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Strain measurements by gauges can play an important role in analyzing the fatigue damage of the hydroelectric turbine runner. However, these measurements cannot cover every steady-state operating condition due to the experimental limitations. Thus, the aim of this research is to predict the strain signal on runner over every possible steady operating condition using available experimental measurements. The strain signal measured during steady state involves several components (such as periodicity, vortex rope component and stochastic components) which can generate difficulties during the prediction. This paper proposes a solution to predict the runner strain signals by independently interpolating each physical phenomenon over different turbine operating conditions. These components are extracted using cyclostationary decomposition operators. A case study is performed on a Francis hydroelectric turbine to verify the interpolation performance. The proposed methodology can contribute to the fatigue assessment and help to reduce the requirements of infield measurements.

This paper deals with a continuously degrading turbine runner due to cavitation that is inspected at fixed time intervals. The degradation of the system is modelled with a gamma process. This paper is focused on comparing the influence of maintenance parameters with the long-time cost criterion. A case study, based on simulated degradation paths, shows that there exists a set of parameters that minimize maintenance costs.

Although hydroelectric generating units are highly reliable, being able to accurately model their degradation level represents a real asset in industrial and financial risk management. This paper presents and models a common degradation phenomenon observed on hydraulic Francis turbine runners: erosive cavitation. It gives an application of stochastic processes for degradation modeling framework in presence of real laboratory experimental data. For degradation modeling, a non homogeneous gamma process is proposed. The model calibration is explained and asymptotic confidence intervals for the model estimate are assessed. Because of the limited size of available dataset, bootstrap techniques are also used to evaluate statistical estimation uncertainties on the model parameters. These uncertainties on the degradation model are then propagated in order to analyze how they impact the distribution of the system lifetime, characterized by the hitting time for a given degradation threshold.

Strain measurements on turbine blades are difficult and costly tasks. Such measurements, when carried out, generally only happen during the runner commissioning. This gives rise to two problems. The first is that some of the sensors often stop functioning properly during the measurement campaign, which leads to distorted data, and the second is that runner blade strains are not available for long-term monitoring after the measurement campaign. To alleviate the consequences of distorted or missing values, we propose the use of neural networks to automate the imputations of missing values in measurement campaign data using virtual sensors. Three types of network architecture are proposed: Long Short-Term Memory (LSTM) in different multi-stage/multi-layer configurations in Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), injector multi-scale attention network (Injector MA-Net), and a combined architecture using both. The performance of these architectures will be compared in four situations: the loss of strain gauge rosette branches; the loss of a complete strain gauge rosette; the loss of data on a complete blade; and the absence of strain data, which is related to the problem of identifying which sensors could be used for long-term monitoring. The performance of the proposed algorithms will be evaluated on real case scenarios from a measurement campaign during a recent unit commissioning.

The measurement of stress/strain on blade plays an essential role in the fatigue assessment of the hydroelectric turbine runner. However, a typical measurement campaign does not cover all the possible operating conditions due to the field measurement requirements and limitations, which leads to fatigue evaluation missing for unmeasured conditions. In this paper, kriging interpolation method is used to estimate the missing fatigue information based on the available measured operating conditions of the turbine. The rainflow counting matrix, which contains the fatigue cycles of strain signal, is considered as the input of the interpolation process. For validation, the fatigue cycles are randomly generated, using bivariate Gaussian distributions, from the interpolated matrix for comparison with the experimental values under similar operating conditions. The developed approach will help plan experimental campaign and might contribute to decreasing the measurement requirements for runner fatigue assessment.

The purpose of this paper is to estimate hydroelectric turbine runner modal characteristics from experimental measurements coming from asynchronous regimes. This is achieved by investigating resonances generated by the interaction of a structural mode with harmonics of the rotating speed. Resonances are extracted using order tracking and processed with a Fast Bayesian algorithm in an ambient manner to estimate modal parameters and related uncertainties. Since data collection and effective processing of hydropower turbine field measurements is in its early stages, this paper lays some foundations in the treatment of transient regime measurements. A novelty in this approach lies in the use of a probabilistic identification tool in Order Based Modal Analysis (OBMA). A numerical experiment and a study from a hydroelectric Francis turbine field measurements are introduced to illustrate the method.

