Looking for a PhD student to join our group: a fully funded studentship is available now

Sensing Unsteadiness in Buoyant PLUMES (SUBPLUMES)

Apply at https://www.findaphd.com/search/ProjectDetails.aspx?PJID=97616

Project Description

Understanding the environmental impacts of wastewater discharges into oceans, dredged material disposal in coastal marine waters and atmospheric emissions from industrial stacks and volcanoes, requires detailed knowledge of how the multiphase (fluid + particulate) behaviour of buoyant jets and plumes is controlled by the source conditions (e.g. momentum and buoyancy fluxes, plume geometry, etc.). Obtaining reliable measurements at source can, however, prove difficult due to their inaccessibility (e.g. discharges from deep ocean outfalls, oil spills from seabed pipeline fractures or eruptions from volcanic vents). As such, well-established theories for buoyant jet and plume behaviour typically assume time-averaged source conditions, thus disconnecting any inherent source unsteadiness from the more accessible measurements of downstream plume behaviour, such as entrainment characteristics, rise and spreading heights, umbrella cloud formation and collapse mechanisms, and particulate fallout and deposition patterns. This project aims to address this disconnect through the development and application of an inverse modelling approach that will utilise new datasets covering a wide range of unsteady discharge environments, with a spectrum of frequencies and magnitudes to mimic relevant source conditions (e.g. ocean outfalls, volcanic vents). It will combine scaled, parametric experiments in existing laboratory facilities that permit a wide range of environmentally-relevant conditions to be tested, and detailed CFD modelling to enhance links between these analogue laboratory data and field-scale volcanic plume data provided by British Geological Survey (BGS). The laboratory tests will be conducted in an existing large-scale recirculating flow facility within the refurbished Environmental Fluid Mechanics laboratory at the University of Dundee, and will utilise sophisticated measurement techniques such as particle image velocimetry (PIV), laser-induced fluorescence (LIF), laser doppler anemometry (LDA) and micro-conductivity profiling to measure detailed velocity and density fields within the evolving plumes. The study will also focus on identifying and optimising the number, location and period of sensor measurements of particle-laden plume dynamics, both at lab and field scales, to ascertain the extent to which the spectrum of unsteady source conditions can be recovered from these downstream plume measurements. The overall goals of the study will be to improve the dynamic links between plume evolution, particulate fall out characteristics and the temporal variability in source conditions, and implement this new knowledge to improve integral plume models, currently utilised in relevant fields (e.g. ocean engineering, volcanology).

Over the past 16 years, Civil Engineering research at Dundee has maintained its ranking as 1st in Scotland and in the top 10 in the UK. A major contributor to this success has been the internationally-recognised research in Environmental Fluid Mechanics with applications in ocean, coastal, offshore and estuarine flows, fluid-structure interactions, sediment transport processes, marine renewable energy, and computational fluid dynamics combining big data and machine learning. The Fluid Mechanics group has a highly successful track record in winning external research grants from national and international funding agencies and industry. It has excellent research and testing facilities, including wave flumes and recirculating flow tanks, as well as the £2M Scottish Marine and Renewables Testing (SMART) Centre, for research at the fluid-structure or fluid-soil interfaces.

The successful candidate will be supervised jointly by Drs Alan Cuthbertson (UoD), Roger Wang (UoD), and Fabio Dioguardi (British Geological Survey, Edinburgh). Some experience in either experimental fluid mechanics, computational fluid dynamics or numerical simulation is highly desirable.

To be eligible for a fully-funded studentship, covering tuition fees and an annual stipend, the candidate must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship (with some further constraint regarding residence for education, further guidance can be found on the EPSRC website). Applicants from EU countries other than the UK who do not comply with the residency criteria are generally only eligible for a fees-only PhD studentship award.

Applications will be reviewed and shortlisted applicants will be invited to interview. Interviews are expected to be held between 18th – 22nd June 2018.

It is anticipated that the successful candidate will start on or around 01 October 2018.

Interested applicants should send a cover letter and CV to .

