A Review of Computational Methods and Reduced Order Models for Flutter Prediction in Turbomachinery

Aeroelastic phenomena in turbomachinery are one of the most challenging problems to model using computational fluid dynamics (CFD) due to their inherent nonlinear nature, the difficulties in simulating fluid–structure interactions and the considerable computational requirements. Nonetheless, accurate modelling of self-sustained flow-induced vibrations, known as flutter, has proved to be crucial in assessing stability boundaries and extending the operative life of turbomachinery. Flutter avoidance and control is becoming more relevant in compressors and fans due to a well-established trend towards lightweight and thinner designs that enhance aerodynamic efficiency. In this paper, an overview of computational techniques adopted over the years is first presented. The principal methods for flutter modelling are then reviewed; a classification is made to distinguish between classical methods, where the fluid flow does not interact with the structure, and coupled methods, where this interaction is modelled. The most used coupling algorithms along with their benefits and drawbacks are then described. Finally, an insight is presented on model order reduction techniques applied to structure and aerodynamic calculations in turbomachinery flutter simulations, with the aim of reducing computational cost and permitting treatment of complex phenomena in a reasonable time.

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Model reduction of aerothermodynamic for hypersonic aerothermoelasticity based on POD and Chebyshev method

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410018808634 ◽

2018 ◽

Vol 233 (10) ◽

pp. 3734-3748

Author(s):

Yan Xiaoxuan ◽

Han Jinglong ◽

Zhang Bing ◽

Yuan Haiwei

Keyword(s):

Computational Cost ◽

Maximum Error ◽

Control Surface ◽

Alternative Approach ◽

Average Maximum ◽

Optimization And Control ◽

Efficient Alternative ◽

Computational Fluid Dynamics Cfd ◽

And Control ◽

Proper Orthogonal

Accurate modeling of aerothermodynamics with low computational cost takes on a crucial role for the optimization and control of hypersonic vehicles. This study examines three reduced-order models (ROMs) to provide a reliable and efficient alternative approach for obtaining the aerothermodynamics of a hypersonic control surface. Coupled computational fluid dynamics (CFD) and computational thermostructural dynamics (CTSD) approaches are used to generate the snapshots for ROMs considering the interactions between aerothermodynamics, structural dynamics and heat transfer. One ROM adopts a surrogate approach named Kriging. The second ROM is constructed by the combination of Proper Orthogonal Decomposition (POD) and Kriging, namely, POD-Kriging. The accuracy of Kriging-based ROM is higher than that of POD-Kriging-based ROM, but the efficiency is lower. Therefore, to address the shortcomings of the above two approaches, a new ROM is developed that is composed of POD and modified Chebyshev polynomials, namely, POD-Chebyshev. The ROM based on POD-Chebyshev has the best precision and efficiency among the three ROMs and generally has less than 2% average maximum error for the studied problem.

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(Generalized) Bloch mode synthesis for the fast dispersion curve calculation of 3D periodic metamaterials

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2052 ◽

2021 ◽

Vol 263 (4) ◽

pp. 2102-2113

Author(s):

Vanessa Cool ◽

Lucas Van Belle ◽

Claus Claeys ◽

Elke Deckers ◽

Wim Desmet

Keyword(s):

Dispersion Curve ◽

Periodic Structures ◽

Cell Model ◽

Stop Band ◽

Computational Cost ◽

Order Reduction ◽

Element Model ◽

Unit Cell ◽

Reduction Techniques ◽

Bloch Mode

Metamaterials, i.e. artificial structures with unconventional properties, have shown to be highly potential lightweight and compact solutions for the attenuation of noise and vibrations in targeted frequency ranges, called stop bands. In order to analyze the performance of these metamaterials, their stop band behavior is typically predicted by means of dispersion curves, which describe the wave propagation in the corresponding infinite periodic structure. The input for these calculations is usually a finite element model of the corresponding unit cell. Most common in literature are 2D plane metamaterials, which often consist of a plate host structure with periodically added masses or resonators. In recent literature, however, full 3D metamaterials are encountered which are periodic in all three directions and which enable complete, omnidirectional stop bands. Although these 3D metamaterials have favorable vibro-acoustic characteristics, the computational cost to analyze them quickly increases with unit cell model size. Model order reduction techniques are important enablers to overcome this problem. In this work, the Bloch Mode Synthesis (BMS) and generalized BMS (GBMS) reduction techniques are extended from 2D to 3D periodic structures. Through several verifications, it is demonstrated that dispersion curve calculation times can be strongly reduced, while accurate stop band predictions are maintained.

