Bayesian quantification of thermodynamic uncertainties in dense gas flows
Article dans une revue avec comité de lecture
JournalReliability Engineering and System Safety
A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the thermodynamic behavior of so-called dense gas flows, – i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curve–, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data –specifically, wall pressure data– are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood func-tion includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the true phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to infor-mative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.
Files in this item
Showing items related by title, author, creator and subject.
Communication avec acteBUFI, Elio Antonio; CINNELLA, Paola; MERLE, Xavier; CINNELLA, Paola (ASME, 2015)The design of an efficient organic rankine cycle (ORC) expander needs to take properly into account strong real gas effects that may occur in given ranges of operating conditions, which can also be highly variable. In this ...
Sparse Bayesian Learning of Explicit Algebraic Reynolds-Stress models for turbulent separated flows Article dans une revue avec comité de lectureA novel Sparse Bayesian Learning (SBL) framework is introduced for generating parsimonious stochastic algebraic stress closures for the Reynolds-Averaged Navier–Stokes (RANS) equations from high-fidelity data. The models ...
Article dans une revue avec comité de lectureMERLE, Xavier; CINNELLA, Paola (Elsevier, 2019)A Bayesian approach is developed to quantify uncertainties associated with the thermodynamic models used for the simulation of dense gas flows, i.e. flows of gases characterized by complex molecules of moderate to high ...
Article dans une revue avec comité de lectureMERLE, Xavier; CINNELLA, Paola (Elsevier, 2014)A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the ...
Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models Article dans une revue avec comité de lectureDE ZORDO-BANLIAT, Maximilien; MERLE, Xavier; DERGHAM, Grégory; CINNELLA, Paola (Elsevier BV, 2020-04)The Reynolds-Averaged Navier-Stokes (RANS) equations represent the computational workhorse for engineering design, despite their numerous flaws. Improving and quantifying the uncertainties associated with RANS models is ...