Monograph records
https://feeds.library.caltech.edu/people/Bae-Hyunji-Jane/monograph.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenMon, 27 Nov 2023 17:37:15 +0000Self-critical machine-learning wall-modeled LES for external aerodynamics
https://resolver.caltech.edu/CaltechAUTHORS:20210315-144531131
Authors: Lozano-DurĂ¡n, A.; Bae, H. J.
Year: 2021
DOI: 10.48550/arXiv.2012.10005
The prediction of aircraft aerodynamic quantities of interest remains among the most pressing challenges for computational fluid dynamics. The aircraft aerodynamics are inherently turbulent with mean-flow three-dimensionality, often accompanied by laminar-to-turbulent transition, flow separation, secondary flow motions at corners, and shock wave formation, to name a few. However, the most widespread wall models are built upon the assumption of statistically-in-equilibrium wall-bounded turbulence and do not faithfully account for the wide variety of flow conditions described above. This raises the question of how to devise models capable of accounting for such a vast and rich collection of flow physics in a feasible manner. In this work, we propose tackling the wall-modeling challenge by devising the flow as a collection of building blocks, whose information enables the prediction of the stress as the wall. The model relies on the assumption that simple canonical flows contain the essential flow physics to devise accurate models. Three types of building block units were used to train the model: turbulent channel flows, turbulent ducts and turbulent boundary layers with separation. This limited training set will be extended in future versions of the model. The approach is implemented using two interconnected artificial neural networks: a classifier, which identifies the contribution of each building block in the flow; and a predictor, which estimates the wall stress via non-linear combinations of building-block units. The output of the model is accompanied by the confidence in the prediction. The latter value aids the detection of areas where the model underperforms, such as flow regions that are not representative of the building blocks used to train the model. The model is validated in a unseen case representative of external aerodynamic applications: the NASA Juncture Flow Experiment.https://authors.library.caltech.edu/records/397p3-jea62A nonlinear subgrid-scale model for large-eddy simulations of rotating turbulent flows
https://resolver.caltech.edu/CaltechAUTHORS:20210504-092729293
Authors: Silvis, Maurits H.; Bae, H. Jane; Trias, F. Xavier; Abkar, Mahdi; Verstappen, Roel
Year: 2021
DOI: 10.48550/arXiv.1904.12748
Rotating turbulent flows form a challenging test case for large-eddy simulation (LES). We, therefore, propose and validate a new subgrid-scale (SGS) model for such flows. The proposed SGS model consists of a dissipative eddy viscosity term as well as a nondissipative term that is nonlinear in the rate-of-strain and rate-of-rotation tensors. The two corresponding model coefficients are a function of the vortex stretching magnitude. Therefore, the model is consistent with many physical and mathematical properties of the Navier-Stokes equations and turbulent stresses, and is easy to implement. We determine the two model constants using a nondynamic procedure that takes into account the interaction between the model terms. Using detailed direct numerical simulations (DNSs) and LESs of rotating decaying turbulence and spanwise-rotating plane-channel flow, we reveal that the two model terms respectively account for dissipation and backscatter of energy, and that the nonlinear term improves predictions of the Reynolds stress anisotropy near solid walls. We also show that the new SGS model provides good predictions of rotating decaying turbulence and leads to outstanding predictions of spanwise-rotating plane-channel flow over a large range of rotation rates for both fine and coarse grid resolutions. Moreover, the new nonlinear model performs as well as the dynamic Smagorinsky and scaled anisotropic minimum-dissipation models in LESs of rotating decaying turbulence and outperforms these models in LESs of spanwise-rotating plane-channel flow, without requiring (dynamic) adaptation or near-wall damping of the model constants.https://authors.library.caltech.edu/records/qpyhg-k8t52Wavelet-based resolvent analysis for statistically-stationary and temporally-evolving flows
https://resolver.caltech.edu/CaltechAUTHORS:20230327-235358200
Authors: Ballouz, Eric; Lopez-Doriga, Barbara; Dawson, Scott T. M.; Bae, H. Jane
Year: 2023
This work introduces a formulation of resolvent analysis that uses wavelet transforms rather than Fourier transforms in time. This allows resolvent analysis to be extended to turbulent flows with non-stationary means in addition to statistically-stationary flows. The optimal resolvent modes for this formulation correspond to the potentially time-transient structures that are most amplified by the linearized Navier-Stokes operator. We validate this methodology for turbulent channel flow and show that the wavelet-based and Fourier-based resolvent analyses are equivalent for statistically-stationary flows. We then apply the wavelet-based resolvent analysis to study the transient growth mechanism in the buffer layer of a turbulent channel flow by windowing the resolvent operator in time and frequency. The method is also applied to temporally-evolving parallel shear flows such as an oscillating boundary layer and three-dimensional channel flow, in which a lateral pressure gradient perturbs a fully-developed turbulent flow in a channel.https://authors.library.caltech.edu/records/m4n8m-7cz15A sparsity-promoting resolvent analysis for the identification of spatiotemporally-localized amplification mechanisms
https://resolver.caltech.edu/CaltechAUTHORS:20230328-000129999
Authors: Lopez-Doriga, Barbara; Ballouz, Eric; Bae, H. Jane; Dawson, Scott T. M.
Year: 2023
This work introduces a variant of resolvent analysis that identifies forcing and response modes that are sparse in both space and time. This is achieved through the use of a sparse principal component analysis (PCA) algorithm, which formulates the associated optimization problem as a nonlinear eigenproblem that can be solved with an inverse power method. We apply this method to parallel shear flows, both in the case where we assume Fourier modes in time (as in standard resolvent analysis) and obtain spatial localization, and where we allow for temporally-sparse modes through the use of a linearized Navier-Stokes operator discretized in both space and time. Appropriate choice of desired mode sparsity allows for the identification of structures corresponding to high amplification that are localized in both space and time. We report on the similarities and differences between these structures and those from standard methods of analysis. After validating this space-time resolvent analysis on statistically-stationary channel flow, we next implement the methodology on a time-periodic Stokes boundary layer, demonstrating the applicability of the approach to non-statistically-stationary systems.https://authors.library.caltech.edu/records/xx50t-27h26