Blog posts

Archive video footage from EU projects
... We are in the last twelve months of the MOTIVATE project and we have started producing video clips about the technology that is being developed.  So, if you missed my presentations at the conference in the US then you can watch the videos online using the links below . We have been making videos describing the outputs of our EU project for about 20 years; so, if you want to see some vintage footage of me twenty years younger then watch a video from the INDUCE project that was active from 1998 to 2001....

MOTIVATE videos: Introduction; Industrial calibration of DIC measurements using a calibration plate or using an LCD screen

 This blog post appeared on June 5th, 2019, on
In Einstein's footsteps?
... I have been involved in organising a number of workshops in Glasgow, London, Munich and Shanghai over the last decade.  The next one will be in Zurich in November 2019 in Guild Hall of Carpenters (Zunfthaus zur Zimmerleuten) where Einstein lectured in November 1910 to the Zurich Physical Society ‘On Boltzmann’s principle and some of its direct consequences‘.  Our subject will be different: ‘Validation of Computational Mechanics Models’; ...  This blog post appeared on February 27th, 2019, on
Industrial uncertainty
Last month I spent almost a week in Zurich. ... I was there for the mid-term meeting of the MOTIVATE project and to conduct some tests and demonstrations in the laboratories of our host, EMPA, the Swiss Federal Laboratories for Materials Science and Technology.  Two of our project partners, Dantec Dynamics GmbH based in Ulm, Germany, and the Athena Research Centre in Patras, Greece, have developed methods for quantifying the uncertainty present in measurements of deformation made in an industrial environment using digital image correlation ... This blog post appeared on December 12th, 2018, on
Spontaneously MOTIVATEd
Some posts arise spontaneously, stimulated by something that I have read or done, while others are part of commitment to communicate on a topic related to my research... This blog post on a new validation metric and flowchart appeared on June 27th, 2018, on
Brave New World
In the brave new world of digital engineering, some engineers are attempting to conceive of a world in which experiments have become obsolete because we can rely on computational modelling to simulate engineering systems. ... This blog post on a world without experiments appeared on January 10th, 2018, on
Getting smarter
Garbage in, garbage out (GIGO) is a perennial problem in computational simulations of engineering structures... This blog post is on how predictions from the simulation have to be tested against reality in order to establish confidence. It appeared on August 2nd, 2017, on

4th Workshop on Validation

After Shanghai 2011, London 2013 and Munich 2014, the Fourth International Workshop on Validation of Computational Mechanics Models will be held in Zurich, Switzerland, on November 5th, 2019. Keynote Speakers include Linden Harris (Airbus), Anna Pandolfi (Politecnico di Milano) and Yakov Ben-Haim (Technion).

The event is free of charge, and a drink reception is included. For organizational reasons please by email.

The program below includes the Keynote as well as other confirmed speakers for the three sessions.


Session one:  Credible Models for Engineering Decision-Making

Keynote: The assessment of smarter testing for credibility and maturity
                  Linden Harris, Airbus, FR

Validation and credibility challenges in applied biology and toxicology
                  Maurice Whelan, JRC Ispra, IT

Experimental detection and quantification of crack networks for numerical simulation in heterogeneous materials using techniques based on 3D digital images
                  Yang Chen and James Marrow, University of Oxford, UK


Session two:   Validation of multi-physics models of engineering systems

Keynote: Validation of multiphysics models: from the material scale to the boundary value problem
                 Anna Pandolfi, Politecnico di Milano, IT

Validation of fluid-structure interaction simulations
                  George Lampeas, University of Patras and Athena Research Center, HE

Model Validation in the UK Nuclear Industry: A Proposed Probabilistic Metric
                  Steve Graham, National Nuclear Laboratory, UK


Session three: The Future of Experimental Data for Model Trustworthiness

Keynote: As our Island of Knowledge Grows, so Does the Shore of our Ignorance
                 Yakov Ben-Haim, Technion, IL

Using Historical Measurement Data for Validation of Engineering Simulations
                 Erwin Hack, Empa, CH

Middle-ear mechanics and sound localization in the lizard: a finite-element approach
                 Pieter Livens, University of Antwerp, BE


DIC Calibration Methods

Methods for quantifying DIC measurement uncertainty in an industrial environment
DIC Measurement uncertainty

We evaluated the state-of-the-art for quantifying measurement uncertainty of digital image correlation and assessed potential candidate techniques for evaluating the uncertainty during 1:1 aircraft scale optical strain and displacement measurements.

From there we developed two specific methods for experiments performed in industrial situations. The methods can be performed at the time of the test and quantify measurement uncertainty in the whole measurement volume in a manner that is relevant to the validation of predictive numerical models. This step significantly improves the quality of laboratory-based DIC measurements.

The first method is based on using a calibration plate in front of the test object and a rigid body displacement of the camera system. The methodology is illustrated in this video. A typical result is displayed in the figure below.

The second method uses an LCD screen in front of the test object which displays simulated speckle patterns that are numerically deformed to represent planar strain states. The methodology is illustrated in this video.


Deviations of position data from DIC measurement after calibrating the system in front of the test object. They form the basis for estimating the DIC measurement uncertainty.

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