Research

DT-HATS structured research

DT-HATS is organised around four research work packages that address the project’s research objectives in a clear step-by-step sequence. It starts by generating experimental data to validate first-principles models, then develops improved hydrogen and ammonia simulations supported by machine learning. Next, multi-fidelity modelling is used to expand and strengthen the data. Finally, the project delivers hybrid and reduced-order models that work as fast, practical design tools for components and complete powertrain systems.

DC1: Experimental characterisation of hydrogen injection, mixing and ignition with final ROM of engine performance

Objective: To experimentally characterise the hydrogen injection, mixing and ignition processes from different injectors under various injection pressures and ignition sources.

Expected Results: Results of H₂ injection measurements in CVC and in optical engine with the outward opening injectors and with the multi-hole injector. Results of H₂ ignition with spark plug systems. Data-driven ROM of H₂ ignition and engine performance.

Academic Supervisor: Prof Zhao (BRUNEL)

Co-Supervisors: Dr Botter (TOYOTA)

DC2: Temperature, velocity and water measurements in H2/air mixture using 1064 nm Laser-Induced Grating Spectroscopy (LIGS) under reactive and non-reactive conditions

Objective: To experimentally characterise the mixing and the combustion process in H₂/air jets for high pressure injection conditions.

Expected Results: Proof of concept to enable water composition measurements by LIGs in H₂/air flame. Comprehensive assessment H₂/air mixing under non-reactive conditions. Experimental database on H₂/air jet mixing and combustion by LIGS at high T.

Academic Supervisor: Dr Lamanna (USTUTT)

Co-Supervisors: Dr Frank (BOSCH)

DC3: Effect of H2-air differential diffusivity on H2 mixing for under-expanded jets under IC engine relevant p-T conditions

Objective: (1) To derive an ANN for H2 and air species mixture properties, including variable diffusivities; (2) To perform numerical simulations for the in-nozzle and fuel plume formation, for conditions relevant to DI hydrogen engines.

Expected Results: Training data and ANN model development for the thermodynamic and transport properties of H₂ – air species mixtures over the wide range of P (up to 1000bar) and T (up to 4000K) conditions. Implementation of the ML model to the CFD solver of CITY and simulations on a numerical grid resolving both the in-nozzle flow and fuel injection will be performed. Validation against the experimental data of partners.

Academic Supervisor: Prof Gavaises (CITY)

Co-Supervisors: Dr Zeh (BOSCH)

H2 fuel system characterization: high-fidelity data and ML-CFD models

WP1

Objective:

Investigation of Hydrogen fuel injection systems by collecting high-fidelity experimental data and developing high-fidelity CFD models aided by ML

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DC4: Atomisation and mixing process of liquid ammonia injection from GDI injectors

Objective: Experimental Study of Atomization, Mixing, and Ignition Processes in High-Pressure Ammonia Injection under Engine-Relevant Conditions, with or without Hydrogen Addition to Enhance Ignition and Combustion.

Expected Results: Full experimental description of liquid NH₃ spray from single hole injectors and multi-hole GDI injectors (droplet size, temperature, air velocity) at different injection/ambient conditions. Full experimental description of ammonia/hydrogen mixing rate for two blending configurations at different injection/ambient conditions. Full experimental description of local NH₃ ignition site from single-hole GDI injectors (chemiluminescence imaging) at different injection/ambient conditions.

Academic Supervisor: Prof Rousselle (UORL)

Co-Supervisors: Dr B. von Rotz (WinGD)

DC5: Optimization of the liquid ammonia and dual-fuel flash-boiling injection strategy: a joint experimental and thermodynamic study on single hole injectors

Expected Results: Full experimental description of liquid NH₃ spray from single-hole injectors and multi-hole GDI injectors (droplet size, temperature, air velocity) at different injection/ambient conditions. Full experimental description of ammonia/hydrogen mixing rate for two blending configurations at different injection/ambient conditions. Full experimental description of local NH₃ ignition site from single-hole GDI injectors (chemiluminescence imaging) at different injection/ambient conditions.

