Numerical simulation of flashing jets atomisation using a unified approach

Lyras, Konstantinos G.; Dembele, Siaka; Wen, Jennifer X.

doi:10.1016/j.ijmultiphaseflow.2018.12.011

Cited by 9 publications

(13 citation statements)

References 44 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper the model proposed by Lyras et al 24 is implemented in the solver as an extra equation to solve within the segregated algorithm. The model reads,…”

Section: Liquid Atomisationmentioning

confidence: 99%

“…where ρ l , ρ g are liquid and gas densities. Analytical expressions and details for the source terms S init , S turb , S coll , S 2nBU , S vap,den and S vap,dil are provided in Lyras et al 24 providing expressions specifically for superheated jets for the evaporation effects.…”

Section: Liquid Atomisationmentioning

confidence: 99%

“…Schmidt et al 23 have proposed an algorithm which links the standard pressure-velocity coupling algorithm to the HRM, developing a compressible solver which was tested for the internal flashing of water for various tests. Based on this work, Lyras et al 24 presented a unified approach for modelling both the internal flow and liquid atomisation that linked the involved processes such as evaporation, turbulent mixing to the algorithm for calculating pressure. The method employed the Eulerian-Lagrangian-Spray-Atomisation (ELSA) model for atomisation and has been validated for various tests for flashing cryogenic liquids and water 25 .…”

Section: Introductionmentioning

confidence: 99%

“…With the inclusion of each additional physical phenomenon (flashing, atomisation, turbulence) the number of equations and thus the computational cost increases. In this paper, the method of Lyras et al 24 is accelerated by substituting part of the simulation by a deep neural network. Specifically, a machine learning surrogate model is used for completely substituting the turbulence model used in previous works in Lyras et al 24 .…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the method of Lyras et al 24 is accelerated by substituting part of the simulation by a deep neural network. Specifically, a machine learning surrogate model is used for completely substituting the turbulence model used in previous works in Lyras et al 24 . Recently, deep learning and other datadriven methods for turbulence closure modelling have shown promise in accelerating LES [26][27][28][29][30][31][32][33][34][35] and RANS simulations [36][37][38][39][40][41] .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine-learning accelerated turbulence modelling of transient flashing jets

Schmidt,

Maulik,

Lyras

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Modelling the sudden depressurisation of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet usually demonstrates a wide range of structures, including ligaments and droplets, due to both mechanical and thermodynamic effects. As the simulation comprises increasingly numerous phenomena, the computational cost begins to increase. One way to moderate the additional cost is to use machine learning surrogacy for specific elements of the calculations. The present study presents a machine learning-assisted computational fluid dynamics approach for simulating the atomisation of flashing liquids accounting for distinct stages, from primary atomisation to secondary break-up to small droplets using the Σ − Y model coupled with the homogeneous relaxation model. Notably, the model for the thermodynamic non-equilibrium (HRM) and Σ −Y are coupled, for the first time, with a deep neural network that simulates the turbulence quantities, which are then used in the prediction of superheated liquid jet atomisation. The data-driven component of the method is used for turbulence modelling, avoiding the solution of the two-equation turbulence model typically used for Reynolds-averaged Navier-Stokes simulations for these problems. Both the accuracy and speed of the hybrid approach are evaluated, demonstrating adequate accuracy and at least 25% faster computational fluid dynamics simulations than the traditional approach. This acceleration suggests that perhaps additional components of the calculation could be replaced for even further benefit.

show abstract

“…In this paper the model proposed by Lyras et al 24 is implemented in the solver as an extra equation to solve within the segregated algorithm. The model reads,…”

Section: Liquid Atomisationmentioning

confidence: 99%

Section: Liquid Atomisationmentioning

confidence: 99%