Mathematical and Computational Applications· 2026Q2
Numerical Integration of FGM Beam Vibration Equations Using a Discrete Variational Approach with Algebraic Constraints
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- Q2SCImago
- 2026year
Short summary
A new constrained differential-quadrature/discrete-variational framework (DVM) accurately integrates functionally graded beam vibration equations, achieving position-level constraint residuals near machine precision and suppressing drift compared to RK4.
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Key points
- Developed a constrained differential-quadrature/discrete-variational framework (DVM) for FGM beam vibration analysis.
- DVM achieves position-level constraint residuals near machine precision and suppresses drift compared to RK4.
- The DVM response remains bounded in simulations up to 10,000 s.
- Spatial refinement from 5 to 11 nodes reduces L∞ displacement error from 4.01×10−3 to 8.26×10−10.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper develops a constrained differential-quadrature/discrete-variational framework for vibration analysis of functionally graded beams. Spatial derivatives are discretized on a Chebyshev–Lobatto grid, while time integration is constructed from Lagrange interpolation and Gauss–Legendre quadrature. Boundary conditions are retained as algebraic constraints and enforced with Lagrange multipliers, leading to the semi-discrete operator K=k1I+kf2A(4)−k2A(2). For the default two-node formulation, the DVM keeps position-level constraint residuals near machine precision and suppresses cumulative position- and velocity-level drift relative to the RK4 comparison, while acceleration-level residuals remain of comparable order. The DVM response remains bounded in simulations up to 10,000 s. An empirical time-step scan is stable through h=0.30 and unstable at h=0.35 for the tested problem. Temporal convergence against an exact-in-time solution of the same constrained semi-discrete system is essentially second order, and pairwise self-convergence gives the same result. Spatial refinement from 5 to 11 Chebyshev–Lobatto nodes reduces the L∞ displacement error from 4.01×10−3 to 8.26×10−10, consistent with spectral-type DQM convergence. Additional sensitivity studies confirm that the principal conclusions are robust to algebraic-solver initialization, stopping tolerance, and several boundary-condition choices within the tested parameter range.
The authors' abstract, as published at the source. Mathematical and Computational Applications, 2026 · DOI ↗
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Field: Numerical Analysis
Numerical AnalysisMathematics