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Saturn's moon Enceladus emits plumes of water vapour and ice particles from fractures near its south pole(1-5), suggesting the possibility of a subsurface ocean(5-7). These plume particles are the dominant source of Saturn's E ring(7,8). A previous in situ analysis(9) of these particles concluded that the minor organic or siliceous components, identified in many ice grains, could be evidence for interaction between Enceladus' rocky core and liquid water(9,10). It was not clear, however, whether the liquid is still present today or whether it has frozen. Here we report the identification of a population of E-ring grains that are rich in sodium salts (similar to 0.5- 2% by mass), which can arise only if the plumes originate from liquid water. The abundance of various salt components in these particles, as well as the inferred basic pH, exhibit a compelling similarity to the predicted composition of a subsurface Enceladus ocean in contact with its rock core(11). The plume vapour is expected to be free of atomic sodium. Thus, the absence of sodium from optical spectra(12) is in good agreement with our results. In the E ring the upper limit for spectroscopy(12) is insufficiently sensitive to detect the concentrations we found.
The discovery of volcanic activity on Enceladus stands out amongst the long list of findings by the Cassini mission to Saturn. In particular the compositional analysis of Enceladus ice particles by Cassini's Cosmic Dust Analyser (CDA) (Srama et al., 2004) has proven to be a powerful technique for obtaining information about processes below the moon's ice crust. Small amounts of sodium salts embedded in the particles' ice matrices provide direct evidence for a subsurface liquid water reservoir, which is, or has been, in contact with the moon's rocky core (Postberg et al., 2009, 2011b).
Jupiter's Galilean satellites Ganymede, Europa, and Callisto are also believed to have subsurface oceans and are therefore prime targets for future NASA and ESA outer Solar System missions. The Galilean moons are engulfed in tenuous dust clouds consisting of tiny pieces of the moons' surfaces (Kruger et al., 1999), released by hypervelocity impacts of micrometeoroids, which steadily bombard the surfaces of the moons. In situ chemical analysis of these grains by a high resolution dust spectrometer will provide spatially resolved mapping of the surface composition of Europa. Ganymede, and Callisto, meeting key scientific objectives of the planned missions. However, novel high-resolution reflectron-type dust mass spectrometers (Sternovsky et al., 2007; Srama et al., 2007) developed for dust astronomy missions (Gran et al., 2009) are probably not robust enough to be operated in the energetic radiation environment of the inner Jovian system. In contrast, CDA's linear spectrometer is much less affected by harsh radiation conditions because its ion detector is not directly facing out into space. The instrument has been continuously operated on Cassini for 11 years. In this paper we investigate the possibility of operating a CDA-like instrument as a high resolution impact mass spectrometer. We show that such an instrument is capable of reliably identifying traces of organic and inorganic materials in the ice matrix of ejecta expected to be generated from the surfaces of the Galilean moons. These measurements are complementary, and in some cases superior, compared to other traditional techniques such as infrared remote sensing or in situ ion or neutral mass spectrometers.
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Many previous studies have shown that the turbulent mixing layer under periodic forcing tends to adopt a lock-on state, where the major portion of the fluctuations in the flow are synchronized at the forcing frequency. The goal of this experimental study is to apply closed-loop control in order to provoke the lock-on state, using information from the flow itself. We aim to determine the range of frequencies for which the closed-loop control can establish the lock-on, and what mechanisms are contributing to the selection of a feedback frequency. In order to expand the solution space for optimal closed-loop control laws, we use the genetic programming control (CPC) framework. The best closed-loop control laws obtained by CPC are analysed along with the associated physical mechanisms in the mixing layer flow. The resulting closed-loop control significantly outperforms open-loop forcing in terms of robustness to changes in the free-stream velocities. In addition, the selection of feedback frequencies is not locked to the most amplified local mode, but rather a range of frequencies around it.
Mixing layer manipulation experiment from open-loop forcing to closed-loop machine learning control
(2015)
Many previous studies have shown that the turbulent mixing layer under periodic forcing tends to adopt a lock-on state, where the major portion of the fluctuations in the flow are synchronized at the forcing frequency. The goal of this experimental study is to apply closed-loop control in order to provoke the lock-on state, using information from the flow itself. We aim to determine the range of frequencies for which the closed-loop control can establish the lock-on, and what mechanisms are contributing to the selection of a feedback frequency. In order to expand the solution space for optimal closed-loop control laws, we use the genetic programming control (CPC) framework. The best closed-loop control laws obtained by CPC are analysed along with the associated physical mechanisms in the mixing layer flow. The resulting closed-loop control significantly outperforms open-loop forcing in terms of robustness to changes in the free-stream velocities. In addition, the selection of feedback frequencies is not locked to the most amplified local mode, but rather a range of frequencies around it.
We propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt, Gunzburger & Lee, Comput. Meth. Appl. Mech. Engng, vol. 196, 2006a, pp. 337-355) and transition matrix models introduced in fluid dynamics in Eckhardt's group (Schneider, Eckhardt & Vollmer, Phys. Rev. E, vol. 75, 2007, art. 066313). CROM constitutes a potential alternative to POD models and generalises the Ulam-Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron-Frobenius operator. The proposed strategy processes a time-resolved sequence of flow snapshots in two steps. First, the snapshot data are clustered into a small number of representative states, called centroids, in the state space. These centroids partition the state space in complementary non-overlapping regions (centroidal Voronoi cells). Departing from the standard algorithm, the probabilities of the clusters are determined, and the states are sorted by analysis of the transition matrix. Second, the transitions between the states are dynamically modelled using a Markov process. Physical mechanisms are then distilled by a refined analysis of the Markov process, e. g. using finite-time Lyapunov exponent (FTLE) and entropic methods. This CROM framework is applied to the Lorenz attractor (as illustrative example), to velocity fields of the spatially evolving incompressible mixing layer and the three-dimensional turbulent wake of a bluff body. For these examples, CROM is shown to identify non-trivial quasi-attractors and transition processes in an unsupervised manner. CROM has numerous potential applications for the systematic identification of physical mechanisms of complex dynamics, for comparison of flow evolution models, for the identification of precursors to desirable and undesirable events, and for flow control applications exploiting nonlinear actuation dynamics.