Cette étude s’intéresse à l’influence des symptômes du trouble de stress posttraumatique (TSPT) dans l’apparition de comportements sexuels problématiques (CSP) chez les enfants victimes d’abus sexuels âgés de 5 à 12 ans. L’échantillon se compose de 256 dyades mère-enfant, dont 173 dyades dans lesquelles les enfants ont une probabilité élevée d’avoir vécu un ou plusieurs abus sexuels et 83 dyades contrôles dans lesquelles les enfants n’ont pas été victime d’abus sexuel. Chaque groupe est évalué avec le Child Behavior Checklist (CBCL) et le Child Sexual Behavior Inventory (CSBI). Les résultats obtenus sont que les enfants victimes d’abus sexuels présentent significativement plus de symptômes du TSPT et développent davantage de CSP que les enfants n’ayant pas vécu d’abus sexuel. De plus, les enfants victimes d’abus sexuels présentant davantage de symptômes du TSPT développent davantage de CSP. Les résultats de cette étude serviront de pistes d’interventions futures afin de prévenir précocement l’apparition de CSP chez les enfants victimes d’abus sexuels.

International audience

Certain elicitation techniques exert some control on expert opinions by leading them to a consensus or to a specific choice. In the absence of such guidelines, experts rely on their own knowledge t...

The welded joints of hydraulic turbine runners have a higher failure probability than other runner regions due to the inevitable presence of welding flaws and dynamic stress concentrations. Welding processes are always associated with flaws caused by technical or metallurgical factors. In order to lower the failure probability, critical welding flaws should be detected and removed before the runner commissioning; otherwise, they will propagate under dynamic stresses and endanger the structure before it reaches its expected life. In such a case, the electricity production process should be halted and a mandatory unplanned maintenance should be applied. The production halt and the maintenance process (or runner replacement) impose high costs to the utility owner. A remedy to this is to have prior knowledge of potential welding flaws so as to select proper nondestructive testing (NDT) methods to detect and characterize them. In addition, better characterization of the potential flaws and their dimensions leads to more precise life estimations. In this study, different NDT methods are used on a series of T-joints designed to be representative in scale of real turbine runner joints. The outcomes of these methods are compared and used to characterize some of the detected indications. In order to confirm the NDT results, part of the joint containing a targeted indication has been extracted from the T-joints to undergo detailed evaluations. The objective is to provide detailed information on some common welding flaws in hydraulic turbine runners and to discuss the capability of different NDT methods used to characterize such flaws.

Spinal disinhibition has been hypothesized to underlie pain hypersensitivity in neuropathic pain. Apparently contradictory mechanisms have been reported, raising questions on the best target to produce analgesia. Here, we show that nerve injury is associated with a reduction in the number of inhibitory synapses in the spinal dorsal horn. Paradoxically, this is accompanied by a BDNF-TrkB-mediated upregulation of synaptic GABAARs and by an α1-to-α2GABAAR subunit switch, providing a mechanistic rationale for the analgesic action of the α2,3GABAAR benzodiazepine-site ligand L838,417 after nerve injury. Yet, we demonstrate that impaired Cl- extrusion underlies the failure of L838,417 to induce analgesia at high doses due to a resulting collapse in Cl- gradient, dramatically limiting the benzodiazepine therapeutic window. In turn, enhancing KCC2 activity not only potentiated L838,417-induced analgesia, it rescued its analgesic potential at high doses, revealing a novel strategy for analgesia in pathological pain, by combined targeting of the appropriate GABAAR-subtypes and restoring Cl- homeostasis. Disinhibition in the dorsal horn of the spinal cord may contribute to chronic pain. Here the authors show that, despite a paradoxical increase in α2/α3 subunits of the GABAA receptor in a neuropathic pain model, inhibition eventually fails due to KCC2 hypofunction.

