scholarly journals Analysis of a Nondestructive Evaluation Technique for Defect Characterization in Magnetic Materials Using Local Magnetic Measurements

2010 ◽  
Vol 2010 ◽  
pp. 1-18 ◽  
Author(s):  
Stephane Durand ◽  
Ivan Cimrák ◽  
Peter Sergeant ◽  
Ahmed Abdallh
Keyword(s):  
Inverse Problem ◽  
Magnetic Measurements ◽  
Optimization Procedure ◽  
Computational Time ◽  
Defect Characterization ◽  
Proper Position ◽  
Sensitivity Equation ◽  
Small Perturbations ◽  
Gradient Based ◽  
Local Measurements

We study a method for nondestructive testing of laminated strips of a nonlinear magnetic material. Based on local measurements of the magnetic induction at the surface, we are able to reconstruct the proper position of defects inside the material, by solving an inverse problem. This inverse problem is solved by minimizing a suitable cost function using a gradient-based optimization procedure. Calculation of the gradient is done either by the standard method of small perturbations or by solving the sensitivity equation. The latter method yields a significant reduction of the computational time. The validity of the proposed algorithm is confirmed by experimental results.

Fluid Dynamic Design Optimization of the Intake of a Small Turbojet

2006 ◽  
Author(s):  
Riccardo Amirante ◽  
Luciano A. Catalano ◽  
Andrea Dadone ◽  
Vito S. E. Daloiso ◽  
Dario Manodoro
Keyword(s):  
Design Optimization ◽  
Electricity Generation ◽  
Fluid Dynamic ◽  
Optimization Procedure ◽  
Black Box ◽  
High Load ◽  
Computational Time ◽  
Design Criteria ◽  
Dynamic Design ◽  
Gradient Based

This paper proposes an efficient gradient-based optimization procedure for black-box simulation codes and its application to the fluid-dynamic design optimization of the intake of a small-size turbojet, at high load and zero flight speed. Two simplified design criteria have been considered, which avoid to simulate the flow in any turbojet components other than the intake itself. Both design optimizations have been completed in a computational time corresponding to that required by eight flow analyses and have provided almost coincident optimal profiles for the intake. The flow fields computed with the original and the optimal profiles are compared to demonstrate the flow pattern improvements that can be theoretically achieved. Finally, the original and the optimal profiles have been mounted on the same small-size turbojet and experimentally tested, to assess the resulting improvements in terms of overall performances. All numerical and experimental results can be obviously extended to the intake of a microturbine for electricity generation.

scholarly journals Optoacoustic image reconstruction: the full inverse problem with variable bases

2018 ◽  
Vol 474 (2219) ◽  
pp. 20180369 ◽  
Author(s):  
S. Schoeder ◽  
I. Olefir ◽  
M. Kronbichler ◽  
V. Ntziachristos ◽  
W. A. Wall
Keyword(s):  
Inverse Problem ◽  
Mass Density ◽  
Optimization Procedure ◽  
Optoacoustic Imaging ◽  
Numerical Studies ◽  
Long Time ◽  
Identification Technique ◽  
Gradient Based ◽  
Ill Conditioning

Optoacoustic imaging was for a long time concerned with the reconstruction of energy density or optical properties. In this work, we present the full inverse problem with respect to optical absorption and diffusion as well as speed of sound and mass density. The inverse problem is solved by an iterative gradient-based optimization procedure. Since the ill-conditioning increases with the number of sought parameters, we propose two approaches to improve the conditioning. The first approach is based on the reduction of the size of the basis for the parameter spaces, that evolves according to the particular characteristics of the solution, while maintaining the flexibility of element-wise parameter selection. The second approach is a material identification technique that incorporates prior knowledge of expected material types and uses the acoustical gradients to identify materials uniquely. We present numerical studies to illustrate the properties and functional principle of the proposed methods. Significant convergence speed-ups are gained by the two approaches countering ill-conditioning. Additionally, we show results for the reconstruction of a mouse brain from in vivo measurements.

