Tag Archives: mechanics

Slowly crossing the valley of death

A view of a valleyThe valley of death in technology development is well-known amongst research engineers and their sponsors. It is the gap between discovery and application, or between realization of an idea in a laboratory and its implementation in the real-world. Some of my research has made it across the valley of death, for example the poleidoscope about 15 years ago (see ‘Poleidoscope (=polariscope+kaleidoscope)‘ on October 14th, 2020).  Our work on quantitative comparisons of data fields from physical measurements and computer predictions is about three-quarters of the way across the valley.  We published a paper in December (see Dvurecenska et al, 2020) on its application to a large panel from the fuselage of an aircraft based on work we completed as part of the MOTIVATE project.  I reported the application of the research in almost real-time in a post in December 2018 (see ‘Industrial Uncertainty‘ on December 12th, 2018) and in further detail in May 2020 as we submitted the manuscript for publication (‘Alleviating industrial uncertainty‘ on May 13th, 2020).  However, the realization in the laboratory occurred nearly a decade ago when teams from Michigan State University and the University of Liverpool came together in the ADVISE project funded by EU Framework 7 programme (see Wang et al, 2011). Subsequently, the team at Michigan State University moved to the University of Liverpool and in collaboration with researchers at Empa developed the technique that was applied in the MOTIVATE project (see Sebastian et al 2013). The work published in December represents a step into the valley of death; from a university environment into a full-scale test laboratory at Empa using a real piece of aircraft.  The MOTIVATE project involved a further step to a demonstration on an on-going test of a cockpit at Airbus which was also reported in a post last May (see ‘The blind leading the blind‘ on May 27th, 2020).  We are now working with Airbus in a new programme to embed the process of quantitative comparison of fields of measurements and predictions into their routine test procedures for aerospace structures.  So, I would like to think we are climbing out of the valley.

Image: not Death Valley but taken on a road trip in 2008 somewhere between Moab, UT and Kanab, UT while living in Okemos, MI.

Sources:

Dvurecenska, K., Diamantakos, I., Hack, E., Lampeas, G., Patterson, E.A. and Siebert, T., 2020. The validation of a full-field deformation analysis of an aircraft panel: A case study. The Journal of Strain Analysis for Engineering Design, p.0309324720971140.

Sebastian, C., Hack, E. and Patterson, E., 2013. An approach to the validation of computational solid mechanics models for strain analysis. The Journal of Strain Analysis for Engineering Design, 48(1), pp.36-47.

Wang, W., Mottershead, J.E., Sebastian, C.M. and Patterson, E.A., 2011. Shape features and finite element model updating from full-field strain data. International Journal of Solids and Structures, 48(11-12), pp.1644-1657.

For more posts on the MOTIVATE project: https://realizeengineering.blog/category/myresearch/motivate-project/

The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660 and the Swiss State Secretariat for Education, Research and Innovation under contract number 17.00064.

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

Credible predictions for regulatory decision-making

detail from abstract by Zahrah ReshRegulators are charged with ensuring that manufactured products, from aircraft and nuclear power stations to cosmetics and vaccines, are safe.  The general public seeks certainty that these devices and the materials and chemicals they are made from will not harm them or the environment.  Technologists that design and manufacture these products know that absolute certainty is unattainable and near-certainty in unaffordable.  Hence, they attempt to deliver the service or product that society desires while ensuring that the risks are As Low As Reasonably Practical (ALARP).  The role of regulators is to independently assess the risks, make a judgment on their acceptability and thus decide whether the operation of a power station or distribution of a vaccine can go ahead.  These are difficult decisions with huge potential consequences – just think of the more than three hundred people killed in the two crashes of Boeing 737 Max airplanes or the 10,000 or so people affected by birth defects caused by the drug thalidomide.  Evidence presented to support applications for regulatory approval is largely based on physical tests, for example fatigue tests on an aircraft structure or toxicological tests using animals.  In some cases the physical tests might not be entirely representative of the real-life situation which can make it difficult to make decisions using the data, for instance a ground test on an airplane is not the same as a flight test and in many respects the animals used in toxicity testing are physiologically different to humans.  In addition, physical tests are expensive and time-consuming which both drives up the costs of seeking regulatory approval and slows down the translation of new innovative products to the market.  The almost ubiquitous use of computer-based simulations to support the research, development and design of manufactured products inevitably leads to their use in supporting regulatory applications.  This creates challenges for regulators who must judge the trustworthiness of predictions from these simulations.  [see ‘Fake facts & untrustworthy predictions‘ on December 4th, 2019]. It is standard practice for modellers to demonstrate the validity of their models; however, validation does not automatically lead to acceptance of predictions by decision-makers.  Acceptance is more closely related to scientific credibility.  I have been working across a number of disciplines on the scientific credibility of models including in engineering where multi-physics phenomena are important, such as hypersonic flight and fusion energy [see ‘Thought leadership in fusion energy‘ on October 9th, 2019], and in computational biology and toxicology [see ‘Hierarchical modelling in engineering and biology‘ on March 14th, 2018]. Working together with my collaborators in these disciplines, we have developed a common set of factors which underpin scientific credibility that are based on principles drawn from the literature on the philosophy of science and are designed to be both discipline-independent and method-agnostic [Patterson & Whelan, 2019; Patterson et al, 2021]. We hope that our cross-disciplinary approach will break down the subject-silos that have become established as different scientific communities have developed their own frameworks for validating models.  As mentioned above, the process of validation tends to be undertaken by model developers and, in some sense, belongs to them; whereas, credibility is not exclusive to the developer but is a trust that needs to be shared with a decision-maker who seeks to use the predictions to inform their decision [see ‘Credibility is in the eye of the beholder‘ on April 20th, 2016].  Trust requires a common knowledge base and understanding that is usually built through interactions.  We hope the credibility factors will provide a framework for these interactions as well as a structure for building a portfolio of evidence that demonstrates the reliability of a model. 

