|
[Sponsors] |
Job Record #19364 | |
Title | ML analysis and adjoint-based optimization for cavitating flows |
Category | PhD Studentship |
Employer | National Technical University of Athens |
Location | Greece, Athens |
International | Yes, international applications are welcome |
Closure Date | Saturday, September 21, 2024 |
Description: | |
Full title: ML enhanced analysis and adjoint-based optimization tools for cavitating flows in hydraulic turbines The Doctoral Candidate (DC10) will be hired for 34 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. DC10 will be enrolled in the PhD program of the School of Mechanical Engineering of the National Technical University of Athens (NTUA), Greece. The main objective of DC10 is to develop physics-informed and numerics-informed DNNs (Deep Neural Networks) and apply them to shape optimization of hydraulic turbomachines. The objective by itself defines the scientific fields which DC10 will be working on: (a) Machine Learning (ML), (b) Computational Fluid Dynamics (CFD) for multiphase flows and (c) gradient-based optimization 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. As written above, the application domain will be that of hydraulic turbomachinery. A 4-month secondment (within the 34-month project) at ANDRITZ HYDRO (AHS-AT), Linz, Austria is scheduled; the purpose of this secondment is to collect data related to previous designs and apply the developed tools to industrial cases. DC10 will use and enhance the in-house GPU-enabled CFD solver PUMA of NTUA for the simulation of steady-state, cavitating flows. PUMA is based on the Transport-Equation-Mixture model (TEM) where liquid and vapor phases are considered as incompressible fluids and the mass transfer rates across them are determined using the Kunz model. PUMA also features a continuous adjoint method developed for shape optimization purposes. Both the prediction and adjoint-based optimization tools have been optimized for GPUs architectures. On the ML side, DC10 work will be based on available tools that use a specific two-branch DNNs to predict scalar fields; these DNNs have been used as surrogates of modeling tools in the framework of multi-disciplinary optimization, for example to replace turbulence and/or transition models to reduce the cost of RANS solvers. Enhancing, modifying and collating the mentioned codes, but also developing new methods to produce an efficient, accurate, reliable and low-cost tool for the optimization of hydraulic turbines will be the central task of DC10. Requirements Research Field Engineering: Mechanical engineering Education Level: Master Degree or equivalent Skills/Qualifications Candidates should have a strong background in CFD, especially for hydraulic turbomachines. In order of importance, they should possess good programming skills in C++ (mandatory) and CUDA-C (i.e. the programming environment of PUMA) 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 incompressible/cavitating flows, in rotating machines, 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) are mandatory (the first) and welcome (the second). Languages ENGLISH Level Excellent Benefits 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 formalized or (iii) dependent children who are actually being maintained by the researcher). The guaranteed (EC) funding is for 34 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 Greece 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 kgianna@mail.ntua.gr (email subject: “SCALE-DC10-Application”). The outcome of the evaluation process will be announced by mid of October 2024. The 34-month contract is expected to start around 01/11/2024. Additional comments DC10 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 PUMA and its adjoint code. Apart from the PhD thesis supervisor (Prof. K. Giannakoglou), researchers of the PCOpt/NTUA Unit with previous experience in similar tasks (CFD code running on GPUs, adjoint methods, shape parameterization, cavitation models, turbomachinery) will support DC10 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 DC10. Work Location Company/Institute: National Technical University of Athens Country: Greece City: Athens |
|
Contact Information: | |
Please mention the CFD Jobs Database, record #19364 when responding to this ad. | |
Name | Kyriakos Giannakoglou |
kgianna@mail.ntua.gr | |
Email Application | Yes |
URL | https://euraxess.ec.europa.eu/jobs/158482 |
Record Data: | |
Last Modified | 09:06:13, Thursday, September 12, 2024 |
[Tell a Friend About this Job Advertisement]