A gradient based optimization procedure for finding axle weights in probabilistic bridge weigh-in-motion method

2020 ◽  
Author(s):  
Matheus Silva Gonçalves ◽  
Felipe Carraro ◽  
Rafael Holdorf Lopez
Keyword(s):  
Computational Complexity ◽  
Optimization Procedure ◽  
Grid Search ◽  
Original Algorithm ◽  
Influence Line ◽  
Weigh In Motion ◽  
Gradient Based ◽  
Bridge Weigh In Motion ◽  
Proposed Modification

Bridge weight in motion (BWIM) consists in the use of sensors on bridges to assess the loads of passing vehicles. Probabilistic Bridge Weight in Motion (pBWIM) is an approach for solving the inverse problem of finding vehicle axle weights based on deformation information. The pBWIM approach uses a probabilistic influence line and seeks the most probable axle weights, given in-situ measurements. To compute such weights, the original pBWIM employed a grid search, which may lead to computational complexity, specially when applied to vehicles with high number of axles. Hence, this note presents an improved version of pBWIM, modifying how the most probable weights are sough. Here, a gradient based optimization procedure is proposed for replacing the computationally expensive grid-search of the original algorithm. The required gradients are fully derived and validated in numerical examples. The proposed modification is shown to highly decrease the computational complexity of the problem.

Nonlinear Analysis and Optimization of Diamond Cell Morphing Wings

Aerospace ◽  
2006 ◽  
Author(s):  
Terrence Johnson ◽  
Mary Frecker ◽  
James Joo ◽  
Mostafa Abdalla ◽  
Brian Sanders ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Optimization Procedure ◽  
Two Stage ◽  
Aerodynamic Loads ◽  
Morphing Wings ◽  
Unit Cells ◽  
Gradient Based ◽  
Element Approach ◽  
Multiple Unit ◽  
Elastic Skin

In this work, a design optimization procedure is developed to maximize the energy efficiency of a scissor mechanism for the NextGen's Batwing application. The unit cells are modeled using a finite element approach. The model considers elastic skin, modeled as linear springs, as well as actuator and aerodynamic loads. A nonlinear large displacement analysis is conducted, and the position of the actuator is optimized using Matlab's gradient based optimization algorithm FMINCON. This optimization procedure is used to investigate the effect of different constraints and load cases. The model is expanded to include multiple unit cells and actuators. A two stage optimization process using a Genetic Algorithm and traditional gradient based optimization (FMINCON) is also developed. The two stage optimization is used to optimize actuator position and placement for different constraints and load cases. Results show that placement and position optimization produce small gains in maximizing energy efficiency; morphing using a soft isotropic skin is more efficient than stiff isotropic and anisotropic skin. In addition, the GA did not use the all of the available actuators to maximize energy efficiency.

scholarly journals Comparison of Two Software Tools for Damage Identification: Gradient-Based vs. Case-Based Approach

2005 ◽  
Vol 293-294 ◽  
pp. 103-110
Author(s):  
Przemysław Kołakowski ◽  
Luis E. Mujica ◽  
Josep Vehí
Keyword(s):  
Damage Identification ◽  
Element Model ◽  
Identification Problem ◽  
Optimization Method ◽  
Software Tools ◽  
Similarity Criteria ◽  
Computational Time ◽  
Case Based Reasoning ◽  
Gradient Based ◽  
Case Based

Two alternative software tools for damage identification are presented. The first tool, developed on the basis of the Virtual Distortion Method (VDM), takes advantage of an analytical formulation of the damage identification problem. Consequently, gradient-based optimization method is applied to solve the resulting dynamic inverse problem in time domain. Finite element model of the structure is necessary for the VDM approach. The second tool utilizes the Case-Based Reasoning (CBR) for damage identification. This method consists in i) extracting principal features of the response signal by wavelet transform, ii) creating a base of representative damage cases, iii) organizing and training the base by neural networks, and finally iv) retrieving and adapting a new case (possible damage) by similarity criteria. Basic description of both approaches is given. A comparison of numerical effectiveness, in terms of accuracy and computational time, is provided for a simple beam structure. Advantages and weaknesses of each approach are highlighted.

scholarly journals Stochastic modeling error reduction using Bayesian approach coupled with an adaptive Kriging-based model

2014 ◽  
Vol 33 (3) ◽  
pp. 856-867 ◽  
Author(s):  
Ahmed Abou-Elyazied Abdallh ◽  
Luc Dupré
Keyword(s):  
Inverse Problem ◽  
Stochastic Modeling ◽  
Approximation Error ◽  
Memory Storage ◽  
Computational Time ◽  
Problem Solution ◽  
Content Type ◽  
Inverse Problem Solution ◽  
Bayesian Approximation ◽  
Adaptive Kriging

Purpose – Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution. Design/methodology/approach – The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique. Findings – The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage. Originality/value – The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction.