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Advances in Hierarchical Model Reduction and combination with other computational reduction methods

10.14293/p2199-8442.1.sop-math.jownzf.v1 ◽

2018 ◽

Author(s):

Giulia Meglioli ◽

Matteo Zancanaro ◽

Francesco Ballarin ◽

Simona Perotto ◽

Gianluigi Rozza

Keyword(s):

Hierarchical Model ◽

Computational Cost ◽

Order Reduction ◽

Reduced Basis ◽

Element Discretization ◽

Modal Expansion ◽

Reduction Techniques ◽

Computational Performance ◽

Reduction Methods ◽

Computational Reduction

In this work we present address the combination of the Hierarchical Model (Hi-Mod) reduction approach with projection-based reduced order methods, exploiting either on Greedy Reduced Basis (RB) or Proper Orthogonal Decomposition (POD), in a parametrized setting. The Hi-Mod approach, introduced in, is suited to reduce problems in pipe-like domains featuring a dominant axial dynamics, such as those arising for instance in haemodynamics. The Hi-Mod approach aims at reducing the computational cost by properly combining a finite element discretization of the dominant dynamics with a modal expansion in the transverse direction. In a parametrized context, the Hi-Mod approach has been employed as the high-fidelity method during the offline stage of model order reduction techniques based on RB or POD. The resulting combined reduction methods, which have been named Hi-RB and Hi-POD, respectively, will be presented with applications in diffusion-advection problems, fluid dynamics and optimal control problems, focusing on the approximation stability of the proposed methods and their computational performance.

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Trim Influence on Kriso Container Ship (KCS): An Experimental and Numerical Study

Volume 7A: Ocean Engineering ◽

10.1115/omae2017-61860 ◽

2017 ◽

Cited By ~ 1

Author(s):

Emil Shivachev ◽

Mahdi Khorasanchi ◽

Alexander H. Day

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Numerical Study ◽

Computational Cost ◽

Breaking Waves ◽

Computational Techniques ◽

Container Ship ◽

Towing Tank ◽

Operational Life ◽

Computational Fluid Dynamics Cfd

There has been a lot of interest in trim optimisation to reduce fuel consumption and emissions of ships. Many existing ships are designed for a single operational condition with the aim of producing low resistance at their design speed and draft with an even keel. Given that a ship will often sail outside this condition over its operational life and moreover some vessels such as LNG carriers return in ballast condition in one leg, the effect of trim on ships resistance will be significant. Ship trim optimization analysis has traditionally been done through towing tank testing. Computational techniques have become increasingly popular for design and optimization applications in all engineering disciplines. Computational Fluid Dynamics (CFD), is the fastest developing area in marine fluid dynamics as an alternative to model tests. High fidelity CFD methods are capable of modelling breaking waves which is especially crucial for trim optimisation studies where the bulbous bow partially emerges or the transom stern partially immerses. This paper presents a trim optimization study on the Kriso Container Ship (KCS) using computational fluid dynamics (CFD) in conjunction with towing tank tests. A series of resistance tests for various trim angles and speeds were conducted at 1:75 scale at design draft. CFD computations were carried out for the same conditions with the hull both fixed and free to sink and trim. Dynamic sinkage and trim add to the computational cost and thus slow the optimisation process. The results obtained from CFD simulations were in good agreement with the experiments. After validating the applicability of the computational model, the same mesh, boundary conditions and solution techniques were used to obtain resistance values for different trim conditions at different Froude numbers. Both the fixed and free trim/sinkage models could predict the trend of resistance with variation of trim angles; however the fixed model failed to measure the absolute values as accurately as the free model. It was concluded that a fixed CFD model, although computationally faster and cheaper, can find the optimum trim angle but cannot predict the amount of savings with very high accuracy. Results concerning the performance of the vessel at different speeds and trim angles were analysed and optimum trim is suggested.

