山崩连续体模式之数值仿真(英文)

<正>Recent advances in landslide/avalanche modeling have been enabling us to investigate the kinematics of such catastrophic events with much more details.Taiwan is located in a region where seismic activities and extreme weather conditions frequently occur.One of their common consequences is the slope failure.In the past decodes,we witnessed at least three giant landslides:Tsaoling,Jiufenershan landslides,triggered by the Chi-Chi earthquake in     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

From well-test data to input to stochastic continuum models: effect of the variable support scale of the hydraulic data

The question of how well the true underlying hydraulic conductivity statistics of heterogeneous media are captured by well tests is addressed. The hydraulic conductivity value and the corresponding support volume associated with a theoretical well are correlated, causing a bias in the statistics derived from well-test analyses. Statistics derived from numerically simulated well tests are compared with the known underlying conductivity statistics and the results indicate an under-prediction by simulations at higher hydraulic conductivities. The deviation starts at about mean conductivity and can be as large as an order of magnitude, with the conductivity in the vicinity of the well defining the upper boundary. In other words, the conductivity value interpreted from the well test cannot be larger than the value that the well test first encounters. Consequently, for data in this simulation exercise, the standard deviation, if only determined for the upper range of the conductivity values, would be underestimated by a factor of 1.6–2. While this specific range is likely to depend on the scale and degree of the underlying heterogeneity as well as the duration of the test, the results should be indicative of a more general behaviour and are likely to occur in other heterogeneous data as well.

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Resumen Se plantea la pregunta de qué tan bien son representadas en las pruebas de pozo, las estadísticas reales de conductividad hidráulica subyacente de medios heterogéneos. Son correlacionados el valor de conductividad hidráulica y el volumen de apoyo correspondiente asociado con un pozo teórico, causando una distorsión en las estadísticas derivadas del análisis de la prueba de pozo. Las estadísticas derivadas de las pruebas de pozo simuladas numéricamente son comparadas con las estadísticas de conductividad subyacente conocidas, y los resultados indican una sub-predicción por las simulaciones hechas con conductividades hidráulicas más altas. La desviación empieza casi con la conductividad media y puede ser tan grande como un orden de magnitud, con la conductividad en la vecindad del pozo definiendo el límite superior. En otras palabras, el valor de conductividad interpretado a partir de la prueba del pozo no puede ser más grande que el valor que la prueba de pozo encuentre primero. Por consiguiente, para los datos en este ejercicio de simulación, la desviación estándar, si solamente fue determinada para el rango superior de los valores de conductividad, se subestimaría en un factor de 1.6–2. Mientras es probable que este rango específico dependa de la escala y del grado de la heterogeneidad subyacente, así como de la duración de la prueba, los resultados deben ser indicativos de un comportamiento más general y son probables también de ocurrir en otros datos heterogéneos.

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Résumé Nous posons ici la question de savoir dans quelle mesure les statistiques de la conductivité hydraulique des milieux hétérogènes pourrait être révélée par des essais de puits. La valeur de la conductivité hydraulique et le volume capté correspondant sont corrélés, créant un biais dans l’analyse des statistiques dérivées des essais de puits. Les statistiques en provenance de simulations numériques d’essais de puits sont comparées avec les statistiques de conductivités connues et les résultats indiquent une sous-évaluation par les simulations, pour les conductivités hydrauliques les plus élevées: la déviation commence à partir de la valeur moyenne de la conductivité et peut atteindre la magnitude d’un ordre de grandeur en considérant la conductivité mesurée au voisinage du puits. Autrement dit, la valeur de la conductivité interprétée via l’essais de pompage ne peut être plus importante que les premières valeurs rencontrées. Par conséquence, pour les données de cet exercice de simulation, la déviation standard sera sous-estimée d’un facteur compris entre 1.6–2 pour les valeurs les plus élevées. Tandis que l’échelle spécifique de valeurs est dépendante de l’échelle et du degré de l’hétérogénéité souterraine, de même que de la durée du test, les résultats pourraient être indicatifs d’un comportement plus général et seraient sans doute observables dans d’autres cas de données hétérogènes.

