Perturbation Method as a Powerful Tool to Solve Highly Nonlinear Problems: The Case of Gelfand’s Equation

2013 ◽  
Vol 6 (2) ◽  
pp. 76-82 ◽  
Author(s):  
U. Filobello- ◽  
H. Vazquez-Le ◽  
K. Boubaker ◽  
Y. Khan ◽  
A. Perez-Sesm ◽  
...  
Keyword(s):  
Perturbation Method ◽  
Nonlinear Problems ◽  
Highly Nonlinear

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

Optimization of the Closure-Weld Region of Cylindrical Containers for Long-Term Corrosion Resistance Using the Successive Heuristic Quadratic Approximation Technique*

2003 ◽  
Vol 125 (3) ◽  
pp. 533-539 ◽  
Author(s):  
Zekai Ceylan ◽  
Mohamed B. Trabia
Keyword(s):  
Finite Element ◽  
Random Search ◽  
Nonlinear Problems ◽  
Search Space ◽  
Induction Coil ◽  
Quadratic Approximation ◽  
Approximation Technique ◽  
General Corrosion ◽  
Highly Nonlinear

Welded cylindrical containers are susceptible to stress corrosion cracking (SCC) in the closure-weld area. An induction coil heating technique may be used to relieve the residual stresses in the closure-weld. This technique involves localized heating of the material by the surrounding coils. The material is then cooled to room temperature by quenching. A two-dimensional axisymmetric finite element model is developed to study the effects of induction coil heating and subsequent quenching. The finite element results are validated through an experimental test. The container design is tuned to maximize the compressive stress from the outer surface to a depth that is equal to the long-term general corrosion rate of the container material multiplied by the desired container lifetime. The problem is subject to several geometrical and stress constraints. Two different solution methods are implemented for this purpose. First, an off-the-shelf optimization software is used. The results however were unsatisfactory because of the highly nonlinear nature of the problem. The paper proposes a novel alternative: the Successive Heuristic Quadratic Approximation (SHQA) technique. This algorithm combines successive quadratic approximation with an adaptive random search within varying search space. SHQA promises to be a suitable search method for computationally intensive, highly nonlinear problems.

scholarly journals Contraction Integral Equation for Three-Dimensional Electromagnetic Inverse Scattering Problems

2019 ◽  
Vol 5 (2) ◽  
pp. 27 ◽  
Author(s):  
Yu Zhong ◽  
Kuiwen Xu
Keyword(s):  
Integral Equation ◽  
Inverse Scattering ◽  
Contraction Mapping ◽  
Three Dimensional ◽  
Nonlinear Problems ◽  
Optimization Method ◽  
Scattering Problems ◽  
Physical Reason ◽  
Inverse Scattering Problems ◽  
Highly Nonlinear

Inverse scattering problems (ISPs) stand at the center of many important imaging applications, such as geophysical explorations, industrial non-destructive testing, bio-medical imaging, etc. Recently, a new type of contraction integral equation for inversion (CIE-I) has been proposed to tackle the two-dimensional electromagnetic ISPs, in which the usually employed Lippmann–Schwinger integral equation (LSIE) is transformed into a new form with a modified medium contrast via a contraction mapping. With the CIE-I, the multiple scattering effects, i.e., the physical reason for the nonlinearity in the ISPs, is substantially suppressed in estimating the modified contrast, without compromising physical modeling. In this paper, we firstly propose to implement this new CIE-I for the three-dimensional ISPs. With the help of the FFT type twofold subspace-based optimization method (TSOM), when handling the highly nonlinear problems with strong scatterers, those with higher contrast and/or larger dimensions (in terms of wavelengths), the performance of the inversions with CIE-I is much better than the ones with the LSIE, wherein inversions usually converge to local minima that may be far away from the solution. In addition, when handling the moderate scatterers (those the LSIE modeling can still handle), the convergence speed of the proposed method with CIE-I is much faster than the one with the LSIE. Secondly, we propose to relax the contraction mapping condition, i.e., different contraction mappings are used in updating contrast sources and contrast, and we find that the convergence can be further accelerated. Several numerical tests illustrate the aforementioned interests.

scholarly journals A parametric acceleration of multilevel Monte Carlo convergence for nonlinear variably saturated flow

2019 ◽  
Vol 24 (1) ◽  
pp. 311-331 ◽  
Author(s):  
Prashant Kumar ◽  
Carmen Rodrigo ◽  
Francisco J. Gaspar ◽  
Cornelis W. Oosterlee
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Nonlinear Problems ◽  
Picard Iteration ◽  
Richards Equation ◽  
Porous Media Flow ◽  
Soil Parameters ◽  
Multilevel Monte Carlo ◽  
Highly Nonlinear ◽  
Non Gaussian

AbstractWe present a multilevel Monte Carlo (MLMC) method for the uncertainty quantification of variably saturated porous media flow that is modeled using the Richards equation. We propose a stochastic extension for the empirical models that are typically employed to close the Richards equations. This is achieved by treating the soil parameters in these models as spatially correlated random fields with appropriately defined marginal distributions. As some of these parameters can only take values in a specific range, non-Gaussian models are utilized. The randomness in these parameters may result in path-wise highly nonlinear systems, so that a robust solver with respect to the random input is required. For this purpose, a solution method based on a combination of the modified Picard iteration and a cell-centered multigrid method for heterogeneous diffusion coefficients is utilized. Moreover, we propose a non-standard MLMC estimator to solve the resulting high-dimensional stochastic Richards equation. The improved efficiency of this multilevel estimator is achieved by parametric continuation that allows us to incorporate simpler nonlinear problems on coarser levels for variance reduction while the target strongly nonlinear problem is solved only on the finest level. Several numerical experiments are presented showing computational savings obtained by the new estimator compared with the original MC estimator.

