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Journal articles on the topic 'ROM (Reduced-Order Model)'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Xie, Xuping, Feng Bao, and Clayton Webster. "Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation." Fluids 3, no. 4 (2018): 84. http://dx.doi.org/10.3390/fluids3040084.

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In this paper, we introduce the evolve-then-filter (EF) regularization method for reduced order modeling of convection-dominated stochastic systems. The standard Galerkin projection reduced order model (G-ROM) yield numerical oscillations in a convection-dominated regime. The evolve-then-filter reduced order model (EF-ROM) aims at the numerical stabilization of the standard G-ROM, which uses explicit ROM spatial filter to regularize various terms in the reduced order model (ROM). Our numerical results are based on a stochastic Burgers equation with linear multiplicative noise. The numerical re

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Menier, Emmanuel, Michele Alessandro Bucci, Mouadh Yagoubi, Lionel Mathelin, and Marc Schoenauer. "CD-ROM: Complemented Deep - Reduced order model." Computer Methods in Applied Mechanics and Engineering 410 (May 2023): 115985. http://dx.doi.org/10.1016/j.cma.2023.115985.

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Bai, Feng, and Yi Wang. "Reduced-Order Modeling Based on Hybrid Snapshot Simulation." International Journal of Computational Methods 18, no. 01 (2020): 2050029. http://dx.doi.org/10.1142/s0219876220500292.

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This paper presents a hybrid snapshot simulation methodology to accelerate the generation of high-quality data for proper orthogonal decomposition (POD) and reduced-order model (ROM) development. The entire span of the snapshot simulation is divided into multiple intervals, each simulated by either high-fidelity full-order model (FOM) or fast local ROM. The simulation then alternates between FOM and local ROM to accelerate snapshot data generation while maintaining the data fidelity and representation. Model switch is determined on-the-fly by evaluating several criteria that monitor the domina

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Le Guennec, Yves, and Simon Morville. "A reduced order model to assist welding parameter setup." Mechanics & Industry 25 (2024): 3. http://dx.doi.org/10.1051/meca/2023045.

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This article demonstrates the time saving in industrial process setup using numerical reduced order modelling (ROM). The numerical simulations may supply useful information to design manufacturing processes but are often time consuming and then not suited with multi-query study such as inverse problem. ROM aims at replacing the original simulation (so-called high-fidelity (HF)) by a low rank model that will run fast according to the HF simulation time. Multi-query studies will use the ROM instead of the HF simulation to save time and deliver a solution consistent with industrial timeline. To v

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Xie, Xuping, Peter J. Nolan, Shane D. Ross , Changhong Mou , and Traian Iliescu. "Lagrangian Reduced Order Modeling Using Finite Time Lyapunov Exponents." Fluids 5, no. 4 (2020): 189. http://dx.doi.org/10.3390/fluids5040189.

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There are two main strategies for improving the projection-based reduced order model (ROM) accuracy—(i) improving the ROM, that is, adding new terms to the standard ROM; and (ii) improving the ROM basis, that is, constructing ROM bases that yield more accurate ROMs. In this paper, we use the latter. We propose two new Lagrangian inner products that we use together with Eulerian and Lagrangian data to construct two new Lagrangian ROMs, which we denote α-ROM and λ-ROM. We show that both Lagrangian ROMs are more accurate than the standard Eulerian ROMs, that is, ROMs that use standard Eulerian in

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Jeong, Hosik, Kanghyuk Ko, Junsung Kim, et al. "Evaluation of Prediction Model for Compressor Performance Using Artificial Neural Network Models and Reduced-Order Models." Energies 17, no. 15 (2024): 3686. http://dx.doi.org/10.3390/en17153686.

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In order to save the time and material costs associated with refrigeration system performance evaluations, a reduced-order model (ROM) using highly accurate numerical analysis results and some experimental values was developed. To solve the shortcomings of these traditional methods in monitoring complex systems, a simplified reduced-order system model was developed. To evaluate the performance of the refrigeration system compressor, the temperature of several points in the system where the compressor actually operates was measured, and the measured values were used as input values for ROM deve

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Colanera, Antonio, Eduardo Di Costanzo, Matteo Chiatto, and Luigi de Luca. "Reduced-Order Model Approaches for Predicting Airfoil Performance." Actuators 13, no. 3 (2024): 88. http://dx.doi.org/10.3390/act13030088.

