arXiv subjects

Steven N. Rodriguez

Publications and source records attributed to Steven N. Rodriguez.

7 recordsLinked to original sources

arXiv2026

Hyper-reduction-free reduced-order Newton solvers for projection-based model-order reduction of nonlinear dynamical systems

This study proposes an intrusive projection-based model-order reduction framework for nonlinear problems with a polynomial structure, solved iteratively using a Newton solver in the reduced space. It is demonstrated that for the targeted class of polynomial nonlinearities, all operators appearing in the projected approximate residual and Jacobian can be precomputed in the offline phase, eliminating the need for hyper-reduction. Additionally, the evaluation of both the projected approximate residual and its Jacobian scales only with the dimension of the reduced space, and does not depend on the dimension of the full-order model, enabling effective offline-online decomposition. The proposed hyper-reduction-free (HRF) framework is applied to both Galerkin (HRF-G) and least-squares Petrov-Galerkin (HRF-LSPG) projection schemes. The accuracy and computational efficiency of the proposed HRF schemes are evaluated in two numerical experiments and compared with a commonly used hyper-reduction scheme, namely the energy-conserving sampling and weighting method, for both the Galerkin and LSPG schemes. In the first numerical example, a parametric Burgers' equation is used to assess the predictive capabilities of the considered model reduction approaches on parameter sets not seen in the training snapshots. In the second example, a parametric heat equation with a cubic reaction term is studied, for which a lifting transformation is employed to expose the desired structure. The efficacy of the HRF methods in accurately reducing the dimensionality of the lifted formulation is investigated. For the studied problems, the results show that HRF-G and HRF-LSPG achieve two and one order of magnitude speedup, respectively, with respect to the full-order model while resulting in state prediction errors below O(10^-2).

cs.CE↗

arXiv2025

Meshless projection model-order reduction via reference spaces for smoothed-particle hydrodynamics

A model-order reduction framework for the meshless smoothed-particle hydrodynamics (SPH) method is presented. The proposed framework introduces the concept of modal reference spaces to overcome the challenges of discovering low-dimensional subspaces from unstructured, dynamic, and mixing numerical topology that occurs in SPH simulations. These reference spaces enable a low-dimensional representation of the field equations while maintaining the inherent meshless qualities of SPH. Modal reference spaces are constructed by projecting snapshot data onto a reference space where low-dimensionality of field quantities can be discovered via traditional modal decomposition techniques (e.g., the proper orthogonal decomposition (POD)). Modal quantities are mapped back to the meshless SPH space via scattered data interpolation during the online predictive stage. The proposed model-order reduction framework is cast into the meshless Galerkin POD and the Adjoint Petrov-Galerkin projection model-order reduction (PMOR) formulation. The PMORs are tested on three numerical experiments: 1) the Taylor--Green vortex; 2) the lid-driven cavity; and 3) the flow past an open cavity. Results show good agreement in reconstructed and predictive velocity fields, which showcase the ability of this framework to evolve the field equations in a low-dimensional subspace on an unstructured, dynamic, and mixing numerical topology. Results also show that the pressure field is sensitive to the projection error due to the stiff weakly-compressible assumption made in the current SPH framework, but this sensitivity can be alleviated through nonlinear approximations, such as the APG approach. The proposed meshless model-order reduction framework reports up to 90,000x dimensional compression within 10% error in quantities of interest, marking a step toward drastic cost reduction in SPH simulations.

cs.CE↗

arXiv2024

Data-Driven Model Identification Using Time Delayed Nonlinear Maps for Systems with Multiple Attractors

This study presents a method, along with its algorithmic and computational framework implementation, and performance verification for dynamical system identification. The approach incorporates insights from phase space structures, such as attractors and their basins. By understanding these structures, we have improved training and testing strategies for operator learning and system identification. Our method uses time delay and non-linear maps rather than embeddings, enabling the assessment of algorithmic accuracy and expressibility, particularly in systems exhibiting multiple attractors. This method, along with its associated algorithm and computational framework, offers broad applicability across various scientific and engineering domains, providing a useful tool for data-driven characterization of systems with complex nonlinear system dynamics.

math.DS↗

arXiv2024

Projection-based model-order reduction via graph autoencoders suited for unstructured meshes

