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► A data-driven entropy-stable discontinuous Galerkin method, ONERA
   26 Oct, 2021 
Job Record #17410
TitleA data-driven entropy-stable discontinuous Galerkin method
LocationFrance, Chatillon (Paris)
InternationalYes, international applications are welcome
Closure DateWednesday, December 15, 2021
High-order numerical methods and in particular the Discontinuous Galerkin (DG) 
Spectral Element Method (DGSEM) have gained popularity in recent years for their 
potential to reduce the computational cost of accurate simulations of complex 
flows. The DG method is a mixed Finite-Volume and Finite-Element method which 
presents a number of interesting properties. These include low dissipation and 
dispersion errors, high parallel efficiency on modern HPC architectures and the 
possibility of adapting the local spatial resolution by modifying the polynomial 
order of the method and the mesh size.Recent works by Fisher et al. [1] and 
Gassner et al. [2] have demonstrated that an entropy-stable DGSEM scheme can be 
obtained by interpreting the DGSEM discretization as an high-order scheme 
obtained by subdividing each cell into a number of sub-cells and introducing a 
numerical flux which is a linear combination of entropy-stable two point fluxes.

In this internship we propose to develop a novel DGSEM scheme for the solution 
of hyperbolic equations where the optimal coefficients of the linear combination 
of the two-point fluxes are identified by a neural network.

For this purpose we will follow an approach recently proposed by Harvard and 
Google in [3] and extended in [4] and [5]. In [3] the authors have demonstrated 
a methodology to use machine learning to systematically derive discretizations 
for continuous physical systems. The data-driven discretizations are able to 
provide accurate solutions with a dramatic drop in the required resolution. The 
design of such schemes and the machine learning algorithms employed for this 
purpose however require a careful choice of the objective functions as well
as physical and numerical constraints. The design of such constraints requires a 
mathematical analysis of the starting and derived discretizations and is 
fundamental to obtain robust and reliable numerical schemes.
The objective of this work is therefore to exploit the numerical properties of 
DGSEM schemes identified in [1] and [2] to obtain a data-driven DGSEM 
discretization ensuring at the same time a minimum order of convergence and the 
entropy-stability property.
Two different approaches will be considered. The first consists in using a 
neural network to compute off-line the optimal two-point flux coefficients to be 
used in the data-driven DGSEM scheme. The second consists in using over the 
course of the simulation a previously trained neural network in order to adapt 
these coefficients as a function of the instantaneous numerical solution at all 
integration points.

The final result of this internship is expected to be a prototype of the desired 
data-driven discretization for hyperbolic equations (Burgers and Euler) in one 
dimension implemented in C++ or Python. The conclusions of this study will then 
be employed to inform future developments for the in-house discontinuous 
Galerkin solver Aghora currently under development.

[1] T.C. Fisher, and M.H. Carpenter, “High-order entropy stable finite 
difference schemes for nonlinear conservation laws: Finite domains”, 2013, 
Journal of Computational Physics, 252.
[2] G.J. Gassner, A.R. Winters, and D.A. Kopriva, “Split form nodal 
discontinuous Galerkin schemes with summation-by-parts property for the 
compressible Euler equations”, 2016, Journal of Computational Physics, 327.
[3] Bar-Sinai, Y., Hoyer, S., Hickey, J. and Brenner, M.P., “Learning data-
driven discretizations for partial differential equations”, 2019, Proceedings of 
the National Academy of Sciences, 116(31).
[4] J. Zhuang, D. Kochkov, Y. Bar-Sinai, M.P. Brenner, S. Hoyer, “Learned 
discretizations for passive scalar advection in a 2-D turbulent flow”, 2020, 
[5] R. Ranade, C. Hill, J. Pathak, “DiscretizationNet: A machine-learning based 
solver for Navier-Stokes equations using Finite Volume discretization”, 2020, 
Contact Information:
Please mention the CFD Jobs Database, record #17410 when responding to this ad.
NameFabio Naddei
Email ApplicationYes
Record Data:
Last Modified10:37:29, Tuesday, October 26, 2021