References:

Patterson EA & Whelan MP, On the validation of variable fidelity multi-physics simulations, J. Sound & Vibration, 448:247-258, 2019.

Patterson EA, Whelan MP & Worth A, The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application, Computational Toxicology, 17: 100144, 2021.

Image: Extract from abstract by Zahrah Resh.

Most valued player performs remote installation

Our Most Valued Player (inset) installing a point sensor in the front section of a fuselage at Airbus in Toulouse under the remote direction of engineers in Switzerland and the UKMany research programmes have been derailed by the pandemic which has closed research laboratories or restricted groups of researchers from working together to solve complex problems. Some research teams have used their problem-solving skills to find new ways of collaborating and to continue to make progress. In the DIMES project we have developed an innovative system for detecting and monitoring the propagation of damage in aircraft structures, and prior to the pandemic, we were planning to demonstrate it on a full-scale test of an aircraft fuselage section at Airbus in Toulouse. However, the closure of our laboratories and travel restrictions across Europe have made it impossible for members of our team based in Liverpool, Chesterfield, Ulm and Zurich to meet or travel to Toulouse to set-up the demonstration. Instead we have used hours of screen-time in meetings to complete our design work and plan the installation of the system in Toulouse. These meetings often involve holding components up to our laptop cameras to show one another what we are doing.  The components of the system were manufactured in various locations before being shipped to Empa in Zurich where they were assembled and the complete system was then shipped to Toulouse.  At the same time, we designed a communication system that included a headset with camera, microphone and earpieces so that our colleague in Toulouse could be guided through the installation of our system by engineers in Germany, Switzerland and the UK.  Amazingly, it all worked and we were half-way through the installation last month when a rise in the COVID infection rate caused a shutdown of the Airbus site in Toulouse.  What we need now is remote-controlled robot to complete the installation for us regardless of COVID restrictions; however, I suspect the project budget cannot afford a robot sufficiently sophisticated to replace our Most Valued Player (MVP) in Toulouse.

The University of Liverpool is the coordinator of the DIMES project and the other partners are Empa, Dantec Dynamics GmbH and Strain Solutions Ltd.

Logos of Clean Sky 2 and EUThe DIMES project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820951.  The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

Image: Our Most Valued Player (inset) installing a point sensor in the front section of a fuselage at Airbus in Toulouse under the remote direction of engineers in Switzerland and the UK.

Scattering electrons reveal dislocations in material structure

Figure 9 from Yang et al, 2012. Map of plastic strain around the crack tip (0, 0) based on the full width of half the maximum of the discrete Fourier transforms of BSE images, together with thermoelastic stress analysis data (white line) and estimates of the plastic zone size based on approaches of Dugdale's (green line) and Irwin's (blue line; dimensions in millimetres).

Figure 9 from Yang et al, 2012. Map of plastic strain around the crack tip (0, 0) based on the full width of half the maximum of the discrete Fourier transforms of BSE images, together with thermoelastic stress analysis data (white line) and estimates of the plastic zone size based on approaches of Dugdale’s (green line) and Irwin’s (blue line; dimensions in millimetres).

