Related papers: Uncertainty quantification of a multi-component Ha…
In the past years we have experienced an increasing interest in understanding of the physical properties of collisionless plasmas, mostly because of the large number of astrophysical environments, e.g. the intracluster medium (ICM),…
We describe here a model for inelastic collisions for electronic excitation and deexcitation processes in a general, multifluid plasma. The model is derived from kinetic theory, and applicable to any mixture and mass ratio. The principle of…
There is wide agreement that the accuracy of turbulence models suffer from their sensitivity with respect to physical input data, the uncertainties of user-elected parameters, as well as the model inadequacy. However, the application of…
The analysis of bubbly two-phase flows is challenging due to their turbulent nature and the need for intrusive phase-detection probes. However, accurately characterizing these flows is crucial for safely designing critical infrastructure…
Bimolecular diffusion coefficients are important parameters used by atmospheric models to calculate altitude profiles of minor constituents in an atmosphere. Unfortunately, laboratory measurements of these coefficients were never conducted…
The objective of this work is to assess the impact of parameter uncertainty on hypersonic aerothermal surface heating predictions in Reynolds-Averaged Navier-Stokes (RANS) simulations using non-intrusive uncertainty quantification (UQ)…
This study aims to comprehensively investigate the deep ensemble approach, an approximate Bayesian inference, in the multi-output regression task for predicting the aerodynamic performance of a missile configuration. To this end, the effect…
Dusty plasma medium turns out to be an ideal system for studying the strongly coupled behavior of matter. The large size and slow response make their dynamics suitable to be captured through simple diagnostic tools. Furthermore, as the…
We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly…
We apply nested-sampling (NS) Bayesian analysis [AshtonEA22] to a model for the transport of MHD-scale solar wind fluctuations. The dual objectives are to obtain improved constraints on parameters present in the turbulence transport model…
In under-balanced drilling, the bottom-hole pressure is kept below pore pressure, causing pressure dependent influx of reservoir gas into the wellbore that makes the system unstable at low drawdowns. In this paper we propose a feedback…
The electrical environment of a ground vacuum testing chamber creates facility effects for gridded ion thrusters. For example, it is well known that the plume from the thruster generates current paths that are very different from what…
In-cylinder flow structures and turbulence characteristics are investigated using direct numerical simulations (DNS) in a laboratory-scale engine at technically relevant engine speeds (1500 and 2500 rpm at full load). The data is computed…
In the event of a nuclear accident, or the detonation of a radiological dispersal device, quickly locating the source of the accident or blast is important for emergency response and environmental decontamination. At a specified time after…
A Bayesian analytics framework that precisely quantifies uncertainty offers a significant advance for financial risk management. We develop an integrated approach that consistently enhances the handling of risk in market volatility…
The transport of sputtered aluminum inside a multi frequency capacitively coupled plasma chamber is simulated by means of a kinetic test multi-particle approach. A novel consistent set of scattering parameters obtained for a modified…
Scientific applications typically generate large volumes of floating-point data, making lossy compression one of the most effective methods for data reduction, thereby lowering storage requirements and improving performance in large-scale…
This work develops a multiphase thermomechanical model of porous silica aerogel and implements an uncertainty analysis framework consisting of the Sobol methods for global sensitivity analyses and Bayesian inference using a set of…
The topology of the applied magnetic field is an important design aspect of Hall thrusters. For modern Hall thrusters, the field topology most often features curved lines with a concave (negative) curvature upstream of the field peak and a…
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model's uncertainty is evaluated using…