Papers by Carl Ollivier-Gooch
22nd AIAA Computational Fluid Dynamics Conference, Jun 18, 2015
In this paper, we perform a numerical estimation of discretization error using the error transpor... more In this paper, we perform a numerical estimation of discretization error using the error transport equation, derived from the primal PDE. The viability of using this method for obtaining higher order estimates for unstructured finite-volume discretizations of scalar linear and nonlinear scalar PDEs has previously been demonstrated, and here we examine how this extends to steady state solutions to the Euler equations, a nonlinear system of PDEs. Considerations for the error transport equation with and without linearization were made. Comparisons of results show that using the fully nonlinear form has verifiable properties as well as being superior in accuracy of the error estimate in some situations, although the Newton linearization can be adequate in others. The major results for 1D and 2D test cases were consistent with scalar problems. With arbitrary choices of discretization orders for the primal and error PDEs and residual source term, the error estimate obtained is in general not sharp and converges to the exact error at the same order as the primal discretization. However, using a discretization scheme where the source term for the error equation is the residual based on a reconstruction of the converged primal solution that is the same order as the error equation discretization leads to a sharp, high order estimate compared to other combinations. Therefore, we demonstrate that there are nominal accuracy combinations for discretizing the primal and error equations, and evaluating the residual source term, that require more computational work but are actually less accurate asymptotically in obtaining an estimate of error, which are choices that one should never make in practice. In addition, some results for the runtime costs are obtained for evaluating the feasibility of applying this error estimation approach compared to higher order primal discretizations.
54th AIAA Aerospace Sciences Meeting, Jan 2, 2016
Journal of Aircraft, May 17, 2023
The current state-of-the-practice technology for high-lift aerodynamic simulations is to solve th... more The current state-of-the-practice technology for high-lift aerodynamic simulations is to solve the Reynolds-averaged Navier–Stokes (RANS) equations on a fixed grid or a refinement sequence of fixed grids. The Fixed-Grid Reynolds-Averaged Navier–Stokes Technology Focus Group set out to determine meshing requirements and best practices, whether RANS can accurately predict the change in aerodynamic performance with changes in flap deflection, whether RANS modeling can produce accurate results near [Formula: see text], and the effects of underconvergence and solution strategy on computed results. Eighteen groups of participants submitted over 100 datasets. Challenges with grid convergence and iterative convergence made it impossible to definitively answer all the questions we had posed. Despite this, we can conclude that meshes with at least half a billion cells (more than one billion degrees of freedom) are required for grid convergence away from stall; that RANS simulations cannot currently be reliably used to predict aerodynamic coefficients near stall, nor changes in coefficients with changes in flap angle; that iterative underconvergence remains a significant source of uncertainty in outputs; and that solution initialization can have an important effect on solution behavior, including flow separation patterns.
AIAA SCITECH 2023 Forum, Jan 19, 2023
Bulletin of the American Physical Society, Nov 23, 2008
We have developed a diffuse-interface algorithm for computing two-component interfacial flows of ... more We have developed a diffuse-interface algorithm for computing two-component interfacial flows of Newtonian and non-Newtonian fluids in 3D. An adaptive meshing scheme produces fine grid near the interface and coarse mesh in the bulk, and leads to accurate resolution of the interface at moderate computational cost. Another advantage of the method is that there is no need for manual intervention during topological changes of the interface such as rupture and coalescence. However, the fully implicit time-stepping results in a large matrix system for complex 3D flows, with high demands for memory and CPU speed. As validating examples, we discuss a drop spreading on a partially wetting substrate and drop deformation in Newtonian and viscoelastic fluids. The results show very good agreement with those from the literature and our own 2D axisymmetric simulations.
AIAA Scitech 2019 Forum, Jan 6, 2019
45th AIAA Aerospace Sciences Meeting and Exhibit, Jan 8, 2007
AIAA SCITECH 2023 Forum, Jan 19, 2023
AIAA SCITECH 2022 Forum, Jan 3, 2022
AIAA Scitech 2020 Forum, Jan 5, 2020
AIAA Journal, Jun 10, 2023
The accuracy of flow simulations is a major concern in computational fluid dynamics (CFD) applica... more The accuracy of flow simulations is a major concern in computational fluid dynamics (CFD) applications. A possible outcome of inaccuracy in CFD results is missing a major feature in the flowfield. Many methods have been proposed to reduce numerical errors and increase overall accuracy, but these are not always efficient or even feasible. In this study, the principal component analysis (PCA) has been performed on compressible flow simulations around an airfoil to map the high-dimensional CFD data to a lower-dimensional PCA subspace. A machine learning classifier based on the extracted principal components has been developed to detect the simulations that miss the separation bubble behind the airfoil. The evaluative measures indicate that the model is able to detect most of the simulations where the separation region is poorly resolved. Moreover, a single mode responsible for the missing flow separation was uncovered that could be the subject of future studies. The results demonstrate that a machine learning model based on the principal components of the dataset is a promising tool for detecting possible missing flow features in CFD.
Computers & Fluids, Mar 1, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
High-order accurate numerical discretization methods are attractive for their potential to signif... more High-order accurate numerical discretization methods are attractive for their potential to significantly reduce the computational costs compared to the traditional second-order methods. Among the various unstructured higher-order discretization schemes, the k-exact reconstruction finite volume method is of interest for its straightforward mathematical formulation, and its compatibility with the current lowerorder industrial solvers. However, current three-dimensional finite volume solvers are limited to the I am thankful for the financial support of the Natural Sciences and Engineering Research Council of Canada and the University of British Columbia through the Canada Graduate Scholarships-Master's (CGS-M) program and the Gartshore Scholarship, respectively. I would like to express my gratitude to my friends and fellow labmates for their valuable help and suggestions. Special thanks to Alireza for patiently helping me with my initial transition into the research environment at ANSLab, and Gary for proofreading this thesis. I wish to thank my dear friend Mohammad. I will never forget your help and advice both inside and outside of work. Also, thank you for your technical suggestions and for proofreading a major portion of this thesis. Last but not least, I would like to thank my parents, Iraj and Mahboubeh, and my sister, Leiana, for their unconditional love, support, and caring. xiv
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Papers by Carl Ollivier-Gooch