Tag Archives: MyResearch

Slowly crossing the valley of death

The 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

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Regulators 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

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Many 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.

The 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.

Digital twins could put at risk what it means to be human

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I have written in the past about my research on the development and use of digital twins. A digital twin is a functional representation in a virtual world of a real world entity that is continually updated with data from the real world [see ‘Fourth industrial revolution’ on July 4^th, 2018 and also a short video at https://www.youtube.com/watch?v=iVS-AuSjpOQ]. I am working with others on developing an integrated digital nuclear environment from which digital twins of individual power stations could be spawned in parallel with the manufacture of their physical counterparts [see ‘Enabling or disruptive technology for nuclear engineering’ on January 1^st, 2015 and ‘Digitally-enabled regulatory environment for fusion power-plants’ on March 20^th, 2019]. A couple of months ago, I wrote about the difficulty of capturing tacit knowledge in digital twins, which is knowledge that is generally not expressed but is retained in the minds of experts and is often essential to developing and operating complex engineering systems [see ‘Tacit hurdle to digital twins’ on August 26^th, 2020]. The concept of tapping into someone’s mind to extract tacit knowledge brings us close to thinking about human digital twins which so far have been restricted to computational models of various parts of human anatomy and physiology. The idea of a digital twin of someone’s mind raises a myriad of philosophical and ethical issues. Whilst the purpose of a digital twin of the mind of an operator of a complex system might be to better predict and understand human-machine interactions, the opportunity to use the digital twin to advance techniques of personalisation will likely be too tempting to ignore. Personalisation is the tailoring of the digital world to respond to our personal needs, for instance using predictive algorithms to recommend what book you should read next or to suggest purchases to you. At the moment, personalisation is driven by data derived from the tracks you make in the digital world as you surf the internet, watch videos and make purchases. However, in the future, those predictive algorithms could be based on reading your mind, or at least its digital twin. We worry about loss of privacy at the moment, by which we probably mean the collation of vast amounts of data about our lives by unaccountable organisations, and it worries us because of the potential for manipulation of our lives without us being aware it is happening. Our free will is endangered by such manipulation but it might be lost entirely to a digital twin of our mind. To quote the philosopher Michael Lynch, you would be handing over ‘privileged access to your mental states’ and to some extent you would no longer be a unique being. We are long way from possessing the technology to realise a digital twin of human mind but the possibility is on the horizon.

Source: Richard Waters, They’re watching you, FT Weekend, 24/25 October 2020.