ENCODING uot;Enabling Sustainable combustion technologies using hybrid physics-based, data-driven

INTRODUCTION

Climate change and global warming, together with the current context of the energy crisis, make the need for energy decarbonization even more pressing. Recently, at COP26, the need to reduce greenhouse gases by 45% by 2030 to achieve the goal of net zero by 2050 was made clear. In this energy revolution, hydrogen and other Renewable Synthetic Fuels (RSFs) will play a key role; however, technology is not mature enough for their use as a fuel. The ENCODING project aims to help predict the impact of RSF in energy-intensive industries (EIIs).

The ENCODING project, an acronym for Enabling Sustainable combustion technologies using hybrid physics-based, data-driven modeling, is funded by the European Commission under the Horizon Europe Framework Programme. The main scientific goal of ENCODING is to develop a data-intensive framework to enable sustainable combustion technologies and RSFs in energy-intensive applications. It involves two European research centers and five academic institutions: ULB in Belgium, UPM in Spain, RWTH in Germany, CNRS - CORIA and NU in France, and CNR STEMS and UNINA in Italy, together with leading industrial partners. We are looking for 10 Doctoral Candidates in the chemistry, physics, engineering, and computer science fields willing to be part of this multi-disciplinary and inter-sectoral project.

If you like combustion science, fluid mechanics, and data-driven modeling, among the 10 Ph.D. positions offered in this multi-disciplinary and inter-sectoral PhD program, there is room for those who would like to combine theory with:

• conducting experimental campaigns (P1, P4, P9),
• working on the computational field (P2, P5, P6, P7), or
• a bit of both (P3, P8, P10).

All the positions offer the opportunity of doing an academic and an industrial secondment during your Ph.D.

REQUIREMENTS:

• Hold or graduate this year with an MSc or equivalent in chemistry, mathematics, engineering, physics, computer science, or any related fields.
• Not be in possession of a doctoral degree.
• Not having resided or exercised your main activity (work, studies, etc.) in the country for which you are applying for the position for more than 12 months in the last 36 months.
• Fluent in English. Any other language, especially the language of the country in which the application is submitted, would be a plus.

Research experience is not required, but it would be a plus.

OTHER IMPORTANT INFORMATION:

• Starting date: July / September 2023. It will last for 36 months.
• You will be a Marie Sklodowska-Curie fellow.
• Gross salary specified under each of the positions.
• The call will be open until all the positions are filled.

ENCODING recruits and trains regardless of race, religion, gender identity, sexual orientation, disability, or national origin. All employment is decided based on qualifications and competence. We are committed to creating a diverse and inclusive environment of mutual respect.

If you would like to be part of this remarkable Doctoral Network, do not hesitate to send an email to encoding.recruitment@gmail.com with:

• your CV,
• a small presentation/purpose letter,
• 2 recommendation letters,
• academic records

or fill in the application form here: https://forms.gle/bdLZ8buRfSTzUWHq8

Please, indicate in the subject of the email which position you are applying for. If you would like to apply for more than one position (no more than 4), please, state the order of preference.

Ph.D Position 1: Combustion of RSF in laboratory scale furnaces. Hosted at ULB in Brussels under the supervision of Alessandro Parente.

Your goal will be to develop a sparse sensing methodology to minimize the number of sensors while extracting the maximum amount of information from the system.  To do so, you will:

1. Collect experimental data from the ULB reverse flow furnace under MILD conditions.
2. Use different Machine Learning (ML) techniques to determine the optimal number of sensors and their placement.
3. Integrate experimental and numerical results that will allow the development of a soft sensor capable of blending experimental and numerical information, which will help the industry to achieve precise combustion control in a cost-efficiency way.

Net Salary:        2950,69 / month or       3610,69 / month with family.  In Brussels, students are exempt of paying taxes.

Ph.D Position 2: Adaptive simulation framework combining dimensionality reduction, classification and model development. Hosted at ULB in Brussels under the supervision of Alessandro Parente and at RWTH in Aachen under the supervision of Heinz Pitsch.

You will develop an adaptive simulation framework to adjust the chemical mechanism complexity and the turbulent combustion closure to the local flow conditions. To do so, you will:

1. Investigate strategies combining dimensionality reduction and optimization.
2. Develop multi-objective classifiers to adapt models to the desired level of fidelity.
3. Develop a turbulent combustion closure based on the DNS data available.
4. Perform RANS and LES simulations.

Net Salary:        2950,69 / month or       3610,69 / month with family. In Brussels, students are exempt of paying taxes.

Ph.D Position 3: Digital twins of industrial furnaces with lifelong learning capabilities. Hosted at ULB in Brussels under the supervision of Alessandro Parente.

You will develop an innovative methodology for digital twins of industrial combustion systems. To do so, you will:

1. Perform dimensionality reduction using modal decomposition and non-linear regression, using GPR and ANN.
2. Define novel approaches for the assimilation of heterogeneous data sources.
3. Update the system modes and non-linear regressions based on new data from both experiments and simulations.
4. Build the digital twin combining numerical simulations of different fidelity (RANS and LES) to predict the behavior of the corresponding physical twin in a wide range of operating conditions.
5. Demonstrate methodology on the ULB furnaces, using experiments and simulations from previous research work.