     

Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

The Indian remote sensing satellite, IRS-P4 (Oceansat-I) launched on May 26th, 1999 carried two sensors on board, i.e., the Ocean Colour Monitor (OCM) and the Multi-frequency Scanning Microwave Radiometer (MSMR) dedicated for oceanographic research. Sequential data of IRS-P4 OCM has been analysed over parts of both east and west coast of India and a methodology to retrieve sea surface current velocities has been applied. The method is based on matching suspended sediment dispersion patterns, in sequential two time lapsed images. The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The MCC technique involves computing matrices of cross-correlation coefficients and identifying correlation peaks. The movement of the pattern can be calculated knowing the displacement of windows required to match patterns in successive images. The technique provides actual flow during a specified period by integrating both tidal and wind influences. The current velocities retrieved were compared with synchronous data collected along the east coast during the GSI cruise ST-133 of R.V. Samudra Kaustubh in January 2000. The current data were measured using the ocean current meter supplied by the Environmental Measurement and CONtrol (EMCON), Kochi available with the Geological Survey of India, Marine Wing. This current meter can measure direction and magnitude with an accuracy of ±5‡ and 2% respectively. The measurement accuracies with coefficient of determination (R ² ) of 0.99, for both magnitude (cm.s^-1) and direction (deg.) were achieved.     

Conditioning reservoir models on rate data using ensemble smoothers

There are several issues to consider when we use ensemble smoothers to condition reservoir models on rate data. The values in a time series of rate data contain redundant information that may lead to poorly conditioned inversions and thereby influence the stability of the numerical computation of the update. A time series of rate data typically has correlated measurement errors in time, and negligence of the correlations leads to a too strong impact from conditioning on the rate data and possible ensemble collapse. The total number of rate data included in the smoother update will typically exceed the ensemble size, and special care needs to be taken to ensure numerically stable results. We force the reservoir model with production rate data derived from the observed production, and the further conditioning on the same rate data implies that we use the data twice. This paper discusses strategies for conditioning reservoir models on rate data using ensemble smoothers. In particular, a significant redundancy in the rate data makes it possible to subsample the rate data. The alternative to subsampling is to model the unknown measurement error correlations and specify the full measurement error covariance matrix. We demonstrate the proposed strategies using different ensemble smoothers with the Norne full-field reservoir model.     

Search for and study of weak radio galaxies with large angular sizes using the NVSS data

A new method for distinguishing candidate giant radio galaxies is proposed and applied. The method is based on comparing the axes of the extended components of NVSS radio sources with separations exceeding 4′, described in a catalog of presumably independent objects. Objects detected using the proposed algorithm include 16 new weak giant-radio-galaxy candidates, for which optical and radio identifications have been obtained using the CATS, NED, SDSS, and SkyView databases.     

Simulation of debris flow on an instrumented test slope using an updated Lagrangian continuum particle method

We present an updated Lagrangian continuum particle method based on smoothed particle hydrodynamics (SPH) for simulating debris flow on an instrumented test slope. The site is a deforested area near the village of Ruedlingen, a community in the canton of Schaffhausen in Switzerland. Artificial rainfall experiments were conducted on the slope that led to failure of the sediment in the form of a debris flow. We develop a 3D mechanistic model for this test slope and conduct numerical simulations of the flow kinematics using an SPH formulation that captures large deformation, material nonlinearity, and the complex post-failure movement of the sediment. Two main simulations explore the impact of changes in the mechanical properties of the sediment on the ensuing kinematics of the flow. The first simulation models the sediment as a granular homogeneous material, while the second simulation models the sediment as a heterogeneous material with spatially varying cohesion. The variable cohesion is meant to represent the effects of root reinforcement from vegetation. By comparing the numerical solutions with the observed failure surfaces and final free-surface geometries of the debris deposit, as well as with the observed flow velocity, flow duration, and hot spots of strain concentration, we provide insights into the accuracy and robustness of the SPH framework for modeling debris flows.

     

Multilevel Monte Carlo methods using ensemble level mixed MsFEM for two-phase flow and transport simulations

In this paper, we propose multilevel Monte Carlo (MLMC) methods that use ensemble level mixed multiscale methods in the simulations of multiphase flow and transport. The contribution of this paper is twofold: (1) a design of ensemble level mixed multiscale finite element methods and (2) a novel use of mixed multiscale finite element methods within multilevel Monte Carlo techniques to speed up the computations. The main idea of ensemble level multiscale methods is to construct local multiscale basis functions that can be used for any member of the ensemble. In this paper, we consider two ensemble level mixed multiscale finite element methods: (1) the no-local-solve-online ensemble level method (NLSO); and (2) the local-solve-online ensemble level method (LSO). The first approach was proposed in Aarnes and Efendiev (SIAM J. Sci. Comput. 30(5):2319-2339, 2008) while the second approach is new. Both mixed multiscale methods use a number of snapshots of the permeability media in generating multiscale basis functions. As a result, in the off-line stage, we construct multiple basis functions for each coarse region where basis functions correspond to different realizations. In the no-local-solve-online ensemble level method, one uses the whole set of precomputed basis functions to approximate the solution for an arbitrary realization. In the local-solve-online ensemble level method, one uses the precomputed functions to construct a multiscale basis for a particular realization. With this basis, the solution corresponding to this particular realization is approximated in LSO mixed multiscale finite element method (MsFEM). In both approaches, the accuracy of the method is related to the number of snapshots computed based on different realizations that one uses to precompute a multiscale basis. In this paper, ensemble level multiscale methods are used in multilevel Monte Carlo methods (Giles 2008a, Oper.Res. 56(3):607-617, b). In multilevel Monte Carlo methods, more accurate (and expensive) forward simulations are run with fewer samples, while less accurate (and inexpensive) forward simulations are run with a larger number of samples. Selecting the number of expensive and inexpensive simulations based on the number of coarse degrees of freedom, one can show that MLMC methods can provide better accuracy at the same cost as Monte Carlo (MC) methods. The main objective of the paper is twofold. First, we would like to compare NLSO and LSO mixed MsFEMs. Further, we use both approaches in the context of MLMC to speedup MC calculations.     

Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models

In this study, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting system. To illustrate the applicability and effect of using lumped and distributed input data in MLR approach, Koyna river watershed in Maharashtra, India is chosen as a case study. The results are also compared with autoregressive integrated moving average (ARIMA) models. MLR attempts to model the relationship between two or more independent variables over a dependent variable by fitting a linear regression equation. The main aim of the present study is to see the consequences of development and applicability of simple models, when sufficient data length is available. Out of 47 years of daily historical rainfall and reservoir inflow data, 33 years of data is used for building the model and 14 years of data is used for validating the model. Based on the observed daily rainfall and reservoir inflow, various types of time-series, cause-effect and combined models are developed using lumped and distributed input data. Model performance was evaluated using various performance criteria and it was found that as in the present case, of well correlated input data, both lumped and distributed MLR models perform equally well. For the present case study considered, both MLR and ARIMA models performed equally sound due to availability of large dataset.     

Simulation of groundwater flow and contaminant transport at a landfill site using models

Numerical and analytical modelling studies were conducted for the analysis of groundwater flow and contaminant transport at the Innisfil landfill site in the Town of Innisfil, County of Simcoe, in Ontario, Canada. Previously conducted field studies categorized the upper stratigraphy at the site into three units: upper sand unit, upper silt/clay unit and Intermediate Sand unit. Essentially horizontal groundwater movement in the two sand units and vertical downward flow in the silt/clay unit were reported by the field hydrogeologists. In the following, application of three computer models (FLOWPATH, USGS MOC and POLLUTE) for the simulation of the groundwater flow and contaminant transport processes at the Innisfil landfill site is described. The paper focuses on the calibration of groundwater flow and contaminant transport systems, and demonstrates how the insight gained during the contaminant transport calibration was used to improve the initial groundwater flow characterization of the hydrogeological system.     

Flood forecasting in large rivers with data-driven models

Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.     

Numerical simulations of simple shear with non‐coaxial soil models

This paper investigates the effects of a non‐coaxial model on simulated stress–strain behaviour of granular materials subject to simple shearing under various initial conditions. In most cases, a significant difference of predictions between coaxial and non‐coaxial modelling is found during the early stage in shearing. With the increase in shearing, non‐coaxial simulations approach and tend to coincide with coaxial simulations. It is also found that the roles of non‐coaxial modelling in simulating simple shear behaviour are considerably influenced by hardening rules, flow rules, initial static lateral pressure coefficients. In some cases, the non‐coaxial modelling gives a similar simulation as the coaxial modelling. In other cases, the non‐coaxial modelling decreases the hardening response or softening response of materials, compared with the coaxial modelling. Under certain conditions, the predicted peak strength of materials with non‐coaxial modelling is larger than that for coaxial modelling. Some of these observations can be attributed to the amount of principal stress rotation in various cases analysed. Others can be attributed to the difference between the directions of the non‐coaxial plastic flow and those for coaxial plastic flow. Copyright © 2005 John Wiley & Sons, Ltd.     

The influence of vegetation on turbulence and flow velocities in European salt-marshes

Flow hindrance by salt‐marsh vegetation is manifested in the structure of the tidal current; it has a significant impact on sediment transport and it has been related to increased sediment accretion. The flow characteristics in three different vegetation types (Spartina maritima, Sp. anglica and Salicornia sp./Suaeda maritima) were measured on three salt‐marshes in Portugal and England. These in situ measurements differ from laboratory flume experiments with ‘clean’ vegetation by the complexity of natural canopies. Skimming flow develops above the Spartina canopy when the vegetation is fully submerged. In this situation, a low turbulence zone with nearly constant velocity in the denser canopy is separated from the skimming flow above by an interface characterized by high Reynolds stresses. In the low turbulence zone, a positive relationship exists between turbulence intensity and shoot density, which is due to wake turbulence generated locally in the canopy. The rate of particle settling should be increased in that zone. The lower limit of skimming flow is best predicted by the height within the canopy that includes 85% of the biomass. For emergent Spartina canopies and the short Salicornia/Suaeda marsh, the maximal velocity‐gradient is shifted upwards compared to a standard boundary layer over bare sediment and the turbulence is attenuated near the bed, but to a lesser extent than for fully submerged Spartina canopies. A turbulence reduction near the bed was observed in all measured profiles; that should enhance sediment deposition and protects the bed against subsequent erosion.     

