Abstract

The hydraulic performance of a labyrinth weir was analysed using numerical simulation. The head-discharge relation was compared to the results of an empirical formula, and the flow 3D pattern was studied. The results showed the reliability of the numerical model results, especially for intermediate to high heads. Moreover, they were used to unveil complex phenomena that are difficult to observe in laboratory tests.

Abstract

Spillways with highly-converging chutes are a non-conventional kind of spillway in which the length of the crest is greater than the width of the stilling basin. A pair of converging chutes leads the water into the stilling basin. This typology has two possible advantages: increase of the discharge capacity or reduction of the hydraulic head for a given discharge flow. The first improves dam safety, whereas the second results in higher storage capacity and power generation. In this proceeding, the authors present the investigation performed in order to study and characterize this kind of spillways. The utilization of this typology as an overtopping protection has emerged as a third possibility, presenting some particularities that are introduced here.

Abstract

The technology of Wedge Shaped Blocks (WSB) for spillways had a slow development through decades but has proven its practical reliability. Ongoing additional research aims to complete the theoretical knowledge, improve current design criteria and make this technology available to practitioners as one more solution to consider when designing an embankment dam spillway. Here a summary is provided about recent physical and numerical modelling of WSB spillways performed by UPM and CIMNE.  

Abstract

In order to comply with the new safety regulations a significant number of Spanish dam spillways must be upgraded. In this scenario and with the aim of increasing the discharge capacity with a reduced investment innovative designs become interesting solutions. One of these innovative designs are the labyrinth weirs. Project POLILAB is carrying out with the objective of optimize the design of labyrinth weirs, physical and numerical tests exposed in this article were developed within this framework. The most relevant results are related with the discharge capacity, the flow pattern and the structural reinforcement achieved by the implementation of a polyhedral bottom.

Abstract

The construction of double-curvature arch dams is an attractive solution from an economic viewpoint due to the reduced volume of concrete necessary for their construction as compared to conventional gravity dams. Due to their complex geometry, many criteria have arisen for their design. However, the most widespread methods are based on recommendations of traditional technical documents without taking into account the possibilities of computer-aided design. In this paper, an innovative software tool to design FEM models of double-curvature arch dams is presented. Several capabilities are allowed: simplified geometry creation (interesting for academic purposes), preliminary geometrical design, high-detailed model construction, and stochastic calculation performance (introducing uncertainty associated with material properties and other parameters). This paper specially focuses on geometrical issues describing the functionalities of the tool and the fundamentals of the design procedure with regard to the following aspects: topography, reference cylinder, excavation depth, crown cantilever thickness and curvature, horizontal arch curvature, excavation and concrete mass volume, and additional elements such as joints or spillways. Examples of application on two Spanish dams are presented and the results obtained analyzed.

Abstract

We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79–0.80 and 0.43–0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

Abstract

Water balances have been identified as an effective tool for assessing the amount of water and its availability in a region. In particular, the System of Environmental-Economic Accounting for Water (SEEA-W) has emerged as one of the most complete methodologies to undertake this action. Despite the great effort made to offer a common methodology for elaborating the SEEA-W tables, there are multiple aspects that prevent its standardization in a generalized way. This leads both to multiple approaches in the realization of water balances and to the introduction of high uncertainty in the accounts obtained.The data series variability, both temporal and spatial, is one of the factors that can introduce large uncertainty into the water balances if they are not correctly evaluated. This aspect has not yet been specifically studied, and most of the case studies in the literature only provide the water balance values for a short period (usually a natural or hydrological year) and for a specific basin. In the case of having data corresponding to several years, average values are typically used to elaborate the calculations. In the present study, the SEEA-W methodology was applied to the Spanish part of Duero river basin. Each component of the water asset accounts was calculated using simulated data from three models: SIMPA (rainfall-runoff model), ASTER (snow-related processes model) and SIMGES (water allocation and management model). These models were developed and calibrated by different Spanish entities. Then, the uncertainty associated with the temporal and spatial irregularity of the data series was also estimated.The Duero basin is located in a semi-arid Mediterranean region, where hydrological processes are affected by high intra- and inter-annual variability. To analyse this issue both monthly and annual resolutions were used. The first allowed identifying a significant intra-annual variability. The second was analysed using a period of 26 years, from 1980 to 2006. The results show a high variability for certain hydrological components which can lead to a great degree of uncertainty. In addition, each hydrological year was classified as dry, average or wet and water balances were calculated under the same classification. This approach would improve the estimation of water resources including the prediction of future climate changes scenarios.Regarding the spatial variability, the water resources of the basin, like many of the large Mediterranean rivers, are not evenly distributed and showed large differences in hydrological processes. These differences are caused by climatic and geomorphological differences (the area comprises an extensive central plateau with a peripheral mountain arch). In addition, the analysis considers the different human activities coexisting within the basin and the heterogeneity of their spatial distribution. The water balances split into a more disaggregated territorial units could help to detect the areas with both positive and negative balances and improve the water resources management in the whole basin.

