arXiv subjects

David E. Rival

Publications and source records attributed to David E. Rival.

8 recordsLinked to original sources

arXiv2026

Exemplar-based objective classification of gust-induced loads across multiple flight conditions

Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such as the flight attitude? Our approach encodes a large number of experimental observations through a machine-learned representation and applies a summarization procedure to select a minimal subset of highly significant exemplars. The exemplars provide a similarity-based objective classification criterion of all the observations, they can be more conveniently inspected by experts and can become subject of more refined experiments. We demonstrate the approach on a database of 3480 pressure-load measurements induced by random gusts on a flying-wing model across six flight attitudes. We find nine fundamental response types that recur across multiple attitudes; analysis of a type's transient response enables physical intuition into the underlying fluid mechanics.

cs.LG↗

arXiv2026

Control-oriented cluster-based reduced-order modelling

This work addresses the challenge of learning reduced-order models (ROMs) capable of generalizing to unobserved dynamical regimes across unseen control parameters. We introduce the Control-oriented Cluster-based Network Model (CNMc), a framework for synthesizing reduced-order dynamics at held-out operating conditions without requiring simulation data at those conditions. While the traditional Cluster Network Model (CNM) is limited to observed regimes, CNMc enables generalization by fitting supervised regression models to the transition probabilities and transition times of the CNM as functions of the control parameter. A key enabler is a Procrustes transformation that maps each operating condition's state space to a common coordinate system in which trajectories across all conditions are standardised and shape-aligned, permitting a shared cluster partition to be learned. We evaluate CNMc on two fluid dynamics benchmarks, the Lorenz-63 system and a controlled turbulent boundary layer, demonstrating that the predicted statistics at the withheld condition closely match those of a CNM trained directly on test data. CNMc also outperforms the competing interpolation-based CNM approaches under identical conditions. These results represent a step toward parameter-aware ROMs suitable for real-time flow control and the acceleration of parametric design studies.

physics.flu-dyn↗

arXiv2026

The search for the gust-wing interaction "textbook"

We address whether complex physical relations can be investigated through the synergy of automated high-volume experiments and the reduction of large datasets to a concise, representative subset of canonical examples -- a "textbook". To this end, we consider the unsteady aerodynamics of wing-gust interactions, which is characterized by its rich, high-dimensional physics. We take advantage of a purpose-built gust generator to systematically produce over 1,000 distinct random gust events and to measure the unsteady loads induced on a delta wing. We then employ a data summarization procedure to identify representative subsets of increasing size from the large-scale database, which then serve as training data for a machine-learning model of the aerodynamic loads from sparse pressure measurements. An appropriately selected "textbook" of a few events can achieve predictive accuracy comparable to random training sets up to two orders of magnitude larger, capturing the intrinsic diversity of the full-scale data and enhancing modeling efficiency and interpretability. Our methodology evidences the potential of distilling the essential information contained in large amounts of experimental observations.

physics.flu-dyn↗

arXiv2024

Wake dynamics of wind turbines in unsteady streamwise flow conditions

The unsteady flow physics of wind-turbine wakes under dynamic forcing conditions are critical to the modeling and control of wind farms for optimal power density. Unsteady forcing in the streamwise direction may be generated by unsteady inflow conditions in the atmospheric boundary layer, dynamic induction control of the turbine, or streamwise surge motions of a floating offshore wind turbine due to floating-platform oscillations. This study seeks to identify the dominant flow mechanisms in unsteady wakes forced by a periodic upstream inflow condition. A theoretical framework for the problem is derived, which describes traveling-wave undulations in the wake radius and streamwise velocity. These dynamics encourage the aggregation of tip vortices into large structures that are advected along in the wake. Flow measurements in the wake of a periodically surging turbine were obtained in an optically accessible towing-tank facility, with an average diameter-based Reynolds number of 300,000 and with surge-velocity amplitudes of up to 40% of the mean inflow velocity. Qualitative agreement between trends in the measurements and model predictions is observed, supporting the validity of the theoretical analyses. The experiments also demonstrate large enhancements in the recovery of the wake relative to the steady-flow case, with wake-length reductions of up to 46.5% and improvements in the available power at 10 diameters downstream of up to 15.7%. These results provide fundamental insights into the dynamics of unsteady wakes and serve as additional evidence that unsteady fluid mechanics can be leveraged to increase the power density of wind farms.

