Scoping study on the significance of mesh resolution vs. scenario uncertainty in the CFD modelling of residential smoke control systems

Computational fluid dynamics (CFD) modelling is a commonly applied tool adopted to support the specification and design of common corridor ventilation systems in UK residential buildings. Inputs for the CFD modelling of common corridor ventilation systems are typically premised on a ‘reasonable worst case’, i.e. no specific uncertainty quantification process is undertaken to evaluate the safety level. As such, where the performance of a specific design sits on a probability spectrum is not defined. Furthermore, mesh cell sizes adopted are typically c. 100 – 200 mm. For a large eddy simulation (LES) based CFD code, this is considered coarse for this application and creates a further uncertainty in respect of capturing key behaviours in the CFD model. Both co-existing practices summarised above create uncertainty, either due to parameter choice or the (computational fire and smoke) model. What is not clear is the relative importance of these uncertainties. This paper summarises a scoping study that subjects the noted common corridor CFD application to a probabilistic risk assessment (PRA), using the MaxEnt method. The uncertainty associated with the performance of a reference design is considered at different grid scales (achieving different ‘a posteriori’ mesh quality indicators), with the aim of quantifying the relative importance of uncertainties associated with inputs and scenarios, vs. the fidelity of the CFD model. For the specific case considered herein, it is found that parameter uncertainty has a more significant impact on the confidence of a given design solution relative to that arising from grid resolution, for grid sizes of 100 mm or less. Above this grid resolution, it was found that uncertainty associated with the model dictates. Given the specific ventilation arrangement modelled in this work care should be undertaken in generalising such conclusions

Uncertainty quantification of leakages in a multistage simulation and comparison with experiments

The present paper presents a numerical study of the impact of tip gap uncertainties in a multistage turbine. It is well known that the rotor gap can change the gas turbine efficiency but the impact of the random variation of the clearance height has not been investigated before. In this paper the radial seals clearance of a datum shroud geometry, representative of steam turbine industrial practice, was systematically varied and numerically tested. By using a Non-Intrusive Uncertainty Quantification simulation based on a Sparse Arbitrary Moment Based Approach, it is possible to predict the radial distribution of uncertainty in stagnation pressure and yaw angle at the exit of the turbine blades. This work shows that the impact of gap uncertainties propagates radially from the tip towards the hub of the turbine and the complete span is affected by a variation of the rotor tip gap. This amplification of the uncertainty is mainly due to the low aspect ratio of the turbine and a similar behavior is expected in high pressure turbines

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins

In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, non-linear, monodimensional (depth-only) system of PDEs with uncertain parameters. While in previous works (Formaggia et al. 2013, Porta et al., 2014) we assumed a simplified depositional history with only one material, in this work we consider multi-layered basins, in which each layer is characterized by a different material, and hence by different properties. This setting requires several improvements with respect to our earlier works, both concerning the deterministic solver and the stochastic discretization. On the deterministic side, we replace the previous fixed-point iterative solver with a more efficient Newton solver at each step of the time-discretization. On the stochastic side, the multi-layered structure gives rise to discontinuities in the dependence of the state variables on the uncertain parameters, that need an appropriate treatment for surrogate modeling techniques, such as sparse grids, to be effective. We propose an innovative methodology to this end which relies on a change of coordinate system to align the discontinuities of the target function within the random parameter space. The reference coordinate system is built upon exploiting physical features of the problem at hand. We employ the locations of material interfaces, which display a smooth dependence on the random parameters and are therefore amenable to sparse grid polynomial approximations. We showcase the capabilities of our numerical methodologies through two synthetic test cases. In particular, we show that our methodology reproduces with high accuracy multi-modal probability density functions displayed by target state variables (e.g., porosity).Comment: 25 pages, 30 figure

Multi-scale control variate methods for uncertainty quantification in kinetic equations

Kinetic equations play a major rule in modeling large systems of interacting particles. Uncertainties may be due to various reasons, like lack of knowledge on the microscopic interaction details or incomplete informations at the boundaries. These uncertainties, however, contribute to the curse of dimensionality and the development of efficient numerical methods is a challenge. In this paper we consider the construction of novel multi-scale methods for such problems which, thanks to a control variate approach, are capable to reduce the variance of standard Monte Carlo techniques

An efficient surrogate model for emulation and physics extraction of large eddy simulations

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as {simplifying assumptions} for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.Comment: Submitted to JASA A&C