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This study delves into the construction of reduced-order models (ROMs) of a flow field over a NACA 0012 airfoil at a moderate Reynolds number and an angle of attack of 8∘. Numerical simulations were computed through the finite-volume solver OpenFOAM. The analysis considers two different reduction techniques: the standard Galerkin projection method, which involves projecting the governing equations onto proper orthogonal decomposition modes (POD−ROMs), and the cluster-based network model (CNM), a fully data-driven nonlinear approach. An analysis of the topology of the dominant POD modes was con

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Dou, Yi Bin, and Min Xu. "Nonlinear Aerodynamics Reduced-Order Model Based on Multi-Input Volterra Series." Advanced Materials Research 748 (August 2013): 421–26. http://dx.doi.org/10.4028/www.scientific.net/amr.748.421.

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This paper gives a low-order approximations of multi-input Volterra series as a nonlinear reduced-order model (ROM) based on wavelet multiresolution analysis. The band-limited pseudorandom multilevel sequence (PRMS) is used as the identification signal and the QR decomposition recursive least square (QRD-RLS) method is utilized as identification method. At last, the ROM is applied to model the nonlinear aerodynamic moment of an airfoil undergoing simultaneous forced pitch and plunge harmonic oscillation. The results show that including the second-order Volterra cross kernels in ROM can capture

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9

Solán-Fustero, Pablo, José Luis Gracia, Adrián Navas-Montilla, and Pilar García-Navarro. "A POD-based reduced order model applied to 1D shallow water equations." IOP Conference Series: Earth and Environmental Science 1136, no. 1 (2023): 012036. http://dx.doi.org/10.1088/1755-1315/1136/1/012036.

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Abstract Many environmental problems involving free surface flow can be solved using the shallow water equations (SWE) often involving high computational costs due to the large spatial and temporal scales of the events. In recent times, reduced order models (ROM) techniques are increasingly used to improve the computational efficiency of simulation models. The Proper Orthogonal Decomposition (POD) method provides an orthogonal basis for representing a given set of data and constructing the ROM by means of the method of snapshots. In this work, a POD-based intrusive ROM strategy is applied to t

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10

Rodrigues, Guilherme V., and Marcelo A. Savi. "Reduced-Order Model Description of Origami Stent Built with Waterbomb Pattern." International Journal of Applied Mechanics 13, no. 02 (2021): 2150016. http://dx.doi.org/10.1142/s1758825121500162.

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Origami-inspired structures have found many innovative applications in engineering fields. The expressive volume changes intrinsically related to their geometry is very useful for different purposes. Nevertheless, the mathematical description of origami structures is complex, which makes the design a challenging topic. This work deals with the use of reduce-order models for the origami description. A cylindrical origami structure with waterbomb pattern, called origami stent, is of concern. A reduced-order model (ROM) is developed based on kinematics and symmetry hypotheses. Afterward, a finite

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11

ZHOU, QIANG, GANG CHEN, and YUEMING LI. "A REDUCED ORDER MODEL BASED ON BLOCK ARNOLDI METHOD FOR AEROELASTIC SYSTEM." International Journal of Applied Mechanics 06, no. 06 (2014): 1450069. http://dx.doi.org/10.1142/s1758825114500690.

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A reduced-order model (ROM) based on block Arnoldi algorithm to quickly predict flutter boundary of aeroelastic system is investigated. First, a mass–damper–spring dynamic system is tested, which shows that the low dimension system produced by the block Arnoldi method can keep a good dynamic property with the original system in low and high frequencies. Then a two-degree of freedom transonic nonlinear aerofoil aeroelastic system is used to validate the suitability of the block Arnoldi method in flutter prediction analysis. In the aerofoil case, the ROM based on a linearized model is obtained t

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Peters, Nicholas, Christopher Silva, and John Ekaterinaris. "A data-driven reduced-order model for rotor optimization." Wind Energy Science 8, no. 7 (2023): 1201–23. http://dx.doi.org/10.5194/wes-8-1201-2023.

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Abstract. For rotor design applications, such as wind turbine rotors or urban air mobility (UAM) rotorcraft and flying-car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving reasonably accurate but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor distributed loads. The POD

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Dou, Yi Bin, and Min Xu. "Reduced-Order Model for Unsteady Compressible Flow Based on POD-Galerkin Projection." Applied Mechanics and Materials 226-228 (November 2012): 835–39. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.835.

