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Until recently frequency-domain subspace algorithms were limited to identify deterministic models from input/output measurements. In this paper, a combined deterministic-stochastic frequency-domain subspace algorithm is presented to estimate models from input/output spectra, frequency response functions or power spectra for application as experimental and operational modal analysis. The relation with time-domain subspace identification is elaborated. It is shown by both simulations and real-life test examples that the presented method outperforms traditional frequency-domain subspace methods.

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The Ostwald ripening rate of several alkane in water emulsions stabilized by a nonionic surfactant is determined from dynamic light scattering (DLS) measurements. With the aid of computer simulations, the intensity weighted droplet radii obtained with DLS are converted to number averages, by taking the form of the droplet size distributionwhich evolves continuously toward a stationary distributioninto account. Thereby the effect of the transition from an initial, log-normal size distribution toward its stationary form is included. Second a model is proposed to account for the effect of the finite size of the surfactant layer (surrounding each oil droplet) on the measured particle size and thus on the ripening rate. It is found that both the effect of the transition from a nonstationary regime toward the stationary Lifshitz−Slyozov−Wagner regime and the effect of the finite size of the surfactant layer influence the ripening rates significantly.

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