physics.flu-dyn↗

arXiv2024

Relative Fluid Stretching and Rotation for Sparse Trajectory Observations

As most mathematically justifiable Lagrangian coherent structure detection methods rely on spatial derivatives, their applicability to sparse trajectory data has been limited. For experimental fluid dynamicists and natural scientists working with Lagrangian trajectory data via passive tracers in unsteady flows (e.g. Lagrangian particle tracking or ocean buoys), obtaining material measures of fluid rotation or stretching is currently only possible for trajectory concentrations that are often out-of-reach. To facilitate frame-indifferent investigations in unsteady and sparsely sampled flows, we present a novel approach to quantify fluid stretching and rotation via relative Lagrangian velocities. This technique provides a formal objective extension of quasi-objective metrics to unsteady flows by accounting for mean flow behavior. For extremely sparse experimental data, fluid structures may be significantly undersampled, and the mean flow behavior becomes difficult to quantify. We provide a means to maintain the accuracy of our novel sparse flow diagnostics in extremely sparse sampling scenarios, such as ocean buoy data and Lagrangian particle tracking. We use data from multiple numerical and experimental flows to show that our methods can identify structures beyond existing limits of sparse, frame-indifferent diagnostics, and exhibit improved interpretability over common frame-dependent diagnostics.

physics.flu-dyn↗

arXiv2022

Model form uncertainty quantification of Reynolds-averaged Navier-Stokes modeling of flows over a SD7003 airfoil

It is well known that the Boussinesq turbulent viscosity hypothesis can yield inaccurate predictions when complex f low features are involved, e.g. laminar-turbulent transition. The focus of the study is to explore the capability of a physics-based uncertainty quantification (UQ) approach to quantify the model-form uncertainty in Reynolds-averaged Naiver-Stokes (RANS) simulations of laminar-turbulent transitional flows over an Selig-Donovan (SD) 7003 airfoil. This methodology perturbs the modeled Reynolds stress tensor in the momentum equations; perturbations are injected into the amplitude, eigenvalues and eigenvectors of the anisotropy Reynolds stress tensor undergone an eigen-decomposition. In this study, our analyses focus upon the amplitude perturbation. We observed a monotonic behavior of the magnitude of the predicted uncertainty bounds for different quantities of interest. High-order regressions based on the turbulence kinetic energy discrepancies are used to develop a novel switch marker function Mk to introduce perturbations in a non-uniform manner over different regions of the domain based upon prior knowledge of the limitations of the model. Importantly, the compound effect of Mk and eigenvalue perturbations show a synergy behavior, e.g., dramatically increased uncertainty bounds to account for the discrepancy in the RANS prediction; and the Mk function effectively avoids over-perturbation to the amplitude of the anisotropy Reynolds stress tensor. In this context, regression based amplitude perturbation of the anisotropy Reynolds stress tensor makes a new contribution to the RANS UQ methodology in the simulations of the airfoil transitional flows, which shows very encouraging results.

physics.flu-dyn↗

arXiv2022

Quantification of Reynolds-averaged-Navier-Stokes model form uncertainty in transitional boundary layer and airfoil flows

It is well known that Boussinesq turbulent-viscosity hypothesis can introduce uncertainty in predictions for complex flow features such as separation, reattachment, and laminar-turbulent transition. This study adopts a recent physics-based uncertainty quantification (UQ) approach to address such model form uncertainty in Reynolds-averaged Naiver- Stokes (RANS) simulations. Thus far, almost all UQ studies have focused on quantifying the model form uncertainty in turbulent flow scenarios. The focus of the study is to advance our understanding of the performance of the UQ approach on two different transitional flow scenarios: a flat plate and a SD7003 airfoil, to close this gap. For the T3A (flat-plate flow) flow, most of the model form uncertainty is concentrated in the laminar-turbulent transition region. For the SD7003 airfoil flow, the eigenvalue perturbations reveal a decrease as well as an increase in the length of the separation bubble. As a consequence, the uncertainty bounds successfully encompass the reattachment point. Likewise, the region of reverse flow that appear in the separation bubble is either suppressed or bolstered by the eigenvalue perturbations. In this context, the UQ methodology is applied to transition and show great results. This is the first successful RANS UQ study for transitional flows.

physics.flu-dyn↗

arXiv2021

Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data

Inspired by biological swimming and flying with distributed sensing, we propose a data-driven approach for load estimation that relies on complex networks. We exploit sparse, real-time pressure inputs, combined with pre-trained transition networks, to estimate aerodynamic loads in unsteady and highly-separated flows. The transition networks contain the aerodynamic states of the system as nodes along with the underlying dynamics as links. A weighted average-based (WAB) strategy is proposed and tested on realistic experimental data on the flow around an accelerating elliptical plate at various angles-of-attack. Aerodynamic loads are then estimated for angles of attack cases not included in the training dataset so as to simulate the estimation process. An optimization process is also included to account for the system's temporal dynamics. Performance and limitations of the WAB approach are discussed, showing that transition networks can represent a versatile and effective data-driven tool for real-time signal estimation using sparse and noisy signals (such as surface pressure) in realistic flows.

physics.flu-dyn↗