► Improving the performance of the industrial flow solver CODA, ONERA
   26 Oct, 2021 
Job Record #17409
TitleImproving the performance of the industrial flow solver CODA
CategoryPostDoc Position
LocationFrance, Chatillon (Paris)
InternationalYes, international applications are welcome
Closure DateWednesday, December 15, 2021
Presentation of the post-doctoral project, context and objective
Since 2017, ONERA has been developing in collaboration with AIRBUS and DLR 
(German Aerospace Center) a new common software, named CODA, for the simulation 
of flows for complex industrial applications.
The CODA solver includes classic finite volume capabilities and novel high-order 
discontinuous Galerkin schemes, all specifically tailored for aeronautical 
applications, and will be the reference solver for aerodynamics applications 
inside the AIRBUS group including Civil transport aircraft and Helicopters.
In order to be able to treat different and varied complex applications with 
competitive turnaround times, the CODA solver is implemented as a highly 
modularized and flexible C++17 library with a python user interface. Template 
metaprogramming is extensively used to provide high flexibility with generic 
programming while ensuring high runtime performance and reducing to a minimum 
runtime polymorphism. Both distributed and shared parallelism are employed by 
using MPI and OpenMP in order to obtain high-parallel efficiency on modern HPC 
architectures and the possibility of exploiting GPUs is currently under study. 
Finally in order to optimize the collaborative work of more than 40 active 
developers, modern software developing practices are extensively used, which 
include the use of best coding and formatting practices, distributed version 
control and continuous integration practices with automated testing, code 
analysis and benchmarking.

The fundamental objective of the proposed research activity is to increase the 
performance of CODA solver for large scale simulations. Some optimization axes 
will be investigated and several proofs of concept developed before any 
implementation into the solver. Specific attention will be paid to the 
capabilities of the existing multilevel parallelism and the efficiency of the 
current partitioning mesh strategies.These activities take place in the 
framework of the European NextSIM project (2021-2024) ( The 
selected candidate will therefore interact with all partners of the CODA and 
NextSIM projects (which additionally include Cerfacs, Barcelona Supercomputing 
Center, CIMNE and the University of Madrid).

The research activities to be conducted are:
· Evaluate serial and parallel performance of the CODA solver. Numerical 
experiments will be carried out for different applications and on different HPC 
architectures to draw out bottlenecks. Daily interactions with developers 
involved in topics such as discretization schemes or high-performance
computing will help to draw first conclusions.
· Analyse the suitability of commonly used data-structures. Identify 
opportunities of efficiency improvements and do proofs of concept by developing 
external devoted prototypes. This study will require a detailed analysis of the 
dominant kernels of the CODA solver and will ask for a particular knowledge of 
target HPC architectures specificities that could be acquired via other NextSim 
· Contribute to activities related to load balancing of parallel computations 
over the course of the different phases of simulations. Performance models will 
be developed to ensure the mesh partitioning strategy will lead to satisfactory 
levels of load balance. The possibility of performing dynamic load balance, i.e. 
redistributing the computational mesh during the simulation, will also be
considered. Such studies will necessarily take into account technical and 
implementation details of high-order discretizations or linear solvers depending 
on the target applications.
· Interact with all partners involved in the development of CODA and take 
benefit from previous experiences gained from other ONERA solvers. 

Profile and skills required
Minimum qualifications:
· A PhD in computer science, applied mathematics or a closely related topic
· Proficient in programming and working with C/C++, Python, Linux
· Familiar with parallel programming models (MPI and OpenMP) and code 
optimization techniques
· A keen interest in performance analysis, profiling tools, and computing 
· Good level of spoken and written English
Additional qualifications:
· Previous experience with large simulation software (for industrial or research 
· Knowledge of collaborative tools such as Git, GitLab, Gerrit or similar
· Background in Computational Fluid Dynamics, Finite Volume or Discontinuous 
Galerkin methods
· Background in linear algebra and in particular sparse iterative solvers

Duration: 12 months, possibly extendable to 24 months - Net yearly salary: about 
25 k€ (medical insurance included) 

Start of contract: As soon as possible
Contact Information:
Please mention the CFD Jobs Database, record #17409 when responding to this ad.
NameFabio Naddei
Email ApplicationYes
Record Data:
Last Modified10:31:10, Tuesday, October 26, 2021

