namely, a boundary condition to model falling jets on free surfaces, an arbitrary Lagrangian–Eulerian formulation to account for the motion of the free surface because of demand and supply of water, and a coupling of the hydrodynamics with a convection–diffusion–reaction equation modeling the time evolution of chlorine. From the numerical point of view, the equations resulting from the mathematical model are approximated using a finite element formulation, with linear continuous interpolations on tetrahedra for all the unknowns. To make it possible, and also to be able to deal with convection‐dominated flows, a stabilized formulation is used. In order to capture the sharp gradients present in the chlorine concentration, particularly near the injection zone, a discontinuity capturing technique is employed. Copyright © 2015 John Wiley & Sons, Ltd.     

Single determinant N‐representability and the kernel energy method applied to water clusters

The Kernel energy method (KEM) is a quantum chemical calculation method that has been shown to provide accurate energies for large molecules. KEM performs calculations on subsets of a molecule (called kernels) and so the computational difficulty of KEM calculations scales more softly than full molecule methods. Although KEM provides accurate energies those energies are not required to satisfy the variational theorem. In this article, KEM is extended to provide a full molecule single‐determinant N‐representable one‐body density matrix. A kernel expansion for the one‐body density matrix analogous to the kernel expansion for energy is defined. This matrix is converted to a normalized projector by an algorithm due to Clinton. The resulting single‐determinant N‐representable density matrix maps to a quantum mechanically valid wavefunction which satisfies the variational theorem. The process is demonstrated on clusters of three to twenty water molecules. The resulting energies are more accurate than the straightforward KEM energy results and all violations of the variational theorem are resolved. The N‐representability studied in this article is applicable to the study of quantum crystallography. © 2017 Wiley Periodicals, Inc.     

Preparation of dummy template‐imprinted polymers for the rapid extraction of nonsteroidal anti‐inflammatory drugs residues in aquatic environmental samples

A molecularly imprinted polymer was synthesized and applied as a sorbent in the solid‐phase extraction device. The imprinted polymer was characterized by fourier‐transform infrared spectroscopy and scanning electron microscope. The results revealed that imprinted polymer possess sensitive selectivity and reliable adsorption properties for five NSAIDs. The imprinted polymer was successfully applied to the pre‐concentration for five NSAIDs in different water samples prior to UPLC‐MS/MS. In the early studies, several factors were investigated, including pH adjustment, the kind of elution solvent and the volume of elution solvent. Finally, we found that the pH 5 and an aliquot of 2 mL methanol were suitable for the water samples. The limits of detection and limits of quantitation of five nonsteroidal anti‐inflammatory drugs varied from 0.007 to 0.480 μg L⁻¹ and 0.03 to 1.58 μg L⁻¹, respectively. The spiking recoveries of the target analytes were 50.33‐127.64% at the levels of 0.2 μg L⁻¹, 2 μg L⁻¹ and 5 μg L⁻¹. The precision and accuracy of this method showed a great increase compared with traditional solid‐phase extraction. The developed method was successfully applied to extraction and analysis of NSAIDs in different water samples with satisfactory results which could help us better understand their environmental fate and risk to ecological health.     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.