Adjoint Multiphase Shape Optimisation Tool With An Ml-Accelerated Real-Fluid Eos - Applications

Universities and Institutes of Greece
December 01, 2023
Offerd Salary:€3374.4
Working address:N/A
Contract Type:Other
Working Time:Full time
Working type:N/A
Ref info:N/A

23 Oct 2023

Job Information


National Technical University of Athens


School of Mechanical Engineering

Research Field

Engineering » Mechanical engineering

Engineering » Chemical engineering

Researcher Profile

First Stage Researcher (R1)



Application Deadline

1 Dec 2023 - 23:00 (Europe/Athens)

Type of Contract


Job Status


Hours Per Week


Offer Starting Date

1 Apr 2024

Is the job funded through the EU Research Framework Programme?


Marie Curie Grant Agreement Number


Is the Job related to staff position within a Research Infrastructure?


Offer Description

The Doctoral Candidate (DC6) will be hired for 36 months as part of the Industry empowerment to Multiphase fluid dynamics simulations using Artificial intelligence and Statistical methods on modern hardware architectures at Scale ( SCALE ) project being funded through the Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks. The main objective of the DC6 to develop and assess (e.g. in the design of regenerative cooling channels) a continuous adjoint method in OpenFoam for multiphase flows, involving an ML as an EoS-surrogate. The objective by itself defines the three scientific fields which the DC6 will be working on: (a) Machine Learning (ML) and (b) Computational Fluid Dynamics (CFD) particularly for multiphase flows and (c) gradient-based optimisation, based on the adjoint method. Applicants should have adequate skills in all three of them; with regard to CFD, in particular, the candidate should have experience in programming, not only using CFD tools. The application domain will be that of regenerative cooling channels and working fluid will be liquid methane. A 4-month secondment (within the 36-month project) at the City University of London (group of Prof. M. Gavaises) is scheduled; purpose of this secondment is to get experience regarding real fluid EoS and their implementation in CFD codes.

The DC6's research will lead to a Volume-of-Fluid based CFD model in OpenFoam, in which real-gas EoS models (also in the form of tabulated data) will be added. Then, Deep Neural Networks (DNNs) will be trained to replace complicated real fluid EoS, and errors introduced by the use of DNN-base surrogates will be estimated. For demonstration purposes, methane will be used as the working fluid. Studies of real gas effect on the heat transfer will be performed. The continuous adjoint to be above code will be programmed and assessed (on simplified test cases, in flow problems with phase change) in terms of the accuracy of the computed sensitivity derivatives; it will include differentiated DNNs to account for the EoS, linked using the chain rule. Thermal and losses-related objective functions will be used. Finally, optimisation runs will be performed in cases related to the design the regenerative cooling channels for a thrust engine, using methane at typical operating conditions which are not far from its critical point.


Research Field

Engineering » Mechanical engineering

Education Level

Master Degree or equivalent


Candidates should have a strong background in CFD, especially for multiphase flows and cavitation models. In order of importance, they should possess good programming skills in C++ (mandatory) and the OpenFOAM environment and experience in using high-performance computing centers. Experience in using/developing optimization methods is welcome. This is a position in the field of CFD for the above applications, not based on commercial CFD s/w. Though experience in using commercial CFD s/w is welcome and surely helps, this position requires good programming skills and is addressed to programmers, rather than just users, of CFD s/w. Background knowledge regarding Machine Learning and Deep Neural Networks is required. Programming skills in Python and deep learning frameworks (preferably tensorflow and tensorflow C++ API) is mandatory (the first) and welcome (the second).




Additional Information


The selected candidate will receive a salary in accordance with the MSCA regulations for DCs. The minimum gross salary includes a living allowance (€3374.4 per month), a mobility allowance (€600 per month) and a family allowance (€660 per month), if the researcher has family (‘Family' means persons linked to the researcher by (i) marriage or (ii) a relationship with equivalent status to a marriage recognised by the legislation of the country where this relationship was formalised or (iii) dependent children who are actually being maintained by the researcher). The guaranteed (EC) funding is for 36 months.

Eligibility criteria

Applicants can be of any nationality and must hold a Master of Science degree (or equivalent) in engineering. They need to fully respect the following eligibility criteria: (a) Must be doctoral candidates, i.e. not already in possession of a doctoral degree at the date of the recruitment. (b) Must undertake transnational mobility. Researchers must not have resided or carried out their main activity (work, studies, etc.) in Austria for more than 12 months in the 36 months immediately before their date of recruitment. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.

Selection process

The candidates should send a CV, cover letter (in which the applicant's experience in CFD development should become clear), BSc and MSc degrees (certified copies plus translation in English), and two letters of recommendation are necessary. Copies of publications could be sent later on, upon request. Personal interviews might be asked.

All applications should be mailed to [email protected] (email subject: “SCALE-DC6-Application”).

The outcome of the evaluation process will be announced by mid of December 2023. The three-year contract is expected to start early in spring 2024 (~01 April 2024).

Additional comments

The DC6 will be working (excluding the secondment) at the Zografou Campus of NTUA in Athens, Greece. The PCOpt/NTUA group consists of about 15 people, including 4 experienced researchers, among which the major developers of the adjointOptimisationFOAM, i.e. the adjoint code PCOpt/NTUA developed in OpenFOAM and made it publicly available. Apart from the PhD thesis supervisor (Prof. K. Giannakoglou), researchers of the PCOpt/NTUA Unit with previous experience in similar tasks (programming in OpenFOAM, adjoint methods, shape parameterization, cavitation models, gradient-based optimization) will support the DC6 in her/his project/PhD. The PCOpt/NTUA Unit possesses a powerful multiprocessor platform, including both CPU and GPU clusters, which is expected to be upgraded during the project life; this will support research to be performed by the DC6.

Work Location(s)

Number of offers available



National Technical University of Athens





Postal Code



Iroon Polytexneiou


Where to apply


[email protected]





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Iroon Polytexneiou

Postal Code



[email protected]




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