► CFD in chemical reactors with OpenFoam, Nagoya University
   23 Oct, 2021 
Job Record #17408
TitleCFD in chemical reactors with OpenFoam
CategoryPostDoc Position
EmployerNagoya University
LocationJapan, Aichi, Nagoya
InternationalYes, international applications are welcome
Closure DateTuesday, November 30, 2021
We are seeking a full-time post-doctoral fellow who can simulate; 

Chemical reaction, heat and mass transfer in fixed bed catalytic reactors.   
It is also preferable that the candidate has experience in process simulation, reactor design and optimization.

Gross salary: over 400,000 JPY/month.
Profile required: Ph.D. in Chemical Engineering or Fluid dynamics. Profile in simulation and modeling with OpenFOAM is required. 

Please send your CV including the skills with OpenFOAM via email to Koyo Norinaga.
The potential candidates will be invited online interview within a week after the arrival of your application. No feedback from our side after a week since your application means no further process toward engagement.

Contact Information:
Please mention the CFD Jobs Database, record #17408 when responding to this ad.
NameKoyo Norinaga
Email ApplicationYes
Record Data:
Last Modified11:23:40, Saturday, October 23, 2021

► Postdoctoral Research Associate – Aerosols in Medicine, Virginia Commonwealth University
   22 Oct, 2021 
Job Record #17369
TitlePostdoctoral Research Associate – Aerosols in Medicine
CategoryPostDoc Position
EmployerVirginia Commonwealth University
LocationUnited States, Virginia, Richmond
InternationalYes, international applications are welcome
Closure DateMonday, December 20, 2021
Postdoctoral Research Associate – Aerosols in Medicine: CFD Development of a 
Surfactant Aerosol Delivery Device

Description of Job:

The Aerosols in Medicine (AIM) Lab in the VCU College of Engineering 
(directed by Dr. PW Longest) seeks to address significant current challenges 
in the field of medical aerosols in order to improve the treatment of 
respiratory and systemic diseases and conditions including chronic lung 
infections, asthma, chronic obstructive pulmonary disease (COPD), cystic 
fibrosis, surfactant insufficiency and acute respiratory distress syndrome.

To address challenges in the field of aerosolized medicine, the AIM lab 
specializes in a thorough understanding of transport phenomena including 
fluid mechanics, heat and mass transfer, turbulence, thermodynamics, 
pharmacokinetics and multiphase flows, together with aerosol science, lung 
biology and lung physiology.  Specific skills developed by lab members often 
fall into the areas of:

•  Computational fluid dynamics (CFD)
•  Inhaler and aerosol generation/delivery device development
•  Development and use of characteristic and complete airway models
•  Particle engineering

These skills are applied to various aspects of the medical aerosol delivery 
process from particle formation during spray drying and aerosol generation 
within an inhaler through particle deposition in the distal airways and 
post-deposition transport including drug dissolution, absorption and 

The available position involves the use of CFD simulations to understand, 
characterize and improve the administration of a dry powder surfactant 
aerosol to infants.  Key regions of interest include aerosol delivery 
through the existing device, patient interface, and infant extrathoracic 
airways.  CFD simulations are expected to produce design modification that 
improve aerosol formation, reduce device and interface losses and improve 
transmission through extrathoracic airways and to infant lungs.  Recommended 
design changes will be prototyped and tested in the Aerosols in Medicine 
(AIM) – Experimental Lab and in the collaborating VCU Department of 
Pharmaceutics labs. Improved aerosol delivery to the lungs of infants is 
needed in order to realize the benefits of aerosolized surfactant 
replacement therapy instead of the current clinical procedure of liquid 
bolus instillation, which involves subject intubation, administration of 
high volume carrier liquids and subsequent mechanical ventilation.  In this 
project, low cost device and formulation options are being developed for 
applications in low resource settings.

Project funding is through the Bill and Melinda Gates Foundation.  Other 
projects in the AIM Lab are funded by NIH and US FDA.

