Estimating the impact force generated by granular flow on a rigid obstruction

Flowing sediments such as debris and liquefied soils could exert a tremendous amount of force as they impact objects along their paths. The total impact force generally varies with slope angle, velocity at impact, and thickness of the flowing sediment. Estimation of the impact force of flowing sediments against protective measures such as earth retaining structures is an important factor for risk assessment. In this paper, we conduct small-scale laboratory physical modeling of sand flow at different slopes and measure the impact force exerted by this material on a fixed rigid wall. We also conduct numerical simulations in the Eulerian framework using computational fluid dynamics algorithms to analyze and reproduce the laboratory test results. The numerical simulations take into consideration the overtopping of the wall with sand, which influenced the measured impact force–time history responses. In addition, the numerical simulations are shown to capture accurately the change of the impact force with slope angle. Finally, the modeling approach conducted in this study is used to estimate the quasi-static force generated by the sediment as it comes to rest on the wall following impact.     

用优势应力分量逼近方法反演分析某水电站地下厂房区地应力状态

本文叙述了应用有限元优势应力分量逼近反演分析地应力场的方法。该方法通过少数点实测应力结果,结合工程地质资料,采用有限元数值模拟分析的方法反演工程区初始应力场。利用该方法并结合某水电站工程的实际情况进行了电站主厂房的地应力场分析,得到了在优势应力分量上与实测值相拟合的工程区剖面最大、最小主应力量值及其方向的分布图,并对工程区整体应力分布状况做了分析与探讨。     

Photoreduction of Fe(III) in the Acidic Mine Pit Lake of San Telmo (Iberian Pyrite Belt): Field and Experimental Work

Light-induced reduction of dissolved and particulate Fe(III) has been observed to occur in the surface waters of the acidic mine pit lake of San Telmo (143,600 m², pH 2.8, Fe_total = 2.72 mM). This photochemical production of Fe(II) is directly related to the intensity of solar radiation and competes with biologically catalyzed reactions (i.e., bacterial re-oxidation of Fe(II)) and physical processes (including ionic diffusion, advection, and convection, which tend to homogenize the epilimnetic concentration of Fe(II) at every moment). Therefore, diel cycles of Fe(II) concentration are observed at the lake surface, with minimum values of 10–20 μM Fe(II) (0.35–0.70% Fe_total) at the sunrise and sunset, and maximum values of 90 μM Fe(II) (3.2% Fe_total) at midday in August 2005. Field and experimental work conducted in San Telmo and other pit lakes of the Iberian Pyrite Belt (IPB) (pH 2.3–3.1, Fe_total = 0.34–17 mM) indicate that the kinetics of the photoreductive reaction is zero-order and is independent of the Fe(III) concentration, but highly dependent on the intensity of solar radiation and temperature. Experimental work conducted with natural Fe(III) minerals (schwertmannite, goethite, and lepidocrocite) suggests that dissolved organic matter is an important factor contributing to the photochemical production of Fe(II). The wavelengths involved in the photoreduction of Fe(III) include not only the spectrum of UV-A radiation (315–400 nm), but also part of the photosynthetically active radiation (PAR, 400–700 nm). This finding is of prime importance for the understanding of the photoreduction processes in the pit lakes of the IPB, because the photo-reactive depth is not limited to the penetration depth of UV-A radiation (upper 1–10 cm of the water column depending on the TDS content), but it is approximately equal to the penetration depth of PAR (e.g., first 4–6 m of the water column in San Telmo on July 2007); thus, increasing the importance of photochemical processes in the hydro(bio)geochemistry of pit lakes.     

Representing geographical objects with scale‐induced indeterminate boundaries: A neural network‐based data model

The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network‐based data model was investigated for representing geographical objects with scale‐induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi‐resolution, sub‐pixel maps that are typically derived from remote sensing techniques.     

An adaptive approach to selecting a flow‐partition exponent for a multiple‐flow‐direction algorithm

Most multiple‐flow‐direction algorithms (MFDs) use a flow‐partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow‐partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow‐partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow‐partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD‐md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow‐partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow‐partition exponent. MFD‐md is quantitatively evaluated using four types of mathematical surfaces and their theoretical ‘true’ value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD‐md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD‐md is better adapted to terrain conditions based on visual judgement.