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(biāo)簽： Optimization and Uncertainty Quantification
上傳時(shí)間： 2016-04-08
上傳用戶：huhu123456
Lithium–sulfur batteries are a promising energy-storage technology due to their relatively low cost and high theoretical energy density. However, one of their major technical problems is the shuttling of soluble polysulfides between electrodes, resulting in rapid capacity fading. Here, we present a metal–organic framework (MOF)-based battery separator to mitigate the shuttling problem. We show that the MOF-based separator acts as an ionic sieve in lithium–sulfur batteries, which selectively sieves Li+ ions while e ciently suppressing undesired polysulfides migrating to the anode side. When a sulfur-containing mesoporous carbon material (approximately 70 wt% sulfur content) is used as a cathode composite without elaborate synthesis or surface modification, a lithium–sulfur battery with a MOF-based separator exhibits a low capacity decay rate (0.019% per cycle over 1,500 cycles). Moreover, there is almost no capacity fading after the initial 100 cycles. Our approach demonstrates the potential for MOF-based materials as separators for energy-storage applications.
上傳時(shí)間： 2017-11-23
上傳用戶：653357637
In this book for the optimisation of assembly conveyor lines we are dealing with series part production featured by a medium complexity degree and a medium number of individual components and assembly technique alternatives. Modern production techniques for medium to large series products or mass production usually involve assembly conveyor lines. They still use hand labour more or less automated. The aim is to have monotonous and similar in type operations or such causing fatigue, stress and production traumas, gradually replaced by automated assembly cycles, means and techniques. This usually widely involves industrial robots and handlers. Higher productivity, lower cost and higher quality of assembled products are usually required.
標(biāo)簽： automation-and-robotics
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
意法半導(dǎo)體STM8系列參考手冊(cè)Program memory: 8 Kbyte Flash memory; dataretention 20 years at 55 °C after 100 cycles? RAM: 1 Kbyte? Data memory: 128 bytes true data EEPROM;endurance up to 100 k write/erase cycles
標(biāo)簽： stm8
上傳時(shí)間： 2022-04-27
上傳用戶：zhaiyawei
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