228,194 research outputs found
Power Flow Modelling of Dynamic Systems - Introduction to Modern Teaching Tools
As tools for dynamic system modelling both conventional methods such as
transfer function or state space representation and modern power flow based
methods are available. The latter methods do not depend on energy domain, are
able to preserve physical system structures, visualize power conversion or
coupling or split, identify power losses or storage, run on conventional
software and emphasize the relevance of energy as basic principle of known
physical domains. Nevertheless common control structures as well as analysis
and design tools may still be applied. Furthermore the generalization of power
flow methods as pseudo-power flow provides with a universal tool for any
dynamic modelling. The phenomenon of power flow constitutes an up to date
education methodology. Thus the paper summarizes fundamentals of selected power
flow oriented modelling methods, presents a Bond Graph block library for
teaching power oriented modelling as compact menu-driven freeware, introduces
selected examples and discusses special features.Comment: 12 pages, 9 figures, 4 table
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
In systems biomedicine, an experimenter encounters different potential
sources of variation in data such as individual samples, multiple experimental
conditions, and multi-variable network-level responses. In multiparametric
cytometry, which is often used for analyzing patient samples, such issues are
critical. While computational methods can identify cell populations in
individual samples, without the ability to automatically match them across
samples, it is difficult to compare and characterize the populations in typical
experiments, such as those responding to various stimulations or distinctive of
particular patients or time-points, especially when there are many samples.
Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous
modeling and registration of populations across a cohort. JCM models every
population with a robust multivariate probability distribution. Simultaneously,
JCM fits a random-effects model to construct an overall batch template -- used
for registering populations across samples, and classifying new samples. By
tackling systems-level variation, JCM supports practical biomedical
applications involving large cohorts
On the non-local geometry of turbulence
A multi-scale methodology for the study of the non-local geometry of eddy structures in turbulence is developed. Starting from a given three-dimensional field, this consists of three main steps: extraction, characterization and classification of structures. The extraction step is done in two stages. First, a multi-scale decomposition based on the curvelet transform is applied to the full three-dimensional field, resulting in a finite set of component three-dimensional fields, one per scale. Second, by iso-contouring each component field at one or more iso-contour levels, a set of closed iso-surfaces is obtained that represents the structures at that scale. The characterization stage is based on the joint probability density function (p.d.f.), in terms of area coverage on each individual iso-surface, of two differential-geometry properties, the shape index and curvedness, plus the stretching parameter, a dimensionless global invariant of the surface. Taken together, this defines the geometrical signature of the iso-surface. The classification step is based on the construction of a finite set of parameters, obtained from algebraic functions of moments of the joint p.d.f. of each structure, that specify its location as a point in a multi-dimensional âfeature spaceâ. At each scale the set of points in feature space represents all structures at that scale, for the specified iso-contour value. This then allows the application, to the set, of clustering techniques that search for groups of structures with a common geometry. Results are presented of a first application of this technique to a passive scalar field obtained from 5123 direct numerical simulation of scalar mixing by forced, isotropic turbulence (Reλ = 265). These show transition, with decreasing scale, from blob-like structures in the larger scales to blob- and tube-like structures with small or moderate stretching in the inertial range of scales, and then toward tube and, predominantly, sheet-like structures with high level of stretching in the dissipation range of scales. Implications of these results for the dynamical behaviour of passive scalar stirring and mixing by turbulence are discussed
Metric for attractor overlap
We present the first general metric for attractor overlap (MAO) facilitating
an unsupervised comparison of flow data sets. The starting point is two or more
attractors, i.e., ensembles of states representing different operating
conditions. The proposed metric generalizes the standard Hilbert-space distance
between two snapshots to snapshot ensembles of two attractors. A reduced-order
analysis for big data and many attractors is enabled by coarse-graining the
snapshots into representative clusters with corresponding centroids and
population probabilities. For a large number of attractors, MAO is augmented by
proximity maps for the snapshots, the centroids, and the attractors, giving
scientifically interpretable visual access to the closeness of the states. The
coherent structures belonging to the overlap and disjoint states between these
attractors are distilled by few representative centroids. We employ MAO for two
quite different actuated flow configurations: (1) a two-dimensional wake of the
fluidic pinball with vortices in a narrow frequency range and (2)
three-dimensional wall turbulence with broadband frequency spectrum manipulated
by spanwise traveling transversal surface waves. MAO compares and classifies
these actuated flows in agreement with physical intuition. For instance, the
first feature coordinate of the attractor proximity map correlates with drag
for the fluidic pinball and for the turbulent boundary layer. MAO has a large
spectrum of potential applications ranging from a quantitative comparison
between numerical simulations and experimental particle-image velocimetry data
to the analysis of simulations representing a myriad of different operating
conditions.Comment: 33 pages, 20 figure
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