Tag Archives: simulation

Diving into three-dimensional fluids

My research group has been working for some years on methods that allow straightforward comparison of large datasets [see ‘Recognizing strain’ on October 28th 2015].  Our original motivation was to compare maps of predicted strain over the surface of engineering structures with maps of measurements.  We have used these comparison methods to validate predictions produced by computational models [see ‘Million to one’ on November 21st 2018] and to identify and track changes in the condition of engineering structures [see ‘Out of the valley of death into a hype cycle’ on February 24th 2021].  Recently, we have extended this second application to tracking changes in the environment including the occurance of El Niño events [see ‘From strain measurements to assessing El Niño events’ on March 17th, 2021].  Now, we are hoping to extend this research into fluid mechanics by using our techniques to compare flow patterns.  We have had some success in exploring the use of methods to optimise the design of the mesh of elements used in computational fluid dynamics to model some simple flow regimes.  We are looking for a PhD student to work on extending our model validation techniques into fluid mechanics using volumes of data from measurement and predictions rather than fields, i.e., moving from two-dimensional to three-dimensional datasets.  If you are interested or know someone who might be interested then please get in touch.

There is more information on the PhD project here.

Ice bores and what they can tell us

Map of Greenland sheet showing depth of iceAbout forty years ago, I was lucky enough to be involved in organising a scientific expedition to North-East Greenland.  Our basecamp was on the Bersaerkerbrae Glacier in Scoresby Land, which at 72 degrees North is well within the Arctic Circle and forty years ago was only accessible in summer when the snow receded.  We measured ablation rates on the glacier [1], counted muskoxen in the surrounding landscape [2] [see ‘Reasons for publishing scientific papers‘ on April 21st 2021] and drilled boreholes in the ice of the glacier.  We performed mechanical tests on the ice cores obtained from different depths in the glacier and in various locations in order to assess the spatial distribution of the material properties of the ice in the glacier. This is important information for producing accurate simulations of the flow of the glacier, although our research did not extend to modelling the glacier.  We could also have used our ice cores to investigate the climatic history of the region.  The Greenland ice sheet contains an archive record of the climate on Earth for about the last half million years, stored in the snow and trapped air bubbles accumulated over that time period.  If the ice sheet melts then that unique record will be lost forever.

The thumbnail image is a map of the depth of ice in the Greenland ice sheet.  The map is about five years old and has a wide green fringe along the east coast.  Scoresby Land is the penisula to the north of the large fiord in the middle of the east coast.  In 1982, the edge of the ice sheet was about 80 miles from the Bersaerkerbrae Glacier, whereas today it is at least twice that distance because the ice sheet is receding.

References:

[1] Patterson EA, 1984, A mathematical model for perched block formation. Journal of Glaciology. 30(106):296-301.

[2] Patterson EA, 1984, ‘Sightings of Muskoxen in Northern Scoresby Land, Greenland’, Arctic, 37(1): 61-63.

Image: https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Greenland_ice_sheet_AMSL_thickness_map-en.svg/2000px-Greenland_ice_sheet_AMSL_thickness_map-en.svg.png

Happy New Year!

Decorative photograph of sculpture of a skeletal person leading a skeletal dinosaurThis year I have written about 20,000 words in 52 posts (including this one); and, since this is the last post of the year, I thought I would take a brief look back at what has preoccupied me in 2021.  Perhaps, not surprisingly the impact of the coronavirus on our lifestyle has featured regularly – almost every week for a month between mid-March and mid-April when we were in lockdown in the UK.  However, the other topics that I have written about frequently are my research on the dynamics of nanoparticles and, in the last six months, on dealing with uncertainty in digital engineering and decision making.  I have also returned several times to innovation processes and transitioning lab-based research into industry.  While following the COP26 in early November, I wrote a series of three posts focussed on energy consumption and the paradigm shifts required to slow down climate change.  There are some connections between these topics: viruses are nanoparticles whose transport and dynamics we do not fully understand; and, digital engineering tools are being used to explore zero-carbon approaches to, for example, energy generation and air transport.  The level of complexity, innovation and urgency associated with developing solutions to these challenges mean that there are always some unknowns and uncertainty when making associated decisions.

The links below are grouped by the topics mentioned above.  I expect there will be more on all of these topics in 2022; however, the topic of next week’s post is unknown because I have not written any posts in advance.  I hope that the uncertainty about the topic of the next post will keep you reading in 2022! 

Coronavirus pandemic: ‘Distancing ourselves from each other‘ on January 13th, 2021; ‘On the impact of writing on well-being‘ on March 3rd, 2021; ‘Collegiality as a defence against pandemic burnout‘ on March 24th, 2021; ‘It’s tiring looking at yourself‘ on March 31st, 2021; ‘Switching off and walking in circles‘ on April 7th, 2021; ‘An upside to lockdown‘ on April 14th, 2021; ‘A brief respite in a long campaign to overcome coronavirus‘ on June 23rd, 2021; and ‘It is hard to remain positive‘ November 3rd 2021.

