Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications

Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for CFD applications. A particular obstacle for uncertainty quantifications in CFD problems is the large model discrepancies associated with the CFD models used for uncertainty propagation. Neglecting or improperly representing the model discrepancies leads to inaccurate and distorted uncertainty distribution for the Quantities of Interest. High-fidelity models, being accurate yet expensive, can accommodate only a small ensemble of simulations and thus lead to large interpolation errors and/or sampling errors; low-fidelity models can propagate a large ensemble, but can introduce large modeling errors. In this work, we propose a multi-model strategy to account for the influences of model discrepancies in uncertainty propagation and to reduce their impact on the predictions. Specifically, we take advantage of CFD models of multiple fidelities to estimate the model discrepancies associated with the lower-fidelity model in the parameter space. A Gaussian process is adopted to construct the model discrepancy function, and a Bayesian approach is used to infer the discrepancies and corresponding uncertainties in the regions of the parameter space where the high-fidelity simulations are not performed. The proposed multi-model strategy combines information from models with different fidelities and computational costs, and is of particular relevance for CFD applications, where a hierarchy of models with a wide range of complexities exists. Several examples of relevance to CFD applications are performed to demonstrate the merits of the proposed strategy. Simulation results suggest that, by combining low- and high-fidelity models, the proposed approach produces better results than what either model can achieve individually.Comment: 18 pages, 8 figure

Evaluation of human obstructive sleep apnea using computational fluid dynamics.

Obstructive sleep apnea (OSA) severity might be correlated to the flow characteristics of the upper airways. We aimed to investigate the severity of OSA based on 3D models constructed from CT scans coupled with computational fluid dynamics (CFD) simulations. The CT scans of seven adult patients diagnosed with OSA were used to reconstruct the 3D models of the upper airways and CFD modeling and analyses were performed. Results from the fluid simulations were compared with the apnea-hypopnea index. Here we show a correlation between a CFD-based parameter, the adjusted pressure coefficient (Cp*), and the respective apnea-hypopnea index (Pearson's r = 0.91, p = 0.004), which suggests that the anatomical-based model coupled with CFD could provide functional and localized information for different regions of the upper airways

Partial CFD models of cardiovascular stents

Copyright @ 2002 Wiley BlackwellThis paper outlines the use of a partial CFD stent model in order to improve discretisation of important small features. The effects of mesh size on the performance measure are investigated. The results are compared with those from full models and also comparisons with clinical trials are made. It is shown that partial models provide a better approximation to reality than full models when using modest PC workstation resources. The general conclusion is that computer-based design of medical devices must take into account the variations in geometry between patients by means of, for example, a flat performance curve against noise

RANS Turbulence Model Development using CFD-Driven Machine Learning

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method (Weatheritt and Sandberg, 2016), but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated way, rather than using an algebraic function. Unlike other data-driven methods that fit the Reynolds stresses of trained models to high-fidelity data, the cost function for the CFD-driven training can be defined based on any flow feature from the CFD results. This extends the applicability of the method especially when the training data is limited. Furthermore, the resulting model, which is the one providing the most accurate CFD results at the end of the training, inherently shows good performance in RANS calculations. To demonstrate the potential of this new method, the CFD-driven machine learning approach is applied to model development for wake mixing in turbomachines. A new model is trained based on a high-pressure turbine case and then tested for three additional cases, all representative of modern turbine nozzles. Despite the geometric configurations and operating conditions being different among the cases, the predicted wake mixing profiles are significantly improved in all of these a posteriori tests. Moreover, the model equation is explicitly given and available for analysis, thus it could be deduced that the enhanced wake prediction is predominantly due to the extra diffusion introduced by the CFD-driven model.Comment: Accepted by Journal of Computational Physic

Study of a regenerative pump using numerical and experimental techniques

Regenerative pumps are the subject of increased interest in industry as these pumps are low cost, low specific speed, compact and able to deliver high heads with stable performance characteristics. The complex flow-field within the pump represents a considerable challenge to detailed mathematical modelling as there is significant flow separation in the impeller blading. This paper presents the use of a commercial CFD code to simulate the flow within the regenerative pump and compare the CFD results with new experimental data. The CFD results demonstrate that it is possible to represent the helical flowfield for the pump which has only been witnessed in experimental flow visualisation until now. The CFD performance results also demonstrate reasonable agreement with the experimental tests. The CFD models are currently being used to optimise key geometric features to increase pump efficiency