Hydroelectric turbine start-up protocols have a significant influence on the fatigue reliability of such asset. Fatigue cracking, is one of the two main degradation mechanisms and often considered the most difficult to predict. Using fatigue reliability model previously developed by the authors, we study the influence of changes in the start-up protocol. Two units are studied. For the first unit, two protocols are studied and for the second unit three protocols are considered. The goal of the study is to look at the expected monetary value (EMV) as a metric able to combine the expected probability of failure with the monetary consequence of such failure. Our results shows that such metric is suitable to identify which start-up protocol is the most advantageous and why?

Large utilities need to optimize investments to maintain their assets. For a utility like Hydro-Quebec (37 GW of installed power) an important part of those investments is used to maintain their hydroelectric facilities. To minimize the maintenance costs, technico-economic model are essentials. They allow to propagate the uncertainties associated with the degradation processes for a given component. Therefore, we developed two technico-economic models for Francis hydroelectric turbine runners: one for crack propagation and one for cavitation. Since these are the main degradation mechanisms leading to failure of Francis runners, they enable us to study the effect of maintenance strategies on the maintenance cost. The models have been created using VME, an asset management software developed by EDF R&D (Electricite de France). VME uses Monte-Carlo simulations to generate stochastic failure dates and obtains probabilistic indicators of the net present value of a given management strategy. Using data based on a Hydro-Quebec (Quebec, Canada) facility, we illustrate the importance of the proper assessment of current and expected long-term reliability on maintenance cost.

International audience

International audience; Reaction turbines, of which Francis turbines, constitute a large proportion of low and medium head turbines installed in hydropower plants. Managing these machines represents a real challenge in terms of efficiency, competitiveness and demands on the energy market. Turbines runner blades exhibit loss of performance from damage due to several reasons. One common source of damage is erosion due to the cavitation phenomenon. Indeed, at a given operating region, rapid changes of velocity can create bubbles in the water flow due to local low pressures. When cavitation bubbles reach pressure recovery, they collapse and may induce wear or erosion in these regions. Even if this phenomenon has been intensively studied in the past decades, cavitation erosion is not fully understood as it is driven by several parameters such as flow dynamic, turbine design, environment, or material properties. Some of these parameters can be studied in laboratory to compare materials resistance between each other. This article aims to model the cavitation by a stochastic model using erosion experimental data observed in the laboratory. The benefit of such models is to consider both the uncertainties and natural fluctuations of the phenomenon. With the proposed framework, the study will highlight the differences observed in cavitation erosion experiments of two common materials used to manufacture Francis's runners. This study is the first step in a project aiming at the prediction of turbines mass loss due to cavitation erosion on actual operating Francis turbines.

This paper focuses on the analysis of dynamic pressure fluctuations in hydro-electric power plants. Pressure fluctuations are usually measured with remote sensors and the dynamic behaviour of the remote tubing line modifies the readings of dynamic pressure and thus introduces a bias. A method to characterize the remote tubing line and to correct the actual pressure is proposed. The corrected measurements are compared with those obtained using intrusive flush-mounted sensors as well as non-intrusive sensors (PVDF wires and strain gauges). Then, the technique to separate the forward and backward components of the obtained pressure pulsations is outlined. It is further shown how the pulsations and the hoop stresses can be reconstructed along the penstock. Finally, measurements carried out in a hydropower plant are presented to demonstrate the proposed techniques applicability for the analysis of dynamic pressure in a penstock. The results show that a pressure profile can be mapped along the penstock length which allows a comparison with the direct pressure sensor measurements.

Experimental characterization of structures provides information about the real dynamic behavior of machineries. This results in a better estimation of dynamic stress levels, used to predict lifespan, reliability and optimal operating ranges. Experimental results can also be used to improve and calibrate simulations and calculations. In particular, calculation of shaft bending frequencies is needed to define the critical rotation speed. However, natural frequency computation requires a prior knowledge on many subcomponent properties, that are not precisely known in practice. This leads to a great uncertainty on the predicted critical speed. The purpose of this study is to use experimental in-situ measurements as a means to reduce uncertainties on physical properties of the shaft line and increase confidence in the prediction of natural frequencies and critical speed. This involves ambient modal analysis, calibration techniques and statistical approaches to gain insight on the true shaft physical properties.

