S. L. Vorlet / Directeur(s) : P. Perona; G. De Cesare 

In the context of energy transition, hydropower is poised to have a major importance in the coming decades. It therefore requires the development of reliable, innovative solutions for maintenance and rehabilitation. Geomembrane systems are a promising technology to reduce water and energy losses, thus increasing the flexibility and efficiency of existing schemes. In hydropower waterways, which tend to experience a reduction of efficiency with time, geomembrane systems usually consist of an exposed hyperelastic geomembrane applied to the existing lining and in contact with the water, in addition to anchor elements. This type of application presents numerous challenges, due to extreme flow conditions related to hydropower operations. Fluid-structure interactions are therefore paramount for hyperelastic geomembranes used for the rehabilitation of hydraulic structures. This research project aims at assessing fluid-structure interactions of hyperelastic geomembranes in pressure flow by means of experimental and numerical modeling. The objective is to characterize the dynamic response of hyperelastic geomembranes. The thesis provides four main contributions. First, a comprehensive review of the application of geomembrane systems as rehabilitation technology for hydraulic structures is provided, and the main processes and parameters governing the deformations of hyperelastic geomembranes are identified. The governing equations are derived, and the constitutive models accounting for hyperelasticity and viscoelasticity are presented. Second, the effects of viscoelastic properties and hyperelasticity on geomembranes mechanics and deformations are described. Experimental findings demonstrate first that geomembranes undergo large and reversible deformations when subjected to external loads. In addition to the hyperelastic deformation, viscoelastic properties modify the geomembranes’ response to external loads, particularly through stress relaxation, creep additional deflection, or hysteresis in case of cyclic loading. Third, the modal characteristics are presented with low prestrain. The numerical data depict that the modal properties of hyperelastic geomembranes are mainly governed by the tension in the geomembranes, as they are thin and flexible structures with no flexural rigidity. When considering low prestrain, resulting in low tension in the geomembranes, the eigenfrequencies fall into the lower frequency range and are closely spaced in frequency for higher modes of vibrations. Fourth, fluid-structure interactions are investigated. The analysis distinguishes flow-induced steady deformations and flow-induced vibrations and characterizes the resulting effects of the geomembrane deflection on flow characteristics. Experimental results confirm that fluid-structure interactions have a major role in the dynamic response of hyperelastic geomembranes. When hyperelastic geomembranes are subjected to fluid-structure interactions, they undergo large, steady deflections coupled with relatively low-amplitude vibrations. Fluid-structure interactions thus result in a combination of steady deformations around which the geomembranes vibrate at a specific frequency. These findings contribute to the better understanding of the complex interplay that occurs between hyperelastic geomembranes and steady flow in pressure waterways and should help to integrate the concepts of fluid-structure interactions in the design of geomembrane systems.

DOI : 10.5075/epfl-thesis-11290

C. M. Gotelli Alvial / Directeur(s) : C. Ancey 

Braided rivers switch between quiet and active periods of bedload transport while their planform changes quickly. This makes both simple descriptive indices and heavy morphodynamic models hard to use in practice. This thesis offers a practical middle path: it treats channel change as a sequence of morphological states read from planform images and models how the river switches between them with a continuous-time Markov model. The aim is to turn images into probabilistic forecasts of sediment transport with stated uncertainty. From flume imagery, binary water masks are compared with two complementary measures that capture edge movement and area overlap. We reduce these pairwise differences and cluster the images to obtain a small, readable set of recurring states, ranging from narrow and simple to wide and partitioned. The time the river stays in a given state is well described by an exponential law, which allows us to estimate transition rates and jump probabilities for the Markov model. The resulting ensemble recovers means, variances, extremes, and main time scales, and it shows clear morphological control of transport, including a negative link with wetted width and with a braiding index. Splitting the variance indicates that differences between states explain a meaningful part of the instant variability, while the rest arises within states. The learned states and transitions remain stable across independent runs. For image-only cases, two variants extend the method: one that preserves the long-term mean, and another that uses stream power from images to scale state-wise means. Weighting by how long the river stays in each state keeps the overall mean accurate when direct bedload data are missing. Overall, the framework provides a clear path from images to forecasts, explains intermittency as switching among states with different export capacity, and enables practical predictions with quantified uncertainty.

DOI : 10.5075/epfl-thesis-11096