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To present a poster, the participants should submit a one-page abstract electronically by email to This email address is being protected from spambots. Acceptance of the poster will be announced to the corresponding author by December 1st, Abstracts should summarize the main concepts of the work, key results, and future perspectives. The presented work might not necessarily include the latest innovative developments and contributions: we also encourage submissions by early career researchers.

Application of Fluid Mechanics_2015

The abstract must be written in English following the template provided. Times Roman font 12pt must be used, with the exceptions of the title 14pt , affiliations, and references 11pt. The text width is mm. The file must be converted into PDF format; other formats are not accepted. The abstract must contain the full name s and affiliation s of the author s. In the case of joint authorship, the name of the author who will present the poster should be indicated with an asterisk.

The final acceptance of the poster publication and presentation requires the reception of the payment of the presenting author's registration fee until 1 November, Poster boards size A0, cm high by 84 cm wide will be available, including mounting material.

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All the posters will be on display for four days starting on Tuesday 25 February In case of doubts, please contact the organizers at This email address is being protected from spambots. The fee includes lunch, coffee break and notes. Early bird until November 1st, included. Normal fee after November 1st, Type 3: Permanent residents of non-NATO countries, need to have the formal approval of the VKI director before online registration by sending an email to the VKI secretariat This email address is being protected from spambots. To benefit from the PhD and Undergraduate Student fee, the participant should join a recommendation letter from his or her professor.

If not, the request will not be taken into consideration.

Introduction Big data and machine learning are driving extensive economic and social changes and are permeating every area of applied science. Abstract Submission All the participants to the Lecture Series "Machine Learning for Fluid Mechanics" are invited to present their work at the poster session, which will represent the central forum of discussion between the participants and lecturers.

MECH0024 Advanced Thermodynamics and Fluid Mechanics

Not applicable when early bird. Subscribe to RSS Feed. Of equal importance is the manner in which results are interpreted.

  • Introduction to Computational Fluid Dynamics Analysis (CFD): Theory and Applications.
  • Computational fluid dynamics;
  • An Advanced Introduction with OpenFOAM® and Matlab.

Advice is provided which allows the correct decisions to be taken, based on results which are known to be reliable. Interaction is encouraged throughout the course, with the planning and design of a complete CFD project and examples of simple hand calculations, mesh designs and solution designs being set for the class to complete. The course is completely code independent. Practising engineers who wish to understand CFD methods and learn how to apply CFD techniques to their particular problems in the most effective manner.

Attendees should have a graduate engineering background including some knowledge of fluid dynamics and engineering mathematics.

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Get in touch to discuss your next steps with our experienced training team. We can work closely with you to understand your specific requirements, cater for your specific industry sector or analysis type, and produce a truly personalised training solution for your organisation. All NAFEMS training courses are entirely code independent , meaning they are suitable for users of any software package.

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Courses are available to both members and non-members of NAFEMS, although member organisations will enjoy a significant discount on all fees. NAFEMS course tutors enjoy a world-class reputation in the engineering analysis community, and with decades of experience between them, will deliver tangible benefits to you, your analysis team, and your wider organisation. Find out more.

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