基于数据包络分析的统计预测模型有效性评价

统计预测模型是进行中长期水文预报的主要手段之一,在统计预测模型建模过程中面临的一个重要问题是如何从诸多待选模型中挑选出一个预测投入较低、预测精度较高的模型。针对这一多属性综合评价问题,提出了利用数据包络分析中的CCR模型进行水文统计预测模型综合评价的方法。模型的输入指标包括预测因子指标和模型参数指标,输出指标为模型精度评价指标,评价结果为模型的相对效率。作为典型案例,对参考作物腾发量预测的20个径向基函数网络模型的有效性进行了评价,结果表明该评价方法是可行的。模型中预测旬参考作物腾发量的关键因子是最高、最低温度,其次是风速,再次是日照时数;将预测时段所属的旬序号作为网络输入可显著提高模型预测精度和相对效率。     

Estimates of heat flow from Cenozoic seafloor using global depth and age data

The total heat output of the Earth constrains models of mantle and core dynamics. Previously published estimates (42–44 TW) have recently been questioned because the measured conductive heat flow on young oceanic lithosphere is about a factor of 2 less than the expected heat flow based on half-space cooling models. Taking the conductive ocean heat flow values at face value reduces the global heat flow from 44 to 31 TW, which has major implications for geodynamics and Earth history. To help resolve this issue, we develop a new method of estimating total oceanic heat flow from depth and age data. The overall elevation of the global ridge system, relative to the deep ocean basins, provides an independent estimate of the total heat content of the lithosphere. Heat flow is proportional to the measured subsidence rate times the heat capacity divided by the thermal expansion coefficient. The largest uncertainty in this method is due to uncertainties in the thermal expansion coefficient and heat capacity. Scalar subsidence rate is computed from gradients of depth and age grids. The method cannot be applied over very young seafloor (< 3 Ma) where age gradient is discontinuous and the assumption of isostasy is invalid. Between 3 and 66 Ma, the new estimates are in agreement with half-space cooling model. Our model-independent estimate of the total heat output of Cenozoic seafloor is 18.6 to 20.5 TW, which leads to a global output of 42 to 44 TW in agreement with previous studies.     

Assessment of 210Pb data from Canadian lakes using the CIC and CRS models

 Much discussion has centered around which ²¹⁰Pb dating method should be used, the constant initial concentration (CIC) model or the constant rate of supply (CRS) model. In this study, the activity data from 22 lacustrine sediment cores from the Canadian prairies were used to compare the determination of sediment accumulation using the two models. Other relative and absolute dating techniques have been used to calibrate the methodology. For half of the core sites examined, the mass sedimentation rate was constant, and thus both the CIC and CRS models were found to be valid. For the other half, variability was observed in the CRS mass accumulation rate trend. The validity of the CIC model for these cores was dependent on the degree of variability of the mass sedimentation rate. Where the variability is moderate to high, the CRS model may be more satisfactory. Caution should be exercised when using chronological data determined with the CRS model, however, as the accuracy of chronology in the lower reaches of a profile is questionable. Received: 11 May 1995 · Accepted: 16 August 1995     

On the weak turbulent motions of an isothermal dry granular dense flow with incompressible grains: part II. Complete closure models and numerical simulations

The complete thermodynamically consistent turbulent closure models of isochoric and isothermal dry granular dense flows with incompressible grains and weak turbulent intensity are established on the basis of a linearized theory with respect to the granular coldness for the dynamic responses of the closure conditions. The models are applied to study a gravity-driven stationary turbulent flow down an inclined moving plane, and the numerical simulations are compared with the experimental outcomes. It shows that while the mean velocity decreases monotonically from its boundary value on the moving plane toward the free surface, the mean porosity and granular coldness display more “exponential-like” increasing/decreasing tendencies. Of particular interest is that the granular coldness evolves from its maximum value on the moving plane toward its minimum value on the free surface, leading to the turbulent dissipation evolving in a similar manner, while the turbulent kinetic energy demonstrate a reverse tendency. The obtained results show good agreements to the experimental outcomes and are similar to the characteristics of conventional Newtonian fluids in turbulent shear flows.