This is the pre-peer reviewed version of the following article:Vidal, Y.; Bonet, J.; Huerta, A. Stabilized updated Lagrangian corrected SPH for explicit dynamic problems. "International journal for numerical methods in engineering", Març 2007, vol. 69, núm. 13, p. 2687-2710, which has been published in final form at http://www3.interscience.wiley.com/journal/112777203/abstractSmooth Particle Hydrodynamics with a total Lagrangian formulation are, in general, more robust than nite elements for large distortion problems. Nevertheless, updating the reference con¯guration\ud may still be necessary in some problems involving extremely large distortions. However, as discussed here a standard updated formulation suffers the presence of zero energy modes that are activated\ud and may spoil completely the solution. It is important to note that, unlike an Eulerian formulation,the updated Lagrangian does not present tension instability but only zero energy modes. Here an stabilization technique is incorporated to the updated formulation to obtain an improved method\ud without mechanisms and capable to solve problems with extremely large distortions.Peer ReviewedPostprint (author’s final draft

This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data-the baseline and the data from the current wind turbine-are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.

Incompressible modelling in finite elements has been a major concern since its early developments and has been extensively studied. However, incompressibility in meshfree methods is still an open topic. Thus, instabilities or locking can preclude the use of mesh-free approximations in such problems. Here, a novel mesh-free formulation is proposed for incompressible flow. It is based on defining a pseudo-divergence-free interpolation space. That is, the finite dimensional interpolation space approaches a divergence-free space when the discretization is refined. Note that such an interpolation does not include any overhead in the computations. The numerical evaluations are performed using the inf-sup numerical test and two well-known benchmark examples for Stokes flow.

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly o↵shore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of o↵shore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to di↵erent faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variablespeed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5MW o↵shore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.