The large operation range of hydroelectric turbine, caused by the increasing changes in the electrical network usage, leads to higher dynamic stress fluctuation on runner. However, the experimental data cannot cover all the possible operating conditions. The missing data in some turbine operation zones generates a challenge for the recovery of the highest dynamic strain. This paper proposes a methodology for the interpolation of such strains between operating conditions by using the kriging method. The case study focuses on the interpolation of the part load rope which is an important component in the dynamic strain of a Francis turbine runner. Two interpolation approaches, inspired by spatio-temporal kriging and cokriging, are applied and compared. Finally, some suggestions are proposed to improve the recovery of operating conditions using interpolation process.

Reply to The small molecule CLP257 does not modify activity of the K + –Cl − co-transporter KCC2 but does potentiate GABA A receptor activity

Large utilities need to optimize the investment made to maintain their assets. For a utility like Hydro-Québec (37 GW) an important part of those investments are made to maintain their hydroelectric facilities. To minimize the maintenance cost, technico-economic model enabling the propagation of uncertainty associated with the degradation processes of a given component seems essential. Therefore, for Francis hydroelectric turbine runners, we developed two technico-economic models: one for crack propagation and one for cavitation. Since these are the main degradation mechanisms leading to failure of Francis runners, they enable us to study the effect of maintenance strategies on the maintenance cost of these components. The model has been created using VME, an asset management software developed by EDF R&D (Électricité de France). VME uses Monte-Carlo simulations to generate stochastic failure dates and obtains probabilistic indicators of the net present value of a given management strategy. We will use a study case based on a Hydro-Québec (Québec, Canada) facility to illustrate the importance of the proper assessment of current and expected long-term reliability on maintenance cost.The paper will be structured as follows. First, an overview of the modelling strategy will be presented. Then, we will have a closer look on how VME, the tool used for Monte-Carlo simulations, derive its results. Finally, we will present a study case and discuss the results obtained in terms of the sensitivity to the reliability assessment uncertainties.

Spinal disinhibition has been hypothesized to underlie pain hypersensitivity in neuropathic pain. Apparently contradictory mechanisms have been reported, raising questions on the best target to produce analgesia. Here, we show that nerve injury is associated with a reduction in the number of inhibitory synapses in the spinal dorsal horn. Paradoxically, this is accompanied by a BDNF-TrkB-mediated upregulation of synaptic GABAARs and by an α1-to-α2GABAAR subunit switch, providing a mechanistic rationale for the analgesic action of the α2,3GABAAR benzodiazepine-site ligand L838,417 after nerve injury. Yet, we demonstrate that impaired Cl- extrusion underlies the failure of L838,417 to induce analgesia at high doses due to a resulting collapse in Cl- gradient, dramatically limiting the benzodiazepine therapeutic window. In turn, enhancing KCC2 activity not only potentiated L838,417-induced analgesia, it rescued its analgesic potential at high doses, revealing a novel strategy for analgesia in pathological pain, by combined targeting of the appropriate GABAAR-subtypes and restoring Cl- homeostasis., Disinhibition in the dorsal horn of the spinal cord may contribute to chronic pain. Here the authors show that, despite a paradoxical increase in α2/α3 subunits of the GABAA receptor in a neuropathic pain model, inhibition eventually fails due to KCC2 hypofunction.

The failure of hydraulic turbine runners is a rare event. So in order to assess the reliability of these components one cannot rely solely on the number of observed failures in a given population. However, as there is a limited number of degradation mechanisms involved, it is possible to use physically-based reliability models. Such models are often more complicated but are able to account for physical parameters in the degradation process. They can therefore help provide solutions to improve reliability. With such models, the effect of materials properties on runner reliability can be highlighted. This paper presents a brief review of the Kitagawa-Takahashi diagram which links the damage tolerance approach, based on fracture mechanics, to the stress or strainlife approaches. Using simplified response spectra based on runner stress measurements, we will show how fatigue reliability is sensitive to materials fatigue properties, namely fatigue crack propagation behaviour and fatigue limit obtained on S-N curves. Furthermore, we will review the influence of the main microstructural features observed in 13%Cr-4%Ni stainless steels commonly used for runner manufacturing. The goal is ultimately to identify the most influential microstructural features and to quantify their effect on fatigue reliability of runners.