More Information about the AIM Lab and group publications can be found at:

Required Qualifications

•  PhD in Mechanical Engineering, Biomedical Engineering or similar field
•  Experience with multiphase CFD, commercial CFD codes requiring user-
defined function development and use, and complex mesh generation
•  Excellent communication skills
•  Ability to mentor junior team members
•  Demonstrated ability to work in multi-disciplinary teams

Preferred Qualifications

•  Experience with CFD applied to aerosol science
•  Experience with CFD applied to inhaler analysis, design, and/or aerosol 
transport in the respiratory airways
•  Experience with constructing small scale devices with high resolution 3D 
•  Experience with aerosol characterization and quantification experiments
•  Publications in the areas of aerosol science or pharmaceutical aerosols

Job Open Date: September 2021

Applications Instructions:  Interested candidates should email a cover 
letter of interest, CV, graduate transcript (unofficial) and three 
professional references that include emails and phone numbers to Prof. 
Longest ( Applications will be reviewed upon receipt and 
accepted until the position is filled. Employment will require successful 
completion of background check(s) in accordance with University policies.

Additional Information

The Mechanical and Nuclear Engineering Department at VCU offers a highly 
interdisciplinary research experience, which is essential for the study of 
pharmaceutical aerosols.  Areas of expertise include transport modeling, 
computational fluid dynamics, life science engineering, and pharmaceutics.  
This project is being performed in collaboration with the VCU Department of 
Pharmaceutics, which is located on the VCU Medical Campus.  The VCU 
Department of Pharmaceutics is widely recognized for its Aerosol Research 
Group, which specializes in the area of respiratory drug delivery, and is 
the developer of the Respiratory Drug Delivery (RDD) Conference series 
(, which currently hosts ~700 attendees to 
national and international conference sites each year.

VCU is an Equal-Opportunity Affirmative Action Employer.  Women, minorities 
and persons with disabilities are strongly encouraged to apply.

Contact Information:
Please mention the CFD Jobs Database, record #17369 when responding to this ad.
NameProf. Longest
Email ApplicationYes
Record Data:
Last Modified16:29:34, Friday, October 22, 2021

► Aerospace or Mechanical Engineer, Mathematician (f/m/x), German Aerospace Center (DLR)
   22 Oct, 2021 
Job Record #17407
TitleAerospace or Mechanical Engineer, Mathematician (f/m/x)
CategoryContract Work
EmployerGerman Aerospace Center (DLR)
LocationGermany, Lower Saxony, Göttingen
InternationalYes, international applications are welcome
Closure Date* None *
Start your mission with DLR

The German Aerospace Center (DLR) is the national aeronautics and space research 
centre and the space agency of the Federal Republic of Germany. Here, 10,000 
employees work together on a unique variety of topics in the fields of 
aeronautics, space, energy, transport, security and digitalisation. Their 
missions range from basic research to the development of innovative applications 
and products for tomorrow. Cutting-edge research requires excellent minds – 
particularly more females – at all levels, who fully achieve their potential in 
an inspiring environment. Launch your mission with us.

For our Institute of Aerodynamics and Flow Technology

in Göttingen, we wish to recruit a qualified

Aerospace or Mechanical Engineer, Mathematician, Physicist (f/m/x)

Development, Implementation and Application of Mesh Adaptation for Numerical 
Flow Simulation

Your mission:
The Institute of Aerodynamics and Flow Technology is a leading research 
institute in the field of aerodynamics/aeroacoustics of airplanes and 
aerothermodynamics of space vehicles.

The C²A²S²E department develops numerical methods for multidisciplinary 
simulation and optimization of air vehicles. Besides physical modeling of 
complex flows and the development of modern numerical algorithms, our research 
activities cover the software-based integration of all relevant disciplines, the 
development of efficient optimization strategies as well as surrogate modeling 
based on higher fidelity methods.

The core task of the position is the further development, implementation and 
application of an adaptation capability of CFD computational meshes in the 
framework of the cooperation between DLR, ONERA and Airbus for the CFD-code 
CODA. The focus of this work is on the extending the adaptation capabilities by 
including the curvature of surface geometry and by the implementation of 
anisotropic element division. Another important aspect is the continuous 
application of the mesh adaptation to industrially relevant aerodynamic 
testcases to evaluate its robustness and the accuracy of CFD-solutions as well 
as identifying room for improvement. The status of work will be regularly 
presented and discussed within the CODA-cooperation.


