Numerical exploration and modelling of a novel environmentally friendly combustion technique: droplet-laden MILD combustion
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
People |
ORCID iD |
| Nedunchezhian Swaminathan (Principal Investigator) |
Publications
Awad H
(2023)
A Priori Direct Numerical Simulation Assessment of MILD Combustion Modelling in the Context of Reynolds Averaged Navier-Stokes Simulations
in Flow, Turbulence and Combustion
Dave H
(2022)
Interpretation and characterization of MILD combustion data using unsupervised clustering informed by physics-based, domain expertise
in Combustion and Flame
Deng X
(2023)
A new open-source library based on novel high-resolution structure-preserving convection schemes
in Journal of Computational Science
Deng X
(2023)
A unified framework for non-linear reconstruction schemes in a compact stencil. Part 1: Beyond second order
in Journal of Computational Physics
Doan N
(2021)
Identification of combustion mode under MILD conditions using Chemical Explosive Mode Analysis
in Proceedings of the Combustion Institute
Fiorina B
(2023)
A joint numerical study of multi-regime turbulent combustion
in Applications in Energy and Combustion Science
Iavarone S
(2021)
An a priori assessment of the Partially Stirred Reactor (PaSR) model for MILD combustion
in Proceedings of the Combustion Institute
| Description | Not yet since this grant started only in Sep. 2019 |
| Exploitation Route | yet to be evaluated |
| Sectors | Aerospace, Defence and Marine,Energy,Environment,Transport |
| Description | none yet - since the grant is still active and started only in Sep. 2019 |
| Title | new skeletal mechanism for n-heptane MILD combustion |
| Description | A chemical kinetic mechanism describing the combustion of n-hetane liquid fuel under MILD condition is developed using a well established computer code/methdology called CARM which used steady state and partial equilibrium approximations for species and reactions with fast time scales. The mechanism developed is suitable for high fidelity simulations of turbulent combustion of practical interest. A manuscript describing the methodology, the mechanism and results are published (under review in journal Combustion and Flame) and thus this information will become available to others. |
| Type Of Material | Technology assay or reagent |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | An accurate description of chemical kinetics for n-heptable combustion in turbulent flows with large diluent species and this condition is of interest for next generation of combustion systems. |
| Title | chemical kinetic skeletal mechanism for n-heptane |
| Description | This combustion chemistry model allows us to represent the chemical pathways of n-heptane oxidation in turbulent combustion simulations at a low computational cost with very good accuracy. Also, the fundamental characteristics such as flame speed, ignition delay, high-temperature ignition, which are relevant for MILD (or "green") combustion technology can be computed easily and quickly. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | This combustion chemistry model allows us to represent the chemical pathways of n-heptane oxidation in turbulent combustion simulations at a low computational cost with very good accuracy. Also, the fundamental characteristics such as flame speed, ignition delay, high-temperature ignition, which are relevant for MILD (or "green") combustion technology can be computed easily and quickly. |
| Description | Berkeley - JYChen |
| Organisation | University of California, Berkeley |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Joint research work to develop chemical kinetics mechanism for combustion of n-heptane under MILD conditions. The identification and conceptualization of this work was proposed by my research team. |
| Collaborator Contribution | Executing those tasks through a computer programme available with Prof. J.Y. Chen at UC Berkeley leading a a development a new chemical kinetic mechanism. |
| Impact | A paper is submitted to high impact journal - Combustion and Flame. The manuscript is under peer review. |
| Start Year | 2020 |
| Description | Norway - Trondheim |
| Organisation | Norwegian University of Science and Technology (NTNU) |
| Country | Norway |
| Sector | Academic/University |
| PI Contribution | Hosted and trained a PhD student from NTNU, Department of Department of Energy and Process Engineering. We developed the research idea conceptualization for joint work. |
| Collaborator Contribution | The research student worked with the researcher employed on this project to execute the required scientific tasks. |
| Impact | A paper is written and published in Proceedings of Combustion Institute based on this joint work. This paper is available at https://doi.org/10.1016/j.proci.2020.06.298 |
| Start Year | 2019 |
| Description | ULB - collaborations |
| Organisation | University Libre Bruxelles (Université Libre de Bruxelles ULB) |
| Country | Belgium |
| Sector | Academic/University |
| PI Contribution | A highly efficient modelling approach is developed to explore a novel combustion concept, which is investigated experimentally at ULB. |
| Collaborator Contribution | Providing experimental data and machine learning algorithms to develop Machine Learning Approach for MILD combustion |
| Impact | 1. N. Swaminathan and A. Parente (2023). Introduction, in Lecture Notes in Energy: Machine Learning and its Application to Reacting Flows, N. Swaminathan and A. Parente (Eds.), ISBN - 978-3-031-16247-3, Springer Nature, Heidelberg, Germany. 2. S. Iavarone, H. Yang, Z. Li, Z. X. Chen and N. Swaminathan (2023). On the use of machine learning for subgrid scale filtered density function modelling in large eddy simulations of combustion systems, in Energy: Machine Learning and its Application to Reacting Flows, N. Swaminathan and A. Parente (Eds.), ISBN - 978-3-031-16247-3, Springer Nature, Heidelberg, Germany. 3. A. Parente and N. Swaminathan (2023). Summary, in Energy: Machine Learning and its Application to Reacting Flows, N. Swaminathan and A. Parente (Eds.), ISBN - 978-3-031-16247-3, Springer Nature, Heidelberg, Germany. 4. S. Iavarone, A. Pequin, N. Swaminathan and A. Parente. A data-driven partially, physics-informed framework for subgrid combustion closure using artificial neural network. Paper No. MCS12-082-TC, presented in 12th Mediterranean Combustion Symposium, January 23-26, 2023, Luxor, Egypt. |
| Start Year | 2018 |