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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.
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 o
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
In this paper we consider the inverse electromagnetic scattering for a cavity surrounded by an inhomogeneous medium in three dimensions. The measurements are scattered wave fields measured on some surface inside the cavity, where such scattered wave
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 th
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 modeli