Your qualifications:

• Master or Diploma in Engineering (AeroSpace, Mechanical Engineering or 
similar) or Science (Mathematics, Physics)
• expertise and experience in the field of mesh generation, e.g. for numerical 
flow simulation
• expertise in the field of numerical methods, preferably for numerical flow 
• substantial knowledge in advanced programming languages (Python, C++) and 
parallel programming
• strong teamwork and results-oriented working skills
• very good oral and written communication skills in English
• ideally expertise and experience in the field of development and application 
of mesh adaptation techniques
• expertise in the field of aerodynamics and flow technology welcome
• experience in software development in a team preferred
• German skills are a plus

Your benefits:

Look forward to a fulfilling job with an employer who appreciates your 
commitment and supports your personal and professional development. Our unique 
infrastructure offers you a working environment in which you have unparalleled 
scope to develop your creative ideas and accomplish your professional 
objectives. Our human resources policy places great value on a healthy family 
and work-life-balance as well as equal opportunities for persons of all genders 
(f/m/x). Individuals with disabilities will be given preferential consideration 
in the event their qualifications are equivalent to those of other candidates.

If you have any questions concerning specific aspects of the job, please contact 
Dr. Cornelia Grabe by calling +49 551 709-2628. Please find further information 
on this vacancy with the reference number 60288, and details regarding the 
application procedure, at
Contact Information:
Please mention the CFD Jobs Database, record #17407 when responding to this ad.
NameDr. Cornelia Grabe
Email ApplicationNo
Record Data:
Last Modified12:42:40, Friday, October 22, 2021

► Fully Funded PhD on Internally-Geared Screw Compressors, City, University of London. Centre for Compressor Technology.
   21 Oct, 2021 
Job Record #17406
TitleFully Funded PhD on Internally-Geared Screw Compressors
CategoryPhD Studentship
EmployerCity, University of London. Centre for Compressor Technology.
LocationUnited Kingdom, England, London
InternationalYes, international applications are welcome
Closure DateWednesday, December 01, 2021
Project title: 
Development of Low and High Order Models for Internally-Geared Screw Compressors

Background and aims:
The Centre for Compressor Technology has conducted ground breaking research on 
internally-geared screw compressors [1-3].  Internally-geared screw compressors 
have a number of potential advantages over conventional twin screw 
configurations, including reduced leakage areas, co-directional thermal 
expansion, reduced rotor deflection, reduced viscous losses, and higher swept 
volume for a given machine envelope.  Initial research has focused on 
characterizing the rotor geometry for internally-geared screw compressors and 
understanding its influence on power transfer between rotors, porting losses and 
leakage paths.  This has resulted in the development of computational tools for 
the preliminary design of these machines, based on a simplified model of the 
compression process.  Further work is required to develop detailed numerical 
models for prediction of machine performance using real fluids with and without 
oil injection in order to better understand the potential performance, the 
losses that occur in the internally geared machine must be understood and 
quantified. Many of the loss mechanism in these machines are similar in nature 
to conventional twin screw machines, but must be characterised due to the unique 
aspects of sealing line geometry and co-rotation (leading to much lower sliding 
velocities at contact points).  Along with the implementation of a low-order 
chamber model, the proposed project will advance this technology to full 3D 
evaluation procedures [4] to enable detailed design and optimisation of rotor 
and port geometry.

Objectives of research:
•	Develop 1D performance prediction tools for internally geared machines 
to include effects of working fluid, leakage, porting losses, oil injection, and 
estimates of bearing, seal and viscous losses.
•	Develop capability for 3D Computational Fluid Dynamics analysis of 
internally-geared machines and use to develop understanding of internal flows 
and loss mechanisms. 
•	Validate software using experimental data for oil-injected air 
compressor and compare with conventional twin-screw machines.
•	Apply validated models to the optimisation of machine geometry for a 
range of real-world applications in air and refrigeration systems.

