1988
DOI: 10.1002/nme.1620260104
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Variable‐order variable‐step algorithms for second‐order systems. Part 1: The methods

Abstract: SUMMARYWe develop a new class of multistage algorithms for second-order systems of ordinary differential equations (ODES) based on methods by Zienkiewicz etOur new methods are designed to provide a set of efficient methods which can be used with local error estimators based on embedding techniques. In a companion paper, we provide a set of subroutines implementing the algorithms derived and a discussion of numerical experience with the resulting codes.

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Cited by 43 publications
(30 citation statements)
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“…This attempts to choose the time steps such that, for a given mesh, the temporal integration error in the displacements lies close to a specified tolerance. For each time step, the local integration error in the displacements is found by taking the difference between a first-order accurate backward Euler solution and a second-order accurate Thomas and Gladwell [35] solution. By choosing the integration parameters in the latter scheme judiciously, this error measure can be computed at negligible additional cost.…”
Section: Coupled Equationsmentioning
confidence: 99%
“…This attempts to choose the time steps such that, for a given mesh, the temporal integration error in the displacements lies close to a specified tolerance. For each time step, the local integration error in the displacements is found by taking the difference between a first-order accurate backward Euler solution and a second-order accurate Thomas and Gladwell [35] solution. By choosing the integration parameters in the latter scheme judiciously, this error measure can be computed at negligible additional cost.…”
Section: Coupled Equationsmentioning
confidence: 99%
“…Thomas and Gladwell [1] presented a family of multistage time-stepping schemes for the solution of second-order ODE systems …”
Section: Introductionmentioning
confidence: 99%
“…where C is given by (19). Although much slower to converge, this approach does not require a fresh factorisation for each iteration and always has a well conditioned Jacobian matrix.…”
Section: Theory For Elastoplastic Algorithmmentioning
confidence: 99%