Development of a real-time latching control algorithm based on wave force prediction

Optimal wave energy control is noncausal as the control command is optimized based on incoming wave force. Therefore, implementation of wave energy control requires forecasting of future wave force. A real-time latching control algorithm based on short-term wave force prediction is developed in this study to tackle such noncausality. The future wave forces are forecasted using a gray model. The receding horizon strategy is used to optimize the control command online, over the prediction horizon interval. Based on the predicted wave forces, the power extraction is maximized by locking and releasing the buoy alternately according to the optimized control command. Simulation results show that the power extraction is increased substantially with implementation of the developed real-time latching control algorithm, even if the future wave forces are predicted. Effects of prediction length and prediction error on the energy conversion are examined. It is found that more wave energy is harvested when a long prediction length is employed while prediction error decreases the control efficiency. The extreme load of power takeoff system increases when the wave energy control is implemented although its travel distance is hardly varied

Wave force prediction effect on the energy absorption of a wave energy converter with real-time control

Real-time control has been widely adopted to enlarge the energy extraction of a wave energy converter (WEC). In order to implement a real-time control, it is necessary to predict the wave excitation forces in the close future. In many previous studies, the wave forces over the prediction horizon were assumed to be already known, while the wave force prediction effect has been hardly examined. In this paper, we investigate the effect of wave force prediction on the energy absorption of a heaving point-absorber WEC with real-time latching control. The real-time control strategy is based on the combination of optimal command theory and first order-one variable grey model GM(1,1). It is shown that a long prediction horizon is beneficial to the energy absorption whereas the prediction deviation reduces extracting efficiency of the WEC. Further analysis indicates that deviation of wave force amplitude has little influence on the WEC performance. It is the phase deviation that leads to energy loss. Since the prediction deviation accumulates over the horizon, a moderate horizon is thus recommended

Congested Traffic States in Empirical Observations and Microscopic Simulations

We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber, Phys. Rev. Lett. {\bf 82}, 4360 (1999)]. We simulate these situations with a novel continuous microscopic single-lane model, the ``intelligent driver model'' (IDM), using the empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has practically the same effect as an on-ramp.Comment: Now published in Phys. Rev. E. Minor changes suggested by a referee are incorporated; full bibliographic info added. For related work see http://www.mtreiber.de/ and http://www.helbing.org

Noise Induced Phenomena in the Dynamics of Two Competing Species

Noise through its interaction with the nonlinearity of the living systems can give rise to counter-intuitive phenomena. In this paper we shortly review noise induced effects in different ecosystems, in which two populations compete for the same resources. We also present new results on spatial patterns of two populations, while modeling real distributions of anchovies and sardines. The transient dynamics of these ecosystems are analyzed through generalized Lotka-Volterra equations in the presence of multiplicative noise, which models the interaction between the species and the environment. We find noise induced phenomena such as quasi-deterministic oscillations, stochastic resonance, noise delayed extinction, and noise induced pattern formation. In addition, our theoretical results are validated with experimental findings. Specifically the results, obtained by a coupled map lattice model, well reproduce the spatial distributions of anchovies and sardines, observed in a marine ecosystem. Moreover, the experimental dynamical behavior of two competing bacterial populations in a meat product and the probability distribution at long times of one of them are well reproduced by a stochastic microbial predictive model.Comment: 23 pages, 8 figures; to be published in Math. Model. Nat. Phenom. (2016

Oscillating epidemics in a dynamic network model: stochastic and mean-field analysis

An adaptive network model using SIS epidemic propagation with link-type-dependent link activation and deletion is considered. Bifurcation analysis of the pairwise ODE approximation and the network-based stochastic simulation is carried out, showing that three typical behaviours may occur; namely, oscillations can be observed besides disease-free or endemic steady states. The oscillatory behaviour in the stochastic simulations is studied using Fourier analysis, as well as through analysing the exact master equations of the stochastic model. By going beyond simply comparing simulation results to mean-field models, our approach yields deeper insights into the observed phenomena and help better understand and map out the limitations of mean-field models

A water level relationship between consecutive gauge stations along Solim\~oes/Amazonas main channel: a wavelet approach

Gauge stations are distributed along the Solim\~oes/Amazonas main channel to monitor water level changes over time. Those measurements help quantify both the water movement and its variability from one gauge station to the next downstream. The objective of this study is to detect changes in the water level relationship between consecutive gauge stations along the Solim\~oes/Amazonas main channel, since 1980. To carry out the analyses, data spanning from 1980 to 2010 from three consecutive gauges (Tefe, Manaus and Obidos) were used to compute standardized daily anomalies. In particular for infra-annual periods it was possible to detect changes for the water level variability along the Solim\~oes/Amazonas main channel, by applying the Morlet Wavelet Transformation (WT) and Wavelet Cross Coherence (WCC) methods. It was possible to quantify the waves amplitude for the WT infra-annual scaled-period and were quite similar to the three gauge stations denoting that the water level variability are related to the same hydrological forcing functions. Changes in the WCC was detected for the Manaus-Obidos river stretch and this characteristic might be associated with land cover changes in the floodplains. The next steps of this research, will be to test this hypotheses by integrating land cover changes into the floodplain with hydrological modelling simulations throughout the time-series

Extinction in neutrally stable stochastic Lotka-Volterra models

Populations of competing biological species exhibit a fascinating interplay between the nonlinear dynamics of evolutionary selection forces and random fluctuations arising from the stochastic nature of the interactions. The processes leading to extinction of species, whose understanding is a key component in the study of evolution and biodiversity, are influenced by both of these factors. In this paper, we investigate a class of stochastic population dynamics models based on generalized Lotka-Volterra systems. In the case of neutral stability of the underlying deterministic model, the impact of intrinsic noise on the survival of species is dramatic: it destroys coexistence of interacting species on a time scale proportional to the population size. We introduce a new method based on stochastic averaging which allows one to understand this extinction process quantitatively by reduction to a lower-dimensional effective dynamics. This is performed analytically for two highly symmetrical models and can be generalized numerically to more complex situations. The extinction probability distributions and other quantities of interest we obtain show excellent agreement with simulations.Comment: 14 pages, 7 figure

Variations in mid-ocean ridge CO2 emissions driven by glacial cycles

The geological record shows links between glacial cycles and volcanic productivity, both subaerially and at mid-ocean ridges. Sea-level-driven pressure changes could also affect chemical properties of mid-ocean ridge volcanism. We consider how changing sea-level could alter the CO2 emissions rate from mid-ocean ridges, on both the segment and global scale. We develop a simplified transport model for a highly incompatible element through a homogenous mantle; variations in the melt concentration the emission rate of the element are created by changes in the depth of first silicate melting. The model predicts an average global mid-ocean ridge CO2 emissions-rate of 53 Mt/yr, in line with other estimates. We show that falling sea level would cause an increase in ridge CO2 emissions with a lag of about 100 kyrs after the causative sea level change. The lag and amplitude of the response are sensitive to mantle permeability and plate spreading rate. For a reconstructed sea-level time series of the past million years, we predict variations of up to 12% (7 Mt/yr) in global mid-ocean ridge CO2 emissions. The magnitude and timing of the predicted variations in CO2 emissions suggests a potential role for ridge carbon emissions in glacial cycles