MATLAB PROGRAMS FOR "vibration SIMULATION USING MATLAB AND ANSYS"
All the M-files which are listed in the book are available on this site. The
ANSYS-related files and some non-listed utility M-files are available for download
from the author s website, www.hatchcon.con.
The Engineering vibration Toolbox is a set of educational programs
written in Octave by Joseph C. Slater. Also included are a number of help files,
demonstration examples, and data files containing raw experimental data. The
codes include single degree of freedom response, response spectrum, finite
elements, numerical integration, and phase plane analysis.
Windows Mobile NLED driver proxy. Created for customization of incoming call ring vibration.
While original Windows Mobile ring vibration constant vibrating during all ring, this driver help to apply any user vibrating pattern.
Abstract: A resistive feedback network is often used to set the output voltage of a power supply. A mechanical potentiometer (pot)conveniently solves the problem of adjusting a power supply. For easier automatic calibration, a mechanical pot can be replaced witha digital pot. This application note presents a calibration solution that uses a digital pot, because digipots are smaller, do not movewith age or vibration, and can be recalibrated remotely. This proposed solution reduces the susceptibility of the system to thetolerance of the digital pot's end-to-end resistance, making the solution optimal fordesigners. This application note also explainssome of the equations required to calculate the resistor chain values and to use a digital pot in this way. A spreadsheet withstandard reisistor values is available for easy calculations.
Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
