6,775 research outputs found
Efficient simulation of incompressible viscous flow over multi-element airfoils
The incompressible, viscous, turbulent flow over single and multi-element airfoils is numerically simulated in an efficient manner by solving the incompressible Navier-Stokes equations. The computer code uses the method of pseudo-compressibility with an upwind-differencing scheme for the convective fluxes and an implicit line-relaxation solution algorithm. The motivation for this work includes interest in studying the high-lift take-off and landing configurations of various aircraft. In particular, accurate computation of lift and drag at various angles of attack, up to stall, is desired. Two different turbulence models are tested in computing the flow over an NACA 4412 airfoil; an accurate prediction of stall is obtained. The approach used for multi-element airfoils involves the use of multiple zones of structured grids fitted to each element. Two different approaches are compared: a patched system of grids, and an overlaid Chimera system of grids. Computational results are presented for two-element, three-element, and four-element airfoil configurations. Excellent agreement with experimental surface pressure coefficients is seen. The code converges in less than 200 iterations, requiring on the order of one minute of CPU time (on a CRAY YMP) per element in the airfoil configuration
Robust Filtering and Smoothing with Gaussian Processes
We propose a principled algorithm for robust Bayesian filtering and smoothing
in nonlinear stochastic dynamic systems when both the transition function and
the measurement function are described by non-parametric Gaussian process (GP)
models. GPs are gaining increasing importance in signal processing, machine
learning, robotics, and control for representing unknown system functions by
posterior probability distributions. This modern way of "system identification"
is more robust than finding point estimates of a parametric function
representation. In this article, we present a principled algorithm for robust
analytic smoothing in GP dynamic systems, which are increasingly used in
robotics and control. Our numerical evaluations demonstrate the robustness of
the proposed approach in situations where other state-of-the-art Gaussian
filters and smoothers can fail.Comment: 7 pages, 1 figure, draft version of paper accepted at IEEE
Transactions on Automatic Contro
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
We present a framework for specifying, training, evaluating, and deploying
machine learning models. Our focus is on simplifying cutting edge machine
learning for practitioners in order to bring such technologies into production.
Recognizing the fast evolution of the field of deep learning, we make no
attempt to capture the design space of all possible model architectures in a
domain- specific language (DSL) or similar configuration language. We allow
users to write code to define their models, but provide abstractions that guide
develop- ers to write models in ways conducive to productionization. We also
provide a unifying Estimator interface, making it possible to write downstream
infrastructure (e.g. distributed training, hyperparameter tuning) independent
of the model implementation. We balance the competing demands for flexibility
and simplicity by offering APIs at different levels of abstraction, making
common model architectures available out of the box, while providing a library
of utilities designed to speed up experimentation with model architectures. To
make out of the box models flexible and usable across a wide range of problems,
these canned Estimators are parameterized not only over traditional
hyperparameters, but also using feature columns, a declarative specification
describing how to interpret input data. We discuss our experience in using this
framework in re- search and production environments, and show the impact on
code health, maintainability, and development speed.Comment: 8 pages, Appeared at KDD 2017, August 13--17, 2017, Halifax, NS,
Canad
Issues of Architectural Description Languages for Handling Dynamic Reconfiguration
Dynamic reconfiguration is the action of modifying a software system at
runtime. Several works have been using architectural specification as the basis
for dynamic reconfiguration. Indeed ADLs (architecture description languages)
let architects describe the elements that could be reconfigured as well as the
set of constraints to which the system must conform during reconfiguration. In
this work, we investigate the ADL literature in order to illustrate how
reconfiguration is supported in four well-known ADLs: pi-ADL, ACME, C2SADL and
Dynamic Wright. From this review, we conclude that none of these ADLs: (i)
addresses the issue of consistently reconfiguring both instances and types;
(ii) takes into account the behaviour of architectural elements during
reconfiguration; and (iii) provides support for assessing reconfiguration,
e.g., verifying the transition against properties.Comment: 6\`eme Conf\'erence francophone sur les architectures logicielles
(CAL'2012), Montpellier : France (2012
Complex Grid Computing
This article investigates the performance of grid computing systems whose
interconnections are given by random and scale-free complex network models.
Regular networks, which are common in parallel computing architectures, are
also used as a standard for comparison. The processing load is assigned to the
processing nodes on demand, and the efficiency of the overall computing is
quantified in terms of the respective speed-ups. It is found that random
networks allow higher computing efficiency than their scale-free counterparts
as a consequence of the smaller number of isolated clusters implied by the
former model. At the same time, for fixed cluster sizes, the scale free model
tend to provide slightly better efficiency. Two modifications of the random and
scale free paradigms, where new connections tend to favor more recently added
nodes, are proposed and shown to be more effective for grid computing than the
standard models. A well-defined correlation is observed between the topological
properties of the network and their respective computing efficiency.Comment: 5 pages, 2 figure
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