Adjoint‐weighted variational formulation for the direct solution of inverse problems of general linear elasticity with full interior data

We describe a novel variational formulation of inverse elasticity problems given interior data. The class of problems considered is rather general and includes, as special cases, plane deformations, compressibility and incompressiblity in isotropic materials, 3D deformations, and anisotropy. The strong form of this problem is governed by equations of pure advective transport. The variational formulation is based on a generalization of the adjoint‐weighted variational equation (AWE) formulation, originally developed for flow of a passive scalar. We describe how to apply AWE to various cases, and prove several properties. We prove that the Galerkin discretization of the AWE formulation leads to a stable, convergent numerical method, and prove optimal rates of convergence. The numerical examples demonstrate optimal convergence of the method with mesh refinement for multiple unknown material parameters, graceful performance in the presence of noise, and robust behavior of the method when the target solution is C^∞, C⁰, or discontinuous. Copyright © 2009 John Wiley & Sons, Ltd.     

Solving inverse electromagnetic problems using FDTD and gradient‐based minimization

We address time‐domain inverse electromagnetic scattering for determining unknown characteristics of an object from observations of the scattered field. Applications include non‐destructive characterization of media and optimization of material properties, for example, the design of radar absorbing materials. Another application is model reduction where a detailed model of a complex geometry is reduced to a simplified model. The inverse problem is formulated as an optimal control problem where the cost function to be minimized is the difference between the estimated and observed fields, and the control parameters are the unknown object characteristics. The problem is solved in a deterministic gradient‐based optimization algorithm using a parallel 2D FDTD scheme. Highly accurate analytical gradients are computed from the adjoint formulation. The inverse method is applied to the characterization of layered dispersive media and the determination of parameters in subcell models for thin sheets and narrow slots. Copyright © 2006 John Wiley & Sons, Ltd.     

NEW REGULARIZATION BY TRANSFORMATION FOR NEURAL NETWORK BASED INVERSE ANALYSES AND ITS APPLICATION TO STRUCTURE IDENTIFICATION

The present authors have been developing an inverse analysis approach using the multilayer neural network and the computational mechanics. This approach basically consists of the following three subprocesses. First, parametrically varying model parameters of a system, their corresponding responses of the system are calculated through computational mechanics simulations such as the finite element analyses, each of which is an ordinary direct analysis. Each data pair of model parameters vs. system responses is called training pattern. Second, a neural network is iteratively trained using a number of training patterns. Here the system responses are given to the input units of the network, while the model parameters to be identified are shown to the network as teacher data. Finally, some system responses measured are given to the well-trained network, which immediately outputs appropriate model parameters even for untrained patterns. This is an inverse analysis. This paper proposes a new regularization method suitable for the inverse analysis approach mentioned above. This method named the Generalized-Space-Lattice (GSL) transformation transforms original input and/or output data points of all training patterns onto uniformly spaced lattice points over a multi-dimensional space. The topological relationships among all the data points are maintained through this transformation. The neural network is then trained using the GSL-transformed training patterns. Since this method significantly remedies localization of training patterns caused due to strong nonlinearity of problem, the neural network can learn the training patterns efficiently as well as accurately. Fundamental performances of the present inverse analysis approach combined with the GSL transformation are examined in detail through the identification of a vibrating non-uniform beam in Young's modulus based on the observation of its multiple eigenfrequencies and eigenmodes.     

Productivity improvement through a sequencing generalised assignment in an assembly line system

This paper considers the assignment of heterogeneous workers to workstations of an assembly line in order to minimise the total production time. As the structure of the system implies that each of the workstations needs at least one worker, thus the problem can be considered as a generalised assignment problem (GAP). The objective is to perform an efficient human resource planning for a specified horizon consisting of several periods. Hence, we present an extension of the generalised assignment problem, consisting of a set of GAPs (one for each planning period) in which each GAP depends on the previous ones. A mixed integer mathematical model is presented for this sequencing assignment problem. The model is solved by an exact algorithm using Gurobi solver. It is proved that the problem is NP-hard and solving the medium and large size instances is not possible by the exact algorithms. Hence, two matheuristic approaches based on the disaggregated formulation of GAP are proposed. The first approach solves the problem through two sub-problems as the transportation formulation and assignment formulation. The second approach solves the problem by decomposition of the problem into several classical GAPs. The approaches are examined by a total of 27 instances. The results illustrate the efficiency of the proposed algorithms in the computational time and accuracy of the solutions.     