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Italic textIn this paper, the reduced-order model (ROM) of unsteady compressible flow based on POD-Galerkin projection has been investigated. The Euler equation formulated with the conservative variables for compressible flow has been reformulated using modified primitive variables. The POD modes are computed using snapshot method and then an explicit quadratic ROM is constructed by applying the Galerkin projection to the modified Euler equation. Because of lacking any dissipation in POD-Galerkin projection, the flow calibration method is introduced to account for the numerical dissipation to

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14

Che Hasan, Mohd Hanif, Mohd Khair Hassan, Fauzi Ahmad, Mohammad Hamiruce Marhaban, Sharil Izwan Haris, and Ehsan Arasteh. "Simplifying the electronic wedge brake system model through model order reduction techniques." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 893–904. http://dx.doi.org/10.11591/eei.v13i2.5815.

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The electronic wedge brake (EWB) uses self-reinforcement principles to optimise stopping power, but its mathematical model has various actuation angles and system dynamics making controller design complex and computationally burdensome. Therefore, the model order reduction (MOR) is made based on three factors that may have a negligible influence on the EWB system: the motor inductance, lead screw axial damping, and wedge mass. Six reduced order model (ROM) types were proposed when one, two, or all factors were ignored. The ROM accuracy was analysed using the frequency and time domain. The perc

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15

Borcea, Liliana, Josselin Garnier, Alexander V. Mamonov, and Jörn Zimmerling. "Reduced order model approach for imaging with waves." Inverse Problems 38, no. 2 (2021): 025004. http://dx.doi.org/10.1088/1361-6420/ac41d0.

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Abstract We introduce a novel, computationally inexpensive approach for imaging with an active array of sensors, which probe an unknown medium with a pulse and measure the resulting waves. The imaging function is based on the principle of time reversal in non-attenuating media and uses a data driven estimate of the ‘internal wave’ originating from the vicinity of the imaging point and propagating to the sensors through the unknown medium. We explain how this estimate can be obtained using a reduced order model (ROM) for the wave propagation. We analyze the imaging function, connect it to the t

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Behere, Ameya, Dushhyanth Rajaram, Tejas G. Puranik, Michelle Kirby, and Dimitri N. Mavris. "Reduced Order Modeling Methods for Aviation Noise Estimation." Sustainability 13, no. 3 (2021): 1120. http://dx.doi.org/10.3390/su13031120.

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A key enabler for sustainable growth of aviation is the mitigation of adverse environmental effects. One area of concern is community noise exposure at large hub airports serving growing population centers. Traditionally, community noise exposure is computed using noise contours around airports, which requires knowledge of a large dataset pertaining to the air traffic operations at the airport of interest. Due to the underlying variability in real-world aircraft operations, numerous assumptions need to be made which adversely affect the accuracy of the model. Reduced-Order Modeling (ROM) metho

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17

Wang, Yuepeng, Yue Cheng, Zongyuan Zhang, and Guang Lin. "Calibration of reduced-order model for a coupled Burgers equations based on PC-EnKF." Mathematical Modelling of Natural Phenomena 13, no. 2 (2018): 21. http://dx.doi.org/10.1051/mmnp/2018023.

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The proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM) are applied to coupled Burgers equations to develop its reduced-order model (ROM) by the Galerkin projection. A calibrated POD ROM is developed in the current study through adding and multiplying a set of time-dependent random parameters to recover the loss of accuracy due to the truncation of the POD modes. Calibrating the ROM becomes essentially a high-dimensional statistical inverse inference problem. To reduce the computational effort, the polynomial chaos based ensemble Kalman filter (PC-EnKF)

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18

Brusca, Sebastian, Antonio Galvagno, Stefano Mauro, Michele Messina, and Rosario Lanzafame. "OWC Systems Savonius Turbine Reduced Order Model Implementation by Means of Experimental Data." Journal of Physics: Conference Series 2893, no. 1 (2024): 012013. https://doi.org/10.1088/1742-6596/2893/1/012013.

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Abstract The present paper discusses the implementation of a Reduced Order Model (ROM) for an OWC Power Take-Off Savonius turbine. The turbine’s ROM relies primarily on experimental data. An ad hoc laboratory-scale oscillating flow simulator was constructed to replicate the behaviour of the OWC power take-off turbine under various operating conditions. A laboratory-scale Savonius turbine with a diameter of 0.09 m, an aspect ratio of 1, and an overlap ratio of 1/3 was subjected to testing. Performance evaluations were conducted using the laboratory-scale oscillating flow simulator. In this pape

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Vernekar, Kiran, Neeharika Kurumoju, and Kavya H M. "Reduced Order Modeling with AI for Model-in-the-Loop Simulation." ARAI Journal of Mobility Technology 5, no. 2 (2025): 1516–26. https://doi.org/10.37285/ajmt.5.2.2.

