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CAD Modeling and CAE Simulation
Published in Jose Martin Herrera Ramirez, Luis Adrian Zuñiga Aviles, Designing Small Weapons, 2022
Jose Martin Herrera Ramirez, Luis Adrian Zuñiga Aviles
Multibody dynamics (MBD) consists of solid bodies (links) connected by joints that restrict their relative motion. This study describes the movements of mechanism caused by forces relative to forward and reverse dynamics. MBD focuses on the entire firearm (macroscopic behavior), performance, relatively large motion, calculation of displacement, velocity, acceleration, and loads of several components and transient analysis for a long duration [23]. As a case study, the following figures show different results achieved in Simscape® multibody module of MATLAB® for a pistol, where the CAD models were imported from SolidWorks® using the code to import the model into MATLAB® (smimport, “pistol.xml”): block diagram of the pistol (Figure 4.50), pistol simulation in four views (Figure 4.51), and pistol simulation in one view (Figure 4.52).
Added value from virtual sensors
Published in Juhani Ukko, Minna Saunila, Janne Heikkinen, R. Scott Semken, Aki Mikkola, Real-time Simulation for Sustainable Production, 2021
Janne Heikkinen, Emil Kurvinen, Jussi Sopanen
Real-time simulation can give the operator an experience similar to that of actual machine operation. In addition, it makes it possible to study, in real time, how different parameter settings will affect the machine’s dynamic behaviors. It also enables timely analysis of the machine’s condition during or after fault events. Simulation tools offer the possibility of studying systems in real time using computationally efficient simulation methods. At the cost of accuracy, some of the deployed methods provide computational efficiency by employing a simplified methodology. However, the most advanced simulation methods, such as multibody dynamics simulation, are capable of simulating behaviors in real time with rather complex models and high accuracy. Additionally, the real-time simulation models can run in parallel with the actual machine making it possible to provide real-time information from the virtual sensor readings to be used for predicting machine condition and assisting in decision-making during controlled maneuvers.
Introduction
Published in Mingjun Xie, Flexible Multibody System Dynamics—Theory and Applications, 2017
In recent years, the topic of flexible multibody dynamic analysis of mechanical and structural systems has gained tremendous attention from a number of researchers. This is primarily due to the emphasis placed by industry on realistic modeling and to the requirement of prompted solution techniques for nonlinear structural analysis. The emphasis of researchers working in the area of multibody dynamic systems has been on improvement of the generality of mathematical models and formulation of equations of motion that are amenable to computer solution. In this book, concepts of multibody dynamics are used to study general robotic manipulators, rotorcraft systems, and human bodies.
Scalable musculoskeletal model for dynamic simulations of upper body movement
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Ali Nasr, Arash Hashemi, John McPhee
To model and simulate human MSK systems, several commercial (e.g., AnyBody, ADAMS-LifeMOD, MuJoCo, SIMM, SimMechanics, MapleSim) and open-source (e.g., OpenSim, MSMS, RBDL, BiomechZoo) multibody dynamics simulation software have been used (Febrer-Nafría et al. 2022). Except for SimMechanics and MapleSim, it is challenging to use the above software for augmenting the human model with sports tools, active prostheses, or exoskeleton models (Febrer-Nafría et al. 2022), especially when using hydraulic, pneumatic, or flexible components such as bike tires (Jansen and McPhee 2020) or golf shafts (Brown et al. 2020). MapleSim is based on symbolic computing and provides analytic derivatives when necessary, such as for optimization or sensitivity analyses, whereas the other software mentioned are based on numerical computation.
A vehicle/track/soil model using co-simulation between multibody dynamics and finite element analysis
Published in International Journal of Rail Transportation, 2020
Bryan Olivier, Olivier Verlinden, Georges Kouroussis
In railway dynamics, there exist models or virtual prototypes focused on the vehicle dynamics. Those models can be, for example, dedicated to the characterization of the vehicle performance in a specific situation [1]. It can also provide the motion of a car containing passengers in order to prevent any discomfort due to an undesirable motion. In all cases, railway vehicles are complex structures involving a substantial number of bodies, suspension elements linking those bodies and then many relative motions. This kind of mechanical system is usually modeled using multibody dynamics techniques. Besides its own dynamics, the vehicle is usually rolling over a track that lays on a soil. When focusing on vehicle dynamics, the motion of the soil is frequently neglected since its effect remains small in comparison with the vehicle motion or the track motion in mid- and high-frequency. This effect is higher but still limited in low frequency [2]. Moreover, in terms of their mathematical representation, the vehicle, the track and the soil are clearly different structures. Indeed, the vehicle involves a limited number of equations of motion depending on the number of degrees of freedom used to represent the motion of the bodies, usually supposed to be rigid, constituting the entire vehicle. Meanwhile, the track and the soil are continuous and flexible structures that are commonly represented using finite element techniques. Since the accuracy of a finite element representation of a structure directly depends on the number of elements taken into account, the number of degrees of freedom involved is much higher than in a rigid multibody representation.
Whole-body momentum control with linear quadratic state incremental walking pattern generation and a centroidal moment pivot balancing strategy for humanoid robots
Published in Advanced Robotics, 2022
Huan-Kun Hsu, Yun-Han Wang, Han-Pang Huang
We used adams software which is a multibody dynamics simulation engine to construct a simulation environment. The robot model is constructed by importing the CAD model, which was built using SOLIDWORKS as illustrated in Figure 9. Figure 10 depicts the simulation flow chart.