Expected outcomes of research:
1.	Detailed comparison of current CFD and lower order analysis tools used 
for classical twin-screw with proposed technology and evaluation of the 
accuracy, computational and data processing, results presentation capabilities.
2.	Evaluation and comparison of performance of internally geared screw 
machine configurations as compressors and expanders.
3.	Identification of best practices for 3D CFD and lower order performance 
modelling of internally geared screw machines and recommendation of flow solver 
settings, boundary conditions, treatment of volumetric deformation, post-
processing etc.

Contact for further information:
Dr Matthew Read, Principle Investigator

[1] 	Read M.G., Smith, I.K. and Stosic, N., “The influence of rotor geometry 
on power transfer between rotors in gerotor-type screw compressors”, Journal of 
Mechanical Design, in print, 2019
[2] 	Read MG, Smith IK, Stosic N. “Influence of rotor geometry on tip leakage 
and port flow areas in gerotor-type twin screw compressors”. Proceedings of the 
Institution of Mechanical Engineers, Part E: Journal of Process Mechanical 
Engineering. 2020 Oct 12:0954408920962412.
[3] 	Read, M.G., Smith, I.K. and Stosic, N., “Geometrical Comparison of 
Conventional and Gerotor-Type Positive Displacement Screw Machines”. In IOP 
Conference Series: Materials Science and Engineering (Vol. 604, No. 1, p. 
011011). 2019
[4] 	Rane, S., Kovacevic A., Stosic N., Grid Generation for CFD Analysis and 
Design of a Variety of Twin Screw Machines, MDPI Designs 2019, 3, 30

Contact Information:
Please mention the CFD Jobs Database, record #17406 when responding to this ad.
NameDr Matthew Read
Email ApplicationYes
Phone+44 (0)20 7040 8746
AddressDr Matthew Read
Lecturer, Department of Mechanical Engineering & Aeronautics
Engineering Stage 1 Programme Coordinator
City, University of London
Northampton Square
London EC1V 0HB
Record Data:
Last Modified20:23:38, Thursday, October 21, 2021

► Turbidity and Oxygenation in Estuary, INRS
   21 Oct, 2021 
Job Record #17405
TitleTurbidity and Oxygenation in Estuary
CategoryPostDoc Position
LocationCanada, Québec, québec city
InternationalYes, international applications are welcome
Closure DateFriday, December 31, 2021
Title : 3D-Numerical study of dissolved oxygen transport and salinity in the St. 
Lawrence river estuary

Context : The St. Lawrence is a major river in North America connecting the 
Great Lakes to the Atlantic Ocean. The river estuary part is located between 
city of Trois-Rivières (South, 20’000 m3/s annual mean discharge) and Baie 
Saint-Paul (North, 5m tidal range): it is centered to city of Québec in Canada. 
This fluvial estuarine area corresponds to maximum turbidity zone (MTZ) with 
mixture of salt/freshwater. Due to turbidity, concentration of dissolved oxygen 
is depleted. The objective of this study is to develop a 3D numerical model 
capturing the both, hydrodynamic and  dissolved oxygen, for prediction under 
climate change.

Methodology : The actual model on the site study was developped under Telemac-2D 
open-source system. The actual model is a classical shallow water flow model 
(depth integrated, or Saint-Venant). The project aims to extend the existing 
model in 3D by using the sigma (vertical) transform. Then the 3D hydrodynamic 
model will be further coupled with sediment transport module (Gaia) to compute 
suspended sediment concentration (SSC) and turbidity? Finally, the passive 
tracer equation for dissolved Oxygen will be coupled with Water quality module 
(Telwaq) in order to consider speciation and reaction of molecules. Field 
(recent) data on turbidity and salinity that are available through local partner 
will serve for validation and publication.

Prospects : The 18-month post-doctoral project is co-funded by a France-Quebec 
collaboration. There are possibility to extend that collaboration.

