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Hudson Young
Hudson Young

Dynamics Of Mechanical Systems


This book represents a truly massive collection of material presenting many of the basic procedures in modern engineering dynamics, numerous examples, and introductions to more advanced topics. The text represents a collection of work and experience from the authors that spans roughly 30 years and fills 20 chapters and 750-plus pages. This text is massive in size and impressive in scope. The text is intended for mid to upper level undergraduate students in engineering and physics. The stated objective of the book is to give the reader a working knowledge of dynamics, enabling them to analyze a broad range of mechanical and biodynamic systems. The emphasis of the book is on presenting the fundamental procedures, but not so much the theory, underlying the dynamic analyses. The authors attempt to convey and develop the skills associated with potentially sophisticated dynamics analysis through the presentation and study of the fundamental engineering components (pendulums, cams, gears, balancing, and the like) which comprise many more complex systems. The book is also intended to serve as an independent study and/or a reference book for either beginning graduate students or practicing engineers.




Dynamics of Mechanical Systems



The book begins with an introduction to the basic concepts and assumptions, basic terminology, a review of units, as well as the concepts of reference frames and coordinate systems. Chapter 2 continues with a review of vector algebra. The next three chapters are devoted to kinematics, with the last of these being on the planar motion of rigid bodies. Chapter 6 discusses force systems and equivalent representation of these force systems. While Chapter 7 presents a detailed review of rigid body inertia properties, including inertia dyadics.


MECE 3338 - Dynamics and Control of Mechanical SystemsCredit Hours: 3.0Lecture Contact Hours: 3 Lab Contact Hours: 0Prerequisite: MECE 2361 and MECE 3336 . Description Design of system parameters and feedback control gains to satisfy transient and steadystate response specifications for mechanical systems. Transfer functions, time and frequency response, vibration isolation, automatic control systems. Design project required.


Customization: The notation and content can be customized. Course and textbook evaluations reflect a highly positive experience. --> Other textbooks for dynamics & control Reviews on these books Interactive and guided. Homework is interactive and guided so students continually make progress and steadily succeed. The 280+ meaningful problems are synthesized via small intelligible steps to reduce frustration, promote confidence that dynamics is more than a "bunch of tricks", and provide a positive learning experience. Instruction is effective (empowering students with useful skills ) and efficient (high-quality, focused interaction).


MECH 3350 Kinematics and Dynamics of Mechanical Systems (3 semester credit hours) Lecture course. Motion and interaction of machine elements and mechanisms. Kinematics, statics, and dynamics are applied for analysis and design of the parts of machines such as planar mechanisms, cams and gears. Prerequisites: ENGR 2300 and MATH 2420 and MECH 2330 and ENGR 3300. (3-0) S


Dynamic mechanical analysis (abbreviated DMA) is a technique used to study and characterize materials. It is most useful for studying the viscoelastic behavior of polymers. A sinusoidal stress is applied and the strain in the material is measured, allowing one to determine the complex modulus. The temperature of the sample or the frequency of the stress are often varied, leading to variations in the complex modulus; this approach can be used to locate the glass transition temperature[1] of the material, as well as to identify transitions corresponding to other molecular motions.


Polymers composed of long molecular chains have unique viscoelastic properties, which combine the characteristics of elastic solids and Newtonian fluids. The classical theory of elasticity describes the mechanical properties of elastic solid where stress is proportional to strain in small deformations. Such response of stress is independent of strain rate. The classical theory of hydrodynamics describes the properties of viscous fluid, for which the response of stress is dependent on strain rate.[2] This solidlike and liquidlike behavior of polymers can be modeled mechanically with combinations of springs and dashpots.[3]


The viscoelastic property of a polymer is studied by dynamic mechanical analysis where a sinusoidal force (stress σ) is applied to a material and the resulting displacement (strain) is measured. For a perfectly elastic solid, the resulting strain and the stress will be perfectly in phase. For a purely viscous fluid, there will be a 90 degree phase lag of strain with respect to stress.[4] Viscoelastic polymers have the characteristics in between where some phase lag will occur during DMA tests.[4] When the strain is applied and the stress lags behind, the following equations hold:[4]