Human environmental heat transfer simulation with CFD – the advances and challenges

The modelling and prediction of human thermoregulatory responses and comfort have gone a long way during the past decades. Sophisticated and detailed human models, i.e. the active multi-nodal thermal models with physiological regulatory responses, have been developed and widely adopted in both research and industrial practice. The recent trend is to integrate human models with environmental models in order to provide more insight into the thermal comfort issues, especially in the non-homogeneous and transient conditions. This paper reviews the logics and expectations of coupling human models with computational fluid dynamics (CFD) models. One of main objectives of such approaches is to take the advantage of the high resolution achievable with the CFD, to replace the empirical methods used in the human models. We aim to initiate debates on the validity of this objective, and to identify the technical requirements for achieving this goal. A simple experiment with 3D human models of different sizes and shapes is also reported. Initial results shows the presence of arms may be important. Further experiments are required to establish the impact of size and shape on simulation result

Improved droplet breakup models for spray applications

The current study examines the performance of two zero-dimensional (0D) aerodynamically-induced breakup models, utilized for the prediction of droplet deformation during the breakup process in the bag, multi-mode and sheet-thinning regimes. The first model investigated is an improved version of the widely used Taylor analogy breakup (TAB) model, which compared to other models has the advantage of having an analytic solution. Following, a model based on the modified Navier–Stokes (M-NS) is examined. The parameters of both models are estimated based upon published experimental data for the bag breakup regime and CFD simulations with Diesel droplets performed as part of this work for the multi-mode and sheet-thinning regimes, for which there is a scarcity of experimental data. Both models show good accuracy in the prediction of the temporal evolution of droplet deformation in the three breakup regimes, compared to the experimental data and the CFD simulations. It is found that the best performance of the two is achieved with the M-NS model. Finally, a unified secondary breakup model is presented, which incorporates various models found in the literature, i.e. TAB, non-linear TAB (NLTAB), droplet deformation and breakup (DDB) and M-NS, into one equation using adjustable coefficients, allowing to switch among the different models

A New Actuator Surface Model with Improved Wake Model for CFD Simulations of Rotorcraft

Simulations of rotorcraft operating in unsteady flow-fields, manoeuvring flight, or with complex rotor configurations pose a significant challenge to current simulation methods. Simplified rotor models lack the generality required for the diverse range of operating conditions that a rotor may be exposed to, while higher-fidelity Navier-Stokes CFD simulations with fully-resolved rotors are expensive in terms of computational resources, simulation time, and pre-processing time. Here we present a new rotor and wake model which is fully-coupled to a CFD solver and is based on the actuator surface model. This model is designed to reduce the cost of complex rotorcraft simulations in comparison with fully-resolved simulations and provide greater generality than other rotor models. Results from simulations using the new actuator surface and wake model provide validation of the concept for hover and forward flight. The spanwise loading distribution, thrust coefficient, and wake geometry are shown to be reasonable in comparison with data from experiments, fully-resolved simulations, and prescribed wake models

Computational fluid dynamics model of a quad-rotor helicopter for dynamic analysis

The control and performance of a quad-rotor helicopter UAV is greatly influenced by its aerodynamics, which in turn is affected by the interactions with features in its remote environment. This paper presents details of Computational Fluid Dynamics (CFD) simulation and analysis of a quadrotor helicopter. It starts by presenting how SolidWorks software is used to develop a 3-D Computer Aided Design (CAD) model of the quad-rotor helicopter, then describes how CFD is used as a computer based mathematical modelling tool to simulate and analyze the effects of wind flow patterns on the performance and control of the quadrotor helicopter. For the purpose of developing a robust adaptive controller for the quad-rotor helicopter to withstand any environmental constraints, which is not within the scope of this paper; this work accurately models the quad-rotor static and dynamic characteristics from a limited number of time-accurate CFD simulations