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Register a new userElectromagnetic Crimping on Threaded Surface: FEM Modelling, Validation and Effects of Pitch and Discharge Energy on Deformation in an Empirical Relation
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Added by
Deepak Kumar
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Electromagnetic crimping is a high-velocity joining method to join highly conductive workpieces where a pulsed magnetic field is applied without any working medium or mechanical contact to deform the workpiece. This work explores tube-to-tube joining of Copper outer tube and Stainless steel threaded inner tube using electromagnetic crimping. A non-coupled simulation model is developed for the finite element analysis. ANSYS Maxwell package is used to obtain the magnetic field intensity, which is later converted to pressure using an analytical equation, and this pressure is applied to the two-tube working domain in ANSYS Explicit Dynamics. Numerical simulations are done for different combinations of discharge energies and pitches of the thread to analyse deformation, stress and strain. The converged finite element results are validated using experimental data. The amount of deformation is found to be proportional to discharge energy and the pitch of the thread used. An empirical relation is developed for the deformation as a function of discharge energy and pitch. The relation is able to predict the deformation for other discharge energies, which is later verified with ANSYS simulations.
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During contraction the energy of muscle tissue increases due to energy from the hydrolysis of ATP. This energy is distributed across the tissue as strain-energy potentials in the contractile elements, strain-energy potential from the 3D deformation of the base-material tissue (containing cellular and ECM effects), energy related to changes in the muscles nearly incompressible volume and external work done at the muscle surface. Thus, energy is redistributed through the muscles tissue as it contracts, with only a component of this energy being used to do mechanical work and develop forces in the muscles line-of-action. Understanding how the strain-energy potentials are redistributed through the muscle tissue will help enlighten why the mechanical performance of whole muscle in its line-of-action does not match the performance that would be expected from the contractile elements alone. Here we demonstrate these physical effects using a 3D muscle model based on the finite element method. The tissue deformations within contracting muscle are large, and so the mechanics of contraction were explained using the principles of continuum mechanics for large deformations. We present simulations of a contracting medial gastrocnemius muscle, showing tissue deformations that mirror observations from MRI-based images. This paper tracks the redistribution of strain-energy potentials through the muscle tissue during isometric contractions, and shows how fibre shortening, pennation angle, transverse bulging and anisotropy in the stress and strain of the muscle tissue are all related to the interaction between the material properties of the muscle and the action of the contractile elements.
The cost- and memory-efficient numerical simulation of coupled volume-based multi-physics problems like flow, transport, wave propagation and others remains a challenging task with finite element method (FEM) approaches. Goal-oriented space and time adaptive methods derived from the dual weighted residual (DWR) method appear to be a shiny key technology to generate optimal space-time meshes to minimise costs. Current implementations for challenging problems of numerical screening tools including the DWR technology broadly suffer in their extensibility to other problems, in high memory consumption or in missing system solver technologies. This work contributes to the efficient embedding of DWR space-time adaptive methods into numerical screening tools for challenging problems of physically relevance with a new approach of flexible data structures and algorithms on them, a modularised and complete implementation as well as illustrative examples to show the performance and efficiency.
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We investigate the memory effects associated with the kicks of particles. Recently, the equivalence between the memory effect and soft theorem has been established. By computing the memory effect from the radiation solutions, we explicitly confirm that, in addition to the leading piece, the subleading and subsubleading soft theorems are equivalent to the subleading and subsubleading memory effects, respectively. It is known that the memory effects can be probed by the displacements or kicks of the test particles. We point out that the these memory effects are also probed by the permanent change of the direction of the spin. We also show that the axion memory effect, recently proposed by the current authors, can be detected as the change of the spin of the test particle. We discuss that if we consider the magnetic monopole as an external particle, the parity-odd electromagnetic memory appears.
Gaussian process regression has proven very powerful in statistics, machine learning and inverse problems. A crucial aspect of the success of this methodology, in a wide range of applications to complex and real-world problems, is hierarchical modeling and learning of hyperparameters. The purpose of this paper is to study two paradigms of learning hierarchical parameters: one is from the probabilistic Bayesian perspective, in particular, the empirical Bayes approach that has been largely used in Bayesian statistics; the other is from the deterministic and approximation theoretic view, and in particular the kernel flow algorithm that was proposed recently in the machine learning literature. Analysis of their consistency in the large data limit, as well as explicit identification of their implicit bias in parameter learning, are established in this paper for a Matern-like model on the torus. A particular technical challenge we overcome is the learning of the regularity parameter in the Matern-like field, for which consistency results have been very scarce in the spatial statistics literature. Moreover, we conduct extensive numerical experiments beyond the Matern-like model, comparing the two algorithms further. These experiments demonstrate learning of other hierarchical parameters, such as amplitude and lengthscale; they also illustrate the setting of model misspecification in which the kernel flow approach could show superior performance to the more traditional empirical Bayes approach.
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