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Model-in-the-Loop (MIL) simulation is a testing technique widely used in the automotive industry for developing and testing control systems. It involves integrating a mathematical model of a physical system being developed into a virtual environment and performing rigorous testing. MIL simulation offers benefits in system design and development, but it also has several computational challenges that can impact the efficiency of the simulation. This paper focuses on enhancing the computational efficiency of a MIL simulation environment for Electric Vehicle (EV) Powertrain system using a Reduced

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20

Holemans, Thomas, Zhu Yang, and Maarten Vanierschot. "Efficient Reduced Order Modeling of Large Data Sets Obtained from CFD Simulations." Fluids 7, no. 3 (2022): 110. http://dx.doi.org/10.3390/fluids7030110.

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The ever-increasing computational power has shifted direct numerical simulations towards higher Reynolds numbers and large eddy simulations towards industrially-relevant flow scales. However, this increase in both temporal and spatial resolution has severely increased the computational cost of model order reduction techniques. Reducing the full data set to a smaller subset in order to perform reduced-order modeling (ROM) may be an interesting method to keep the computational effort reasonable. Moreover, non-tomographic particle image velocimetry measurements obtain a 2D data set of a 3D flow f

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Zahn, Rebecca, and Christian Breitsamter. "Neuro-Fuzzy Network-Based Reduced-Order Modeling of Transonic Aileron Buzz." Aerospace 7, no. 11 (2020): 162. http://dx.doi.org/10.3390/aerospace7110162.

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In the present work, a reduced-order modeling (ROM) framework based on a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is applied for the computation of transonic aileron buzz. The training data set for the specified ROM is obtained by performing forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulations. Further, a Monte Carlo-based training procedure is applied in order to estimate statistical errors. In order to demonstrate the method’s fidelity, a two-dimensional aeroelastic model based on the NACA651213 airfoil

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Haida, Michal, Rafal Fingas, Wojciech Szwajnoch, et al. "An Object-Oriented R744 Two-Phase Ejector Reduced-Order Model for Dynamic Simulations." Energies 12, no. 7 (2019): 1282. http://dx.doi.org/10.3390/en12071282.

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The object-oriented two-phase ejector hybrid reduced-order model (ROM) was developed for dynamic simulation of the R744 refrigeration system. OpenModelica software was used to evaluate the system’s performance. Moreover, the hybrid ROM results were compared to the results given by the non-dimensional and one-dimensional mathematical approaches of the R744 two-phase ejector. Accuracy of all three ejector models was defined through a validation procedure for the experimental results. Finally, the dynamic simulation of the hybrid ROM ejector model integrated with the R744 refrigeration system was

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Chen, Gang, Yingtao Zuo, Jian Sun, and Yueming Li. "Support-Vector-Machine-Based Reduced-Order Model for Limit Cycle Oscillation Prediction of Nonlinear Aeroelastic System." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/152123.

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It is not easy for the system identification-based reduced-order model (ROM) and even eigenmode based reduced-order model to predict the limit cycle oscillation generated by the nonlinear unsteady aerodynamics. Most of these traditional ROMs are sensitive to the flow parameter variation. In order to deal with this problem, a support vector machine- (SVM-) based ROM was investigated and the general construction framework was proposed. The two-DOF aeroelastic system for the NACA 64A010 airfoil in transonic flow was then demonstrated for the new SVM-based ROM. The simulation results show that the

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Kheirabadi, Hassan, Roohollah Noori, Jamal M. V. Samani, Jan Franklin Adamowski, Mohammad Hassan Ranjbar, and Naser Hajizadeh Zaker. "A reduced-order model for the regeneration of surface currents in Gorgan Bay, Iran." Journal of Hydroinformatics 20, no. 6 (2018): 1419–35. http://dx.doi.org/10.2166/hydro.2018.149.

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Abstract This study developed a hydrodynamic reduced-order model (ROM) to regenerate surface currents in Gorgan Bay, Iran. The developed ROM was based on linking a three-dimensional hydrodynamic model, MIKE3-FM, with a data reduction technique, proper orthogonal decomposition (POD). The MIKE3-FM model was first run to simulate surface currents in the bay under a real wind scenario for two years starting July 1, 2010. Thereafter, time and space steps of 6 hours and 500 m, respectively, were chosen to capture 2,920 snapshots of the simulated surface currents using the MIKE3-FM model on 1,937 gri

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Girfoglio, Michele, Annalisa Quaini, and Gianluigi Rozza. "Pressure Stabilization Strategies for a LES Filtering Reduced Order Model." Fluids 6, no. 9 (2021): 302. http://dx.doi.org/10.3390/fluids6090302.