Improved centrifugal compressor impeller optimization with a radial basis function network and principle component analysis

2010 ◽  
Vol 224 (4) ◽  
pp. 935-945 ◽  
Author(s):  
Y Ma ◽  
A Engeda ◽  
M Cave ◽  
J-L Di Liberti
Keyword(s):  
Radial Basis Function ◽  
Centrifugal Compressor ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Optimization Procedure ◽  
Component Analysis ◽  
Computational Time ◽  
Radial Basis ◽  
Compressor Impeller ◽  
Function Network

The development of a fast and reliable computer-aided design and optimization procedure for centrifugal compressors has attracted a great deal of attention both in the industry and in academia. Artificial neural networks (ANNs) have been widely used to create an approximate performance map to substitute the direct application of flow solvers in the optimization procedure. Although ANNs greatly decrease the computational time for the optimization, their accuracies still limit their applications. Furthermore, ANNs also bring errors to the final results. In this study, principal component analysis (PCA) or independent component analysis (ICA) is applied to transform the training database and make a radial basis function network (RBFN), a type of ANN, trained in a new coordinate system. The present study compares the accuracies of three different trained ANNs: RBFN, RBFN with PCA, and RBFN with ICA. Furthermore, the total performances of the centrifugal compressor impeller optimization procedures using these three different trained ANNs are compared. Genetic algorithm (GA) is used as an optimization method in the optimization procedure and influences of GA parameters on the optimization procedure performances are also studied. All results demonstrate that the application of PCA significantly increases the accuracy of trained ANN as well as the total performance of the centrifugal compressor impeller optimization procedure.

An Inverse Problem for the Mechanical Characterization of Steels From the Taylor Test

2010 ◽  
Author(s):  
C. Hernandez ◽  
A. Maranon ◽  
I. A. Ashcroft ◽  
J. P. Casas-Rodriguez
Keyword(s):  
Inverse Problem ◽  
Material Characterization ◽  
Impact Test ◽  
Kinematic Hardening ◽  
Optimization Procedure ◽  
Material Model ◽  
Hardening Material ◽  
Final Shape ◽  
Taylor Impact ◽  
Taylor Impact Test

Material characterization procedures are often complicated processes. In particular, dynamic material characterization usually requires many complicated and expensive tests. One of the tools used to characterize the behavior of materials under dynamic loading is the Taylor impact test. In this experiment, a flat-ended cylinder of initial uniform cross-sectional area is fired at a rigid target. The terminal geometry of the deformed cylinder is used to determine the material strength at different strain rates. This paper presents the formulation and solution of a first class inverse problem for the identification of the kinematic hardening material model from a Taylor impact test of a steel cylinder. The inverse problem is formulated as an optimization procedure for the determination of the optimal set of the model constants. The input parameter of the procedure is the final shape of a Taylor impact test specimen, in terms of central geometric moments, at a given impact velocity. The output parameters are the material model constants, which are determined by fitting the final shape of a numerically simulated Taylor specimen to the final shape of the experimental specimen. This optimization procedure is performed by a real-coded genetic algorithm. The paper includes a numerical example of the characterization procedure for a steel 1018 Taylor specimen of 8 mm diameter and 20 mm length, impacted at a velocity of 250 m/s. This simulation demonstrates the performance of the algorithm and the ability to estimate the kinematic hardening material model constants.

scholarly journals Optimization of shot peen forming patterns to achieve complex target shapes the inverse problem

2021 ◽  
Author(s):  
Hong Yan Miao ◽  
Martin levesque ◽  
Frederick Gosselin
Keyword(s):  
Inverse Problem ◽  
Optimization Procedure ◽  
Spherical Shape ◽  
Forming Process ◽  
Peen Forming ◽  
Out Of Plane ◽  
Order Of Magnitude ◽  
Complex Target ◽  
Wing Skin ◽  
Relative Differences

The inverse problem of determining how to shot peen a plate such that it deforms into a desired target shape is a challenge in the peen forming industry. While peening thick plates uniformly on one side results in a spherical shape, with the same curvature in all directions, complex peening patterns are required to form other shapes, such as cylinders and saddles found on fuselages and wing skin panels. In this study, we present an optimization procedure to automatically compute shot peening patterns. This procedure relies on an idealized model of the peen forming process, where the effect of the treatment is modeled by in-plane expansion of the peened areas, and on an off-the-shelf optimization algorithm. For validation purposes, we peen formed three 305 X 305 X 4.9 mm and two 762 X 762 X 4.9mm 2024--T3 aluminium alloy plates into cylindrical and saddle shapes using the same peening treatment. The obtained shapes qualitatively match simulations. For 305 X 305 X 4.9mm plates, the relative differences had the same distribution and were of the same order of magnitude as initial out-of-plane deviations measured on the as-received plates.

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