Article
Mechanics
Shijun Chu, Chao Xia, Hanfeng Wang, Yajun Fan, Zhigang Yang
Summary: The study reveals that the seal-vibrissa-shaped cylinder has a more stable three-dimensional separation wake, longer vortex formation length, and weaker vortex strength compared to a circular cylinder at a Reynolds number of 20000. The mean drag and fluctuation of the lift coefficient of the seal-vibrissa-shaped cylinder are significantly reduced, and SPOD can extract four typical vortex shedding patterns.
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
Engineering, Marine
Yan Zheng, Dan Zhang, Tianbo Wang, Hiroka Rinoshika, Akira Rinoshika
Summary: In this study, a hybrid two-dimensional orthogonal wavelet multiresolution and proper orthogonal decomposition technique is developed to analyze the wake flow behind a semi cylinder. The study investigates the modal energy distributions, flow patterns, and PSD distributions of multi-scale flow structures using the proposed technique. The results show the changes in dominance, symmetry, and energy distribution of the flow structures as they vary from large-scale to small-scale.
Article
Computer Science, Interdisciplinary Applications
Elizabeth H. Krath, Forrest L. Carpenter, Paul G. A. Cizmas, David A. Johnston
Summary: This study introduces a novel, more efficient reduced-order model for compressible flows based on proper orthogonal decomposition (POD). By using specific volume instead of density, the coefficients of the system of ODEs in the reduced-order model were pre-computed. Various methods were used to enhance ODE solver stability. Validation was done for two cases, showing a speedup exceeding four orders of magnitude compared to the full-order model.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Mechanics
Marc Olbrich, Markus Baer, Kilian Oberleithner, Sonja Schmelter
Summary: This study investigates different cases of slug flow in horizontal pipes to find statistical characteristics of the slugs in time and space, including slug frequencies, averaged slug body length, and an energy representation. The use of snapshot proper orthogonal decomposition with an additional mode coupling algorithm provides accurate and reliable characterization of the slug flow patterns.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2021)
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
Mathematics, Applied
Xi Li, Yan Luo, Minfu Feng
Summary: In this paper, an efficient proper orthogonal decomposition based reduced-order model (POD-ROM) for nonstationary Stokes equations is proposed. The new scheme combines the classical projection method with POD technique, resulting in low computational costs and improved efficiency.
JOURNAL OF SCIENTIFIC COMPUTING
(2022)
Article
Mathematics, Applied
Y. Zhang, M. Vanierschot
Summary: A three-dimensional incompressible annular jet was simulated using the large eddy simulation (LES) method at a Reynolds number Re = 8 500. The study focused on the flow dynamics of the wake flow, with particular attention to the proper orthogonal decomposition (POD) analysis of the velocity fluctuation vectors. The results showed that the first four eigenmodes captured significant turbulent kinetic energy, impacting the wake dynamics, with modes 1 and 2 related to a radial shift of the stagnation point, and modes 3 and 4 involving stretching or squeezing effects in the radial direction of the recirculation region.
APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION
(2021)
Article
Engineering, Multidisciplinary
Spenser Anderson, Cristina White, Charbel Farhat
Summary: This article presents an alternative and complementary approach to accelerate projection-based model order reduction (PMOR) methods by introducing sparsity into the reduced-order basis. The proposed approach enhances computational efficiency by partitioning the computational domain and demonstrates significant acceleration and CPU time speedup compared to high-dimensional models in turbulent flow applications.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2023)
Article
Mechanics
Muting Hao, Joshua Hope-Collins, Luca di Mare
Summary: This study presents a novel synthetic inflow generator that can produce a random field matching realistic two-point statistics with minimal input. The method is based on inferring realistic two-point covariance tensors from available data and efficient eigen-decomposition of the correlation tensor. The proposed method is shown to generate realistic turbulent flow and has the potential for generalization to complex geometries.
Article
Mechanics
Zhengxiao Ma, Jian Yu, Ruoye Xiao
Summary: This paper proposes a nonintrusive reduced basis (RB) method based on dynamic mode decomposition (DMD) for parameterized time-dependent flows. The offline stage involves extracting the reduced basis functions and introducing a novel hybrid DMD regression model for the temporal evolution of the RB coefficients. To enhance stability for complex nonlinear problems, a threshold value is used to modify the DMD eigenvalues and eigenvectors. Additionally, interpolation of the coefficients in parameter space is performed using a feedforward neural network or random forest algorithm. The online stage enables the prediction of the RB solution at a new time/parameter value with low computational cost and complete decoupling from the high-fidelity dimension. The proposed model is demonstrated with two cases, showing reasonable efficiency and robustness.
Article
Mathematics, Applied
Xiang Sun, Xiaomin Pan, Jung-Il Choi
Summary: The proposed method utilizes POD and PCE to construct an efficient stochastic representation model through non-intrusive methods, significantly reducing computational costs and storage requirements for high-dimensional physical and random spaces, while demonstrating similar accuracy in predicting statistical quantities as classical sparse PCE.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2021)
Article
Thermodynamics
Jiacheng Ma, Donghun Kim, James E. Braun
Summary: This paper presents a computationally efficient and accurate dynamic modeling approach for vapor compression systems using model order reduction techniques. By reformulating the heat exchanger model and applying POD, reduced order models for evaporator and condenser are constructed with system stability and numerical efficiency in mind. Transient simulations conducted under various operating conditions show that the reduced order model can execute faster with negligible prediction errors compared to the high-fidelity finite volume model.