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We present a stabilized POD–Galerkin reduced order method (ROM) for a Leray model. For the implementation of the model, we combine a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method. In both steps of the EF algorithm, velocity and pressure fields are approximated using different POD basis and coefficients. To achieve pressure stabilization, we consider and compare two strategies: the pressure Poisson equation and the supremizer enrichment of the velocity space. We show that the evolve and filtered velocity spaces have to be enriched with the su

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Akkari, Nissrine, Fabien Casenave, Elie Hachem, and David Ryckelynck. "A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders." Fluids 7, no. 10 (2022): 334. http://dx.doi.org/10.3390/fluids7100334.

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This paper presents a new nonlinear projection based model reduction using convolutional Variational AutoEncoders (VAEs). This framework is applied on transient incompressible flows. The accuracy is obtained thanks to the expression of the velocity and pressure fields in a nonlinear manifold maximising the likelihood on pre-computed data in the offline stage. A confidence interval is obtained for each time instant thanks to the definition of the reduced dynamic coefficients as independent random variables for which the posterior probability given the offline data is known. The parameters of th

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Van Damme, Christopher, Alecio Madrid, Matthew Allen, and Joseph Hollkamp. "Simultaneous Regression and Selection in Nonlinear Modal Model Identification." Vibration 4, no. 1 (2021): 232–47. http://dx.doi.org/10.3390/vibration4010016.

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High fidelity finite element (FE) models are widely used to simulate the dynamic responses of geometrically nonlinear structures. The high computational cost of running long time duration analyses, however, has made nonlinear reduced order models (ROMs) attractive alternatives. While there are a variety of reduced order modeling techniques, in general, their shared goal is to project the nonlinear response of the system onto a smaller number of degrees of freedom. Implicit Condensation (IC), a popular and non-intrusive technique, identifies the ROM parameters by fitting a polynomial model to s

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Sampedro Llopis, Hermes, Allan P. Engsig-Karup, Cheol-Ho Jeong, Finnur Pind, and Jan S. Hesthaven. "Reduced basis methods for numerical room acoustic simulations with parametrized boundaries." Journal of the Acoustical Society of America 152, no. 2 (2022): 851–65. http://dx.doi.org/10.1121/10.0012696.

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The use of model-based numerical simulations of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iteratively, which is computationally expensive. We present a reduced basis method (RBM) to achieve a computational cost reduction relative to a traditional full-order model (FOM) for wave-based room acoustic simulations with parametrized boundaries. The FOM solver is based on the spectral-element method; however, other numerical methods could be applied. The RBM reduces the computational burden by solving the problem in

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Persoons, Tim, Rick Cressall, and Sajad Alimohammadi. "Validating a Reduced-Order Model for Synthetic Jet Actuators Using CFD and Experimental Data." Actuators 7, no. 4 (2018): 67. http://dx.doi.org/10.3390/act7040067.

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Synthetic jet actuators (SJA) are emerging in various engineering applications, from flow separation and noise control in aviation to thermal management of electronics. A SJA oscillates a flexible membrane inside a cavity connected to a nozzle producing vortices. A complex interaction between the cavity pressure field and the driving electronics can make it difficult to predict performance. A reduced-order model (ROM) has been developed to predict the performance of SJAs. This paper applies this model to a canonical configuration with applications in flow control and electronics cooling, consi

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Ahmed, Shady E., Omer San, Kursat Kara, Rami Younis, and Adil Rasheed. "Multifidelity computing for coupling full and reduced order models." PLOS ONE 16, no. 2 (2021): e0246092. http://dx.doi.org/10.1371/journal.pone.0246092.

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Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity de

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Degao, Hou, Qian Jiyu, and Wei Tao. "Digital twins design for water cold plate using reduced order model." Journal of Physics: Conference Series 2825, no. 1 (2024): 012009. http://dx.doi.org/10.1088/1742-6596/2825/1/012009.

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Abstract Water cold plates are widely used for heat dissipation in electronic devices. The design of water cold plats includes fluid characterization, heat transfer analysis, and fluid-solid coupling analysis, which are mostly carried out by fluid or thermal design software simulation, resulting in low simulation efficiency. This paper proposes a water cold plate simulation method based on digital twins, which realizes the downgrading of the traditional 3D CFD simulation model to a 1D mathematical model (ROM) and greatly shortens the thermal simulation calculation time. Meanwhile, real-time ma

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Daniel, Thomas, Fabien Casenave, Nissrine Akkari, David Ryckelynck, and Christian Rey. "Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models." Mechanics & Industry 23 (2022): 3. http://dx.doi.org/10.1051/meca/2022001.