A complementary note on Gegenbauer polynomial approximation for random response problem of stochastic structure

Recently, Gegenbauer polynomial approximation was proposed for solving the evolutionary random response problem of a random structure with bounded random parameters under evolutionary random excitations. The bounded random parameters used there are supposed to be proportional to a random variable with λ-PDF (probability density function). For this kind of random parameter, the Gegenbauer polynomial approximation is the unique correct choice for transforming a random structure into its deterministic equivalent system, which plays a central role in solving the response problem. Actually, the Gegenbauer polynomial approximation bridges the gap between the random structural response problem and the conventional methods. Just through its deterministic equivalent system, the random response problem of a random structure can be solved by any available, analytical and numerical method developed for deterministic systems. But the simple assumption on proportionality to λ-PDF may bring some unnecessary limitation on symmetry. Since λ-PDF is symmetrical about its center axis, so are these random parameters. However, not all random parameters have this kind of symmetry. Then, what can we do to lessen the limitation, if random parameters are non-symmetric per se? Besides, the graph of λ-PDF (see Fig. 1 in the text) shows its features’ dependence on λ, that is, the smaller the λ, the more dispersive the PDF. Then, what is the influence of different values of λ on the evolutionary random responses of the stochastic system? Moreover, is there any qualitative information about the dispersion in response with respect to every individual random parameter? This note is devoted to answering the above two questions and giving a supplementary note on Gegenbauer polynomial approximation. Our study shows that the symmetric limitation can be partly lessened by putting the random parameter into a polynomial function, even a quadratic one, of a random variable with λ-PDF. On the other hand, the second-order moment of the random responses of the stochastic structure relative to that of a nominal one can be used to describe qualitatively their relative dispersion with respect to each individual random parameter.     

Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem

The goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface measurements and a mathematical model of torso–heart geometry that relates the sources to the measurements. This problem is ill-posed due to attenuation and smoothing that occur inside the thorax, and small errors in the measurements yield large reconstruction errors. To overcome this, ill-posedness, traditional regularization methods such as Tikhonov regularization and truncated singular value decomposition and statistical approaches such as Bayesian Maximum A Posteriori estimation and Kalman filter have been applied. Statistical methods have yielded accurate inverse solutions; however, they require knowledge of a good a priori probability density function, or state transition definition. Minimum relative entropy (MRE) is an approach for inferring probability density function from a set of constraints and prior information, and may be an alternative to those statistical methods since it operates with more simple prior information definitions. However, success of the MRE method also depends on good choice of prior parameters in the form of upper and lower bound values, expected uncertainty in the model and the prior mean. In this paper, we explore the effects of each of these parameters on the solution of inverse ECG problem and discuss the limitations of the method. Our results show that the prior expected value is the most influential of the three MRE parameters.     

An accurate penalty-based approach for reliability-based design optimization

A typical reliability-based design optimization (RBDO) problem is usually formulated as a stochastic optimization model where the performance of a system is optimized with the reliability requirements being satisfied. Most existing RBDO methods divide the problem into two sub-problems: one relates to reliability analysis, the other relates to optimization. Traditional approaches nest the two sub-problems with the reliability analysis as the inner loop and the optimization as the outer loop. Such nested approaches face the challenge of prohibitive computational expense that drives recent research focusing on decoupling the two loops or even fundamentally transforming the two-loop structure into one deterministic optimization problem. While promising, the potential issue in these computationally efficient approaches is the lowered accuracy. In this paper, a new decoupled approach, which performs the two loops sequentially, is proposed. First, a deterministic optimization problem is solved to locate the means of the uncertain design variables. After the mean values are determined, the reliability analysis is performed. A new deterministic optimization problem is then restructured with a penalty added to each limit-state function to improve the solution iteratively. Most existing research on decoupled approaches linearizes the limit-state functions or introduces the penalty into the limit-state functions, which may suffer the approximation error. In this research, the penalty term is introduced to change the right hand side (RHS) value of the deterministic constraints. Without linearizing or transforming the formulations of limit-state function, this penalty-based approach effectively improves the accuracy of RBDO. Comparison experiments are conducted to illustrate how the proposed method obtains improved solutions with acceptable computational cost when compared to other RBDO approaches collected from literature.     

