Two self-starting single-solve third-order explicit integration algorithms for second-order nonlinear dynamics

TL;DR

This paper introduces two novel third-order explicit algorithms for second-order nonlinear dynamics, achieving higher accuracy and better numerical dissipation than existing methods, suitable for large-scale structural dynamic problems.

Contribution

The paper develops two new self-starting single-solve third-order explicit algorithms, filling a gap in existing methods and demonstrating improved accuracy and efficiency in nonlinear dynamic simulations.

Findings

Algorithms outperform existing explicit methods in accuracy.

Built-in numerical dissipation filters high-frequency noise.

Algorithms deliver superior precision at low computational cost.

Abstract

The single-step explicit time integration methods have long been valuable for solving large-scale nonlinear structural dynamic problems, classified into single-solve and multi-sub-step approaches. However, no existing explicit single-solve methods achieve third-order accuracy. The paper addresses this gap by proposing two new third-order explicit algorithms developed within the framework of self-starting single-solve time integration algorithms, which incorporates 11 algorithmic parameters. The study reveals that fully explicit methods with single-solve cannot reach third-order accuracy for general dynamic problems. Consequently, two novel algorithms are proposed: Algorithm 1 is a fully explicit scheme that achieves third-order accuracy in displacement and velocity for undamped problems; Algorithm 2, which employs implicit treatment of velocity and achieves third-order accuracy for general dynamic problems. Across a suite of both linear and nonlinear benchmarks, the new algorithms consistently outperform existing single-solve explicit methods in accuracy. Their built-in numerical dissipation effectively filters out spurious high-frequency components, as demonstrated by two wave propagation problems. Finally, when applied to the realistic engineering problem, both of them deliver superior numerical precision at minimal computational cost.