Review on erosion phenomenon, maintenance, and financial calculation of lifetime as an asset for Pelton turbines

Prevention of greenhouse emissionsis the top priority for all countries, which urges them to switch to renewable energy as much as possible. Hydropower is one of the renewables that have high flexibility and at the same time compatibility to be used with any other renewable sources. Moreover, hydropower plants operating in the Himalayas, Andes, and Alps are facing operational challenges due to the high concentration of sediment loads in rivers. Although the arrangement of traditional sediment control mechanisms like dams and sand traps, the erosion tendency of hydroturbine components operating in this sediment-laden water increases with the increased concentration of sediments. Much past research has been directed towards understanding sediment behaviors, investigation of flow, and effect of concentration, shape, and size, especially with Francis turbines. However, there are very fewer studies regarding sediment erosion and flow behavior in the case of the Pelton turbine. Hence, delving deeper into the flow characteristics, sediment behavior, and performance of the Pelton turbine is important to better understand the flow and sediment pattern of these types of turbines. The paper consists of the evaluation of studies conducted on the flow pattern in the Pelton turbine buckets and its validation with the numerical analysis models using image processing. It is being used in the Waterpower Laboratory at the Norwegian University of Science and Technology, NTNU. This paper also evaluates the scope of investigations about erosion by sediments in Pelton buckets using image analysis and state-of-the-art technology in the hydropower sector. In addition, a review is done about the predictability of erosion based on the measurements of the quantity of sediments that passes through the turbine. This research paper can build a background for quantifying sediment erosion in Pelton turbines with a certain degree of error, which can be utilized as a reference in future studies. The life cycle estimation of a turbine is also analyzed with the consideration of its location and financial return requirements together with the type of maintenance that it may have and the repair that is foreseen, in the case of a non-coated surface. Erosion, Flow behavior, Image analysis, Pelton turbine, Sediment particlespublishedVersio

Decomposition of the structural response of the Francis-99 runner during resonance

In this work, the complex structural response of the Francis-99 turbine runner is further investigated by decomposition of the output signal from previous Laser Doppler Vibrometry (LDV) measurements into the motion of each nodal diameter. During the structural measurements, the non-rotating runner was installed in the turbine pit, submerged in a non-flowing water, and excited with piezoelectric patches mounted on the hub. The patches were excited with phase shifted sinusoidal voltage to create overall excitation of the runner with a desired number of nodal diameters. The deflection of selected locations on the trailing edges were scanned with LDV, one point at a time, and the global movement was reconstructed by combining the data for all points. The Francis-99 runner has its blades bolted to an over-dimensioned hub and shroud, where the hub is not fully axi-symmetrical and has several hollows in it. This, together with the fact that one patch was found to be non-functional, is believed to have excited other ND patterns in addition to the one that was intentionally excited, therefore contaminating the movement of the trailing edges with movements that does not belongs to the excited ND. To mitigate this and create a better representation of the movement of the trailing edge, which is not affected by the bleed from other ND, the LDV signal for each excited frequency of a particular ND is post-processed using discreet Fourier transformation to decompose the motion of each nodal diameter in the range ND0 to ND7. This unveils the contribution of each nodal diameter within the output signal where a spike is seen for the excited ND in all measurements. Influence from other nodal diameters were found, where the failed patched is believed to cause a ND1 like movement. In addition the clustering of multiple eigenmodes with differing nodal diameters previously found in narrow frequency bands were also found as interfering contribution when exciting at the relevant frequencies. © Published under licence by IOP Publishing Ltd.Decomposition of the structural response of the Francis-99 runner during resonancepublishedVersio

Coherent energy and force uncertainty in deep learning force fields

In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quantify aleatoric uncertainty in the predicted energies, a widely used modeling approach involves predicting both a mean and variance for each energy value. However, this model is not differentiable under the usual white noise assumption, so energy uncertainty does not naturally translate to force uncertainty. In this work we propose a machine learning potential energy model in which energy and force aleatoric uncertainty are linked through a spatially correlated noise process. We demonstrate our approach on an equivariant messages passing neural network potential trained on energies and forces on two out-of-equilibrium molecular datasets. Furthermore, we also show how to obtain epistemic uncertainties in this setting based on a Bayesian interpretation of deep ensemble models.Comment: Presented at Advancing Molecular Machine Learning - Overcoming Limitations [ML4Molecules], ELLIS workshop, VIRTUAL, December 8, 2023, unofficial NeurIPS 2023 side-even

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces

Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith et al.) and Transition1x (Schreiner et al.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials

Similar impairments shown on a neuropsychological test battery in adolescents with high-functioning autism and early onset schizophrenia: A two-year follow-up study

Introduction: Cognitive impairments are common in both Autism Spectrum Disorders (ASD) and schizophrenia, but it is unclear whether the pattern of difficulties is similar or different in the two disorders. This cross-sectional and longitudinal study compared the neuropsychological functioning in adolescents with ASD with adolescents with Early Onset Schizophrenia (EOS). Methods: At baseline and at two-year follow-up, participants were assessed with a brief neuropsychological test battery measuring executive functions, visual and verbal learning, delayed recall and recognition and psychomotor speed. Results: We found similar levels of neuropsychological impairment across groups and over time in the adolescents with ASD or EOS. Adolescents in both groups did not improve significantly on verbal learning, verbal delayed recall, visual learning, visual delayed recall or visual delayed recognition, and both groups performed poorer on verbal recognition. Both groups improved on measures of psychomotor processing and executive functions. Conclusion: The findings suggest that it may be difficult to differentiate adolescents with EOS and ASD based on neuropsychological task performance. An implication of the results is that adolescents with either disorder may benefit from a similar approach to the treatment of cognitive impairment in the disorders.acceptedVersio

Retinoic acid has different effects on UCP1 expression in mouse and human adipocytes

BACKGROUND: Increased adipose thermogenesis is being considered as a strategy aimed at preventing or reversing obesity. Thus, regulation of the uncoupling protein 1 (UCP1) gene in human adipocytes is of significant interest. Retinoic acid (RA), the carboxylic acid form of vitamin A, displays agonist activity toward several nuclear hormone receptors, including RA receptors (RARs) and peroxisome proliferator-activated receptor δ (PPARδ). Moreover, RA is a potent positive regulator of UCP1 expression in mouse adipocytes. RESULTS: The effects of all-trans RA (ATRA) on UCP1 gene expression in models of mouse and human adipocyte differentiation were investigated. ATRA induced UCP1 expression in all mouse white and brown adipocytes, but inhibited or had no effect on UCP1 expression in human adipocyte cell lines and primary human white adipocytes. Experiments with various RAR agonists and a RAR antagonist in mouse cells demonstrated that the stimulatory effect of ATRA on UCP1 gene expression was indeed mediated by RARs. Consistently, a PPARδ agonist was without effect. Moreover, the ATRA-mediated induction of UCP1 expression in mouse adipocytes was independent of PPARγ coactivator-1α. CONCLUSIONS: UCP1 expression is differently affected by ATRA in mouse and human adipocytes. ATRA induces UCP1 expression in mouse adipocytes through activation of RARs, whereas expression of UCP1 in human adipocytes is not increased by exposure to ATRA