Nonlinear Structural Dynamics Problems Research Articles

Development of an explicit time integration algorithm based on optimal integration parameter updating

AbstractIntegration algorithm is an important mean to solve the discrete time equations of structural dynamics in earthquake engineering. In this paper, a new model‐based explicit integration algorithm, featured by optimization‐based integration parameter updating, is developed for a more accurate and efficient solution of structural dynamic problems. The new integration algorithm is designed to yield minimum error and controllable numerical dispersion under the premise of stability by defining objective functions and constraints. Numerical properties of the new integration algorithm are investigated in details using the amplification matrix method for a linear elastic single degree‐of‐freedom system. Meanwhile, representative numerical examples and complex structure examples are analyzed and compared with other representative algorithms. As a result, the proposed algorithm yields comparable numerical characteristics with other methods, but exhibits pronounced superiority in controllable algorithmic dissipation, higher precision, and better efficiency. Therefore, the new integration algorithm possesses extensive application prospect in solving complicated linear and nonlinear structural dynamic problems.

Regression-based identification and order reduction method for nonlinear dynamic structural models

In this paper, we propose a regression-based nonlinear reduced-order model for nonlinear structural dynamics problems, called the Nonlinear Identification and Dimension-Order Reduction (NLIDOR) algorithm. We evaluate the algorithm using a simple toy model, a chain of coupled oscillators and an actual three-dimensional flat plate. The results show that NLIDOR can accurately identify the natural frequencies and modes of the system and capture the nonlinear dynamical features, while the linear Dynamic Mode Decomposition (DMD) method can only capture linear features and is influenced by nonlinear terms. Compared with the full-order model (FOM), NLIDOR can effectively reduce computational cost, while compared with DMD, NLIDOR significantly improves computational accuracy. The results demonstrate the effectiveness and potential of NLIDOR for solving nonlinear dynamic problems in various applications.

Retraction Note: An Implicit Unconditionally Stable Integration Method for Nonlinear Structural Dynamics and Earthquake Engineering Problems

Retraction Note: An Implicit Unconditionally Stable Integration Method for Nonlinear Structural Dynamics and Earthquake Engineering Problems

Model order reduction for structural nonlinear dynamic analysis based on Isogeometric analysis

Model order reduction approach generates lower dimensional approximations to the original system while preserving model’s essential information and computational accuracy. For nonlinear structural dynamic problems, where the stiffness matrix is configuration dependent, an iterative solution procedure is inevitable and a revisit to all the elements is essential for updating the stiffness matrix. In this paper, the nonlinear dynamics of the planar curved beams and 3D cylindrical shells are studied based on the isogeometric analysis and their model order reductions are investigated based on the proper orthogonal decomposition and discrete empirical interpolation method (POD-DEIM). Numerical results show that IGA-based POD-DEIM method significantly improves the computational efficiency of the nonlinear dynamic analysis of the beam and shell structures.

RETRACTED ARTICLE: An Implicit Unconditionally Stable Integration Method for Nonlinear Structural Dynamics and Earthquake Engineering Problems

RETRACTED ARTICLE: An Implicit Unconditionally Stable Integration Method for Nonlinear Structural Dynamics and Earthquake Engineering Problems

Isogeometric analysis of 3D solids in boundary representation for problems in nonlinear solid mechanics and structural dynamics

AbstractThis contribution presents an isogeometric approach in three dimensions for nonlinear problems in solid mechanics and structural dynamics. The proposed approach aligns with the boundary representation modeling technique in CAD. Isogeometric analysis is combined with the parameterization of the scaled boundary finite element method. In this way, the boundary description of 3D solids in CAD can be directly utilized for the analysis in an isogeometric framework. The approximation of the solution on the boundary is based on bi‐variate NURBS. We also approximate the interior of the solid with uni‐variate B‐splines to facilitate the solution of nonlinear problems. The nonlinear case is extended to three dimensions in this present work. The method is further extended to dynamic problems. The mass and damping matrices are derived using the same basis functions. The solution of the entire solid is obtained with the Galerkin method. We study several nonlinear and dynamic problems with simple geometries and arbitrary number of boundaries. Moreover, a fiber reinforced composite serves as demonstration for complex shapes.