It is almost impossible to manufacture metal components that are flawless.  Every flaw or imperfection in a metallic component is a potential site for the initiation of a crack that could lead to the failure of the component [see ‘Alan Arnold Griffith’ on April 26th, 2017].  Hence, engineers are very interested in understanding the mechanisms of crack initiation and propagation so that these processes can be prevented or, at least, inhibited.  It is relatively easy to achieve these outcomes by not applying loads that would supply the energy to drive failure processes; however, the very purpose of a metal component is often to carry load and hence a compromise must be reached.  The deep understanding of crack initiation and propagation, required for an effective and safe compromise, needs detailed measurements of evolution of the crack and of its advancing front or tip [depending whether you are thinking in three- or two-dimensions].  When a metal is subjected to repeated cycles of loading, then a crack can grow incrementally with each load cycle; and in these conditions a small volume of material, just ahead of the crack and into which the crack is about to grow, has an important role in determining the rate of crack growth.  The sharp geometry of the crack tip causes localisation of the applied load in the material ahead of the crack thus raising the stress sufficiently high to cause permanent deformation in the material on the macroscale.  The region of permanent deformation is known as the crack tip plastic zone.  The permanent deformation induces disruptions in the regular packing of the metal atoms or crystal lattice, which are known as dislocations and continued cyclic loading causes the dislocations to move and congregate around the crack tip.  Ultimately, dislocations combine to form voids in the material and then voids coalesce to form the next extension of the crack.  In reality, it is an oversimplification to refer to a crack tip because there is a continuous transition from a definite crack to definitely no crack via a network of loosely connected voids, unconnected voids, aggregated dislocations almost forming a void, to a progressively more dispersed crowd of dislocations and finally virgin or undamaged material.  If you know where to look on a polished metal surface then you could probably see a crack about 1 mm in length and, with aid of an optical microscope, you could probably see the larger voids forming in the material ahead of the crack especially when a load is applied to open the crack.  However, dislocations are very small, of the order tens of nanometres in steel, and hence not visible in an optical microscope because they are smaller than the wavelength of light.  When dislocations congregate in the plastic zone ahead of the crack, they disturb the surface of the metal and causing a change its texture which can be detected in the pattern produced by electrons bouncing off the surface.  At Michigan State University about ten years ago, using backscattered electron (BSE) images produced in a scanning electron microscope (SEM), we demonstrated that the change in texture could be measured and quantified by evaluating the frequency content of the images using a discrete Fourier transform (DFT).  We collected 225 square images arranged in a chessboard pattern covering a 2.8 mm by 2.8 mm square around a 5 mm long crack in a titanium specimen which allowed us to map the plastic zone associated with the crack tip (figure 9 from Yang et al, 2012).  The length of the side of each image was 115 microns and 345 pixels so that we had 3 pixels per micron which was sufficient to resolve the texture changes in the metal surface due to dislocation density.  The images are from our paper published in the Proceedings of the Royal Society and the one below (figure 4 from Yang et al, 2012) shows four BSE images along the top at increasing distances from the crack tip moving from left to right.  The middle row shows the corresponding results from the discrete Fourier transform that illustrate the decreasing frequency content of the images moving from left to right, i.e. with distance from the crack.  The graphs in the bottom row show the profile through the centre of the DFTs.  The grain structure in the metal can be seen in the BSE images and looks like crazy paving on a garden path or patio.  Each grain has a particular and continuous crystal lattice orientation which causes the electrons to scatter differently from it compared to its neighbour.  We have used the technique to verify measurements of the extent of the crack tip plastic zone made using thermoelastic stress analysis (TSA) and then used TSA to study ‘Crack tip plasticity in reactor steels’ [see post on March 13th, 2019].

Figure 4 from Yang et al, 2012. (a) Backscattered electron images at increasing distance from crack from left to right; (b) their corresponding discrete Fourier transforms (DFTs) and (c) a horizontal line profile across the centre of each DFT.

Figure 4 from Yang et al, 2012. (a) Backscattered electron images at increasing distance from crack from left to right; (b) their corresponding discrete Fourier transforms (DFTs) and (c) a horizontal line profile across the centre of each DFT.

Reference: Yang, Y., Crimp, M., Tomlinson, R.A., Patterson, E.A., 2012, Quantitative measurement of plastic strain field at a fatigue crack tip, Proc. R. Soc. A., 468(2144):2399-2415.