Proper generalized decomposition solution of the parameterized Helmholtz problem: application to inverse geophysical problems

The identification of the geological structure from seismic data is formulated as an inverse problem. The properties and the shape of the rock formations in the subsoil are described by material and geometric parameters, which are taken as input data for a predictive model. Here, the model is based on the Helmholtz equation, describing the acoustic response of the system for a given wave length. Thus, the inverse problem consists in identifying the values of these parameters such that the output of the model agrees the best with observations. This optimization algorithm requires multiple queries to the model with different values of the parameters. Reduced order models are especially well suited to significantly reduce the computational overhead of the multiple evaluations of the model. In particular, the proper generalized decomposition produces a solution explicitly stating the parametric dependence, where the parameters play the same role as the physical coordinates. A proper generalized decomposition solver is devised to inexpensively explore the parametric space along the iterative process. This exploration of the parametric space is in fact seen as a post‐process of the generalized solution. The approach adopted demonstrates its viability when tested in two illustrative examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Low‐frequency assessment of the in situ acoustic absorption of materials in rooms: an inverse problem approach using evolutionary optimization

The in situ assessment of the acoustic absorption of materials is often a necessity. The need to cover the whole frequency range of interest for the building engineer has led the authors to an approach involving two frequency‐complementary measurement methods. This paper deals with the part dedicated to low frequencies. The measurement is defined here as a boundary inverse interior problem. A numerical model of the room under investigation, allowing for the computation of the pressure field in the volume, given impedance boundary conditions and a point source, is combined to a global optimization algorithm. The algorithm explores the set of possible boundary conditions in order to minimize the difference between the computed pressure values and the one observed at a few measurement points, leading to the determination of all the boundary conditions at a time. In practice, the finite element method (FEM) or the finite difference method (FDM) is used here to model the room and an Evolution Strategy as the optimization tool. After describing the ES operators, a numerical study is carried out on simulated measurements, both on problem‐ and algorithm‐specific parameters, in the case of an academic two‐dimensional room geometry. The method is then applied to a three‐dimensional room with promising results. Copyright © 2002 John Wiley & Sons, Ltd.     

An inverse approach in obtaining shape functions for a superconvergent thin plate element

This article presents an inverse approach to provide shape functions associate with a superconvergent thin plate element formulation. In the proposed approach, candidates for shape functions of an element are selected using a series of functions such as, trigonometric series, simple and hierarchical polynomials or a combination of them. Next, one imposes all the physical, geometrical, compatibility and completeness constraints associated with the concerned element on these function. In the final stage, the unknown parameters of the shape functions are determined by minimizing the discrimination errors in the element formulation. The proposed method is employed to determine the shape functions associate with the superconvergent plate element formulation. The accuracy of the obtained formulation is examined against previously developed plate models using several numerical examples. These comparisons indicate the developed model provides more accurate results both in local and global coordinates system.     

