Article
Computer Science, Interdisciplinary Applications
Quincy A. Huhn, Mauricio E. Tano, Jean C. Ragusa, Youngsoo Choi
Summary: Dynamic Mode Decomposition (DMD) is a model-order reduction technique that extracts spatial modes of fixed temporal frequencies from numerical or experimental data. This paper presents two novel approaches to parametric DMD: one based on interpolation of the reduced-order DMD eigen-pair and the other based on interpolation of the reduced DMD (Koopman) operator. Numerical results are provided for diffusion-dominated nonlinear dynamical problems, including a multiphysics radiative transfer example. The three parametric DMD approaches are compared.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Multidisciplinary
Xinshuai Zhang, Tingwei Ji, Fangfang Xie, Hongyu Zheng, Yao Zheng
Summary: This study proposes a novel compressed sensing reduced-order modeling framework for predicting unsteady flow fields from sparse and noisy sensor measurements. The framework includes an offline learning stage using Long Short Term Memory (LSTM) model and sparsity-promoting Dynamic Mode Decomposition (DMD) algorithm, and an online forecasting stage using Deep Neural Network (DNN) to establish correlations and predict flow fields accurately.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Martin Otter
Summary: This article introduces Signal Tables as a format for exchanging simulation data based on dictionaries and multi-dimensional arrays. It provides a convenient way to store and process various simulation-related data in different programming languages, and allows storing Signal Tables in files.
Article
Computer Science, Interdisciplinary Applications
Robert Kender, Laura Stops, Valentin Krespach, Bernd Wunderlich, Martin Pottmann, Anna-Maria Ecker, Sebastian Rehfeldt, Harald Klein
Summary: This article introduces the Dynamic Edmister Method, a generic numerically robust model reduction approach used for dynamic distillation columns. In illustrative case studies, it is applied to a digital twin of the pressure column of an air separation unit. Both case studies show that the reduced models accurately reproduce the dynamic and steady-state behavior of the underlying rigorous model while reducing the calculation effort by 70%. This model reduction approach is a significant first step towards using digital twins for dynamic optimization.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Philip Pergam, Heiko Briesen
Summary: This study aims to improve the computational efficiency of a complex mathematical cake-filtration model with strong nonlinearities. A hybrid data-driven approach using proper orthogonal decomposition is employed, and optimal, globally defined basis functions are found based on a few sample simulations. The reduced-order model obtained from this approach has a 98% decrease in dimension compared to the full-order model, resulting in a 90% decrease in computational time for solving a benchmark optimization problem. This significant numerical speed-up offers the potential to use the reduced-order model in advanced process control and optimization methods.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Mechanics
Jing Wang, Hairun Xie, Miao Zhang, Hui Xu
Summary: In this paper, a Physics-Assisted Variational Autoencoder is proposed to identify dominant features of transonic buffet, which combines unsupervised reduced-order modeling with additional physical information embedded via a buffet classifier. Statistical results reveal that the buffet state can be determined exactly with just one latent space when a proper weight of classifier is chosen. Based on this identification, the displacement thickness at 80% chordwise location is proposed as a metric for buffet prediction, achieving an accuracy of 98.5% in buffet state classification.
Article
Ecology
Brandon T. Sinn, Sandra J. Simon, Mathilda Santee, Stephen P. DiFazio, Nicole M. Fama, Craig F. Barrett
Summary: Generating densely sampled single nucleotide polymorphism (SNP) data is essential in various fields of biology, but wet-laboratory expertise and bioinformatics training can be limiting factors. ISSRseq, a PCR-based method, offers a straightforward approach to reduced representation of genomic variation, requiring only simple wet-laboratory skills and commonplace instrumentation. This method is highly repeatable, flexible, and capable of genomic-scale variant discovery on par with existing methods that are more complex.
