Computational fluid dynamics analysis of a wire-feed, high-velocity oxygen-fuel (HVOF) thermal spray torch

The fluid and particle dynamics of a High-Velocity Oxygen-Fuel Thermal Spray torch are analyzed using computational and experimental techniques. Three-dimensional Computational Fluid Dynamics (CFD) results are presented for a curved aircap used for coating interior surfaces such as engine cylinder bores. The device analyzed is similar to the Metco Diamond Jet Rotating Wire (DJRW) torch. The feed gases are injected through an axisymmetric nozzle into the curved aircap. Premixed propylene and oxygen are introduced from an annulus in the nozzle, while cooling air is injected between the nozzle and the interior wall of the aircap. The combustion process is modeled using a single-step finite-rate chemistry model with a total of 9 gas species which includes dissociation of combustion products. A continually-fed steel wire passes through the center of the nozzle and melting occurs at a conical tip near the exit of the aircap. Wire melting is simulated computationally by injecting liquid steel particles into the flow field near the tip of the wire. Experimental particle velocity measurements during wire feed were also taken using a Laser Two-Focus (L2F) velocimeter system. Flow fields inside and outside the aircap are presented and particle velocity predictions are compared with experimental measurements outside of the aircap

Quantifying simulator discrepancy in discrete-time dynamical simulators

When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum-likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall-runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules. This article has supplementary material online

Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis.

This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models

Verification of an agent-based disease model of human mycobacterium tuberculosis infection

Agent-Based Models are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for Agent-Based Models that aims at evaluating the numerical errors associated with the model. A step-by-step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS-TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS-TB and, in general, with any other ABMs