Two-Step Unconditionally Stable Noniterative Dissipative Displacement Method for Analysis of Nonlinear Structural Dynamics Problems

When solving structural dynamic problems, the displacement algorithm needs only calculating and storing structure’s displacements in the main calculation process, which makes the displacement algorithm have advantages over multivariable algorithms in calculation efficiency and storage requirements. By using a novel approach based on dimensional analysis firstly given by the first author, a one-parameter family of two-step unconditionally stable noniterative displacement algorithms, referred to as the CQ-2x method, is developed. Compared with other unconditionally stable noniterative multivariable algorithms such as the representative KR- α method, the proposed method has advantages in several aspects. The CQ-2x method is unconditionally stable regardless of stiffness hardening or stiffness weakening, while the KR- α method is only conditionally stable in case of stiffness hardening. The CQ-2x method needs only one solver within one time step, while the KR- α method needs two solvers within one time step, which makes the CQ-2x method show higher efficiency. Numerical examples are presented to demonstrate the potential of the proposed method.

A truly self-starting implicit family of integration algorithms with dissipation control for nonlinear dynamics

In this paper, a novel implicit family of composite two sub-step algorithms with controllable dissipations is developed to effectively solve nonlinear structural dynamic problems. The primary superiority of the present method over other existing integration methods lies that it is truly self-starting and so the computation of initial acceleration vector is avoided, but the second-order accurate acceleration responses can be provided. Besides, the present method also achieves other desired numerical characteristics, such as the second-order accuracy of three primary variables, unconditional stability and no overshoots. Particularly, the novel method achieves adjustable numerical dissipations in the low and high frequency by controlling its two algorithmic parameters ( $$ \gamma $$ and $$ \rho _{\infty }$$ ). The classical dissipative parameter $$ \rho _{\infty }$$ determines numerical dissipations in the high-frequency while $$ \gamma $$ adjusts numerical dissipations in the low-frequency. Linear and nonlinear numerical examples are given to show the superiority of the novel method over existing integration methods with respect to accuracy and overshoot.

A Time Integration Method Based on Galerkin Weak Form for Nonlinear Structural Dynamics

This paper presents a step-by-step time integration method for transient solutions of nonlinear structural dynamic problems. Taking the second-order nonlinear dynamic equations as the model problem, this self-starting one-step algorithm is constructed using the Galerkin finite element method (FEM) and Newton–Raphson iteration, in which it is recommended to adopt time elements of degree m = 1,2,3. Based on the mathematical and numerical analysis, it is found that the method can gain a convergence order of 2m for both displacement and velocity results when an ordinary Gauss integral is implemented. Meanwhile, with reduced Gauss integration, the method achieves unconditional stability. Furthermore, a feasible integration scheme with controllable numerical damping has been established by modifying the test function and introducing a special integral rule. Representative numerical examples show that the proposed method performs well in stability with controllable numerical dissipation, and its computational efficiency is superior as well.

A New Family of Explicit Model-Based Integration Algorithms for Structural Dynamic Analysis

A new family of explicit model-based integration algorithms for solving the equations of motion for linear and nonlinear systems is developed. These algorithms are also known as structure-dependent algorithms because the integration parameters are functions of the complete model of the structural system. A variety of numerical properties of the proposed algorithms, including consistency and local truncation error, stability, numerical dispersion and energy dissipation, overshooting, and frequency response under arbitrary excitation, are investigated using the discrete control theory and amplification matrix for linear elastic systems. In addition, the discrete control theory is applied for assessing the stability of the proposed algorithms for nonlinear structural systems. It is observed that the proposed algorithms exhibit the same numerical characteristics as the well-known Newmark family of integration algorithms. Compared with three existing model-based integration algorithms, i.e. the Chen–Ricles, modified Chen–Ricles, and Gui’s algorithms, the proposed algorithms possess more general and versatile numerical features. As a result, the new family of explicit model-based integration algorithms can be potentially used to solve complicated linear and nonlinear structural dynamics problems.

Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics

SummaryRecently, a novel nonparametric probabilistic method for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics was proposed. Its potential was demonstrated through several uncertainty quantification (UQ) applications in vibration analysis and nonlinear computational structural dynamics. This method, which relies on projection‐based model order reduction to achieve computational feasibility, exhibits a vector‐valued hyperparameter in the probability model of the random reduced‐order basis and associated stochastic projection‐based reduced‐order model. It identifies this hyperparameter by formulating a statistical inverse problem, grounded in target quantities of interest, and solving the corresponding nonconvex optimization problem. For many practical applications, however, this identification approach is computationally intensive. For this reason, this paper presents a faster predictor‐corrector approach for determining the appropriate value of the vector‐valued hyperparameter that is based on a probabilistic learning on manifolds. It also demonstrates the computational advantages of this alternative identification approach through the UQ of two three‐dimensional nonlinear structural dynamics problems associated with two different configurations of a microelectromechanical systems device.