An inverse problem of simultaneously estimating contact conductance and heat transfer coefficient of exhaust gases between engine's exhaust valve and seat

The conjugate gradient method using two search step sizes is used to solve the inverse problem of simultaneously estimating the periodic thermal contact conductance, h_c(t), and the heat transfer coefficient of the exhaust gases, h_g(t), between the exhaust valve and seat in an internal combustion engine. The importance of the determination of h_c(t) and h_g(t) lie in that they are the critical factors for designing the cooling system and the insulation of the exhaust valve. The inverse analysis is based on the temperature measurements taken from the sensors placed in both the valve and seat regions during the transient process of operation. In this study two unknown timewise-varying functions h_c(t) and h_g(t) are to be estimated at the same time, thus two search step sizes with each one corresponding to each unknown function are derived. The results show that the CPU time for the inverse solutions using two search step sizes are greatly reduced than using just one search step size¹ for the determination of two unknowns, besides, it also shows that the inverse solutions are reliable even when the measurement errors are considered. The advantage of the conjugate gradient method is that no a priori information is needed on the variation of the unknown quantities, since the solution automatically determines the functional form over the domain specified. The successful development of the present technique can be applied to any kind of two-dimensional periodic contact problems, such as the determination of a two-dimensional contact conductance problem² and the temperature or heat flux behaviour on the inside wall of internal combustion engines³.     

Proper generalized decomposition of a geometrically parametrized heat problem with geophysical applications

The solution of a steady thermal multiphase problem is assumed to be dependent on a set of parameters describing the geometry of the domain, the internal interfaces and the material properties. These parameters are considered as new independent variables. The problem is therefore stated in a multidimensional setup. The proper generalized decomposition (PGD) provides an approximation scheme especially well suited to preclude dramatically increasing the computational complexity with the number of dimensions. The PGD strategy is reviewed for the standard case dealing only with material parameters. Then, the ideas presented in [Ammar et al., “Parametric solutions involving geometry: A step towards efficient shape optimization.” Comput. Methods Appl. Mech. Eng., 2014; 268 :178–193] to deal with parameters describing the domain geometry are adapted to a more general case including parametrization of the location of internal interfaces. Finally, the formulation is extended to combine the two types of parameters. The proposed strategy is used to solve a problem in applied geophysics studying the temperature field in a cross section of the Earth crust subsurface. The resulting problem is in a 10-dimensional space, but the PGD solution provides a fairly accurate approximation (error ≤1%) using less that 150 terms in the PGD expansion. Copyright © 2015 John Wiley & Sons, Ltd.     

高层建筑损伤识别中的补偿算法

本文在已有研究工作的基础上，提出了处理激励测量信息不完备情况下的补偿算法。此方法主要针对高层建筑动力检测中强迫激励的情况，是一种基于时域的识别方法，该方法实际上是利用客观工程实际中结构系统激励所具有的物理持征作为识别计算的辅助条件，来解决测量信息的不完备性问题。算例分析表明了该方法的可行性。     

A STOCHASTIC MODEL FOR THE SIMULATION OF DRAFTING

Drafting is an important process taking place at several stages of yarn manufacturing. We present a stochastic model which describes the interactions of individual fibres and is inspired by the spin flip process used in statistical mechanics. Simulating on the basis of such a model amounts to seeking an approximate solution of a minimal cut problem which is the formulation of a deterministic approach in term of friction forces.     

Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving

In this paper, a polymorphic uncertain nonlinear programming (PUNP) approach is developed to formulate the problem of maximizing the capacity in a system of V-belt driving with uncertainties. The constructed optimization model is found to consist of a nonlinear objective function and some nonlinear constraints with some parameters which are of uncertain nature. These uncertain parameters are interval parameters, random interval parameters, fuzzy parameters or fuzzy interval parameters. To find a robust solution of the problem, a deterministic equivalent formulation (DEF) is established for the polymorphic uncertain nonlinear programming model. For a given satisfaction level, this DEF turns out to be a nonlinear programming involving only interval parameters. A solution method, called a sampling based interactive method, is developed such that a robust solution of the original model with polymorphic uncertainties is obtained by using standard smooth optimization techniques. The proposed method is applied into a real-world design of V-belt driving, and the results indicate that both the PUNP approach and the developed algorithm are useful to the optimization problem with polymorphic uncertainty.     