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We consider the dictionary-based ROM-net (Reduced Order Model) framework [Daniel et al., Adv. Model. Simul. Eng. Sci. 7 (2020) https://doi.org/10.1186/s40323-020-00153-6] and summarize the underlying methodologies and their recent improvements. The object of interest is a real-life industrial model of an elastoviscoplastic high-pressure turbine blade subjected to thermal, centrifugal and pressure loadings. The main contribution of this work is the application of the complete ROM-net workflow to the quantification of the uncertainty of dual quantities on this blade (such as the accumulated plas

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Shamim, Muhammad Babar, Marian Hörsting, and Stephan Wulfinghoff. "Variational Reduced-Order Modeling of Thermomechanical Shape Memory Alloy Based Cooperative Bistable Microactuators." Actuators 12, no. 1 (2023): 36. http://dx.doi.org/10.3390/act12010036.

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This article presents the formulation and application of a reduced-order thermomechanical finite strain shape memory alloy (SMA)-based microactuator model for switching devices under thermal loading by Joule heating. The formulation is cast in the generalized standard material framework with an extension for thermomechanics. The proper orthogonal decomposition (POD) is utilized for capturing a reduced basis from a precomputed finite element method (FEM) full-scale model. The modal coefficients are computed by optimization of the underlying incremental thermomechanical potential, and the weak f

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Sanayei, Masoud, Akbar Esfandiari, Maryam Vahedi, Saeed Behboodi, and Iman Dabbaghchian. "Modeling error assessment of reduced-order finite element models for SHM: A case study of Milad Tower." Journal of Physics: Conference Series 2647, no. 2 (2024): 022005. http://dx.doi.org/10.1088/1742-6596/2647/2/022005.

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Abstract Condition assessment, model updating, and basic studies of large and complex structures subjected to static and dynamic loadings necessitate establishing a reasonably low order model to enhance computational efficiency. An essential requirement for the development of a low-order model is to preserve the main characteristics of the full-scale model within the frequency range of interest or the desired static and/or dynamic response. In this regard, a novel reduced-order model (ROM) is developed based on a full-scale model (FSM) built using commercial finite element analysis software. U

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CHEN, GANG, YUE-MING LI, and GUI-RONG YAN. "LIMIT CYCLE OSCILLATION PREDICTION AND CONTROL DESIGN METHOD FOR AEROELASTIC SYSTEM BASED ON NEW NONLINEAR REDUCED ORDER MODEL." International Journal of Computational Methods 08, no. 01 (2011): 77–90. http://dx.doi.org/10.1142/s0219876211002435.

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When the amplitude of the oscillation of the unsteady flow is large or there is large perturbation relative to the mean background flow, the traditional proper orthogonal decomposition/reduced order model (POD/ROM) based on linearized time or frequency domain small disturbance solvers cannot capture the main nonlinear features well such as limit cycle oscillation (LCO), which is very dangerous for the structure. Therefore, the traditional linear ROMs are not good enough for limit cycles prediction and active control law design. A new nonlinear ROM based on dynamically nonlinear flow equation N

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Plas, Wito, Toon Demeester, and Michel De Paepe. "Building a reduced order model from CFD data on leaves to evaluate optimal climate conditions." Journal of Physics: Conference Series 2766, no. 1 (2024): 012091. http://dx.doi.org/10.1088/1742-6596/2766/1/012091.

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Abstract Climate control in vertical farms or greenhouses is crucial in order to have the optimal climate for the plants. This climate can be simulated using Computational Fluid Dynamics (CFD). This way, different ventilation methods can for example be compared. Previously, plants were modeled as a porous zone, in which additional source terms are added to the energy or transpiration transport equation. In this study, ten different leaves under different orientations are studied. The heat and mass balance for each leaf is solved and the boundary layer is adequately resolved. Different leaf ori

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Cha, Kyunghwan, Gyeonghyeok Kang, Myungsung Lee, and Joo-Han Kim. "Indoor HVAC Prediction in Multi-Use Building Using Reduced Order Model." Transactions of the KSME C Industrial Technology and Innovation 12, no. 2 (2024): 135–47. http://dx.doi.org/10.3795/ksme-c.2024.12.2.135.