Abstract

Due to complex nature of nearly all infrastructures (and more specifically concrete dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance such as epistemic and aleatory, or spatial and temporal. The objective of this paper is to focus on the material and modeling uncertainties, and to couple them with soft computing techniques aiming to reduce the computational burden of the conventional Monte Carlo-based finite element simulations. Several scenarios are considered in which the concrete and foundation material properties, the water level, and the dam geometry are assumed as random variables. Five soft computing techniques (i.e., random forest, boosted regression trees, multi-adaptive regression splines, artificial neural networks, and support vector machines) are employed to predict various quantities of interest based on different training sizes. It is argued that the artificial neural network is the most accurate algorithm in majority of cases, with enough accuracy as to be useful in reliability analysis as a complement to numerical models. The results with 200 samples in the training set are enough for reaching useful accuracy in most cases. For the simple prediction tasks, the results were predicted with less than 1% error. It is observed that increasing the number of input parameters increases the prediction error. The partial dependence plots provided most sensitive variables in dam design, which were consistent with the physics of the problem. Finally, several practical recommendations are provided for future applications.

Abstract

Labyrinth weirs provide an economic option for flow control structures in a variety of applications, including as spillways at dams. The cycles of labyrinth weirs are typically placed in a linear configuration. However, numerous projects place labyrinth cycles along an arc to take advantage of reservoir conditions and dam alignment, and to reduce construction costs such as narrowing the spillway chute. Practitioners must optimize more than 10 geometric variables when developing a head–discharge relationship. This is typically done using the following tools: empirical relationships, numerical modeling, and physical modeling. This study applied a new tool, machine learning, to the analysis of the geometrically complex arced labyrinth weirs. In this work, both neural networks (NN) and random forests (RF) were employed to estimate the discharge coefficient for this specific type of weir with the results of physical modeling experiments used for training. Machine learning results are critiqued in terms of accuracy, robustness, interpolation, applicability, and new insights into the hydraulic performance of arced labyrinth weirs. Results demonstrate that NN and RF algorithms can be used as a unique expression for curve fitting, although neural networks outperformed random forest when interpolating among the tested geometries.

Abstract

En esta comunicación se expone el estudio realizado para la mejora de la capacidad de desagüe de aliviaderos en tecla de piano mediante la optimización de su diseño geométrico en base a modelación numérica validada físicamente. Se muestra para un cierto diseño base de aliviadero en tecla de piano, el patrón de flujo que induce la geometría y su influencia en la capacidad de desagüe. Se observó una reconducción del flujo por los voladizos del aliviadero, lo que provoca zonas de baja velocidad y concentración de líneas de corriente, dificultando el desagüe. Una vez estudiado el patrón flujo se procedió al rediseño de la geometría del aliviadero, en base a la detección en el modelo numérico de las zonas de baja velocidad inducidas por el contorno geométrico. La nueva geometría de aliviadero optimizada se modeló numéricamente, observándose un aumento de la capacidad de desagüe respecto a la geometría base.