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
標簽：
Optimization and Uncertainty Quantification
上傳時間：
2016-04-08
上傳用戶：huhu123456
DESCRIPTION
The Texas Instruments MSP430 family of ultra-low-power microcontrollers consists of several devices featuring
different sets of peripherals targeted for various applications. The architecture, combined with five low-power
modes, is optimized to achieve extended battery life in portable measurement applications. The device features a
powerful 16-bit RISC CPU, 16-bit registers, and constant generators that contribute to maximum code efficiency.
The digitally controlled oscillator (DCO) allows wake-up from low-power modes to active mode in less than 1 μs.
The MSP430G2x13 and MSP430G2x53 series are ultra-low-power mixed signal microcontrollers with built-in 16-
bit timers, up to 24 I/O capacitive-touch enabled pins, a versatile analog comparator, and built-in communication
capability using the universal serial communication interface. In addition the MSP430G2x53 family members
have a 10-bit analog-to-digital (A/D) converter. For configuration details see Table 1.
Typical applications include low-cost sensor systems that capture analog signals, convert them to digital values,
and then process the data for display or for transmission to a host system.
標簽：
G2553
2553
430G
MSP
430
上傳時間：
2018-12-25
上傳用戶：ygyh