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Gautam, Rudar Kumar, Nitin Singh, Niraj Kumar Choudhary, and Anirudha Narain. "Model order reduction using factor division algorithm and fuzzy c-means clustering technique." Transactions of the Institute of Measurement and Control 41, no. 2 (2018): 468–75. http://dx.doi.org/10.1177/0142331218762605.

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This paper proposes a novel hybrid approach that combines factor division algorithm and fuzzy c-means clustering technique for reducing the model order of high-order linear time invariant system. The process of clustering is used for finding the group of objects with similar nature that can be differentiated from the other dissimilar objects. The numerator of the higher order model is reduced using the factor division algorithm and the denominator of the higher order model is reduced using the fuzzy c-means clustering technique. The stability of the model is also verified using the pole zero s

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Ahn, Seokho, Jaesung Kim, Seokheum Baek, Cheolyong Kim, Hyunsoo Jang, and Seojin Lee. "Toward Digital Twin Development for Implant Placement Planning Using a Parametric Reduced-Order Model." Bioengineering 11, no. 1 (2024): 84. http://dx.doi.org/10.3390/bioengineering11010084.

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Real-time stress distribution data for implants and cortical bones can aid in determining appropriate implant placement plans and improving the post-placement success rate. This study aims to achieve these goals via a parametric reduced-order model (ROM) method based on stress distribution data obtained using finite element analysis. For the first time, the finite element analysis cases for six design variables related to implant placement were determined simultaneously via the design of experiments and a sensitivity analysis. The differences between the minimum and maximum stresses obtained f

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Bladh, R., M. P. Castanier, and C. Pierre. "Reduced Order Modeling and Vibration Analysis of Mistuned Bladed Disk Assemblies With Shrouds." Journal of Engineering for Gas Turbines and Power 121, no. 3 (1999): 515–22. http://dx.doi.org/10.1115/1.2818503.

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This paper presents important improvements and extensions to a computationally efficient reduced order modeling technique for the vibration analysis of mistuned bladed disks. In particular, this work shows how the existing modeling technique is readily extended to turbomachinery rotors with shrouded blades. The modeling technique employs a component mode synthesis approach to systematically generate a reduced order model (ROM) using component modes calculated from a finite element model (FEM) of the rotor. Based on the total number of degrees of freedom, the ROM is typically two or three order

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Yu, Ming, Wei-Xi Huang, and Chun-Xiao Xu. "Data-driven construction of a reduced-order model for supersonic boundary layer transition." Journal of Fluid Mechanics 874 (July 15, 2019): 1096–114. http://dx.doi.org/10.1017/jfm.2019.470.

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In this study, a data-driven method for the construction of a reduced-order model (ROM) for complex flows is proposed. The method uses the proper orthogonal decomposition (POD) modes as the orthogonal basis and the dynamic mode decomposition method to obtain linear equations for the temporal evolution coefficients of the modes. This method eliminates the need for the governing equations of the flows involved, and therefore saves the effort of deriving the projected equations and proving their consistency, convergence and stability, as required by the conventional Galerkin projection method, wh

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Shu, Jung I., Yi Wang, William C. Krolick, and Kapil Pant. "Parametric Aeroelastic Reduced-Order Model with State-Consistence Enforcement." AIAA Journal, December 22, 2022, 1–20. http://dx.doi.org/10.2514/1.j062274.

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State-space reduced-order models (ROMs) constructed by traditional system identification methods suffer from the state-inconsistence issue and poor ROM interpolatability for varying flight conditions. This paper presents a novel system identification method and a state-consistence enforcement (SCE) algorithm to generate a state-space aeroelastic (AE)-ROM within a broad flight parameter space. A new regularization term is proposed to modify the traditional autoregressive exogenous formulation and specifically penalize state inconsistence between ROMs at varying flight conditions. The new formul

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Coscia, Dario, Nicola Demo, and Gianluigi Rozza. "Generative adversarial reduced order modelling." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-54067-z.

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AbstractIn this work, we present GAROM, a new approach for reduced order modeling (ROM) based on generative adversarial networks (GANs). GANs attempt to learn to generate data with the same statistics of the underlying distribution of a dataset, using two neural networks, namely discriminator and generator. While widely applied in many areas of deep learning, little research is done on their application for ROM, i.e. approximating a high-fidelity model with a simpler one. In this work, we combine the GAN and ROM framework, introducing a data-driven generative adversarial model able to learn so

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Bai, Feng, and Yi Wang. "DEIM-embedded hybrid snapshot simulation for reduced order model generation." Engineering Computations, November 2, 2022. http://dx.doi.org/10.1108/ec-11-2021-0647.

