Estimating Severity of Damage in Lattice Structures Utilizing Substructure Modal Data

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Hui Fang
Keyword(s):  
Synthetic Data ◽  
Iterative Solution ◽  
Complete Information ◽  
Solution Procedure ◽  
Estimation Methods ◽  
Lattice Structures ◽  
Modal Data ◽  
Severity Estimation ◽  
Beam Type ◽  
Spe Method

This paper newly develops a method for the damage severity estimate for lattice structures based on the employment of the substructure potential energy (SPE). While all existing damage severity estimation methods that utilize modal data are either employing an iterative solution procedure or requiring spatially complete information, the SPE method is an exact, noniterative solution method and only requires substructure modal data. The performance of the proposed method is presented for beam-type and plate-type lattice structures based on synthetic data generated from finite element models.

scholarly journals Self-updated four-node finite element using deep learning

2021 ◽  
Author(s):  
Jaeho Jung ◽  
Hyungmin Jun ◽  
Phill-Seung Lee
Keyword(s):  
Finite Element ◽  
Deep Learning ◽  
Iterative Solution ◽  
Solution Procedure ◽  
Element Formulation ◽  
Zero Energy ◽  
Energy Mode ◽  
Bending Modes ◽  
Solution Accuracy ◽  
Iterative Solution Procedure

AbstractThis paper introduces a new concept called self-updated finite element (SUFE). The finite element (FE) is activated through an iterative procedure to improve the solution accuracy without mesh refinement. A mode-based finite element formulation is devised for a four-node finite element and the assumed modal strain is employed for bending modes. A search procedure for optimal bending directions is implemented through deep learning for a given element deformation to minimize shear locking. The proposed element is called a self-updated four-node finite element, for which an iterative solution procedure is developed. The element passes the patch and zero-energy mode tests. As the number of iterations increases, the finite element solutions become more and more accurate, resulting in significantly accurate solutions with a few iterations. The SUFE concept is very effective, especially when the meshes are coarse and severely distorted. Its excellent performance is demonstrated through various numerical examples.

scholarly journals A Subspace Preconditioned LSQR Gauss-Newton Method with a Constrained Line Search Path Applied to 3D Biomedical Microwave Imaging

2015 ◽  
Vol 2015 ◽  
pp. 1-21
Author(s):  
Jürgen De Zaeytijd ◽  
Ann Franchois
Keyword(s):  
Complex Permittivity ◽  
Line Search ◽  
Synthetic Data ◽  
Iterative Solution ◽  
Initial Guess ◽  
Microwave Imaging ◽  
Single Frequency ◽  
Problem Solution ◽  
Source Position ◽  
Search Path

Three contributions that can improve the performance of a Newton-type iterative quantitative microwave imaging algorithm in a biomedical context are proposed. (i) To speed up the iterative forward problem solution, we extrapolate the initial guess of the field from a few field solutions corresponding to previous source positions for the same complex permittivity (i.e., “marching on in source position”) as well as from a Born-type approximation that is computed from a field solution corresponding to one previous complex permittivity profile for the same source position. (ii) The regularized Gauss-Newton update system can be ill-conditioned; hence we propose to employ a two-level preconditioned iterative solution method. We apply the subspace preconditioned LSQR algorithm from Jacobsen et al. (2003) and we employ a 3D cosine basis. (iii) We propose a new constrained line search path in the Gauss-Newton optimization, which incorporates in a smooth manner lower and upper bounds on the object permittivity, such that these bounds never can be violated along the search path. Single-frequency reconstructions from bipolarized synthetic data are shown for various three-dimensional numerical biological phantoms, including a realistic breast phantom from the University of Wisconsin-Madison (UWCEM) online repository.

scholarly journals WHSP-Net: A Weakly-Supervised Approach for 3D Hand Shape and Pose Recovery from a Single Depth Image

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3784 ◽  
Author(s):  
Jameel Malik ◽  
Ahmed Elhayek ◽  
Didier Stricker
Keyword(s):  
Pose Estimation ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Depth Image ◽  
Estimation Methods ◽  
Hand Shape ◽  
Pose Recovery ◽  
Real World Datasets ◽  
Weakly Supervised

Hand shape and pose recovery is essential for many computer vision applications such as animation of a personalized hand mesh in a virtual environment. Although there are many hand pose estimation methods, only a few deep learning based algorithms target 3D hand shape and pose from a single RGB or depth image. Jointly estimating hand shape and pose is very challenging because none of the existing real benchmarks provides ground truth hand shape. For this reason, we propose a novel weakly-supervised approach for 3D hand shape and pose recovery (named WHSP-Net) from a single depth image by learning shapes from unlabeled real data and labeled synthetic data. To this end, we propose a novel framework which consists of three novel components. The first is the Convolutional Neural Network (CNN) based deep network which produces 3D joints positions from learned 3D bone vectors using a new layer. The second is a novel shape decoder that recovers dense 3D hand mesh from sparse joints. The third is a novel depth synthesizer which reconstructs 2D depth image from 3D hand mesh. The whole pipeline is fine-tuned in an end-to-end manner. We demonstrate that our approach recovers reasonable hand shapes from real world datasets as well as from live stream of depth camera in real-time. Our algorithm outperforms state-of-the-art methods that output more than the joint positions and shows competitive performance on 3D pose estimation task.