Net Salary:        2950,69 / month or       3610,69 / month with family. In Brussels, students are exempt of paying taxes.

Ph.D Position 4: Combustion of RSF in laboratory-scale vitiated co-flow burners. Hosted at RWTH in Aachen under the supervision of Heinz Pitsch.

You will conduct an advanced experimental campaign combining optical in-flame and post-combustion measurements and different sensing techniques in the ITV partially premixed turbulent jet facility. To do so, you will:

1. Study hydrogen in neat form and blend it with methane and ammonia.
2. Perform combined optical diagnostics: OH-LIF and PIV with gas sampling and different sensors.
3. Quantify and understand the correlation between sensor signals and performance characteristics.
4. Create a complete database for the development of data-driven soft-sensor models based on Machine Learning (ML) techniques and validation of data.

Gross Salary:        4.788,38 / month or       5.448,38 / month with family

Ph.D Position 5: High-fidelity simulation of RSF combustion with DNS. Hosted at RWTH in Aachen under the supervision of Heinz Pitsch.

You will study the impact of hydrogen blends with methane and ammonia on complex turbulent combustion systems with different set-ups employing diluted and pre-heated combustion regimes. To do so, you will:

1. Perform DNS on canonical configurations.
2. Analyze data from DNS simulations.
3. Extract principal combustion characteristics useful for the formulation of physics-informed Machine Learning (ML) approaches.
4. Formulate combustion closures for LES.
5. Perform LES simulations of a laboratory-scale vitiated co-flow burner and validate with experimental data.

Gross Salary:        4.788,38 / month or       5.448,38 / month with family

Ph.D Position 6: High-fidelity simulation of RSF combustion with DNS: effect of hydrogen addition on NOx emissions. Hosted at CNRS-CORIA in Rouen under the supervision of Pascale Domingo and Luc Vervisch.

You will study the impact of hydrogen addition in the fuel stream of a turbulent non-premixed synthetic-methane flame using DNS. To do so, you will:

1. Analyze the impact of hydrogen fuel enrichment on NOx emissions.
2. Analyze the impact of ammonia injection in the post-flame region on NO reduction.
3. Perform DNS simulation of a representative configuration of turbulent non-premixed burners.
4. Generate a complete database with the results obtained from the DNS simulations.

Gross Salary:              3.029,48 / month or       3.459,60 / month with family

Ph.D Position 7: Digital twin from DNS data: application to H2-enriched combustion and selective non-catalytic reduction. Hosted at CNRS-CORIA in Rouen under the supervision of Pascale Domingo and Luc Vervisch.

Your objective will be to develop digital twins for controlling industrial systems subject to time-varying fuel charges. To do so, you will:

1. Develop an advanced process to optimize in real-time the after-treatment of NOx and other harmful emissions.
2. Perform RANS simulations to deduct the digital twin.
3. Validate the model against DNS data.
4. Further develop the digital twin based on physics combining system-dependent and physics-related information.

Gross Salary:              3.029,48 / month or       3.459,60 / month with family

Ph.D Position 8: Dimensionality reduction and feature extraction in massive combustion data sets. Hosted at UPM in Madrid under the supervision of Soledad Le Clainche Mart nez.

Your objective will be to formulate novel feature extraction algorithms, able to classify and optimally parametrize the different processes occurring in a combustion system. To do so, you will:

• Develop approaches to optimize and regularize low dimensional manifolds by combining the feature extraction process with sampling techniques and non-linear scaling using kernel methods.
• Develop hybrid methods for combustion diagnostics combining data-based approaches with first principle approaches. This potential technique will be used to parametrize turbulent combustion systems.

Gross Salary:              2.882 / month or     3.053 / month with family

Ph.D Position 9: Combustion of RSF in laboratory scale cyclonic reactors. Hosted at CNR-STEMS in Naples under the supervision of Mara de Joannon.

You will collect and analyze the combustion characteristics of methane-hydrogen-ammonia mixtures in canonical and cyclonic reactors. To do so, you will:

1. Identify the matrix of operating conditions (initial conditions and fuels) for the experimental campaign.
2. Conduct the experimental campaign in which in situ spatially resolved chemical sampling and optical diagnostic techniques will be used.
3. Analyze the dataset from the experimental campaigns and identify global parameters representative of the system.

Gross Salary including employer s social contribution:       3.911,60 / month or            4571,6 / month with family

Ph.D Position 10: Low-cost sensors for monitoring and soft-sensing strategies for control of RSF combustion. Hosted at CNR-STEMS in Naples under the supervision of Mara de Joannon.

Your objective will be to develop soft-sensing strategies for industrial combustion systems. To do so, you will:

1. Identify and screen available sensors based on their cost, reliability and, effectiveness.
2. Using the data collected, build and train software models using Machine Learning (ML) techniques.
3. Test the soft sensors in the CNR cyclonic burner to verify their accuracy and robustness.
4. Develop a tool for data collection based on the soft sensors used for monitoring and control.

Gross Salary including employer s social contribution:       3.911,60 / month or            4571,6 / month with family