HAM Solutions for Vortex-Induced Vibration of a Circular Cylinder

2014 ◽  
Author(s):  
Zhiliang Lin ◽  
Longbin Tao
Keyword(s):  
Circular Cylinder ◽  
Practical Importance ◽  
Nonlinear Problems ◽  
Van Der Pol Oscillator ◽  
Fluid Structure ◽  
Vortex Induced Vibration ◽  
Highly Nonlinear ◽  
Homotopy Analysis ◽  
Simple Linear Equation ◽  
Wake Oscillation

The vortex-induced vibration (VIV) phenomenon is result of fluid-structure interaction which occurs in many engineering fields. The study of VIV of a circular cylinder is of practical importance (such as in marine cables and flexible risers in petroleum production). In this paper, one classical phenomenological VIV model — the motion of the cylinder is modeled by a simple linear equation, and the fluctuating nature of the vortex wake oscillation is modeled by a van der Pol oscillator, is analyzed. Firstly, the homotopy analysis method (HAM), a powerful technique for highly nonlinear problems, is developed to solve the coupled fluid-structure dynamical system with the convergence of the homotopy series solutions being demonstrated. Based on the HAM solutions, some properties of the fully nonlinear classical coupled VIV model are presented. All the results proved that the proposed HAM scheme has potential to be an effective analytic technique to study the VIV problems.

scholarly journals Response Analysis of the Free Field under Fault Movements

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
H. L. Qu ◽  
Y. Wu ◽  
B. K. Zhang ◽  
Q. D. Hu ◽  
Z. L. Xiao
Keyword(s):  
Free Field ◽  
Development Program ◽  
Normal Fault ◽  
Nonlinear Problems ◽  
Secondary Development ◽  
Response Analysis ◽  
Soil Parameters ◽  
Reverse Fault ◽  
Critical Displacement ◽  
Highly Nonlinear

A quasistatic simulation of highly nonlinear problems under fault movements was carried out using the EXPLICIT module of ABAQUS. Combined with the secondary development program of the software, the application of the strain softening Mohr–Coulomb model in the simulation was realized. Free field-fault systems were simulated with two types of fault types (normal and reverse faults), four fault dip angles (45°, 60°, 75°, and 90°), and two kinds of soil (sand and clay). Moreover, the rupture laws and sensitivities of the sand and clay were studied with different soil thicknesses and different fault dip angles in the free field. The results show that the width of the ground zone with obvious deformation, which represents the point of the fault outcrop, the critical displacement of the fault, and the rupture characteristics of the overlying soil are closely related to the fault type and soil parameters. The critical displacement of the reverse fault is larger than that of the normal fault. The width of the ground zone with obvious deformation varies from 0.65 to 1.3 and does not exhibit a regular relationship with the type of soil. Compared with a normal fault, the rupture of a reverse fault is not prone to exposure at the surface.

scholarly journals Ellipsoidal and Gaussian Kalman Filter Model for Discrete-Time Nonlinear Systems

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1168 ◽  
Author(s):  
Ligang Sun ◽  
Hamza Alkhatib ◽  
Boris Kargoll ◽  
Vladik Kreinovich ◽  
Ingo Neumann
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Discrete Time ◽  
Probability Distributions ◽  
Nonlinear Problems ◽  
The State ◽  
Filter Model ◽  
Highly Nonlinear ◽  
Correction Step

In this paper, we propose a new technique—called Ellipsoidal and Gaussian Kalman filter—for state estimation of discrete-time nonlinear systems in situations when for some parts of uncertainty, we know the probability distributions, while for other parts of uncertainty, we only know the bounds (but we do not know the corresponding probabilities). Similarly to the usual Kalman filter, our algorithm is iterative: on each iteration, we first predict the state at the next moment of time, and then we use measurement results to correct the corresponding estimates. On each correction step, we solve a convex optimization problem to find the optimal estimate for the system’s state (and the optimal ellipsoid for describing the systems’s uncertainty). Testing our algorithm on several highly nonlinear problems has shown that the new algorithm performs the extended Kalman filter technique better—the state estimation technique usually applied to such nonlinear problems.

scholarly journals Optimal solution error covariance in highly nonlinear problems of variational data assimilation

2012 ◽  
Vol 19 (2) ◽  
pp. 177-184 ◽  
Author(s):  
V. Shutyaev ◽  
I. Gejadze ◽  
G. J. M. Copeland ◽  
F.-X. Le Dimet
Keyword(s):  
Nonlinear Dynamics ◽  
Data Assimilation ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Variational Data Assimilation ◽  
Model Parameters ◽  
Efficient Computation ◽  
Viscous Term ◽  
Highly Nonlinear ◽  
The Cost

Abstract. The problem of variational data assimilation (DA) for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition, boundary conditions and/or model parameters. The input data contain observation and background errors, hence there is an error in the optimal solution. For mildly nonlinear dynamics, the covariance matrix of the optimal solution error can be approximated by the inverse Hessian of the cost function. For problems with strongly nonlinear dynamics, a new statistical method based on the computation of a sample of inverse Hessians is suggested. This method relies on the efficient computation of the inverse Hessian by means of iterative methods (Lanczos and quasi-Newton BFGS) with preconditioning. Numerical examples are presented for the model governed by the Burgers equation with a nonlinear viscous term.

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