Search results

This chapter aims to explore how emotional language construing experiences of UK COVID-19 lockdown in the present frames expectations for future behaviours and intended memories. We analyse 102 responses collected through an online narrative survey during the first lockdown in the United Kingdom. The survey asked participants to articulate ‘an image to remember lockdown by’. Taking a positive discourse analysis approach, using corpus linguistics and systemic functional linguistics tools, we challenge the primarily negative mainstream discourses of COVID-19 and lockdown experiences and explore how language choices evaluating different aspects of life in lockdown evoke emotion to construe a desired projected future. Findings indicate that respondents actively and selectively articulate primarily positive intended memories based on kinship peace and nature that contrast with normal life experiences. Such choices are framed within emotional states enacted through language choices. We argue that these projected memories act as a ‘time capsule’ whereby decisions to retain positive memories help to promote adaptive well-being in the face of potentially overwhelmingly negative circumstances.

The purpose of this paper is to simulate in the time domain three‐dimensional electrical, thermal, mechanical coupled contact problems arising in electric resistance welding (ERW) processes.

A three‐dimensional multiphysical numerical model for analyzing contact problems is proposed. Electrical and thermal field equations in bulk domains are discretized with the cell method (CM). Welding resistance at contact interfaces is described locally by synthetic statistic parameters and contacting domains are matched together by a non‐overlapping domain decomposition method. Contact pressure distribution is resolved by a finite‐element procedure. The model is validated with 3D FEM software package.

The semi‐analytical model describing the electric and thermal resistances at contact interfaces can be easily embedded in CM formulations, where problem variables are expressed directly in integral form. Compatibility conditions between contact members are enforced by a domain decomposition approach. System conditioning and computing time are improved by a solution strategy based on the Schur complement method.

The electrical‐thermal analysis is not coupled strongly with the mechanical analysis and contact pressure distribution is assumed to be not depending on thermal stresses, which can be considerable near the contact area where localized joule heating occurs.

Resistance welding processes involve mechanical, electrical, and thermal non‐linear coupled effects that cannot be simulated by standard commercial software packages. The proposed numerical model can be used instead for designing and optimizing ERW processes.

The paper shows that numerical modeling of ERW processes requires a careful prediction of the localized joule heating occurring at the electrode‐material interface. This effect is reconstructed by the proposed approach simulating coupled electrical, thermal, and mechanical effects on different spatial scales.

This paper aims to propose a fast and accurate simulation of large-scale induction heating problems by using nonlinear reduced-order models.

A projection space for model order reduction (MOR) is quickly generated from the first kernels of Volterra’s series to the problem solution. The nonlinear reduced model can be solved with time-harmonic phasor approximation, as the nonlinear quadratic structure of the full problem is preserved by the projection.

The solution of induction heating problems is still computationally expensive, even with a time-harmonic eddy current approximation. Numerical results show that the construction of the nonlinear reduced model has a computational cost which is orders of magnitude smaller than that required for the solution of the full problem.

Only linear magnetic materials are considered in the present formulation.

The proposed MOR approach is suitable for the solution of industrial problems with a computing time which is orders of magnitude smaller than that required for the full unreduced problem, solved by traditional discretization methods such as finite element method.

The most common technique for MOR is the proper orthogonal decomposition. It requires solving the full nonlinear problem several times. The present MOR approach can be built directly at a negligible computational cost instead. From the reduced model, magnetic and temperature fields can be accurately reconstructed in whole time and space domains.