This paper presents the development of a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) using a nonlinear manifold least-squares Petrov-Galerkin (LSPG) projection scheme. The architecture is particularly useful for advection-dominated flows modeled by unstructured meshes, as it provides a robust nonlinear mapping that can be leveraged in a PMOR setting. The presented graph autoencoder is constructed with a two-part process that consists of (1) generating a hierarchy of reduced graphs to emulate the compressive abilities of convolutional neural networks (CNNs) and (2) training a message passing operation at each step in the hierarchy of reduced graphs to emulate the filtering process of a CNN. The resulting framework provides improved flexibility over traditional CNN-based autoencoders because it is readily extendable to unstructured meshes. We provide an analysis of the interpretability of the graph autoencoder's latent state variables, where we find that the Jacobian of the decoder for the proposed graph autoencoder provides interpretable mode shapes akin to traditional proper orthogonal decomposition modes. To highlight the capabilities of the proposed framework, which is named geometric deep least-squares Petrov-Galerkin (GD-LSPG), we benchmark the method on a one-dimensional Burgers' model with a structured mesh and demonstrate the flexibility of GD-LSPG by deploying it on two test cases for two-dimensional Euler equations that use an unstructured mesh. The proposed framework is more flexible than using a traditional CNN-based autoencoder and provides considerable improvement in accuracy for very low-dimensional latent spaces in comparison with traditional affine projections.

cs.CE↗

arXiv2021

Projection-tree reduced order modeling for fast N-body computations

This work presents a data-driven reduced-order modeling framework to accelerate the computations of $N$-body dynamical systems and their pair-wise interactions. The proposed framework differs from traditional acceleration methods, like the Barnes-Hut method, which requires online tree building of the state space, or the fast-multipole method, which requires rigorous $a$ $priori$ analysis of governing kernels and online tree building. Our approach combines Barnes-Hut hierarchical decomposition, dimensional compression via the least-squares Petrov-Galerkin (LSPG) projection, and hyper-reduction by way of the Gauss-Newton with approximated tensor (GNAT) approach. The resulting $projection-tree$ reduced order model (PTROM) enables a drastic reduction in operational count complexity by constructing sparse hyper-reduced pairwise interactions of the $N$-body dynamical system. As a result, the presented framework is capable of achieving an operational count complexity that is independent of $N$, the number of bodies in the numerical domain. Capabilities of the PTROM method are demonstrated on the two-dimensional fluid-dynamic Biot-Savart kernel within a parametric and reproductive setting. Results show the PTROM is capable of achieving over 2000$\times$ wall-time speed-up with respect to the full-order model, where the speed-up increases with $N$. The resulting solution delivers quantities of interest with errors that are less than 0.1$\%$ with respect to full-order model.

cs.CE↗

arXiv2021

Stability of Helical Vortex Structures Shed from Flexible Rotors

The presented investigation is motivated by the need to uncover connections between underlying rotor fluid-structure interactions and vortex dynamics to fatigue performance and characterization of flexible rotor blades, their hub, and their supporting superstructure. Towards this effort, temporal stability characteristics of tip vortices shed from flexible rotor blades are investigated numerically. An aeroelastic free-vortex wake method is employed to simulate the helical tip vortices and the associated velocity field. A linear eigenvalue stability analysis is employed to quantify stability trends (growth rate v. perturbation wavenumber) and growth-rate temporal evolution of tip vortices. Simulations of a canonical rotor with rigid blades and its generation of tip vortices are first conducted to validate the stability analysis employed herein. Next, a stationary wind turbine is emulated using the National Renewable Energy Laboratory 5MW reference wind turbine base design to investigate the impact rotor aeroelasticity has on tip vortex stability evolution in time. Blade flexibility is shown to reduce the sensitivity of tip vortex destabilization to low wavenumber perturbations, also blade-pitch reduces growth-rate magnitude and alters the growth-rate peak dependence on perturbation wavenumber, all of which have in the past not been reported in the rotorcraft literature. The current investigation aims to develop insight into rotorcraft tip vortex kinematics and stability to work towards quantifying fatigue loading and its effects, minimizing adverse blade-vortex interaction effects, such as excessive noise emission and large blade vibrations also affecting fatigue performance.

physics.flu-dyn↗

arXiv2019

Strongly-coupled aeroelastic free-vortex wake framework for floating offshore wind turbine rotors. Part 2: Application

This two-part paper presents the integration of the free-vortex wake method (FVM) with an aeroelastic framework suitable to model the rotor-wake interactions engendered by floating offshore wind turbine (FOWT) rotors in operation. Part 1 of this paper introduces the numerical development and validation of an aeroelastic framework. Due to a lack of experimental aeroelastic benchmarks for FOWTs, a series of validation studies are conducted against the rotor aerodynamic and structural performance of the National Renewable Energy Laboratory (NREL) 5-MW reference wind turbine. Part 2 of this paper focuses on the modeling and simulating different aeroelastic operational conditions of FOWTs. Numerical results of the current framework capture consistently the aerodynamic rotor performance, such as power, thrust, and torque of wave-induced pitching FOWTs. In addition, the presented aeroelastic framework yields additional information about the power, thrust, and torque fluctuations due to the out-of-phase blade passing frequency and corresponding blade deflections. The fidelity of the presented framework demonstrates, for the first time, an FVM-based aeroelastic method capable of carrying out investigations on rotor-wake interactions and relevant aeroelastic phenomena of FOWTs.

physics.flu-dyn↗