Abstract

El método del Caudal Mínimo Nocturno (CMN) es un concepto ampliamente utilizado para la estimación de fugas y otros objetivos estratégicos en empresas operadoras de agua. Si bien es un procedimiento de fácil aplicabilidad, existe un gran abanico de submétodos de aplicación, pudiendo introducirse una gran incertidumbre en los cálculos si no se seleccionan correctamente los criterios a utilizar. En este estudio se presenta un algoritmo capaz de asistir en la elección del mejor enfoque para el cálculo de CMN en función de los datos disponibles, las características de cada caso y el objetivo perseguido. Para elaborar dicho algoritmo se han ejecutado tres acciones: (i) revisión exhaustiva de literatura especializada y recopilación de submétodos utilizados en casos reales a nivel global, (ii) elaboración de un algoritmo multi-criterio capaz de seleccionar la mejor opción en cada fase del cálculo y (iii) presentación de una plataforma informática, llamada WatEner, en la que se integrará progresivamente el algoritmo propuesto.

Abstract

El empleo en aliviaderos de presas de fábrica de canales laterales para la recogida de los vertidos en la zona de estribos ha sido una solución utilizada con relativa frecuencia lo largo de la historia en casos en los que la longitud de vertido era sensiblemente superior a la anchura del valle aguas abajo de la presa. Este tipo de aliviaderos en presas existentes puede tener como objetivo el aumento de capacidad de su aliviadero o bien el aumento de los niveles máximos de explotación del embalse manteniendo la capacidad de desagüe con los resguardos exigidos. A pesar de estos antecedentes, no existen estudios metodológicos que proporcionen criterios para su diseño,  ni conclusiones sobre el efecto que producen este tipo de aliviaderos en la disipación de energía en cuencos de pie de presa. Ante esta situación, se está desarrollando el proyecto de investigación aplicada CALA, cuyo objeto principal es definir criterios para el diseño de ese tipo de aliviaderos y el desarrollo de un software de cálculo que permita su predimensionamiento y optimización.

Abstract

El rendimiento de los dispositivos de monitorización ha experimentado mejoras permitiendo disponer de más información sobre el comportamiento de las estructuras. Sin embargo, las inversiones realizadas en modernización de sistemas de medida no se recuperan a menos que se complementen con aplicaciones capaces de manejar una información tan amplia y diversa. En esta contribución, se presenta una herramienta de software para importar, explorar, limpiar y analizar datos de monitorización. Además, permite la generación de modelos de predicción basados en machine learning, así como la interpretación de la respuesta del sistema a las acciones o cargas en funcionamiento. La metodología y la estructura general se dividen en dos fases: i) carga, depuración, completado y análisis datos, y ii) generación e interpretación de modelos predictivos basados en machine learning. El mismo modelo puede utilizarse para la detección de anomalías comparando las predicciones con el comportamiento registrado.

Abstract

We present some developments in the Particle Finite Element Method (PFEM) for analysis of complex coupled problems in mechanics involving fluid-soil-structure interaction (FSSI). The PFEM uses an updated Lagrangian description to model the motion of nodes (particles) in both the fluid and the solid domains (the later including soil/rock and structures). A mesh connects the particles (nodes) defining the discretized domain where the governing equations for each of the constituent materials are solved as in the standard FEM. The stabilization for dealing with an incompressibility continuum is introduced via the finite calculus (FIC) method. An incremental iterative scheme for the solution of the non linear transient coupled FSSI problem is described. The procedure to model frictional contact conditions and material erosion at fluid-solid and solid-solid interfaces is described. We present several examples of application of the PFEM to solve FSSI problems such as the motion of rocks by water streams, the erosion of a river bed adjacent to a bridge foundation, the stability of breakwaters and constructions sea waves and the study of landslides.

Abstract

The paper focuses on the analysis of radial-gated spillways, which is carried out by the solution of a numerical model based on the finite element method (FEM). The Oliana Dam is considered as a case study and the discharge capacity is predicted both by the application of a level-set-based free-surface solver and by the use of traditional empirical formulations. The results of the analysis are then used for training an artificial neural network to allow real-time predictions of the discharge in any situation of energy head and gate opening within the operation range of the reservoir. The comparison of the results obtained with the different methods shows that numerical models such as the FEM can be useful as a predictive tool for the analysis of the hydraulic performance of radial-gated spillways.

