·[一些機器人方面的PDF].Introduction.to.Robotics,.Mechanics.and.Control.JOHN.J.CRAIG
標簽： Introduction Mechanics Robotics Control
上傳時間： 2013-06-08
上傳用戶：uuuuuuu
Engineering Mechanics : dynamics / R.C. Hibbeler. 此書的兩張配套軟盤
標簽： R.C. Engineering Mechanics dynamics
上傳時間： 2014-12-06
上傳用戶：kiklkook
Chapman - Advanced Mathematics and Mechanics Applications Using MATLAB, 3rd Ed - 2003
標簽： Applications Mathematics Mechanics Advanced
上傳時間： 2015-11-19
上傳用戶：onewq
that is a very useful book which explain some basic rules in statistical Mechanics.
標簽： statistical Mechanics explain useful
上傳時間： 2014-11-17
上傳用戶：zhangjinzj
該文檔為MATLAB Sim Mechanics機構動態仿真概述文檔，是一份很不錯的參考資料，具有較高參考價值，感興趣的可以下載看看………………
標簽： matlab
上傳時間： 2022-01-11
上傳用戶：
OFELI is an object oriented library of C++ classes for development of finite element codes. Its main features are : * Various storage schemes of matrices (dense, sparse, skyline). * Direct methods of solution of linear systems of equations as well as various combinations of iterative solvers and preconditioners. * Shape functions of most "popular" finite elements * Element arrays of most popular problems (Heat Transfer, Fluid Flow, Solid Mechanics, Electromagnetics, ...).
標簽： development oriented classes element
上傳時間： 2015-03-03
上傳用戶：kbnswdifs
ABAQUS is a general purpose finite element analysis program which is widely used to analyses mechanical, structure and civil engineering problems. Abaqus has some special feature for analysing fracture Mechanics problems, and therefore it is a main tools for the FE-analysis in the Fracture Group at the Mechanical Engineering at Glasgow Universtity. The software which can transfer data from Abaqus into a Matlab readable environment has been developed as a part of a research program in Constraint Estimation in Fracture Mechanics. This research program was funded by a grant from the Defence Research Agency through Prof. J. Sumpter.
標簽： analysis analyses general element
上傳時間： 2015-05-13
上傳用戶：xfbs821
monte carlo 仿真英文電子書 AGuidetoMonteCarloSimulationsinStatisticalPhysics,Second EditionThis new and updated deals with all aspects of Monte Carlo simulation ofcomplexphysicalsystemsencounteredincondensed-matterphysicsandsta-tistical Mechanics as well as in related ?elds, for example polymer science,lattice gauge theory and protein folding
標簽： AGuidetoMonteCarloSimulationsinSt atisticalPhysics EditionThis Second
上傳時間： 2016-04-25
上傳用戶：xmsmh
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
This texts contemporary approach focuses on the concepts of linear control systems, rather than computational Mechanics. Straightforward coverage includes an integrated treatment of both classical and modern control system methods. The text emphasizes design with discussions of problem formulation, design criteria, physical constraints, several design methods, and implementation of compensators.Discussions of topics not found in other texts--such as pole placement, model matching and robust tracking--add to the texts cutting-edge presentation. Students will appreciate the applications and discussions of practical aspects, including the leading problem in developing block diagrams, noise, disturbances, and plant perturbations. State feedback and state estimators are designed using state variable equations and transfer functions, offering a comparison of the two approaches. The incorporation of MATLAB throughout the text helps students to avoid time-consuming computation and concentrate on control system design and analysis
標簽： 控制系統
上傳時間： 2021-12-15
上傳用戶：
