On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

Abstract

A classic approach to computational fluid dynamics is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study. To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.

Conjugate Heat Transfer and Fluid Flow Modeling for Liquid Microjet Impingement Cooling with Alternating Feeding and Draining Channels

“…The reason is that the transition SST model in ANSYS Fluent extends the traditional SST k-ω transport equations by tracking two additional variables for intermittency and transition onset using empirical correlations developed by Menter et al [36]. Various authors have shown that the k-ω SST model shows unsatisfactory performance for jets, both free jets [37] and impinging jets [38]. This arises due to the eddy-viscosity hypothesis used in two-equation turbulence models, that over-predict the mixing rate in the CFD simulation [39].…”

Propagation of uncertainty in a rotating pipe mechanism to generate an impinging swirling jet flow for heat transfer from a flat plate

“…It is well known that Computational Fluid Dynamics (CFD) is a powerful tool in fluid-related fields such as optimisation [1,2], aerospace & aerodynamics industry [3,4,5], fire safety modelling [6], heat transfer [7] or nuclear energy [8], amongst many others. Much effort has been spent to develop numerical algorithms for CFD, leading to more reliable simulations for decision-making and validation purposes, where uncertainty plays an important role.…”

“…Their outcomes encouraged us to undertake the quantification of experimental uncertainties in our simulations. The impact of uncertainty in CFD simulations of jets has been also studied by the authors in [5], where the simulation of a compressible jet flow under uncertain conditions is analysed, demonstrating that there is a relationship between the input random variables and the spatial distribution of pressure and velocity arising due to the propagation of uncertainty in the simulation. Other papers that also offered a reference and motivation are [45], where synthetic jets by means of polynomial chaos are studied, [46] where underexpanded jets in a crossflow for turbulent mixing are investigated, and [47], where uncertainty estimation is developed in RANS simulations of high-speed aircraft nozzle jets.…”

Propagation of Uncertainty in a Rotating Pipe Mechanism to Generate an Impinging Swirling Jet Flow for Heat Transfer from a Flat Plate

“…Such development tests may involve the building of several prototypes and may become dangerous if some conditions are extreme (for instance, in a nuclear reactor). For this reason, CFD simulations are a powerful tool in fields such as optimisation [1,2], aerospace & aerodynamics industry [3,4,5], fire safety modelling [6], heat transfer [7] or nuclear energy [8], amongst many others. Much effort has been spent to develop numerical algorithms for CFD, leading to more reliable simulations for decision-making and validation purposes, where uncertainty plays an important role.…”

“…Their outcomes encouraged us to undertake the quantification of experimental uncertainties in our simulations. The impact of uncertainty in CFD simulations of jets has been also studied by the authors in [5], where the simulation of a compressible jet flow under uncertain conditions is analysed, demonstrating that there is a relationship between the input random variables and the spatial distribution of pressure and velocity arising due to the propagation of uncertainty in the simulation. Other papers that also offered a reference and motivation are [24], where synthetic jets by means of polynomial chaos are studied, [25] where underexpanded jets in a crossflow for turbulent mixing are investigated, and [26], where uncertainty estimation is developed in RANS simulations of high-speed aircraft nozzle jets.…”