Conservation laws for arbitrary objectives with application to fracture resistant design
Source abstract
Abstract
In this paper, we pose a configurational optimization problem to derive the sensitivity of an arbitrary objective to arbitrary motions of one or more finite-sized heterogeneities inserted into a homogeneous domain. In the derivation, we pose an adjoint boundary value problem and utilize the adjoint fields as well as the definition of a generalized Eshelby energy-momentum tensor for arbitrary objectives to express the final result. The resulting sensitivity may be expressed as surface integrals with jump terms across the heterogeneity boundaries that vanish on homogeneous domains yielding generalized conservation laws for arbitrary objectives. We then derive the specific path-independent forms of the sensitivity of the objective to arbitrary translation, rotation or scaling of the inserted heterogeneities. We next illustrate the application of the derived sensitivities to specific objectives common to fracture mechanics as well as to structural optimization. The chosen objectives include strain energy, trade-off between structural compliance and mass, and an arbitrary objective defined entirely on the boundary of the domain. We show that for the strain energy objective, the derived sensitivities naturally yield the classical J-, L- and M-integrals of fracture mechanics. The theory is implemented within an Isogeometric computational framework for fracture modeling termed Enriched Isogeometric analysis (EIGA). The EIGA computational technique is used to optimally identify worst-case locations for line cracks that are inserted into the domain as well as to optimally mitigate the risk of fracture due to a crack at its worst-case location by sequentially inserting and optimizing the configurations of circular/elliptical stiff/soft inclusions.
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Research context
OpenAlex topics: Numerical methods in engineering · Structural Response to Dynamic Loads · Probabilistic and Robust Engineering Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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