Informal enquiries can be made to Dr Alan Cuthbertson () or Dr Roger Wang ()

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A short research visit to Orkney

I will conduct a short research visit to Orkney with funding support of UK Fluids Network. This visit will connect my research to tidal energy. More details are below:

Sensitivity study of the tidal circulation pattern around the Orkney Islands – is there a threshold of chaos?

Dr Ruo-Qian Wang, School of Science and Engineering, University of Dundee, Visiting: Dr David K Woolf, International Centre Island Technology, Heriot-Watt University, Orkney Campus

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Co-supervised Post-Doctoral Associate in Abu Dhabi

Post-Doctoral Associate in Division of Engineering, NYU Abu Dhabi – Dr. Samer Madanat Research Group and Dr. Ruo-Qian (Roger) Wang Research Group

Applications are invited for a Post-Doctoral Associate position for a collaborative project between New York University Abu Dhabi and the University of Dundee in the area of Physical Coastal Oceanography to develop and apply models that inform decision-making about coastal risk reduction.

The anticipated appointment is from September 1, 2018. Responsibilities of this position include to conduct a series of sensitivity analyses for the local coastline alternation using a coastal hydrodynamic model (e.g. Delft3D) of the Arabian Gulf at different sea levels with the uncertainty of coastal morphologies, and support other team members at NYUAD working on a game-theoretic study on regional public policy schemes. In addition, will also explore the application of uncertain quantification and machine learning techniques to develop innovative methods of model calibration, surrogating, and data analysis. The ideal candidate will be co-supervised by Professor Samer Madanat at NYUAD and Dr. Ruo-Qian (Roger) Wang at University of Dundee, Scotland.

Successful applicants will be a motivated and intelligent individual who is excited about working on collaborative projects, with excellent written and oral communication skills. Other required qualifications include:

·      Ph.D. in Civil Engineering, Geophysics, Oceanography, or a similar field.
·      Experience building and calibrating coastal hydrodynamic models.
·      Excellent programming skills in C/C++, Matlab, Python, or a suitable substitute.
·      Good collaborative skills and interest in working with interdisciplinary teams.

The terms of employment include highly competitive salary, housing allowance, and other benefits. Applications will be accepted immediately and candidates will be considered until the position is filled. To be considered, all applicants must submit a cover letter, curriculum vitae, transcript of degree, a one-page summary of research accomplishments and interests, and at least 2 letters of recommendation, all in PDF format. If you have any questions, please email: samer.madanat@nyu.edu or r.u.wang@dundee.ac.uk

About NYUAD:

NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities.

EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity Employer

UAE Nationals are encouraged to apply.


This position is not located in the United States. You must be willing to relocate to Abu Dhabi, United Arab Emirates.

Please apply through this link: https://apply.interfolio.com/48609

Public media report: Twitter + Citizen Science + AI = improved flood data collection

Check the public news report on our new paper:

The Sunday Times: https://www.thetimes.co.uk/article/flood-alert-scientists-tapping-in-to-twitter-hc7txl5zl

University News of University of Dundee: https://www.dundee.ac.uk/scienceengineering/news/2017/article/twitter–citizen-science–ai–improved-flood-data-collection.php

Phys.org: https://phys.org/news/2017-12-twitter-citizen-science-ai.html

Times of India: https://timesofindia.indiatimes.com/home/science/researchers-use-twitter-ai-to-develop-flood-warning-system/articleshow/62253456.cms

Digital Trends: https://www.digitaltrends.com/cool-tech/ai-twitter-urban-flooding/

Yahoo news: https://uk.news.yahoo.com/sea-levels-rise-researchers-monitor-211825808.html

TreeHugger: https://www.treehugger.com/climate-change/forecasting-urban-inundation-sunny-day-flood-tweets.html

The University Network: https://www.tun.com/blog/tweets-ai-flood-early-warning-system/

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Big Data of Urban Flooding: Dance with Social Media, Crowdsourcing Data and Artificial Intelligence

Our first big data paper has been published online at Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data

To our knowledge, this is the first paper in the world using computer vision to extract urban flooding data from crowdsourcing photos collected from Smart Phone Apps.

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PhD Student Scholarship Opportunity

Our school has a few openings for PhD student scholarship in collaboration with Chinese Government. Interested students can find more details in here.

More PhD studentship opportunities will come soon. Please stay in tune.