An Interfacial Tracking Method for Ultrashort Pulse Laser Melting and Resolidification of a Thin Metal Film

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
Yuwen Zhang ◽  
J. K. Chen
Keyword(s):  
Metal Film ◽  
Laser Energy ◽  
Film Surface ◽  
Iterative Solution ◽  
Coupling Factor ◽  
Solution Procedure ◽  
Thin Metal Film ◽  
Solidification Temperature ◽  
Ultrashort Pulse Laser ◽  
Tracking Method

An interfacial tracking method was developed to model rapid melting and resolidification of a freestanding metal film subject to an ultrashort laser pulse. The laser energy was deposited to the electrons near thin film surface, and subsequently diffused into a deeper part of the electron gas and transferred to the lattice. The energy equations for the electron and lattice were coupled through an electron-lattice coupling factor. Melting and resolidification were modeled by considering the interfacial energy balance and nucleation dynamics. An iterative solution procedure was employed to determine the elevated melting temperature and depressed solidification temperature in the ultrafast phase-change processes. The predicted surface lattice temperature, interfacial location, interfacial temperature, and interfacial velocity were compared with those obtained by an explicit enthalpy model. The effects of the electron thermal conductivity models, ballistic range, and laser fluence on the melting and resolidification were also investigated.

Delamination Onset and Growth Prediction of Advanced Composite Materials With a Fabrication Flaw

2007 ◽  
Author(s):  
Jim Lua ◽  
Jagannathan Sankar
Keyword(s):  
Failure Load ◽  
Iterative Solution ◽  
Material Model ◽  
Solution Procedure ◽  
Model Parameters ◽  
Component Level ◽  
Cohesive Model ◽  
Advanced Composite ◽  
Fracture Parameters ◽  
Cohesive Models

The delamination failure mode is particularly significant in the damage tolerance design of advanced composite, since manufacturing flaws and in-service damage most often manifest themselves as interlaminar cracks. The primary goal of this paper is to evaluate the validity and accuracy of the developed cohesive interface model in predicting the fracture parameters at coupon and component level. To capture crack initiation and growth under mixed mode loading, a cohesive model based on a bi-linear constitutive material law is implemented in LSDYNA via a user-defined material model. The cohesive model parameters and the associated fracture toughness are determined for both primary and secondary bond coupons subjected to double cantilever beam and end notch flexure loading. An iterative solution procedure is used to determine the cohesive parameters by matching the failure load/displacement prediction with the observed test data. To explore the feasibility of using coupon level fracture parameters for fracture prediction at component level, the determined cohesive models are used to predict the critical failure load associated with delamination onset and growth of doubler specimens under axial and bending loads.

scholarly journals Estimation of soil properties by coupled inversion of electrical resistance, temperature, and moisture content data

2019 ◽  
Author(s):  
Elchin E. Jafarov ◽  
Dylan R. Harp ◽  
Ethan T. Coon ◽  
Baptiste Dafflon ◽  
Anh Phuong Tran ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Soil Properties ◽  
Electrical Resistance ◽  
Synthetic Data ◽  
The Arctic ◽  
Estimation Methods ◽  
Coupled Models ◽  
Using Data ◽  
Parameter Estimation Methods ◽  
Initial Starting

Abstract. Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback (PCF). However, large uncertainties exist regarding the timing and magnitude of the PCF, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE framework that estimates soil properties from time-series of soil temperature, moisture, and electrical resistance measurements. The framework utilizes the PEST (Model Independent Parameter Estimation and Uncertainty Analysis) toolbox and coupled hydro-thermal-geophysical modeling. We test the framework against synthetic data, providing a proof-of-concept for the approach. We use specified subsurface parameters and coupled models to setup a synthetic state, perturb the parameters, then verify that our PE framework is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we evaluate the relative worth of including various types and amount of data needed to improve predictions. The results of the PE tests suggest that using data from multiple observational datasets improves the accuracy of the estimated parameters.

scholarly journals Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8477
Author(s):  
Roozbeh Mohammadi ◽  
Claudio Roncoli
Keyword(s):  
Traffic Control ◽  
Partial Information ◽  
Control Strategies ◽  
Synthetic Data ◽  
Real Data ◽  
Significant Loss ◽  
Data Driven ◽  
Estimation Methods ◽  
Connected Vehicles ◽  
Vehicle Data

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

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