Development of a high-order compact finite-difference total Lagrangian method for nonlinear structural dynamic analysis

• A compact finite-difference total Lagrangian method (CFDTLM) is developed for nonlinear structural dynamics analysis. • The large deformation of elastic structures for Hookean and neo-Hookean materials is accurately simulated using the CFDTLM. • The CFDTLM is extended and assessed for simulating thermo-elastodynamic problems. • The numerical solution procedure adopted is simpler than high-order finite volume and finite element formulations . • The solution methodology proposed is accurate for simulating nonlinear structural dynamics problems. A high-order compact finite-difference total Lagrangian method (CFDTLM) is developed and applied to nonlinear structural dynamic analysis. The two-dimensional simulation of thermo-elastodynamics is numerically performed in generalized curvilinear coordinates by taking into account the geometric and material nonlinearities. The spatial discretization is carried out by a fourth-order compact finite-difference scheme and an implicit second-order accurate dual time-stepping method is applied for the time integration. The accuracy and capability of the proposed solution methodology for the nonlinear structural analysis is investigated through simulating different static and dynamic benchmark problems including large deformations, large displacements and Hookean/neo-Hookean materials. The solution method is demonstrated to be free of shear-locking behavior. The results obtained by the present solution algorithm are compared with the analytical solution and the numerical results of the finite element and finite volume methods to examine the accuracy and robustness of the solution method proposed. A grid study is also performed to investigate the grid size effect on the accuracy and performance of the solution. Indications are that the solution methodology proposed is accurate for simulating nonlinear structural dynamics problems.

Unconditionally Stable Explicit Displacement Method for Analyzing Nonlinear Structural Dynamics Problems

AbstractThis paper introduces a novel approach based on dimensional analysis for designing explicit displacement algorithms for use in analyzing nonlinear structural dynamics problems. Using this a...

Generalization of quadratic manifolds for reduced order modeling of nonlinear structural dynamics

In this paper, a generalization of the quadratic manifold approach for the reduction of geometrically nonlinear structural dynamics problems is presented. This generalization is obtained by a linearization of the static force with respect to the generalized coordinates, resulting in a shift of the quadratic behavior from the force to the manifold. In this framework, static derivatives emerge as natural extensions to the modal derivatives for displacement fields other than the vibration modes, such as the Krylov subspace vectors. In the nonlinear projection framework employed here, the dynamic problem is projected onto the tangent space of the quadratic manifold, allowing for a much lower number of generalized coordinates compared to linear basis methods. The potential of the quadratic manifold approach is investigated in a numerical study, where several variations of the approach are compared on different examples, giving a clear indication of where the proposed approach is applicable.

Accelerated mesh sampling for the hyper reduction of nonlinear computational models

SummaryIn nonlinear model order reduction, hyper reduction designates the process of approximating a projection‐based reduced‐order operator on a reduced mesh, using a numerical algorithm whose computational complexity scales with the small size of the projection‐based reduced‐order model. Usually, the reduced mesh is constructed by sampling the large‐scale mesh associated with the high‐dimensional model underlying the projection‐based reduced‐order model. The sampling process itself is governed by the minimization of the size of the reduced mesh for which the hyper reduction method of interest delivers the desired accuracy for a chosen set of training reduced‐order quantities. Because such a construction procedure is combinatorially hard, its key objective function is conveniently substituted with a convex approximation. Nevertheless, for large‐scale meshes, the resulting mesh sampling procedure remains computationally intensive. In this paper, three different convex approximations that promote sparsity in the solution are considered for constructing reduced meshes that are suitable for hyper reduction and paired with appropriate active set algorithms for solving the resulting minimization problems. These algorithms are equipped with carefully designed parallel computational kernels in order to accelerate the overall process of mesh sampling for hyper reduction, and therefore achieve practicality for realistic, large‐scale, nonlinear structural dynamics problems. Conclusions are also offered as to what algorithm is most suitable for constructing a reduced mesh for the purpose of hyper reduction. Copyright © 2016 John Wiley & Sons, Ltd.