Energy and climate change: ‘When you invent the ship, you invent the shipwreck‘ on August 25th, 2021; ‘It is hard to remain positive‘ November 3rd 2021; ‘Where we are and what we have‘ on November 24th, 2021; ‘Disruptive change required to avoid existential threats‘ on December 1st, 2021; and ‘Bringing an end to thermodynamic whoopee‘ on December 8th, 2021.

Innovation processes: ‘Slowly crossing the valley of death‘ on January 27th, 2021; ‘Out of the valley of death into a hype cycle?‘ on February 24th, 2021; ‘Innovative design too far ahead of the market?‘ on May 5th, 2021 and ‘Jigsaw puzzling without a picture‘ on October 27th, 2021.

Nanoparticles: ‘Going against the flow‘ on February 3rd, 2021; ‘Seeing things with nanoparticles‘ on March 10th, 2021; and ‘Nano biomechanical engineering of agent delivery to cells‘ on December 15th, 2021.

Uncertainty: ‘Certainty is unattainable and near-certainty is unaffordable‘ on May 12th, 2021; ‘Neat earth objects make tomorrow a little less than certain‘ on May 26th, 2021; ‘Negative capability and optimal ambiguity‘ on July 7th, 2021; ‘Deep uncertainty and meta ignorance‘ on July 21st, 2021; ‘Somethings will always be unknown‘ on August 18th, 2021; ‘Jigsaw puzzling without a picture‘ on October 27th, 2021; and, ‘Do you know RIO?‘ on November 17th, 2021.

Jigsaw puzzling without a picture

A350 XWB passes Maximum Wing Bending test

A350 XWB passes Maximum Wing Bending test

Research sometimes feels like putting together a jigsaw puzzle without the picture or being sure you have all of the pieces.  The pieces we are trying to fit together at the moment are (i) image decomposition of strain fields [see ‘Recognising strain’ on October 28th 2015] that allows fields containing millions of data values to be represented by a feature vector with only tens of elements which is useful for comparing maps or fields of predictions from a computational model with measurements made in the real-world; (ii) evaluation of the variation in measurement uncertainty over a field of view of measured displacements or strains in a large structure [see ‘Industrial uncertainty’ on December 12th 2018] which provides information about the quality of the measurements; and (iii) a probabilistic validation metric that provides a measure of how well predictions from a computational model represent measurements made in the real world [see ‘Million to one’ on November 21st 2018].  We have found some of the missing pieces of the jigsaw, for example we have established how to represent the distribution of measurement uncertainty in the feature vector domain [see ‘From strain measurements to assessing El Niño events’ on March 17th 2021] so that it can be used to assess the significance of differences between measurements and predictions represented by their feature vectors – this connects (i) and (ii) together.  Very recently we have demonstrated a generic technique for performing image decomposition of irregularly shaped fields of data or data fields with holes [see Christian et al, 2021] which extends the applicability of our method for comparing measurements and predictions to real-world objects rather than idealised shapes.  This allows (i) to be used in industrial applications but we still have to work out how to connect this to the probabilistic metric in (iii) while also incorporating spatially-varying uncertainty.  These techniques can be used in a wide range of applications, as demonstrated in our recent work on El Niño events [see Alexiadis et al, 2021], because, by treating all fields of data as images, the techniques are agnostic about the source and format of the data.  However, at the moment, our main focus is on their application to ground tests on aircraft structures as part of the Smarter Testing project in collaboration with Airbus, Centre for Modelling & Simulation, Dassault Systèmes, GOM UK Ltd, and the National Physical Laboratory with funding from the Aerospace Technology Institute.  Together we are working towards digital continuity across virtual and physical testing of aircraft structures to provide live data fusion and enable condition-led inspections, test control and validation of computational models.  We anticipate these advances will reduce time and costs for physical tests and accelerate the development of new designs of aircraft that will contribute to global sustainability targets (the aerospace industry has committed to reduce CO2 emissions to 50% of 2005 levels by 2050).  The Smarter Testing project has an ambitious goal which reveals that our pieces of the jigsaw puzzle belong to a small section of a much larger one.

For more on the Smarter Testing project see:

https://www.aerospacetestinginternational.com/news/structural-testing/smarter-testing-research-program-to-link-virtual-and-physical-aerospace-testing.html

https://www.aerospacetestinginternational.com/opinion/how-integrating-the-virtual-and-physical-will-make-aerospace-testing-and-certification-smarter.html

References

Alexiadis A, Ferson S, Patterson EA. Transformation of measurement uncertainties into low-dimensional feature vector space. Royal Society open science. 8(3):201086, 2021.

Christian WJ, Dean AD, Dvurecenska K, Middleton CA, Patterson EA. Comparing full-field data from structural components with complicated geometries. Royal Society open science. 8(9):210916, 2021.

Image: http://www.airbus.com/galleries/photo-gallery