by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English
|Statement||Shane A. Dunn.|
|Series||NASA technical memorandum -- 109116.|
|Contributions||Langley Research Center.|
|The Physical Object|
A challenging issue concerning the sensitivity‐based finite element model updating (FEMU) is to create a well‐established framework for updating the inherent structural properties of FE models. A Finite Element Model Updating Method Considering Environmental Impacts 27 May k Downloads; Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS and brings insight to how environment factors influence structural modal properties and how to consider them in the model updating Author: Shanglian Zhou, Wei Song. Model updating is a common method to improve the correlation between finite element (F.E.) models and measured data. F.E. model updating is a technique that is used to identify and correct. A new adaptive response surface methodology, especially adapted for thermal problems, is used to update the experimental setup parameters in a finite element model to the state of the test sample measured by pulsed thermography. Poly Vinyl Chloride (PVC) test samples are used to examine the results for thermal insulator models.
Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Computational cost reduction and best model updating method seeking are the key issues during model updating for different kinds of bridges. This paper presents a combined method, Kriging model and Latin hypercube sampling method, for finite element (FE) model updating. For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum . An experimental verification of a parameter estimation algorithm for finite-element (FE) model updating for damage quantification of a concrete beam with extensive damage is presented using the measured frequency response function (FRF) data. In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the.
The proposed updating method was validated experimentally by updating a finite-element model (FEM) of an existing steel truss bridge that utilized the vibration data obtained from a car-running test. proposed model-updating framework was then investigated by considering the data from a simulated damaged bridge and the experimental data from. Finite element model updating has emerged in the s as a subject of immense importance to the design, construction and maintenance of mechanical systems and civil engineering structures. This book, the first on the subject, sets out to explain the principles of model updating, not only as a research text, but also as a guide for the. Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies . Basic Finite Element Method as Applied to Injury Biomechanics provides a unique introduction to finite element methods. Unlike other books on the topic, this comprehensive reference teaches readers to develop a finite element model from the beginning, including all the appropriate theories that are needed throughout the model development process.