Academic Supervisor: Dr Lamanna (USTUTT)

Co-Supervisors: Dr Mouyeke, Dr Ruoff (WLO)

DC6: Development of multicomponent real-fluid machine leaning (RFM-DAL) approach for LES of NH3-H2 inj. and mixing

Objective: 1) To develop and validate a multicomponent RFM-DAL approach for the LES modelling of ammonia-hydrogen injection and mixing using DT-HATS experimental databases; 2) To perform different LES simulations of ammonia-hydrogen injection and mixing using DT-HATS experimental databases.

Expected Results: Deep and Active Learning (DAL) methodology capable of providing the thermodynamic properties required by the RFM framework during runtime. Large-eddy Simulations (LES) of NH₃-H₂ injection and mixing with air and H₂.

Academic Supervisor: Dr Habchi (IFPEN)

Co-Supervisors: Dr J. R. Martinez (WinGD), Dr B. Delhom (IFPEN)

DC7: Development of a model for H2-NH3 flash-boiling, using a non-equilibrium approach with heterogeneous nucleation

Objective: 1) To develop a non-equilibrium model with heterogeneous nucleation for H₂-NH₃, coupled to ANN-based properties for ammonia; 2) To perform numerical simulations for conditions relevant to DI ammonia-hydrogen engines, predicting hydrogen dispersion.

Expected Results: A methodology for non-equilibrium phase transition with heterogeneous nucleation for H₂-NH₃ and real-fluid ammonia properties. RANS and LES simulation results, validated against experimental data.

Academic Supervisor: Prof Battistoni (UNIPG)

Co-Supervisors: Dr Nambully, Dr Senecal (CONVERGE)

NH3 fuel system characterization: high-fidelity data and ML-CFD models

WP2

Objective:

Investigation of Ammonia fuel injection systems by collecting high-fidelity experimental data and developing high-fidelity CFD models aided by ML.

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DC8: Experimental characterisation of ammonia ignition using nanosecond barrier discharges

Objective: To experimentally characterise the ignition and combustion process of ammonia using nanosecond barrier discharges.

Expected Results: Full experimental description of mixing state in real SI engine conditions. Full experimental description of early flame stage and local extinction as a function of ammonia injection strategy. Chemiluminescence of radical species with high speed visible camera, to reach local equivalence ratio information.

Academic Supervisor: Prof Zhao (BRUNEL)

Co-Supervisors: Dr Drouvin (TOYOTA)

DC9: Ammonia flame propagation characteristics as a function of mixing process and hydrogen additions in optical engine

Objective: To experimentally characterise ammonia flame development in SI GDI engine: mixing, equivalence ratio, early flame stage, flame propagation, species.

Expected Results: Full experimental description of mixing state in real SI engine conditions. Full experimental description of ethe arly flame stage and local extinction as a function of ammonia injection strategy. Chemiluminescence of radical species with high speed visible camera, to reach local equivalence ratio information.

Academic Supervisor: Prof Rousselle (UORL)

Co-Supervisors: Dr Watts-Farmer (CARNOT)

DC10: Development of a low temperature plasma ignition model for NH3 and H2

Objective: 1) To develop a plasma-assisted ignition model for NH₃. 2) To perform RANS simulations for conditions relevant to DI ammonia-hydrogen engines, predicting mixing and ignition.

Expected Results: Thermal and non-equilibrium discharge plasma characterisation in terms of electron temperature and density, plasma temperature, radicals and ions; and CFD model development based on energy and species deposition. RANS simulation results, validated against experimental data, and parametric investigations of the effect of NH₃ injection strategies on ignitability.

Academic Supervisor: Prof Battistoni (UNIPG)

Co-Supervisors: Dr Scarcelli (ARGONNE)

DC11: Development of a LES combustion model for NH3/H2/air flames accounting for preferential diffusion and high Karlovitz regime coupled to a multicomponent Real Fluid Model (RFM)

Objective: 1) Develop and validate a LES combustion model (CFM-HK) able to account for preferential diffusion and high Karlovitz regime for NH₃-H₂-air premixed flames. 2) Couple CFM-HK and RFM frameworks and evaluate it on realistic dual-fuel NH₃-H₂-air experiments.