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PurposeThe purpose of this paper is to establish an intelligent framework to generate the data representatives in snapshot simulation in order to construct the online reduced-order model based on the generated data information. It could greatly reduce the computational time in snapshot simulation and accelerate the computational efficiency in the real-time computation of reduced-order modeling.Design/methodology/approachThe snapshot simulation, which generates the data to construct reduced-order models (ROMs), usually is computationally demanding. In order to accelerate the snapshot generation

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Borcea, Liliana, Josselin Garnier, Alexander V. Mamonov, and Jörn Zimmerling. "Waveform inversion via reduced order modeling." GEOPHYSICS, November 24, 2022, 1–91. http://dx.doi.org/10.1190/geo2022-0070.1.

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We introduce a novel approach to waveform inversion, based on a data driven reduced order model (ROM) of the wave operator. The presentation is for the acoustic wave equation, but the approach can be extended to elastic or electromagnetic waves. The data are time resolved measurements of the pressure wave gathered by an acquisition system which probes the unknown medium with pulses and measures the generated waves. We propose to solve the inverse problem of velocity estimation by minimizing the square misfit between the ROM computed from the recorded data and the ROM computed from the modeled

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Ocana, M., D. Alonso, and A. Velazquez. "Reduced Order Model for a Power-Law Fluid." Journal of Fluids Engineering 136, no. 7 (2014). http://dx.doi.org/10.1115/1.4026666.

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This article describes the development of a reduced order model (ROM) based on residual minimization for a generic power-law fluid. The objective of the work is to generate a methodology that allows for the fast and accurate computation of polymeric flow fields in a multiparameter space. It is shown that the ROM allows for the computation of the flow field in a few seconds, as compared with the use of computational fluid dynamics (CFD) methods in which the central processing unit (CPU) time is on the order of hours. The model fluid used in the study is a polymeric fluid characterized by both i

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Kitsios, Vassili, Laurent Cordier, and Terence J. O’Kane. "Proper orthogonal decomposition reduced-order model of the global oceans." Theoretical and Computational Fluid Dynamics, September 19, 2024. http://dx.doi.org/10.1007/s00162-024-00719-9.

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AbstractA reduced-order model (ROM) of the global oceans is developed by projecting the hydrostatic Boussinesq equations of motion onto a proper orthogonal decomposition (POD) basis. Three-dimensional POD modes are calculated from the ocean fields of an ensemble climate reanalysis dataset. The coefficients in the POD ROM are calculated using a regression approach. The performance of various POD ROM configurations are assessed. Each configuration is derived from an alternate sea-water equation of state, linking the density and temperature fields. POD ROM variants incorporating an equation of st

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Kadeethum, Teeratorn, Daniel O’Malley, Francesco Ballarin, et al. "Enhancing high-fidelity nonlinear solver with reduced order model." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-22407-6.

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AbstractWe propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM’s prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions. To evaluate our approach, four physical problems with varying d

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Beck, Joseph A., Jeffrey M. Brown, Alex A. Kaszynski, Emily B. Carper, and Daniel L. Gillaugh. "Geometric Mistuning Reduced-Order Model Development Utilizing Bayesian Surrogate Models for Component Mode Calculations." Journal of Engineering for Gas Turbines and Power 141, no. 10 (2019). http://dx.doi.org/10.1115/1.4044454.

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AbstractIntegrally bladed rotors (IBRs) are meant to be rotationally periodic structures. However, unique variations in geometries and material properties from sector-to-sector, called mistuning, destroy the structural periodicity. This results in mode localization that can induce forced response levels greater than what is predicted with a tuned analysis. Furthermore, mistuning and mode localization are random processes that require stochastic treatments when analyzing the distribution of fleet responses. Generating this distribution can be computationally intensive when using the full finite

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Kim, Jin-Gyun, M. Faizan Baqir, and K. C. Park. "Method for Reduced-Order Modeling with Mode Selection Criterion." AIAA Journal, August 5, 2024, 1–13. http://dx.doi.org/10.2514/1.j064414.

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A reduced-order modeling (partitioned reduced-order modeling [P-ROM]) method for structural systems is presented, which employs recently developed displacement-only partitioned (DP) equations of motion. It is shown that the DP equations directly yield the same eigenvalues as the assembled equations of finite element model, and the corresponding eigenvectors are a partitioned form of the assembled eigenvectors. A unique feature of the proposed P-ROM method is its a priori mode selection criterion that can be utilized with the desired target accuracy assigned by the modeler. Thus, the proposed P

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