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Faster prediction of wildfire behaviour by physical models through application of proper orthogonal decomposition

International Journal of Wildland Fire ◽

10.1071/wf15150 ◽

2016 ◽

Vol 25 (11) ◽

pp. 1181 ◽

Cited By ~ 8

Author(s):

Elisa Guelpa ◽

Adriano Sciacovelli ◽

Vittorio Verda ◽

Davide Ascoli

Keyword(s):

Proper Orthogonal Decomposition ◽

Physical Model ◽

Computational Cost ◽

Computation Time ◽

Order Reduction ◽

Orthogonal Decomposition ◽

Physical Models ◽

Reduction Techniques ◽

Rate Of Spread ◽

Proper Orthogonal

Physical models of wildfires are of particular interest in fire behaviour research and have applications in firefighting, rescue and evacuation. However, physical models present a challenge as a result of the large computational resources they often require, especially for the analysis of large areas or when multiple scenarios are investigated. The objective of this paper is to explore the opportunity to reduce the computation time requested by physical wildfire models through application of a model order reduction technique, specifically the proper orthogonal decomposition (POD) technique. POD is here applied to a simple one-dimensional physical model. The full physical model for illustration of the concept is first tested with experimental data to check its ability to simulate wildfire behaviour; it is then reduced using the POD technique. It is shown that the reduced model is able to simulate fire propagation with only small deviations in results in comparison with the physical model (~6.4% deviation in the rate of spread, ROS) and a drastic reduction (~85%) in computational cost. The results demonstrate the advantages of applying effective reduction techniques to create new generations of fire models based on reduced physical approaches. The potential applicability of POD to more complex models is also discussed.

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Model Order Reduction in Computational Multiscale Fracture Mechanics

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.713.248 ◽

2016 ◽

Vol 713 ◽

pp. 248-253

Author(s):

M. Caicedo ◽

J. Oliver ◽

A.E. Huespe ◽

O. Lloberas-Valls

Keyword(s):

Model Order Reduction ◽

High Performance ◽

Computational Cost ◽

Order Reduction ◽

Cementitious Materials ◽

Reduced Model ◽

Computational Time ◽

Strong Discontinuity ◽

Model Order ◽

Reduction Techniques

Nowadays, the model order reduction techniques have become an intensive research eld because of the increasing interest in the computational modeling of complex phenomena in multi-physic problems, and its conse- quent increment in high-computing demanding processes; it is well known that the availability of high-performance computing capacity is, in most of cases limited, therefore, the model order reduction becomes a novelty tool to overcome this paradigm, that represents an immediately challenge in our research community. In computational multiscale modeling for instance, in order to study the interaction between components, a di erent numerical model has to be solved in each scale, this feature increases radically the computational cost. We present a reduced model based on a multi-scale framework for numerical modeling of the structural failure of heterogeneous quasi-brittle materials using the Strong Discontinuity Approach (CSD). The model is assessed by application to cementitious materials. The Proper Orthogonal Decomposition (POD) and the Reduced Order Integration Cubature are the pro- posed techniques to develop the reduced model, these two techniques work together to reduce both, the complexity and computational time of the high-delity model, in our case the FE2 standard model

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Advanced Model Order Reduction Techniques in VLSI Design

10.1017/cbo9780511541117 ◽

2007 ◽

Cited By ~ 77

Author(s):

Sheldon Tan ◽

Lei He

Keyword(s):

Model Order Reduction ◽

Vlsi Design ◽

Order Reduction ◽

Model Order ◽

Reduction Techniques ◽

Advanced Model

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Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

10.26434/chemrxiv.13047863 ◽

2020 ◽

Author(s):

Jingbai Li ◽

Patrick Reiser ◽

André Eberhard ◽

Pascal Friederich ◽

Steven Lopez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Excited State ◽

Adaptive Sampling ◽

Computational Cost ◽

Ground Truth ◽

Absolute Error ◽

Photochemical Reactions ◽

Computational Techniques ◽

Full Potential

Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries.

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