METHODS IN ECOLOGY AND EVOLUTION
(2022)
Article
Engineering, Mechanical
Linus Andersson, Peter Persson, Kent Persson
Summary: This paper investigates strategies for reduced order modeling of geometrically nonlinear finite element models. Simulation-free, non-intrusive approaches are considered, which do not require access to the source code of a finite element program. The study focuses on flat structures and proposes a methodology for generating computationally efficient reduced order models. The concepts are validated on solid beam models and continuously supported shell models, and strategies for efficient time integration are discussed and evaluated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Chemical
Peng Qiu, Fuchen Wang, Qinghua Guo, Andreas Richter, Jianliang Xu, Zhenghua Dai
Summary: The study investigated the modeling approach of ROM framework in turbulent reacting flow within CIJR, dividing the reactor space into different reaction units to predict high-order reactions. The performance of ROM in turbulent flow reactors was tested, suggesting the importance of flow mechanism in reactor modeling.
CHEMICAL ENGINEERING SCIENCE
(2022)
Article
Engineering, Multidisciplinary
David R. Brandyberry, Xiang Zhang, Philippe H. Geubelle
Summary: This paper proposes a two-step optimization method for the design of multiscale heterogeneous materials with nonlinear macroscopic response driven by volumetric and interfacial damage at the microstructural level. The method includes a reduced-order design phase using Eigendeformation-based reduced-order Homogenization Method (EHM) and a high-fidelity optimization phase using Interface-Enriched Generalized Finite Element Method (IGFEM).
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Sebastien Riffaud, Michel Bergmann, Charbel Farhat, Sebastian Grimberg, Angelo Iollo
Summary: The DGDD method couples high-fidelity polynomial approximation with low-dimensional resolution for solving conservation laws numerically. It uses a reduced-order model to predict solutions in low-fidelity regions and a high-dimensional model for regions not amenable to low-dimensional representation. The coupling between the two models is done through numerical fluxes at discrete cell boundaries, showing stability, accuracy, and reduced computational cost compared to the high-dimensional model.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Engineering, Mechanical
Giorgio Gobat, Andrea Opreni, Stefania Fresca, Andrea Manzoni, Attilio Frangi
Summary: In this study, the Proper Orthogonal Decomposition (POD) method is applied to efficiently simulate the nonlinear behavior of Micro-Electro-Mechanical-Systems (MEMS) in various scenarios involving geometric and electrostatic nonlinearities. The POD method reduces the polynomial terms up to cubic order associated with large displacements through exact projection onto a low-dimensional subspace spanned by the Proper Orthogonal Modes (POMs). Electrostatic nonlinearities are modeled using precomputed manifolds based on the amplitudes of the electrically active POMs. The reliability of the assumed linear trial space is extensively tested in challenging applications such as resonators, micromirrors, and arches with internal resonances. Comparisons are made between the periodic orbits computed with POD and the invariant manifold approximated with Direct Normal Form approaches, highlighting the reliability and remarkable predictive capabilities of the technique, particularly in terms of estimating the frequency response function of selected output quantities of interest.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Interdisciplinary Applications
Arash Massoudieh, Khiem Nguyen, Sudhir Murthy
Summary: Sub-disciplines within the water sector are divided into specialty domains, resulting in modeling tools for flow and water quality based on pre-determined equations. However, there is a need to integrate different sets of equations to address complex problems that involve interactions between multiple domains. This paper presents an extensible modeling framework that allows user-defined model components to be added using plugins, enabling adaptability to specific needs and objectives. The framework's data structure for describing model components, properties, and equations used for computation is also discussed. Four examples highlighting the multi-domain capability of the framework are provided.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Acoustics
Wei Xu, Min Xu, Xiaomin An, Weigang Yao
Summary: The study presents a nonlinear reduced-order model for calculating the nonlinear response of isotropic and composite plates, showing nearly an order of magnitude speedup compared to direct FE simulation and predicting shorter sonic fatigue life than White Gaussian Noise (WGN).
JOURNAL OF SOUND AND VIBRATION
(2021)
Article
Engineering, Aerospace
Brandon M. Lowe, David W. Zingg
Summary: This paper introduces a model order reduction framework for flutter-constrained aircraft optimization. By linearizing the Euler equations around a steady-state solution, a linear reduced-order model with fewer degrees of freedom is constructed and coupled with a linear structural model to form a monolithic aeroelastic system. The onset of flutter is determined by analyzing the eigenvalues of the resulting system, and the use of a stabilizing inner product is demonstrated to ensure the stability of the model.