Abstract

Changes in direction and cross section in supercritical hydraulic channels generate shockwaves which result in an increase in flow depth with regard to that for uniform regime. These disturbances are propagated downstream and need to be considered in the design of the chute walls. In dam spillways, where flow rates are often high, this phenomenon can have significant implications for the cost and complexity of the solution. It has been traditionally analysed by means of reduced-scale experimental tests, as it has a clear three-dimensional character and therefore cannot be approached with two-dimensional numerical models. In this work, the ability of the particle finite element method (PFEM) to reproduce this phenomenon is analysed. PFEM has been successfully applied in previous works to problems involving high irregularities in free surface. First, simple test cases available in the technical bibliography were selected to be reproduced with PFEM. Subsequently, the method was applied in two spillways of real dams. The results show that PFEM is capable of capturing the shockwave fronts generated both in the contractions and in the expansions that occur behind the spillway piers. This suggests that the method may be useful as a complement to laboratory test campaigns for the design and hydraulic analysis of dam spillways with complex geometries.

Abstract

Rail transport, both for people and goods, is becoming increasingly significant all over the world, which is reflected in the great growth of conventional and high-speed train lines. Most of these infrastructures are built with railway ballast, a granular material whose main functions are to resist vertical and horizontal loads and to face climatic actions. The growing popularity of these infrastructures has led to the development of numerical models to evaluate their performance. Among a wide range of numerical methods, the Discrete Element Method (DEM) was found to be effective for evaluating the performance of granular materials. This approach considers their discontinuous nature and has proven to be a useful tool to determine the dynamic behaviour of groups of particles. Moreover, the DEM is also used to compute the behaviour of continuum materials. In this work, rails and bearing plates are characterised in the calculations using this methodology, called the bonded DEM. It is a modification of the classical DEM which assumes that bonds exist between particles, resisting their separation. The code used is developed within DEMPack, a specific software tool for modelling physical problems using the DEM. Currently, DEMPack allows the use of two different types of geometry: spheres with rolling friction and clusters of spheres. A previous analysis showed that spheres are more effective for studying the macroscopic behaviour of the ballast layer, while clusters are necessary for small-scale tests involving highly compacted particles since their results are greatly influenced by particles and contacts distribution. After calibrating the code, full-scale tests were performed applying the load of a high-speed train on a railway track section in different situations. Considering the amount of material (about 260,000 particles) and that the aim is to evaluate the deflection of the rails, the calculations are carried out using spheres. The numerical results correctly capture the effect on the deflection of the rails. It can be concluded that the DEM increases the possibilities for analysing innovative solutions since real case-scenarios can be studied with enough accuracy and feasible time.

Abstract

The discrete element method (DEM) is an emerging tool for the calculation of the behaviour of bulk materials. One of the key features of this method is the explicit integration of the motion equations. Explicit methods are rapid, at the cost of a limited time step to achieve numerical stability. First- or second-order integration schemes based on a Taylor series are frequently used in this framework and shown to be accurate for the translational and rotational motion of spherical particles. However, they may lead to relevant inaccuracies when non-spherical particles are used since the orientation implies a modification in the second-order inertia tensor in the inertial reference frame. Specific integration schemes for non-spherical particles have been proposed in the literature, such as the fourth-order Runge–Kutta scheme presented by Munjiza et al. and the predictor–corrector scheme developed by Zhao and van Wachem which applies the direct multiplication algorithm for integrating the orientation. In this work, both methods are adapted to be used together with a velocity Verlet scheme for the translational integration. The performance of the resulting schemes, as well as that of the direct integration method, is assessed, both in benchmark tests with analytical solution and in real-scale problems. The results suggest that the fourth-order Runge–Kutta and the Zhao and van Wachem schemes are clearly more accurate than the direct integration method without increasing the computational time.

Abstract

We present some developments and applications of the Particle Finite Element Method (PFEM) for analysis of complex coupled problems in mechanics involving fluid-soil-structure interaction.

Abstract

Predictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the field of machine learning (ML), which have been applied to solve practical problems. The work aims at testing the prediction capability of some state-of-the-art algorithms to model dam behaviour, in terms of displacements and leakage. Models based on random forests (RF), boosted regression trees (BRT), neural networks (NN), support vector machines (SVM) and multivariate adaptive regression splines (MARS) are fitted to predict 14 target variables. Prediction accuracy is compared with the conventional statistical model, which shows poorer performance on average. BRT models stand out as the most accurate overall, followed by NN and RF. It was also verified that the model fit can be improved by removing the records of the first years of dam functioning from the training set.