Expected Results: DNS NH₃-H₂-air database and validated CFD model with CFM-HK validated against this DNS database. CFD results with CFM-HK coupled to RFM and validation on NH₃-H₂ engine experiments.

Academic Supervisor: Dr Colin (IFPEN)

Co-Supervisors: Prof Rousselle (UORL), Dr K. Truffin (IFPEN)

H2-NH3 ignition and combustion: high-fidelity data and ML-CFD models

WP3

Objective:

Exploration of ignition and combustion of Hydrogen and Ammonia, and mixtures, by collecting high-fidelity experimental data and developing ignition and combustion CFD models with ML acceleration for the combustion chemistry and real-fluid properties.

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DC12: JAX fluids for modelling of dispersion and mixing of Liquefied H2 jets and H2-NH3 mixtures

Objective: 1) To implement the ANN models for H₂, air species and NH₃ mixture properties, into JAX fluid solver. 2) To perform numerical simulations for different mixtures and evaluate the computational accuracy and efficiency of the solver against standard solvers.

Expected Results: Implementation of ANNs for mixture/species properties in solver Python libraries.Validation against experimental data from partners and comparison against predictions from other DCs to evaluate numerical efficiency of the solver.

Academic Supervisor: Prof Gavaises (CITY)

Co-Supervisors: Dr Vidal (AIRBUS)

DC13: Fuel injection strategies for LNH3 and NH3-H2 blends for DFICE with ML

Objective: 1) To derive property table and ML model for the NH₃-H₂mixtures at different concentrations from the PC-SAFT EoS for conditions relevant to DFICEs, and implementation into CFD solver. 2) To populate a CFD simulation database and explore the creation of a ROM based on simulation results.

Expected Results: Tabulated data for the thermodynamic and transport properties of the NH₃-H₂mixtures over the wide range of P (up to 1000bar) and T (up to 3000K) conditions by utilising the PC-SAFT EoS and a liquid-vapour equilibrium model and training of the relevant ML model and implementation of the ML model into the CITY’s CFD solver. Simulations on a numerical grid resolving both the in-nozzle flow and fuel injection will be performed, with validation against network and literature data^,and population of a CFD simulation database for reduced order model development based on opensource tools.

Academic Supervisor: Prof Gavaises (CITY)

Co-Supervisors: Dr Mouyeke (WLO)

DC14: Development of an NH3-H2 engine digital twin based on heterogeneous multi-fidelity and system level simulation data

Objective: 1) To develop the digital twin architecture for complete engine combustion process, integrating both black box and grey-box approaches. 2)To build the DT with real data.

Expected Results: Definition of the digital twin architecture and automation of simulation data generation/collection. Development of the DT and application to NH₃-H₂ complete combustion system.

Academic Supervisor: Prof Battistoni (UNIPG)

Co-Supervisors: Prof Mainini (IMPERIAL), Dr Aglietti (DUMAREY)

DC15: Digital twin for the design exploration and optimisation of a H2 combustion chamber

Objective: 1)To develop a ROM for the exploration of the design space and the identification of optimal design. 2)To perform numerical simulations for conditions relevant to DI hydrogen engines parameters to ensure optimal combustion while minimising the pollutant emissions.

Expected Results: Development of a data-driven ROM which can act as a digital twin of a H₂ combustion chamber. Identification of residual sources of uncertainty. Identification of optimal design parameters and operating conditions.

Academic Supervisor: Prof Parente (ULB)

Co-Supervisors: Dr Nambully, Dr Senecal (CONVERGE)

Digital Twins for H2 and NH3 application in transport

WP4

Objective:

Development of digital twins, based on both physical principles and data, as tools for fast exploration of Hydrogen and Ammonia ecosystems, informed by the previously developed CFD-ML models and experiments.

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