Data-driven generation of synthetic wind speeds: a comparative study

The increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.This work was partly supported by the Research Foundation Flanders (FWO) [grant number 74213/K231719N]

Innovative system identification methods for monitoring applications

Monitoring the modal parameters of civil and mechanical system received plenty of interest the last decades. Several approaches have been proposed and successfully applied in civil engineering for structural health monitoring of bridges (mainly based on the monitoring of the resonant frequencies and mode shapes). In applications such as the monitoring of offshore wind turbines and flight flutter testing the monitoring of the damping ratios are essential. For offshore wind turbine monitoring the presence of time-varying harmonic components, close to the modes of interest, can complicate the identification process. The difficulty related to flight flutter testing is that, in general, only short data records are available. The aim of this contribution is to introduce system identification methods and monitoring strategies that result in more reliable decisions and that can cope with complex monitoring applications. Basic concepts of system identification will be recapitulated with attention for monitoring aspects. The proposed monitoring methodology is based on the recently introduced Transmissibility-based Operational Modal Analysis (TOMA) approach

Quantitative Determination of Temperature in the Approach to Magnetic Order of Ultracold Fermions in an Optical Lattice

We perform a quantitative simulation of the repulsive Fermi-Hubbard model using an ultracold gas trapped in an optical lattice. The entropy of the system is determined by comparing accurate measurements of the equilibrium double occupancy with theoretical calculations over a wide range of parameters. We demonstrate the applicability of both high-temperature series and dynamical mean-field theory to obtain quantitative agreement with the experimental data. The reliability of the entropy determination is confirmed by a comprehensive analysis of all systematic errors. In the center of the Mott insulating cloud we obtain an entropy per atom as low as 0.77k(B) which is about twice as large as the entropy at the Neel transition. The corresponding temperature depends on the atom number and for small fillings reaches values on the order of the tunneling energy

Quantum Monte Carlo Loop Algorithm for the t-J Model

We propose a generalization of the Quantum Monte Carlo loop algorithm to the t-J model by a mapping to three coupled six-vertex models. The autocorrelation times are reduced by orders of magnitude compared to the conventional local algorithms. The method is completely ergodic and can be formulated directly in continuous time. We introduce improved estimators for simulations with a local sign problem. Some first results of finite temperature simulations are presented for a t-J chain, a frustrated Heisenberg chain, and t-J ladder models.Comment: 22 pages, including 12 figures. RevTex v3.0, uses psf.te

Diffusion in the Continuous-Imaginary-Time Quantum World-Line Monte Carlo Simulations with Extended Ensembles

The dynamics of samples in the continuous-imaginary-time quantum world-line Monte Carlo simulations with extended ensembles are investigated. In the case of a conventional flat ensemble on the one-dimensional quantum S=1 bi-quadratic model, the asymmetric behavior of Monte Carlo samples appears in the diffusion process in the space of the number of vertices. We prove that a local diffusivity is asymptotically proportional to the number of vertices, and we demonstrate the asymmetric behavior in the flat ensemble case. On the basis of the asymptotic form, we propose the weight of an optimal ensemble as $1/\sqrt{n}$, where $n$ denotes the number of vertices in a sample. It is shown that the asymmetric behavior completely vanishes in the case of the proposed ensemble on the one-dimensional quantum S=1 bi-quadratic model.Comment: 4 pages, 2 figures, update a referenc

Properties and Detection of Spin Nematic Order in Strongly Correlated Electron Systems

A spin nematic is a state which breaks spin SU(2) symmetry while preserving translational and time reversal symmetries. Spin nematic order can arise naturally from charge fluctuations of a spin stripe state. Focusing on the possible existence of such a state in strongly correlated electron systems, we build a nematic wave function starting from a t-J type model. The nematic is a spin-two operator, and therefore does not couple directly to neutrons. However, we show that neutron scattering and Knight shift experiments can detect the spin anisotropy of electrons moving in a nematic background. We find the mean field phase diagram for the nematic taking into account spin-orbit effects.Comment: 13 pages, 11 figures. (v2) References adde

Universal Statistical Behavior of Neural Spike Trains

We construct a model that predicts the statistical properties of spike trains generated by a sensory neuron. The model describes the combined effects of the neuron's intrinsic properties, the noise in the surrounding, and the external driving stimulus. We show that the spike trains exhibit universal statistical behavior over short times, modulated by a strongly stimulus-dependent behavior over long times. These predictions are confirmed in experiments on H1, a motion-sensitive neuron in the fly visual system.Comment: 7 pages, 4 figure

Localized spin ordering in Kondo lattice models

Using a non-Abelian density matrix renormalization group method we determine the phase diagram of the Kondo lattice model in one dimension, by directly measuring the magnetization of the ground-state. This allowed us to discover a second ferromagnetic phase missed in previous approaches. The phase transitions are found to be continuous. The spin-spin correlation function is studied in detail, and we determine in which regions the large and small Fermi surfaces dominate. The importance of double-exchange ordering and its competition with Kondo singlet formation is emphasized in understanding the complexity of the model.Comment: Revtex, 4 pages, 4 eps figures embedde

Note on the thermodynamic Bethe Ansatz approach to the quantum phase diagram of the strong coupling ladder compounds

We investigate the low-temperature phase diagram of the exactly solved su(4) two-leg spin ladder as a function of the rung coupling $J_{\perp}$ and magnetic field $H$ by means of the thermodynamic Bethe Ansatz (TBA). In the absence of a magnetic field the model exhibits three quantum phases, while in the presence of a strong magnetic field there is no singlet ground state for ferromagnetic rung coupling. For antiferromagnetic rung coupling, there is a gapped phase in the regime H H_{c2} and a Luttinger liquid magnetic phase in the regime H_{c1} < H < H_{c2}. The critical behaviour derived using the TBA is consistent with the existing experimental, numerical and perturbative results for the strong coupling ladder compounds. This includes the spin excitation gap and the critical fields H_{c1} and H_{c2}, which are in excellent agreement with the experimental values for the known strong coupling ladder compounds (5IAP)_2CuBr_4 2H_2 O, Cu_2(C_5 H_{12} N_2)_2 Cl_4 and (C_5 H_{12} N)_2 CuBr_4. In addition we predict the spin gap $\Delta \approx J_{\perp}-{1/2}J_{\parallel}$ for the weak coupling compounds with $J_{\perp} \sim J_{\parallel}$, such as (VO)_2 P_2 O_7, and also show that the gap opens for arbitrary $J_{\perp}/ J_{\parallel}$.Comment: 10 pages, 3 figure