We have a fully funded PhD studentship and look for applicants open to both UK and international students: Engineering doctorate (EngD): Predicting performance of intensified carbon capture inside rotating packed beds using CFD modelling
Carbon capture is considered the only technology able to decarbonise the hard-to-abate industries. many of these industries utilise legacy sites with little space for large new unit operations required in conventional carbon capture. rotating packed beds (RPB) look to solve this problem by intensifying carbon capture and reducing the footprint required by up to ten times, while also significantly decreasing the capital cost of these units. when combined with proprietary solvents, it is believed the cost of capture can be reduced to $30/tonne CO2 in some cases, helping to enable the rapid uptake of carbon capture and progression towards net zero.
RPB are a novel technology that utilise the principles of process intensification to enhance the performance of mass transfer processes between fluids. given the application of rpb to the field of CO2 capture is relatively new, there is uncertainty regarding the impact of different process variables on the performance of the rpb that would otherwise require a significant amount of practical experimentation to investigate. computational fluid dynamics can enable the process to be accurately modelled, allowing for quick and inexpensive prediction of performance under various conditions.
In this project a rotating packed bed absorber will be modelled in ansys fluent, with validation of the model’s outputs through use of the 1 tonnes of co2 per day (TPD) pilot-scale rotating packed bed absorber at the university of sheffield’s translational energy research centre. Initial research will involve investigating the impact of operational conditions and physical properties of the solvent on capture performance. As the project continues, the scope will widen to include sensitivity analysis of design parameters and the impact of scaling the RPB absorber on the existing project outputs and learnings. Additionally, further rotating unit operations could also be investigated.
This project will utilise and develop your knowledge surrounding CFD modelling, mass and heat transfer, reaction kinetics and chemical equilibria. You will work closely with carbon clean, a global leader in the development of carbon capture technology and pioneers in the use of RPB for industrial decarbonisation. Your findings could directly impact the design and operation of commercial working carbon capture facilities, supporting the pathway to net zero.
We are seeking applicants to start in september 2022.
The project will be part of the EPSRC-supported centre for doctoral training in resilient decarbonised fuel energy systems. The student who undertakes it will be one of a cohort of over 50 students in a broad range of disciplines across the universities of sheffield, nottingham and cardiff.
The research work will be based in the energy research group within the department of mechanical engineering and the translational energy research centre (TERC) at sheffield which is a brand new, high profile, innovation focused national research facility. You will be working within an exciting and dynamic group with approximately over 60 researchers undertaking a broad area of energy research with approximately three years' extensive research time in industry, preparing for high-level careers in the energy sector.
The studentship will cover full university fees and a tax-free, enhanced annual stipend of ￡20,352, including ￡16,062 (2022/2023) a year for four years and a stipend enhancement of ￡3,750 per annum.
We provide the opportunity for the joint Ph.D. program between UCSD and SDSU.
Do you know that measurement in a turbulent environment has its own "sixth
sense"? In fluid dynamical systems, measurements can be used to figure out
events that happened far away from the probing location.
Through state-of-art data assimilation techniques, we can trace back the origin
of any information we have measured. We used this technique to locate the
release of a pollution release, and reconstruct unknown flow fields from limited
Are you self-motivated to do a Ph.D. in interdisciplinary researches about fluid
dynamics, inverse problems, and optimization? We are looking for Ph.D. students
that are willing to spend time studying in an encouraging and creative
There has been a long-hovering question about how to combine experimental
measurements with numerical simulations. Especially in terms of designing a
turbulence model that agrees with experimental studies. In addition, the design
of sensor networks or sensor weighting can be optimized in terms of the amount
of information obtained.
In this Ph.D. project, you will develop novel simulation techniques that combine
machine learning techniques with data assimilation.
The required skills and preferred profile
We are looking for self-motivated young researchers from mechanical
engineering, aerospace engineering, computational physics, applied mathematics,
or other closely related areas.
Familiar with MATLAB and FORTRAN with MPI.
C++ and python is a plus.
Good conceptual understanding of calculus and linear algebra.
Experience with simple machine learning algorithms.
Good communication skills including presentation skills, academic writing
with latex or word.
Please note that the GRE is required for all JDP applicants and cannot be
Having a part-time hobby is a plus.
Work is carried out in the Data Assimilation group at Aerospace Engineering, San Diego State University.
We study various inverse problems in fluid dynamics using numerical simulations
with the discrete adjoint operator.
For further information, please visit us at https://qiwang.sdsu.edu/
Information and application
Interested applicants should visit https://www.engineering.sdsu.edu/admissions/jointdoc_areomech.aspx
for more details about applying for the joint program.
Meanwhile, please reach out to Qi Wang (firstname.lastname@example.org), including:
• A short description of your qualifications and motivation to apply for this
• CV or resume.
• Transcripts from your Bachelor and Master degrees.
Selected candidates will be invited to an interview and should prepare a
scientific presentation as part of the requirements.
We highly value diversity at our university. Applicants from all backgrounds are