scholarly journals Human Neck Finite Element Model Development and Validation against Original Experimental Data

2004 ◽  
Author(s):  
F. Meyer ◽  
N. Bourdet ◽  
C. Deck ◽  
R. Willinger ◽  
J.S. Raul
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Development ◽  
Element Model ◽  
Development And Validation ◽  
Original Experimental Data

scholarly journals Investigation of the Thickness Differential on the Formability of Aluminum Tailor Welded Blanks

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 875
Author(s):  
Jie Wu ◽  
Yuri Hovanski ◽  
Michael Miles
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Numerical Model ◽  
Plastic Strain ◽  
Finite Element Model ◽  
Equivalent Plastic Strain ◽  
Element Model ◽  
Dome Height ◽  
Tailor Welded Blanks ◽  
Simulation Results

A finite element model is proposed to investigate the effect of thickness differential on Limiting Dome Height (LDH) testing of aluminum tailor-welded blanks. The numerical model is validated via comparison of the equivalent plastic strain and displacement distribution between the simulation results and the experimental data. The normalized equivalent plastic strain and normalized LDH values are proposed as a means of quantifying the influence of thickness differential for a variety of different ratios. Increasing thickness differential was found to decrease the normalized equivalent plastic strain and normalized LDH values, this providing an evaluation of blank formability.

Risk-Controlled Wellbore Stability Criterion Based on Machine Learning Assisted Finite Element Model

2021 ◽  
Author(s):  
Hussain AlBahrani ◽  
Nobuo Morita
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Finite Element ◽  
Finite Element Model ◽  
Stability Criterion ◽  
Element Model ◽  
Rock Failure ◽  
Wellbore Stability ◽  
Wellbore Instability ◽  
Conventional Methods

Abstract In many drilling scenarios that include deep wells and highly stressed environments, the mud weight required to completely prevent wellbore instability can be impractically high. In such cases, what is known as risk-controlled wellbore stability criterion is introduced. This criterion allows for a certain level of wellbore instability to take place. This means that the mud weight calculated using this criterion will only constrain wellbore instability to a certain manageable level, hence the name risk-controlled. Conventionally, the allowable level of wellbore instability in this type of models has always been based on the magnitude of the breakout angle. However, wellbore enlargements, as seen in calipers and image logs, can be highly irregular in terms of its distribution around the wellbore. This irregularity means that risk-controlling the wellbore instability through the breakout angle might not be always sufficient. Instead, the total volume of cavings is introduced as the risk control parameter for wellbore instability. Unlike the breakout angle, the total volume of cavings can be coupled with a suitable hydraulics model to determine the threshold of manageable instability. The expected total volume of cavings is determined using a machine learning (ML) assisted 3D elasto-plastic finite element model (FEM). The FEM works to model the interval of interest, which eventually provides a description of the stress distribution around the wellbore. The ML algorithm works to learn the patterns and limits of rock failure in a supervised training manner based on the wellbore enlargement seen in calipers and image logs from nearby offset wells. Combing the FEM output with the ML algorithm leads to an accurate prediction of shear failure zones. The model is able to predict both the radial and circumferential distribution of enlargements at any mud weight and stress regime, which leads to a determination of the expected total volume of cavings. The model implementation is first validated through experimental data. The experimental data is based on true-triaxial tests of bored core samples. Next, a full dataset from offset wells is used to populate and train the model. The trained model is then used to produce estimations of risk-controlled stability mud weights for different drilling scenarios. The model results are compared against those produced by conventional methods. Finally, both the FEM-ML model and the conventional methods results are compared against the drilling experience of the offset wells. This methodology provides a more comprehensive and new solution to risk controlling wellbore instability. It relies on a novel process which learns rock failure from calipers and image logs.

Biodynamics of Human Thorax With Body Armors Subject to Bullet Impact

2001 ◽  
Author(s):  
Y. W. Kwon ◽  
J. A. Lobuono
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Body Armor ◽  
Projectile Impact ◽  
Human Thorax ◽  
Trachea And Bronchi ◽  
Armor System ◽  
The Finite Element Model

Abstract The objective of this study is to develop a finite element model of the human thorax with a protective body armor system so that the model can adequately determine the thorax’s biodynamical response from a projectile impact. The finite element model of the human thorax consists of the thoracic skeleton, heart, lungs, major arteries, major veins, trachea, and bronchi. The finite element model of the human thorax is validated by comparing the model’s results to experimental data obtained from cadavers wearing a protective body armor system undergoing a projectile impact.

A Multi-Modality Image Data Collection Protocol for Full Body Finite Element Model Development

2009 ◽  
Author(s):  
F. Scott Gayzik ◽  
Craig A. Hamilton ◽  
Josh C. Tan ◽  
Craig McNally ◽  
Stefan M. Duma ◽  
...  
Keyword(s):  
Finite Element ◽  
Data Collection ◽  
Finite Element Model ◽  
Model Development ◽  
Image Data ◽  
Element Model ◽  
Full Body

scholarly journals Development and validation of a C0-C7 cervical spine finite element model

2017 ◽  
Vol 108 ◽  
pp. 13007
Author(s):  
Wei Wei ◽  
Yin Liu ◽  
Xianping Du ◽  
Na Li
Keyword(s):  
Finite Element ◽  
Cervical Spine ◽  
Finite Element Model ◽  
Element Model ◽  
Development And Validation
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