3,656 research outputs found
Generation of intense negative ion beams
An electron gun is used with a mirror electrostatic field to produce zero or near zero velocity electrons by forming a turning point in their trajectories. A gas capable of attaching zero or near zero velocity is introduced at this turning point, and negative ions are produced by the attachment or dissociative attachment process. Operation may be continuous or pulsed. Ions thus formed are extracted by a simple lens system and suitable biasing of grids
The Formation of the Russian Medical Profession: A Comparison of Power and Plagues in the Eighteenth and Nineteenth Centuries
High-Order Shock Capturing for Computational Aeroacoustics
Jet noise is not only an annoyance to passengers and communities near airports, it is a major contributor to hearing loss in veterans who served on aircraft carriers, as well as a significant limiting factor for the growth of commercial airlines. High-fidelity large eddy simulation (LES) is an important tool for analyzing and predicting jet noise; however the utilized non-dissipative high order finite difference schemes produce instabilities at shock waves. Schemes for capturing shock waves, however, are more dissipative and do a poor job preserving turbulent structures and acoustic waves. To maximize the strengths of both approaches, hybrid methods utilize shock capturing locally where the flow is discontinuous and traditional LES methods over the remaining regions. In this study, a sixth-order compact scheme is hybridized with a weighted essentially non-oscillatory (WENO) method for local shock-capturing. Furthermore, improvements are made to the dissipative and dispersive qualities of WENO. Optimized bandwidth resolution and reduced nonlinearity are found to improve resolution of flow structures passing through shocks on standard test problems. Additionally, proper detection of shock waves is critical to overall performance of hybrid schemes, so several approaches are examined. Superior efficiency and accuracy are obtained through careful selection of shock wave detectors
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories
Data-driven models for nonlinear dynamical systems based on approximating the
underlying Koopman operator or generator have proven to be successful tools for
forecasting, feature learning, state estimation, and control. It has become
well known that the Koopman generators for control-affine systems also have
affine dependence on the input, leading to convenient finite-dimensional
bilinear approximations of the dynamics. Yet there are still two main obstacles
that limit the scope of current approaches for approximating the Koopman
generators of systems with actuation. First, the performance of existing
methods depends heavily on the choice of basis functions over which the Koopman
generator is to be approximated; and there is currently no universal way to
choose them for systems that are not measure preserving. Secondly, if we do not
observe the full state, then it becomes necessary to account for the dependence
of the output time series on the sequence of supplied inputs when constructing
observables to approximate Koopman operators. To address these issues, we write
the dynamics of observables governed by the Koopman generator as a bilinear
hidden Markov model, and determine the model parameters using the
expectation-maximization (EM) algorithm. The E-step involves a standard Kalman
filter and smoother, while the M-step resembles control-affine dynamic mode
decomposition for the generator. We demonstrate the performance of this method
on three examples, including recovery of a finite-dimensional Koopman-invariant
subspace for an actuated system with a slow manifold; estimation of Koopman
eigenfunctions for the unforced Duffing equation; and model-predictive control
of a fluidic pinball system based only on noisy observations of lift and drag
Effects of an active visuomotor steering task on covert attention
In complex dynamic tasks such as driving it is essential to be aware of potentially important targets in peripheral vision. While eye tracking methods in various driving tasks have provided much information about drivers’ gaze strategies, these methods only inform about overt attention and provide limited grounds to assess hypotheses concerning covert attention. We adapted the Posner cue paradigm to a dynamic steering task in a driving simulator. The participants were instructed to report the presence of peripheral targets while their gaze was fixed to the road. We aimed to see whether and how the active steering task and complex visual stimulus might affect directing covert attention to the visual periphery. In a control condition, the detection task was performed without a visual scene and active steering. Detection performance in bends was better in the control task compared to corresponding performance in the steering task, indicating that active steering and the complex visual scene affected the ability to distribute covert attention. Lower targets were discriminated slower than targets at the level of the fixation circle in both conditions. We did not observe higher discriminability for on-road targets. The results may be accounted for by either bottom-up optic flow biasing of attention, or top-down saccade planning.Peer reviewe
Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance
Data-driven reduced-order models often fail to make accurate forecasts of
high-dimensional nonlinear dynamical systems that are sensitive along
coordinates with low-variance because such coordinates are often truncated,
e.g., by proper orthogonal decomposition, kernel principal component analysis,
and autoencoders. Such systems are encountered frequently in shear-dominated
fluid flows where non-normality plays a significant role in the growth of
disturbances. In order to address these issues, we employ ideas from active
subspaces to find low-dimensional systems of coordinates for model reduction
that balance adjoint-based information about the system's sensitivity with the
variance of states along trajectories. The resulting method, which we refer to
as covariance balancing reduction using adjoint snapshots (CoBRAS), is
analogous to balanced truncation with state and adjoint-based gradient
covariance matrices replacing the system Gramians and obeying the same key
transformation laws. Here, the extracted coordinates are associated with an
oblique projection that can be used to construct Petrov-Galerkin reduced-order
models. We provide an efficient snapshot-based computational method analogous
to balanced proper orthogonal decomposition. This also leads to the observation
that the reduced coordinates can be computed relying on inner products of state
and gradient samples alone, allowing us to find rich nonlinear coordinates by
replacing the inner product with a kernel function. In these coordinates,
reduced-order models can be learned using regression. We demonstrate these
techniques and compare to a variety of other methods on a simple, yet
challenging three-dimensional system and a nonlinear axisymmetric jet flow
simulation with state variables
Analysis of amplification mechanisms and cross-frequency interactions in nonlinear flows via the harmonic resolvent
We propose a framework that elucidates the input-output characteristics of
flows with complex dynamics arising from nonlinear interactions between
different time scales. More specifically, we consider a periodically
time-varying base flow, and perform a frequency-domain analysis of periodic
perturbations about this base flow; the response of these perturbations is
governed by the harmonic resolvent, which is a linear operator similar to the
harmonic transfer function introduced by Wereley (1991). This approach makes it
possible to explicitly capture the triadic interactions that are responsible
for the energy transfer between different time scales in the flow. For
instance, perturbations at frequency are coupled with perturbations at
frequency through the base flow at frequency . We draw
a connection with resolvent analsyis, which is a special case of the harmonic
resolvent when evaluated about a steady base flow. We show that the left and
right singular vectors of the harmonic resolvent are the optimal response and
forcing modes, which can be understood as full spatio-temporal signals that
reveal space-time amplification characteristics of the flow. We illustrate the
method on examples, including a three-dimensional system of ordinary
differential equations and the flow over an airfoil at near-stall angle of
attack
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