Turbulence attenuation by large neutrally buoyant particles

Turbulence modulation by inertial-range-size, neutrally-buoyant particles is investigated experimentally in a von K\'arm\'an flow. Increasing the particle volume fraction $\Phi_\mathrm{v}$, maintaining constant impellers Reynolds number attenuates the fluid turbulence. The inertial-range energy transfer rate decreases as $\propto\Phi_\mathrm{v}^{2/3}$, suggesting that only particles located on a surface affect the flow. Small-scale turbulent properties, such as structure functions or acceleration distribution, are unchanged. Finally, measurements hint at the existence of a transition between two different regimes occurring when the average distance between large particles is of the order of the thickness of their boundary layers.Comment: 7 pages, 4 figure

Optical spectroscopy and the nature of the insulating state of rare-earth nickelates

Using a combination of spectroscopic ellipsometry and DC transport measurements, we determine the temperature dependence of the optical conductivity of NdNiO$_3$ and SmNiO$_{3}$ films. The optical spectra show the appearance of a characteristic two-peak structure in the near-infrared when the material passes from the metal to the insulator phase. Dynamical mean-field theory calculations confirm this two-peak structure, and allow to identify these spectral changes and the associated changes in the electronic structure. We demonstrate that the insulating phase in these compounds and the associated characteristic two-peak structure are due to the combined effect of bond-disproportionation and Mott physics associated with half of the disproportionated sites. We also provide insights into the structure of excited states above the gap.Comment: 12 pages, 13 figure

Lagrangian temperature, velocity and local heat flux measurement in Rayleigh-Benard convection

We have developed a small, neutrally buoyant, wireless temperature sensor. Using a camera for optical tracking, we obtain simultaneous measurements of position and temperature of the sensor as it is carried along by the flow in Rayleigh-B\'enard convection, at $Ra \sim 10^{10}$. We report on statistics of temperature, velocity, and heat transport in turbulent thermal convection. The motion of the sensor particle exhibits dynamics close to that of Lagrangian tracers in hydrodynamic turbulence. We also quantify heat transport in plumes, revealing self-similarity and extreme variations from plume to plume.Comment: 4 page

Simultaneous 3D measurement of the translation and rotation of finite size particles and the flow field in a fully developed turbulent water flow

We report a novel experimental technique that measures simultaneously in three dimensions the trajectories, the translation, and the rotation of finite size inertial particles together with the turbulent flow. The flow field is analyzed by tracking the temporal evolution of small fluorescent tracer particles. The inertial particles consist of a super-absorbent polymer that renders them index and density matched with water and thus invisible. The particles are marked by inserting at various locations tracer particles into the polymer. Translation and rotation, as well as the flow field around the particle are recovered dynamically from the analysis of the marker and tracer particle trajectories. We apply this technique to study the dynamics of inertial particles much larger in size (Rp/{\eta} \approx 100) than the Kolmogorov length scale {\eta} in a von K\'arm\'an swirling water flow (R{\lambda} \approx 400). We show, using the mixed (particle/fluid) Eulerian second order velocity structure function, that the interaction zone between the particle and the flow develops in a spherical shell of width 2Rp around the particle of radius Rp. This we interpret as an indication of a wake induced by the particle. This measurement technique has many additional advantages that will make it useful to address other problems such as particle collisions, dynamics of non-spherical solid objects, or even of wet granular matter.Comment: 18 pages, 7 figures, submitted to "Measurement Science and Technology" special issue on "Advances in 3D velocimetry

Markov property of Lagrangian turbulence

Based on direct numerical simulations with point-like inertial particles transported by homogeneous and isotropic turbulent flows, we present evidence for the existence of Markov property in Lagrangian turbulence. We show that the Markov property is valid for a finite step size larger than a Stokes number-dependent Einstein-Markov memory length. This enables the description of multi-scale statistics of Lagrangian particles by Fokker-Planck equations, which can be embedded in an interdisciplinary approach linking the statistical description of turbulence with fluctuation theorems of non-equilibrium stochastic thermodynamics and fluctuation theorems, and local flow structures.Comment: submitted to PRL, 5 pages, 4 figure

Towards a FPGA-controlled deep phase modulation interferometer

Deep phase modulation interferometry was proposed as a method to enhance homodyne interferometers to work over many fringes. In this scheme, a sinusoidal phase modulation is applied in one arm while the demodulation takes place as a post-processing step. In this contribution we report on the development to implement this scheme in a fiber coupled interferometer controlled by means of a FPGA, which includes a LEON3 soft-core processor. The latter acts as a CPU and executes a custom made application to communicate with a host PC. In contrast to usual FPGA-based designs, this implementation allows a real-time fine tuning of the parameters involved in the setup, from the control to the post-processing parameters.Comment: Proceedings of the X LISA Symposium, Gainesville, May 18-23, 201

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

<p>Abstract</p> <p>Background</p> <p>Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved.</p> <p>Method</p> <p>This paper introduces a new hybrid methodology <it>Expert-based Cooperative Analysis </it>(EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by <it>EbCA-Data Envelopment Analysis (EbCA-DEA)</it>, and 2) Case-mix of schizophrenia based on functional dependency using <it>Clustering Based on Rules (ClBR)</it>. In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases.</p> <p>Results</p> <p>EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here <it>Implicit Knowledg </it>-IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases.</p> <p>Discussion</p> <p>This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.</p

Impacts of changes in groundwater recharge on the isotopic composition and geochemistry of seasonally ice-covered lakes: insights for sustainable management

Lakes are under increasing pressure due to widespread anthropogenic impacts related to rapid development and population growth. Accordingly, many lakes are currently undergoing a systematic decline in water quality. Recent studies have highlighted that global warming and the subsequent changes in water use may further exacerbate eutrophication in lakes. Lake evolution depends strongly on hydrologic balance, and therefore on groundwater connectivity. Groundwater also influences the sensitivity of lacustrine ecosystems to climate and environmental changes, and governs their resilience. Improved characterization of groundwater exchange with lakes is needed today for lake preservation, lake restoration, and sustainable management of lake water quality into the future. In this context, the aim of the present paper is to determine if the future evolution of the climate, the population, and the recharge could modify the geochemistry of lakes (mainly isotopic signature and quality via phosphorous load) and if the isotopic monitoring of lakes could be an efficient tool to highlight the variability of the water budget and quality. Small groundwater-connected lakes were chosen to simulate changes in water balance and water quality expected under future climate change scenarios, namely representative concentration pathways (RCPs) 4.5 and 8.5. Contemporary baseline conditions, including isotope mass balance and geochemical characteristics, were determined through an intensive field-based research program prior to the simulations. Results highlight that future lake geochemistry and isotopic composition trends will depend on four main parameters: location (and therefore climate conditions), lake catchment size (which impacts the intensity of the flux change), lake volume (which impacts the range of variation), and lake G index (i.e., the percentage of groundwater that makes up total lake inflows), the latter being the dominant control on water balance conditions, as revealed by the sensitivity of lake isotopic composition. Based on these model simulations, stable isotopes appear to be especially useful for detecting changes in recharge to lakes with a G index of between 50 and 80 %, but response is non-linear. Simulated monthly trends reveal that evolution of annual lake isotopic composition can be dampened by opposing monthly recharge fluctuations. It is also shown that changes in water quality in groundwater-connected lakes depend significantly on lake location and on the intensity of recharge change

Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train

Predicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors' data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance