Brembo RacingMarketThe CRECK Modeling Lab is looking for candidates for a Ph.D. position for a research project in collaboration with Brembo S.p.A., a leading company working in the field of disk brake technology. The project is about the numerical modeling of the Chemical Vapor Infiltration (CVI) process for the production of carbon disk brakes for aircraft and racing car markets.Brembo CarbonDiskBrake

 The research activities are focused on the numerical modeling of CVI reactors through multi-scale/multi-physics CFD analyses, aimed at advancing the understanding of the physical and chemical phenomena governing the densification process, in order to improve and optimize the reactor design or to propose new reactor concepts.

The Ph.D. grant lasts 3 years and provides a monthly scholarship of about € 1350. The candidate is expected to spend about six months at Brembo S.p.A. to collect and analyze experimental data, essential for proper validation of developed numerical tools.

You can find all the information about the call and how to apply at the following link:


If you are interested in this position, please apply. The call is open until October 18th 2021, h14:00.


OpenSMOKE++ was used for simulating the evaporation and combustion of isolated fuel droplets. The work is a collaboration between the CRECK Modeling Lab, Cornell University, University of San Diego California and NASA Glenn Research Center. The main results have been published on Combustion Theory and Modelling in a paper with title: "The role of composition in the combustion of n-heptane/iso-butanol mixtures: experiments and detailed modelling".

Evaporation and combustion of isolated fuel droplets


A. Dalili, J.D. Brunson, S. Guo, M. Turello, F. Pizzetti, L. Badiali, C. T. Avedisian ,K. Seshadri, A. Cuoci, F.A. Williams, A. Frassoldati, M.C. Hicks, The role of composition in the combustion of n-heptane/iso-butanol mixtures: experiments and detailed modelling, (2020), Combustion Theory and Modelling, DOI: 10.1080/13647830.2020.1800823



Experimental data and detailed numerical modelling are presented on the burning characteristics of a model gasoline/biofuel mixture consisting of n-heptane and iso-butanol. A droplet burning in an environment that minimises the influence of buoyant and forced convective flows in the standard atmosphere is used to promote one-dimensional gas transport to facilitate numerical modelling of the droplet burning process. The numerical model includes a detailed combustion kinetic mechanism, unsteady gas and liquid transport, multicomponent diffusion inside the droplet, variable properties, and non-luminous radiative heat transfer from the flame. The numerical simulation was validated by experimental measurements in the standard atmosphere which showed good agreement with the evolutions of droplet and flame diameters. The iso-butanol concentration had a strong effect on formation of particulates. Above ~20% (volume) iso-butanol, flame luminosity was significantly diminished anddecreased with increasing iso-butanol concentration, while CO2 emissions as a representative greenhouse gas were not strongly influenced by the iso-butanol loading. The soot shell was located near a 1350 K isotherm for concentrations up to 20% (volume) iso-butanol, suggesting this value as a possible soot inception temperature for the mixture droplet. The combustion rate decreased with increasing iso-butanol concentration which was attributed to iso-butanol's higher liquid density. No evidence of a low temperature burning regime, or of extinction, was found (in experiments and simulations) for the small droplet sizes investigated.

Congratulations to our PhD Candidate Giampaolo Maio! His paper with title "A virtual chemical mechanism for prediction of NO emissions from flames" has been recently published on Combustion Theory and Modelling.

Temperature and NO profiles along the axis


G. Maio, M. Cailler, A. Cuoci, B. Fiorina, A virtual chemical mechanism for prediction of NO emissions from flames, (2020), Combustion Theory and Modelling, In Press, DOI: 10.1080/13647830.2020.1772509



A reduced order kinetic model for NO (nitric oxide) prediction, based on the virtual chemistry methodology [M. Cailler, N. Darabiha, and B. Fiorina, Development of a virtual optimized chemistry method. Application to hydrocarbon/air combustion, Combust. Flame 211 (2020), pp. 281–302], is developed and applied. Virtual chemistry aims to optimise thermochemical properties and kinetic rate parameters of a network of virtual species and reactions. A virtual main chemical mechanism is dedicated to temperature and heat release prediction and is coupled with the flow governing equations, whereas satellite sub-mechanisms are designed to predict pollutants formation. Two virtual chemistry mechanisms are here employed: a main mechanism for calculating the temperature and heat release rate and a second mechanism dedicated to NO prediction. To recover the chemical structure of multi-mode combustion, both premixed and non-premixed flamelets are included in the learning database used to optimise the virtual NO mechanism. A multi-zone optimisation procedure is developed to accurately capture both fast and slow NO chemistry that include prompt, thermal and reburning pathways. The proposed NO sub-mechanism and optimisation methodology are applied to CH (Formula presented.) /air combustion. Laminar 1-D premixed and non-premixed flamelet configurations are first tested. The approach is then further assessed in 2-D CFD laminar flame simulations, by providing a direct comparison against detailed chemistry. 2-D premixed, non-premixed and partially premixed flame configurations are numerically investigated. For all cases, the virtual mechanism fairly captures temperature and (Formula presented.) chemistry with only 12 virtual species and 8 virtual reactions with a drastic CPU time reduction compared to detailed chemistry.

Congratulations to our PhD Candidate Agnes Livia Bodor! Her paper with title "A post processing technique to predict primary particle size of sooting flames based on a chemical discrete sectional model: Application to diluted coflow flames" has been recently published on Combustion and Flame.

Laminar sooting flame


A.L. Bodor, B. Franzelli, T. Faravelli, A.Cuoci, A post processing technique to predict primary particle size of sooting flames based on a chemical discrete sectional model: Application to diluted coflow flames, (2019) Combustion and Flame, 208, pp. 122-138, DOI: 10.1016/j.combustflame.2019.06.008



A numerical post-processing strategy to reconstruct the size of soot primary particles is presented in this work in the context of the chemical sectional models. The proposed technique is based on solving the transport equation of the primary particle number density for each considered aggregate size. The chemical source terms are derived from the corresponding chemical reactions.The validity of the proposed approach is tested on target flames of the International Sooting Flame (ISF) Workshop. In particular, first, a laminar premixed ethylene flame is considered. Profiles of soot volume fraction and mean primary particle size are compared between simulation and measurements and a satisfactory agreement is observed, validating the proposed post-processing strategy. Second, a laminar coflow ethylene flame is put under the scope. Numerical results are compared to experimental data once again in terms of soot volume fraction and primary particle size. The sensitivity to model parameters, such as accounting for surface rounding and the choice of the smallest aggregating particle size, is explored. Once validated, the effect of dilution on the mean primary particle diameter in laminar diffusion flames is examined. The general trends observed experimentally are recovered. The correlation between temperature, precursor concentrations, soot volume fraction and primary particle diameter is explored. Finally, formation rates and residence time along the particle trajectories are investigated to explain the effect of dilution on the spatial localization of the biggest particles along the flame. The relation between the soot volume fraction and the mean primary particle diameter is examined.

The OpenSMOKE++ framework was used in the context of adaptive reduced chemistry for multidimensional laminar flames in a recent publication on Energies with title: "Impact of the partitioning method on multidimensional adaptive-chemistry simulations". The work is a collaboration between the CRECK Modeling Lab and the BURN Group at Universite' Libre de Bruxelles.

Maps of temperature of an unsteady 2D laminar coflow flame adaptive simulation


G. D'Alessio, A. Cuoci, G. Aversano, M. Bracconi, A. Stagni, A. Parente, Impact of the partitioning method on multidimensional adaptive-chemistry simulations, (2020), Energies, 13(10), no. 2567, DOI: 10.3390/en13102567



The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms' level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In thiswork, two different clustering algorithms for the partitioning of the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteady laminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach based on the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is the Local Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction (i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtained for both the tested methods, but LPCA showed superior performances in terms of reduced mechanisms uniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lower sensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also being optimal, with respect to SKM, for massive dataset clustering tasks.


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H2020 JETSCREENh2020




Find out more about the JETSCREEN Project, which received funding from the European Union's Horizon 2020 research and innovation programme for developing a screening and optimization platform for alternative fuels

H2020 IMPROOFh2020




Find out more about the IMPROOF Project, which received funding from the European Union's Horizon 2020 research and innovation programme for improving the energy efficiency of steam cracking furnaces, while reducing emissions of greenhouse gases and NOx.