INTERNATIONAL JOURNAL OF REFRIGERATION
(2021)
Article
Mathematics, Applied
Birgul Koc, Tomas Chacon Rebollo, Samuele Rubino
Summary: In this paper, we provide evidence of uniform error bounds for proper orthogonal decomposition (POD) reduced order modeling (ROM) of the Burgers equation with the inclusion of difference quotients (DQs). Our study focuses on the behavior of DQ ROM error bounds using different POD spaces and error measures. Numerical tests show that DQ ROM errors are significantly smaller than noDQ errors, and the addition of DQs in the POD process leads to an optimality/super-optimality behavior.
JOURNAL OF SCIENTIFIC COMPUTING
(2023)
Article
Thermodynamics
R. Kapulla, K. H. Manohar, S. Paranjape, D. Paladino
Summary: The study investigates the self-similar behavior of statistical quantities derived from a jet flow using both original data and low-order representations obtained from Proper Orthogonal Decomposition (POD). It is found that statistical properties obtained from low-order representations resemble the shape of the original jet and exhibit asymptotic states with increasing downstream distances. These findings suggest that self-similar behavior is mainly controlled by large-scale vortices, with the exception of axial velocity root-mean-square values.
EXPERIMENTAL THERMAL AND FLUID SCIENCE
(2021)
Article
Mechanics
Nicholas Hamilton, Murat Tutkun, Raul Bayoan Cal
Article
Mechanics
Nicholas Hamilton, Murat Tutkun, Raul Bayoan Cal
Article
Green & Sustainable Science & Technology
Naseem Ali, Nicholas Hamilton, Gerard Cortina, Marc Calaf, Raul Bayoan Cal
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2018)
Article
Energy & Fuels
Nicholas Hamilton, Bianca Viggiano, Marc Calaf, Murat Tutkun, Raul Bayoan Cal
Article
Mechanics
Naseem Ali, Nicholas Hamilton, Marc Calaf, Raul Bayoan Cal
JOURNAL OF TURBULENCE
(2019)
Article
Energy & Fuels
Paula Doubrawa, Eliot W. Quon, Luis A. Martinez-Tossas, Kelsey Shaler, Mithu Debnath, Nicholas Hamilton, Thomas G. Herges, Dave Maniaci, Christopher L. Kelley, Alan S. Hsieh, Myra L. Blaylock, Paul Laan, Soren Juhl Andersen, Sonja Krueger, Marie Cathelain, Wolfgang Schlez, Jason Jonkman, Emmanuel Branlard, Gerald Steinfeld, Sascha Schmidt, Frederic Blondel, Laura J. Lukassen, Patrick Moriarty
Editorial Material
Green & Sustainable Science & Technology
Nicholas Hamilton, Dennice Gayme, Raul Bayoan Cal
Summary: The development of operational strategies for wind farms has attracted a lot of attention in recent years. The focus has been on achieving various goals as an integrated plant system. Wake models and new approaches to wake steering play a crucial role in improving control performance.
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2022)
Editorial Material
Green & Sustainable Science & Technology
Majid Bastankhah, Nicholas Hamilton, Raul Bayoan Cal
Summary: The interaction of wind turbines with turbulent atmospheric boundary layer flows is a complex multi-scale problem. Wind tunnel experiments are valuable tools for studying wind energy aerodynamics.
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2022)
Article
Green & Sustainable Science & Technology
Zein Sadek, Ryan Scott, Nicholas Hamilton, Raul Bayoan Cal
Summary: A new three-dimensional steady-state wake model is proposed, improving the wake description by incorporating local flow acceleration near the rotor. Compound and normal Gaussian functions are used to concisely describe wake structures such as momentum deficit and regions of accelerated flow. With the use of large-eddy simulations as training data, the model is developed for two in-line turbines under various inflow conditions. It demonstrates comparable or even better performance in terms of relative error and mass consistency compared to existing work, and shows a high degree of flexibility in scaling across different inflow conditions by utilizing empirical correlations.
Article
Green & Sustainable Science & Technology
Nicholas Hamilton, Christopher J. Bay, Paul Fleming, Jennifer King, Luis A. Martinez-Tossas
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2020)
Proceedings Paper
Energy & Fuels
K. Shaler, J. Jonkman, N. Hamilton
Proceedings Paper
Engineering, Aerospace
J. Jonkman, P. Doubrawa, N. Hamilton, J. Annoni, P. Fleming
SCIENCE OF MAKING TORQUE FROM WIND (TORQUE 2018)
(2018)
Article
Green & Sustainable Science & Technology
Naseem Ali, Nicholas Hamilton, Dominic DeLucia, Raul Bayoan Cal
WIND ENERGY SCIENCE
(2018)
Article
Mechanics
N. Ali, G. Cortina, N. Hamilton, M. Calaf, R. B. Cal
JOURNAL OF FLUID MECHANICS
(2017)