Category Archives: mechanics

Engineering idiom

Many of us, either as students or as instructors, will have experienced the phenomenon that students are more likely to give a correct answer when the context is familiar [Linn & Hyde, 1989; Chapman et al 1991].  Conversely, a lack of familiarity may induce students to panic about the context and fail to listen in a lecture [Rosser, 2004] or to appreciate the point of a question in an examination.  Your dictionary probably gives two meanings for context: ‘surrounding conditions’ and ‘a construction of speech’.  You would think that the importance of teaching by reference to the surrounding condition is so obvious as to require no comment; except professors forget that conditions experienced by students are different to their own, both now and when they were students [Nathan, 2005 & ‘Creating an evolving learning environment’ on February 21st, 2018].  To get an appreciation of how different consult the ‘Mindset List‘ produced each year by Beloit College; for example as far as the class of 2020 are concerned robots have always been surgical partners in the operating room [#55 on the 2020 Mindset List].

What about the construction of speech?  I think that there is an engineering idiom because engineering education has its own ‘language’ of models and analogies.  Engineering science is usually taught in the context of idealised applications, such as colliding spheres, springs and dashpots, and shafts.  It would be wrong to say that they have no relevance to the subject; but, the relevance is often only apparent to those well-versed in the subject; and, by definition, students are not.  The result is a loss of perceived usefulness of learning which adversely influences student motivation [Wigfield & Eccles, 2000] – they are more likely to switch off, so keep the language simple.

References:

Chipman S, Marshall S, Scott P. Content effects on word problem performance: A possible source of test bias? American Educational Research Journal, 28(4), 897-915, 1991.

Linn M, Hyde J, Gender, mathematics, and science, Educational Researcher, 18(8), 17-19, 22-27, 1989.

Nathan R, My freshman year: what a professor learned by becoming a student, Cornell University Press, Ithaca, New York, 2005.

Rosser SV, Gender issues in teaching science, in S. Rose. and B. Brown (eds.), Report on the 2003 Workshop on Gender Issues in the Sciences, pp. 28-37, 2004.

Wigfield A, Eccles JS, Expectancy-value theory of motivation, Contemporary Educational Psychology, 25(1): 68-81, 2000.

 

CALE #7 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]

Establishing fidelity and credibility in tests & simulations (FACTS)

A month or so ago I gave a lecture entitled ‘Establishing FACTS (Fidelity And Credibility in Tests & Simulations)’ to the local branch of the Institution of Engineering Technology (IET). Of course my title was a play on words because the Oxford English Dictionary defines a ‘fact’ as ‘a thing that is known or proved to be true’ or ‘information used as evidence or as part of report’.   One of my current research interests is how we establish predictions from simulations as evidence that can be used reliably in decision-making.  This is important because simulations based on computational models have become ubiquitous in engineering for, amongst other things, design optimisation and evaluation of structural integrity.   These models need to possess the appropriate level of fidelity and to be credible in the eyes of decision-makers, not just their creators.  Model credibility is usually provided through validation processes using a small number of physical tests that must yield a large quantity of reliable and relevant data [see ‘Getting smarter‘ on June 21st, 2017].  Reliable and relevant data means making measurements with low levels of uncertainty under real-world conditions which is usually challenging.

These topics recur through much of my research and have found applications in aerospace engineering, nuclear engineering and biology. My lecture to the IET gave an overview of these ideas using applications from each of these fields, some of which I have described in past posts.  So, I have now created a new page on this blog with a catalogue of these past posts on the theme of ‘FACTS‘.  Feel free to have a browse!

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 or teaching, such as the CALE series.  The motivation for a post seems unrelated to its popularity.  This post is part of that commitment to communicate.

After 12 months, our EU-supported research project, MOTIVATE [see ‘Getting Smarter‘ on June 21st, 2017] is one-third complete in terms of time; and, as in all research it appears to have made a slow start with much effort expended on conceptualizing, planning, reviewing prior research and discussions.  However, we are on-schedule and have delivered on one of our four research tasks with the result that we have a new validation metric and a new flowchart for the validation process.  The validation metric was revealed at the Photomechanics 2018 conference in Toulouse earlier this year [see ‘Massive Engineering‘ on April 4th, 2018].  The new flowchart [see the graphic] is the result of a brainstorming [see ‘Brave New World‘ on January 10th, 2018] and much subsequent discussion; and will be presented at a conference in Brussels next month [ICEM 2018] at which we will invite feedback [proceedings paper].  The big change from the classical flowchart [see for example ASME V&V guide] is the inclusion of historical data with the possibility of not requiring experiments to provide data for validation purposes. This is probably a paradigm shift for the engineering community, or at least the V&V [Validation & Verification] community.  So, we are expecting some robust feedback – feel free to comment on this blog!

References:

Hack E, Burguete RL, Dvurecenska K, Lampeas G, Patterson EA, Siebert T & Szigeti E, Steps toward industrial validation experiments, In Proceedings Int. Conf. Experimental Mechanics, Brussels, July 2018 [pdf here].

Dvurcenska K, Patelli E & Patterson EA, What’s the probability that a simulation agrees with your experiment? In Proceedings Photomechanics 2018, Toulouse, March 2018.

 

 

Mapping atoms

Typical atom maps of P, Cu, Mn, Ni & Si (clockwise from bottom centre) in 65x65x142 nm sample of steel from Styman et al, 2015.

A couple of weeks ago I wrote about the opening plenary talk at the NNL Sci-Tec conference [‘The disrupting benefit of innovation’ on May 23rd, 2018].  One of the innovations discussed at the conference was the applications of atom probe tomography for understanding the mechanisms underpinning material behaviour.  Atom probe tomography produces three-dimensional maps of the location and type of individual atoms in a sample of material.  It is a destructive technique that uses a high energy pulse to induce field evaporation of ions from the tip of a needle-like sample.  A detector senses the position of the ions and their chemical identity is found using a mass spectrometer.  Only small samples can be examined, typically of the order of 100nm.

A group led by Jonathan Hyde at NNL have been exploring the use of atom probe tomography to understand the post-irradiation annealing of weld material in reactor pressure vessels and to examine the formation of bubbles of rare gases in fuel cladding which trap hydrogen causing material embrittlement.  A set of typical three-dimensional maps of atoms is shown in the thumb-nail from a recent paper by the group (follow the link for the original image).

It is amazing that we can map the location of atoms within a material and we are just beginning to appreciate the potential applications of this capability.  As another presenter at the conference said: ‘Big journeys begin with Iittle steps’.

BTW it was rewarding to see one of our alumni from our CPD course [see ‘Leadership is like shepherding’ on May 10th, 2017] presenting this work at the conference.

Source:

Styman PD, Hyde JM, Parfitt D, Wilford K, Burke MG, English CA & Efsing P, Post-irradiation annealing of Ni-Mn-Si-enriched clusters in a neutron-irradiated RPV steel weld using atom probe tomography, J. Nuclear Materials, 459:127-134, 2015.