Skills required: High Performance Computing; stratified flows; estuarine 

To apply : 1/ Cover letter (1p); 2/ CV ; 3/ scientific papers related to topic

Contact Information:
Please mention the CFD Jobs Database, record #17405 when responding to this ad.
Namedamien pham van bang
Email ApplicationYes
Address490 rue de la couronne,
G1K 9A9 Québec (QC)
Record Data:
Last Modified18:19:07, Thursday, October 21, 2021

► PhD - Digital Twins by Machine Learning and Numerical Modeling, UNITO - Computer Science Department & DOFWARE S.r.l.
   21 Oct, 2021 
Job Record #17404
TitlePhD - Digital Twins by Machine Learning and Numerical Modeling
CategoryPhD Studentship
EmployerUNITO - Computer Science Department & DOFWARE S.r.l.
LocationItaly, Turin
InternationalYes, international applications are welcome
Closure DateThursday, October 28, 2021
One PhD position has been opened in the fields of COMPUTER SCIENCE, MACHINE LEARNING and PHYSICAL-NUMERICAL MODELING. The project is coordinated
in cooperation between Università di Torino (UNITO) - Computer Science Department and DOFWARE S.r.l., a company involved in the implementation of
modeling solutions supporting product and process design and control.

The project title is:

"Implementation of Digital Twins focused on the energy aspects related to indoor environment, widening from the indoor air quality (IAQ) to
the adoption of alternative energies, by means of machine learning techniques and physical-numerical modeling".

The PhD Coordinator is Prof. Marco Grangetto, and the Scientific Supervisor is Prof. Rosa Meo.

For detailed information, please have a look at:

When applying for the position, a draft of the research project must be submitted too.

For any further information concerning examinations, please, check the proposed research sheet or contact the supervisor Prof. Rosa Meo
Contact Information:
Please mention the CFD Jobs Database, record #17404 when responding to this ad.
NameProf. Rosa Meo
Email ApplicationYes
Record Data:
Last Modified17:35:59, Thursday, October 21, 2021

► Mass transfer from core-shell cylinders subjected to flow, China Scholarship Council / UT Compiegne (France)
   21 Oct, 2021 
Job Record #17403
TitleMass transfer from core-shell cylinders subjected to flow
CategoryPhD Studentship
EmployerChina Scholarship Council / UT Compiegne (France)
LocationFrance, Compiegne
InternationalYes, international applications are welcome
Closure Date* None *
Université de Technologie de Compiègne

PhD Grants from the China Scholarship Council: PhD proposal for 2022

Thesis title: Mass transfer from core-shell cylinders subjected to flow

Keywords: Transport phenomena, computational fluid dynamics, lattice Boltzmann method, artificial lung, 
heat sink exchanger

Supervisor and contact person: Dr. Badr Kaoui, Biomechanics and Bioengineering, Université de Technologie 
de Compiègne, Compiègne, France /

Summary: The project aims to study systematically mass transfer from initially loaded cylinders covered with 
a semi-permeable shell and subjected to flow. Two-dimensional computer simulations based on two-
component lattice Boltzmann method will be used to compute the flow around the cylinders and the mass 
transport of a released solute. Mass transfer efficiency will be characterized with the Sherwood number (the 
dimensionless transfer coefficient) as a function of different parameters, such as the shell permeability and 
the cylinders spatial arrangement. An in-house fully parallelized code developed with Fortran 2000 will be 
used together with the emerging techniques of machine learning to screen a wide range of control 
parameters. Access to modern advanced workstations and supercomputer platforms such as PILCAM2 of the 
UTC will be granted.

Planning of the PhD project:

1. First year: Extension of recent studies [1,2] to a large range of the blockage degree (ratio of the cylinder 
diameter to the width of the channel) to investigate numerically the effect of the confinement, which will be 
compared to the theory of Khan et al [3], while considering both cases: constant and varying boundary 
conditions at the surface of the cylinder,
2. Second year: Study the efficiency of mass transfer from a stationary array of core-shell cylinders, with the 
aim to complement the list of existing correlations in literature of heat efficiency in heat sink exchangers, 
while considering cylinders with semi-permeable shells (that adds an interfacial resistance). The model and 
the knowledge of this part of the project will be applied to model oxygen exchange in an array of cylinders for 
future design of artificial lungs,
3. Third year: Development of a numerical method to study mass transfer efficiency from a single core-shell 
cylinder, whose shell undergoes either growth or shrinkage (accumulation of an undesired matter or shell 
degradation due to aging), while coupling the lattice Boltzmann method (used for the transport phenomena) 
and the phase field model (used for the cylinder shape evolution). 

