Identifying Self-excited Vibrations with Evolutionary Computing

AbstractThis study uses differential evolution to identify the coeffic ients of second-order differentia l equations of self-e xc ited vibrations fro m a time signal. The motivation is found in the ample occurrence of this vibration type in engineering and physics, in particu lar in the real -life proble m of v ibrations of hydraulic structure gates. In the proposed method, an equation structure is assumed at the level of the ordinary differentia l equation and a population of candidate coefficient vectors undergoes evolutionary training. In this way the numerical constants of non-linear terms of various self-e xc ited vibration types were recovered fro m the time signal and the velocity value only at the initial t ime. Co mparisons are given regarding accuracy and computing time. Dependency of the test errors on the algorith m para meters is studied in a sensitivity analysis. The presented evolutionary method shows good promise for future applicat ion in engineering systems, in particular operational early -wa rning systems that recognise oscillations with negative damping before they can cause damage

NCR-days 2008 : 10 years NCR: November 20-21

De verschillende subthema’s van de NCR-dagen 2008, (i) Stroomgebied en Overstromingsrisico management (ii) Hydrologie en (iii) Geomorfodynamica en Morfologie, dekken een groot gedeelte van het hedendaagse onderzoek dat in Nederland op rivierkundig gebied wordt uitgevoerd

How to Speed up Optimization? Opposite-Center Learning and Its Application to Differential Evolution

This paper introduces a new sampling technique called Opposite-Center Learning (OCL) intended for convergence speed-up of meta-heuristic optimization algorithms. It comprises an extension of Opposition-Based Learning (OBL), a simple scheme that manages to boost numerous optimization methods by considering the opposite points of candidate solutions. In contrast to OBL, OCL has a theoretical foundation-the opposite center point is defined as the optimal choice in pair-wise sampling of the search space given a random starting point. A concise analytical background is provided. Computationally the opposite center point is approximated by a lightweight Monte Carlo scheme for arbitrary dimension. Empirical results up to dimension 20 confirm that OCL outperforms OBL and random sampling: the points generated by OCL have shorter expected distances to a uniformly distributed global optimum. To further test its practical performance, OCL is applied to differential evolution (DE). This novel scheme for continuous optimization named Opposite-Center DE (OCDE) employs OCL for population initialization and generation jumping. Numerical experiments on a set of benchmark functions for dimensions 10 and 30 reveal that OCDE on average improves the convergence rates by 38% and 27% compared to the original DE and the Opposition-based DE (ODE), respectively, while remaining fully robust. Most promising are the observations that the accelerations shown by OCDE and OCL increase with problem dimensionality

Landscape change and biodiversity values of floodplains along the River Vistula, Poland.

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Modelling flow-induced vibrations of gates in hydraulic structures

The dynamic behaviour of gates in hydraulic structures caused by passing flow poses a potential threat to flood protection. Complex interactions between the turbulent flow and the suspended gate body may induce undesired vibrations. This thesis contributes to a better understanding and prevention of gate vibrations by employing a variety of computational approaches. Simulations with the finite-element method are used to analyse the fluid-structure interaction of a new underflow gate type which exploits leakage flow to temper the excitation. A physical model experiment of the same configuration confirms this beneficial effect for a wider range of conditions. Furthermore, an outline of a control system is given that is based on data from acceleration sensors installed on the gates. It is shown how this system can be trained to classify future states and thus support operational decisions that avoid critical vibrations. Moreover, evolutionary computing is explored as a system identification tool for dynamical systems. The differential evolution algorithm was applied to recover the coefficients of several non-linear motion equations of self-excited oscillations from time signals. The collective results from these different methods help to eliminate problems related to flow-induced gate vibrations and so increase water safety

Particle Tracking in a Shallow Mixing Layer: A Fluid Dynamics Laboratory in the Field

At locations where two natural streams of different velocity come together, a mixing layer develops. In this free shear flow, the velocity difference between the two streams is gradually reduced through the exchange of lateral momentum. This involves different forms of turbulent phenomena. If the width of the flow domain is large compared to the water depth, as is often the case in rivers, the mixing layer is shallow. This shallowness further complicates the flow patterns. In this thesis the development of shallow mixing layers is studied using Particle Tracking in a field study in a natural river. The aim is to improve our understanding of shallow mixing layers and hence to contribute to improvements of computational models of these flows. Field measurements were performed in a lowland section of the river Spree (width 30m, depth 1m) near Berlin, in collaboration with the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB). A 30m long splitter plate was constructed in the middle of the river, parallel to the banks, with an upstream weir on one side to control the discharge. A large shear layer developed with noticeable coherent structures. Three setups were created with varying velocity differences across the splitter. Both single point measurements (Acoustic Doppler Velocimetry, ADV) and whole field measurements (Particle Tracking Velocimetry, PTV) were done. The Particle Tracking consisted of the interval release of floating particles in the beginning of the mixing layer, from the end of the splitter. These measurements were done at night using small tea candle lights, providing sufficient contrast with the water surface. Three runs were filmed from a camera fixed in a tree at 10m above the water surface. A special point of interest in the data processing, which was done using custom-made Matlab programming, was the perspective transformation of the camera images. The analysis of the Particle Tracking data focused on: - mean velocity profiles in the horizontal plane - visualisation of particle streaklines - Lagrangian single particle and two-particle statistics Using Particle Tracking as a whole-field velocimetry tool proved difficult. The particle density was too low and the area of interest was not completely covered. Nevertheless, the characteristic tangential mean velocity profiles were found. The streakline plots proved the existence of large two-dimensional coherent structures at the low-velocity edge of the mixing layer in the two setups with the highest shear. The use of Lagrangian statistics proved a particularly valuable tool for acquiring information on particle dispersion. Moreover the Lagrangian analysis matched well with the Particle Tracking data. It yielded information on the spreading rate of the particle cloud and the ensemble averaged separation in time of initially close particle pairs. Most notably, the result of the latter was identification of two distinct separating regimes using Batchelor time scaling. Lastly, the ADV analysis, performed by IGB in Berlin, showed that the advective stresses of the horizontal velocities were an order of magnitude higher than the plane Reynolds stresses. The ADV analysis, which is not completed yet, will help to gain insight into the role of the composite bed friction on the lateral momentum exchange; thus complementing previous laboratory research at TU Delft.Civil Engineering and Geoscience