Varying the composition of monomers and cross-linking can add or change the functionality of a polymer that can alter the results obtained from DMA. An example of such changes can be seen by blending ethylene propylene diene monomer (EPDM) with styrene-butadiene rubber (SBR) and different cross-linking or curing systems. Nair et al. abbreviate blends as E0S, E20S, etc., where E0S equals the weight percent of EPDM in the blend and S denotes sulfur as the curing agent.[6]


Citation: Faber H, van Soest AJ, Kistemaker DA (2018) Inverse dynamics of mechanical multibody systems: An improved algorithm that ensures consistency between kinematics and external forces. PLoS ONE 13(9): e0204575.


To conclude, no inverse dynamics method is currently available in which i) all residual forces and torques are removed, ii) segment angles at all time nodes are optimized together, and iii) the problem is defined such that it always produces a unique solution, i.e. it results in minimal adaptation of the kinematics while the external forces are not accommodated. The purpose of this study was to develop an algorithm that improves on inverse dynamics while meeting these demands. To show the significance of the inconsistency between kinematics and external forces, the magnitudes of the residual force and torque values of a classical inverse dynamics analysis were obtained from a dataset concerning human gait. The resulting optimization algorithm was evaluated by applying it to the same dataset, comparing the results (kinematics and joint torques) to those obtained using a classical inverse dynamics analysis. In the example application, the dataset consisted of the sagittal plane coordinates of markers attached to body segments, sagittal plane ground reaction force data (including point of application) and segment parameter values. After optimization of the dataset, the measured ground reaction force and kinematics were fully consistent.


We performed a classical inverse dynamics analysis on one complete stride of a subject walking at 1.8 m/s, which yielded the residual forces on the trunk (see Fig 1). This trial will be referred to as the typical example. The onset of the stride was defined by toe off of the right leg. Positive x- and y-forces were defined as in the walking direction (forward) and upward, respectively. From Fig 1 it can be observed that in particular the horizontal component of the residual force at the trunk was substantial. Note again that these forces do not exist in reality.


In a classical inverse dynamics analysis, based on a rigid linked segment model, measured kinematics and external forces are in general not mechanically consistent. In this study, an algorithm was developed to remedy this by modifying the measured kinematics as little as possible such that the resulting optimized kinematics are mechanically consistent with measured external forces. As an example, this algorithm was applied to a dataset of human walking containing 2D joint positions. Our analyses show that the algorithm was capable of completely removing the residual forces and torques during a stride with minor changes to the measured kinematics, while leaving the measured ground reaction forces unchanged. As a result, joint torque profiles before and after optimization showed similar patterns.


The example used in this study was a 2D representation of walking. However, we stress that it is straightforward to extend the algorithm in several directions. First, we note that extension to 3D is straightforward. For example, in walking experiments with ground reaction force and 3D measurement of kinematics, three residual force components and three residual torque components will arise at the trunk. These can be treated the same way as in the planar case. However, due to increased model complexity in 3D applications, it should be established in future work how this affects the calculation time of the optimization. Second, as mentioned in the introduction, several methods exist in which body segment parameter values are added to the variables to be optimized. These were not included in our algorithm because we focused on altering the kinematics and its effect on the residual force and torque values. However, including body segment parameter values and imposing reasonable bounds is a relatively simple extension, which can contribute to improving inverse dynamics analysis. Third, human walking is an example of a (nearly) periodic movement. Conceivably, researchers may want to impose strict periodicity on such a movement. In that case, the external forces should be (minimally) adjusted such that the cycle average of the sums of all forces and torques equal zero. Also, constraints should be added to enforce equal positions and velocities at the start and end of the cycle.


Summarizing, a straightforward algorithm was developed that completely removed residual forces and torques in an inverse dynamics analysis. It was found that small adjustments to the kinematics only, in the order of 1 cm marker displacements, were sufficient to obtain a consistent mechanical description. The algorithm provides a clear improvement over current methods in calculating net joint torques and it should, in our opinion, therefore be included in any rigid body inverse dynamics analysis.


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