International collaborations with: 
CNR (Italy), Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (Germany), Massachusetts General 
Hospital (USA), University of South Africa (South Africa)


[1] C. Bielinski , N. Le, and B. Kaoui, Unsteady mass transfer from a core-shell cylinder in crossflow, Physical 
Review Fluids 6, 023501 (2021)
[2] B. Kaoui, Flow and mass transfer around a core-shell reservoir, Physical Review E 95, 063310 (2017).
[3] W. A. Khan, J. R. Culham, and M. M. Yovanovich, Fluid flow and heat transfer from a cylinder between 
parallel planes, J. Thermophys. Heat Transfer 18, 395 (2004).
Contact Information:
Please mention the CFD Jobs Database, record #17403 when responding to this ad.
NameBadr Kaoui
Email ApplicationYes
AddressBiomechanics and Bioengineering,
Université de Technologie de Compiègne,
60200 Compiègne, France
Record Data:
Last Modified16:52:37, Thursday, October 21, 2021

► NSF Funded PhD Position - CFD of Hurricanes, University of Maryland Dept. of Physics or Mech. E.
   21 Oct, 2021 
Job Record #17402
TitleNSF Funded PhD Position - CFD of Hurricanes
CategoryPhD Studentship
EmployerUniversity of Maryland Dept. of Physics or Mech. E.
LocationUnited States, Maryland, Baltimore
InternationalYes, international applications are welcome
Closure Date* None *
The Geophysical Fluid Dynamics (GFD) group in the Joint Center for Earth Systems 
Technology (JCET) and Department of Physics (Atmospheric Physics Program) at the 
University of Maryland Baltimore County (UMBC) is seeking a PhD student for a 
graduate research assistant position. The student will work on an exciting new 
project that will study the fluid mechanics of turbulence, convection and 
hurricane intensification with cutting-edge numerical models and remote sensing 
data. One of these models, “Climate Machine”, is a new atmospheric model that 
has an advanced computing architecture and optimal numerical properties for 
simulating turbulent flow. The project is funded by the National Science 
Foundation (NSF) for a period of three years with Dr. Steve Guimond (UMBC) and 
Dr. Simone Marras (NJIT) as lead investigators. The selected graduate student 
with receive a stipend (~ $32,000/year) and tuition coverage in addition to 
conference expenses. The selected student will also have connections to NASA 
Goddard Space Flight Center (GSFC), which is in close proximity to UMBC, with 
potential opportunities for internships, collaborations and future job 
prospects. Note that students can also choose to get their PhD degree in the 
Department of Mechanical Engineering if desired.

The atmospheric models being studied in this project utilize high-order 
Continuous Galerkin (CG) or Discontinuous Galerkin (DG) numerical methods along 
with various time integration schemes. Part of the project is to understand the 
value of these high-order methods relative to community-based finite difference 
or finite volume methods for simulating very high Reynolds number flows that 
typify a hurricane. Large eddy simulations of the hurricane intensification 
process will be conducted on supercomputers with these models to understand the 
flow of energy between various space/time scales and the lifecycle of discrete 
convective plumes in a moist atmosphere.

Interested students should have a B.S., M.S. or have started their Ph.D. degree 
in physics, atmospheric science, mechanical engineering, applied mathematics or 
a closely related field. The ideal student will have interests/skills in the 
physics of turbulence, hurricanes (or more generally fluid mechanics) as well as 
computational methods for studying these problems. Significant experience with 
computer coding in Fortran or C as well as Matlab, Python or Julia is required. 
Excellent oral and written communication skills and a passion for working on 
problems through to completion are desired.

For questions and further information, please contact Dr. Steve Guimond at and include a CV with information relevant to the position. 
Positions can start as soon as possible and applications will continually be 
evaluated until a suitable candidate is found. When in doubt, send an email to 
check in.
Contact Information:
Please mention the CFD Jobs Database, record #17402 when responding to this ad.
NameStephen Guimond
Email ApplicationYes
Address1000 Hilltop Cir, Baltimore, MD 21250
Record Data:
Last Modified05:33:42, Thursday, October 21, 2021

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