Semi-implicit numerical simulations of geometrically nonlinear beam, plate, and shell dynamical systems

ABSTRACTThe following article serves three purposes: (i) it presents a simple semi-implicit numerical formulation for nonlinear structural dynamics problems, which is computationally inexpensive and simple to use in nonlinear dynamics and chaos simulations; (ii) it serves as an introduction to numerical studies of nonlinear structural dynamics for engineering students; and (iii) it formulates a nonlinear structural dynamical system for studies of nonlinear dynamics and chaos. Numerical formulations along with results are presented for nonlinear oscillators, beams, Föppl–von Kármán plates, and thin shallow shells.

Application of Weight Functions in Nonlinear Analysis of Structural Dynamics Problems

This paper presents a weighted residual method with several weight functions for solving differential equation of motion in nonlinear structural dynamics problems. Order of variation of acceleration is assumed to be quadratic in each time step in which polynomial of displacement would contain five unknown coefficients. Five equations are required for determination of these coefficients in each time step. These equations are obtained from initial conditions, satisfying equation of motions at both ends, and weighted residual integration. In this study, four procedures are considered for weight function to be used in the weighted residual integration as; unit weight function, Petrov–Galerkin’s weight function, least square weight function, and collocation weight function. Due to higher order of acceleration in the proposed method, the results indicate better and more accurate responses. Among the tested functions, the unit weighted function method demonstrated to be non-dissipative and its numerical dispersion showed to be clearly less than the common Newmark’s linear acceleration method. Also critical time step duration in stability investigation for weighted function procedure showed to be larger than the critical time step duration obtained by other methods used in the nonlinear structural dynamics problems.

Parabolic and cubic acceleration time integration schemes for nonlinear structural dynamics problems using the method of weighted residuals

ABSTRACTTwo algorithms are proposed for direct time integration of an equation of motion of structural dynamics problems. The performance of the proposed methods is examined by evaluating stability, order of accuracy, numerical dissipation, and algorithmic damping. The results show that critical time for instability of the proposed algorithms is larger than those of conditionally stable methods. The numerical dissipation is shown to be explicitly less than other methods. Furthermore, the proposed algorithms are non-dissipative in the low-frequency range and have favorable damping in mid- and high-frequency regimes. Three examples are carried out to evaluate the feasibility and effectiveness of the proposed algorithms.

Stochastic collocation-based finite element of structural nonlinear dynamics with application in composite structures

Stochastic analysis of structures having nonlinearity by means of sampling methods leads to expensive cost in term of computational time. In contrast, non-sampling methods based on the spectral representation of uncertainty are very efficient with comparable accurate results. In this pa- per, the application of spectral methods to nonlinear dynamics of structures with random parameters is investigated. The impact of the parameter randomness on structural responses has been consid- ered. To this end, uncertain parameters and the structure responses are represented using the gPC expansions with unknown deterministic coefficients and random orthogonal polynomial basis. The deterministic finite element model of the structure is used as black-box and it is executed on a set of random collocation points. As the sample structure responses are estimated, a nonlinear optimization process is employed to calculate the unknown coefficients. The method has this main advantage that can be used for complicated nonlinear structural dynamic problems for which the deterministic FEM model has been already developed. Furthermore, it is very time efficient in comparison with sampling methods, as MC simulations. The application of the method is applied to the nonlinear transient analysis of composite beam structures including uncertain quadratic random damping. The results show that the proposed method can capture the large range of uncertainty in input parameters as well as in structural dynamic responses while it is too time-efficient.

A Robust and Efficient Composite Time Integration Algorithm for Nonlinear Structural Dynamic Analysis

This paper presents a new robust and efficient time integration algorithm suitable for various complex nonlinear structural dynamic finite element problems. Based on the idea of composition, the three-point backward difference formula and a generalized central difference formula are combined to constitute the implicit algorithm. Theoretical analysis indicates that the composite algorithm is a single-solver algorithm with satisfactory accuracy, unconditional stability, and second-order convergence rate. Moreover, without any additional parameters, the composite algorithm maintains a symmetric effective stiffness matrix and the computational cost is the same as that of the trapezoidal rule. And more merits of the proposed algorithm are revealed through several representative finite element examples by comparing with analytical solutions or solutions provided by other numerical techniques. Results show that not only the linear stiff problem but also the nonlinear problems involving nonlinearities of geometry, contact, and material can be solved efficiently and successfully by this composite algorithm. Thus the prospect of its implementation in existing finite element codes can be foreseen.