Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

Typical Model Studies

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Digital image correlation technique for the kinematic assessment of laser system optomechanical mounts.

Scientific reports·2025

Same author

Integrating Advanced Technologies for Environmental Valuation in Legacy Mining Sites: The Role of Digital Twins at Lavrion Technological and Cultural Park.

Sensors (Basel, Switzerland)·2025

Same author

New trends of additive manufacturing using materials based-on natural fibers and minerals : A systematic review.

Heliyon·2025

Same author

Using computational learning for non-melanoma skin cancer and actinic keratosis near-infrared hyperspectral signature classification.

Photodiagnosis and photodynamic therapy·2024

Same author

Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation.

PloS one·2024

Same author

Practical RGB-to-XYZ Color Transformation Matrix Estimation under Different Lighting Conditions for Graffiti Documentation.

Sensors (Basel, Switzerland)·2024

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

Surrogate metamodels from digital image correlation for testing high-performance composite vessels.

Javier Pisonero¹, Manuel Rodríguez-Martín², Jose G Fueyo²

¹Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, 05003, Ávila, Spain.

|April 22, 2024

Summary

This summary is machine-generated.

A new workflow generates accurate surrogate metamodels for predicting composite vessel failure, achieving over 95% confidence. This approach fills a testing standardization gap for roll-wrapped cylindrical vessels.

Keywords:

Composites Digital image correlation (DIC)Finite element method (FEM)Reliability engineering Testing Vessels

More Related Videos

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

Published on: October 3, 2018

Related Experiment Videos

Last Updated: Jun 28, 2025

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

Published on: October 3, 2018

Area of Science:

Materials Science and Engineering
Mechanical Engineering
Computational Mechanics

Background:

High-performance composite cylindrical vessels manufactured by Roll Wrapping technology lack standardized testing procedures.
Predicting failure in these vessels under initial service conditions is crucial for safety and reliability.
Existing methods may not adequately address the unique challenges of composite material behavior and manufacturing processes.

Purpose of the Study:

To develop a workflow for generating surrogate metamodels to predict and evaluate failure in composite cylindrical vessels.
To achieve a prediction confidence above 95% for initial service conditions.
To establish a basis for new, standardized testing procedures for these vessels.

Main Methods:

Development of probabilistic numerical models using the Finite Element Method (FEM).
Experimental characterization of material properties using 2D digital image correlation (DIC) on flat specimens.
Generation of surrogate metamodels via stochastic approximations from numerical models.
Experimental validation using a ring-ring tensile test with high-precision 3D DIC measurements.

Main Results:

Surrogate metamodels demonstrated high accuracy, with prediction confidence exceeding 95%.
Parametric and robust tests confirmed no statistically significant differences between metamodel predictions and experimental results.
Errors in predicting rupture were less than 2% compared to experimental data.

Conclusions:

The developed workflow provides a reliable method for predicting failure in composite cylindrical vessels.
The validated surrogate metamodels can serve as a foundation for establishing new standardized testing procedures.
This approach enhances the safety and reliability assessment of composite vessels manufactured via Roll Wrapping.