Regularization methods in image restoration: An application to HST images

The mathematic problem of restoring an image degraded by blurring and noise is ill-posed, so that the solution is affected by numeric instability. As a consequence, the solution provided by the so-called inverse filter is completely contaminated by noise and, in general, is deprived of any physical meaning. If one looks for approximate solutions, the ill-posedness of the problem implies that the set of these solutions is too broad. For this reason, one must look for approximate solutions satisfying some kind of a priori constraints, the so-called a priori information. This fact explains the variety of methods, usually called regularization methods, which have been designed for solving this kind of problems. In this article we briefly review some of the most widely used methods, both deterministic and probabilistic, and show their effectiveness in the restoration of some HST images.     

Fuzzy logic based approach to FRP retrofit of columns

Current design approaches for seismic retrofit use deterministic variables to describe the geometry, material properties and the applied loads on the bridge column. Using a mechanistic model that considers nonlinear material behavior, these deterministic input variables can be directly mapped to the design parameters. However the results often give a false sense of reliability due to neglecting uncertainties related to the input variables of the analysis (data uncertainty), unpredictable fluctuations of loads and natural variability of material properties, and/or the uncertainty in the analytical model itself (model uncertainty). While methods of reliability analysis can provide a means for designing so as not to exceed specific levels of “acceptable” risk, they do not consider the uncertainty in the assumption of distribution functions for each of the input variables and are built on the basic assumption that the models used perfectly describe reality. This, however, still results in significant unknowns and often design models that are not truly validated across their response space. This paper describes the application of a fuzzy probabilistic approach to capture the inherent uncertainty in such applications. The application of the approach is demonstrated through an example and results are compared to those obtained from conventional deterministic analytical models. It is noted that the confidence in the achieved safety of the retrofit system that is based on the use of the fuzzy probabilistic approach is much higher than that achieved using the deterministic approach. This is due to the consideration of uncertainty in the material parameters as well as the consideration of uncertainty in the assumed crack angle during the design process.     

Single machine scheduling problem with stochastic sequence-dependent setup times

In this study, we consider stochastic single machine scheduling problem. We assume that setup times are both sequence dependent and uncertain while processing times and due dates are deterministic. In the literature, most of the studies consider the uncertainty on processing times or due dates. However, in the real-world applications (i.e. plastic moulding industry, appliance assembly, etc.), it is common to see varying setup times due to labour or setup tools availability. In order to cover this fact in machine scheduling, we set our objective as to minimise the total expected tardiness under uncertain sequence-dependent setup times. For the solution of this NP-hard problem, several heuristics and some dynamic programming algorithms have been developed. However, none of these approaches provide an exact solution for the problem. In this study, a two-stage stochastic-programming method is utilised for the optimal solution of the problem. In addition, a Genetic Algorithm approach is proposed to solve the large-size problems approximately. Finally, the results of the stochastic approach are compared with the deterministic one to demonstrate the value of the stochastic solution.     

Coefficient identification for cubically anisotropic elastic media

In this paper, we consider a version of the inverse problem for a homogeneous, cubically anisotropic, elastic half space. Such a medium is characterized by four constants, and our goal is to study the properties of the mapping which associates to these constants the normal component, but not the transverse components, of a corresponding point source response. This may be viewed as the first stage in a ‘layer stripping’ approach to the inverse problem when the four parameters are allowed to be depth dependent.     

基于区间分析的不确定性结构动力学模型修正方法

提出了一种基于区间分析的不确定性有限元模型修正方法。在区间参数结构特征值分析理论和确定性有限元模型修正方法基础上,假设不确定性与初始有限元模型误差均较小,采用灵敏度方法推导了待修正参数区间中点值和不确定区间的迭代格式。以三自由度弹簧-质量系统和复合材料板为例,采用拉丁超立方抽样构造仿真试验模态参数样本,开展仿真研究。结果表明,当仿真试验样本能准确反映结构模态参数的区间特性时,方法的收敛精度和效率均较高;修正后计算模态参数能准确反映试验数据的区间特性。所提出方法适用于解决试验样本较少,仅能得到试验模态参数区间的有限元模型修正问题。