scholarly journals Pressure Stabilization Strategies for a LES Filtering Reduced Order Model

Fluids ◽  
2021 ◽  
Vol 6 (9) ◽  
pp. 302 ◽  
Author(s):  
Michele Girfoglio ◽  
Annalisa Quaini ◽  
Gianluigi Rozza
Keyword(s):  
Poisson Equation ◽  
Computationally Efficient ◽  
Pressure Poisson Equation ◽  
Reduced Order ◽  
Pressure Fields ◽  
Flow Past A Cylinder ◽  
Pressure Stabilization ◽  
Step Algorithm ◽  
Lift And Drag ◽  
Volume Method

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 supremizer solutions related to both evolve and filter pressure fields in order to obtain stable and accurate solutions with the supremizer enrichment method. We test our ROM approach on a 2D unsteady flow past a cylinder at Reynolds number 0≤Re≤100. We find that both stabilization strategies produce comparable errors in the reconstruction of the lift and drag coefficients, with the pressure Poisson equation method being more computationally efficient.

Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

2021 ◽  
pp. 095441002199986
Author(s):  
Hassan F Ahmed ◽  
Hamayun Farooq ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Stokes Equations ◽  
Decomposition Analysis ◽  
Hydrodynamic Forces ◽  
Reduced Order Modeling ◽  
Navier Stokes ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

scholarly journals A Hybrid Analytics Paradigm Combining Physics-Based Modeling and Data-Driven Modeling to Accelerate Incompressible Flow Solvers

Fluids ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 50 ◽  
Author(s):  
Sk. Rahman ◽  
Adil Rasheed ◽  
Omer San
Keyword(s):  
Poisson Equation ◽  
Incompressible Flow ◽  
Data Exchange ◽  
Incompressible Flows ◽  
Stokes Equations ◽  
Time Integration ◽  
Data Driven ◽  
Integration Algorithm ◽  
Reduced Order ◽  
Advection Diffusion Equation

Numerical solution of the incompressible Navier–Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the underlying time integration algorithm, which constitutes the major component of the computational expense. In this study, we propose a hybrid analytics procedure combining a data-driven approach with a physics-based simulation technique to accelerate the computation of incompressible flows. In our approach, proper orthogonal basis functions are generated to be used in solving the Poisson equation in a reduced order space. Since the time integration of the advection–diffusion equation part of the physics-based model is computationally inexpensive in a typical incompressible flow solver, it is retained in the full order space to represent the dynamics more accurately. Encoder and decoder interface conditions are provided by incorporating the elliptic constraint along with the data exchange between the full order and reduced order spaces. We investigate the feasibility of the proposed method by solving the Taylor–Green vortex decaying problem, and it is found that a remarkable speed-up can be achieved while retaining a similar accuracy with respect to the full order model.

Effects of Airfoil’s Polar Data in the Stall Region on the Estimation of Darrieus Wind Turbine Performance

2016 ◽  
Author(s):  
David Marten ◽  
Alessandro Bianchini ◽  
Georgios Pechlivanoglou ◽  
Francesco Balduzzi ◽  
Christian Navid Nayeri ◽  
...  
Keyword(s):  
Unsteady Aerodynamics ◽  
Vertical Axis ◽  
Design Guidelines ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Simulation Methods ◽  
Computationally Efficient ◽  
Urban Context ◽  
Lift And Drag ◽  
Thrust And Torque

Interest in vertical-axis wind turbines (VAWTs) is experiencing a renaissance after most major research projects came to a standstill in the mid 90’s, in favour of conventional horizontal-axis turbines (HAWTs). Nowadays, the inherent advantages of the VAWT concept, especially in the Darrieus configuration, may outweigh their disadvantages in specific applications, like the urban context or floating platforms. To enable these concepts further, efficient, accurate, and robust aerodynamic prediction tools and design guidelines are needed for VAWTs, for which low-order simulation methods have not reached yet a maturity comparable to that of the Blade Element Momentum Theory for HAWTs’ applications. The two computationally efficient methods that are presently capable of capturing the unsteady aerodynamics of Darrieus turbines are the Double Multiple Streamtubes (DMS) Theory, based on momentum balances, and the Lifting Line Theory (LLT) coupled to a free vortex wake model. Both methods make use of tabulated lift and drag coefficients to compute the blade forces. Since the incidence angles range experienced by a VAWT blade is much wider than that of a HAWT blade, the accuracy of polars in describing the stall region and the transition towards the “thin plate like” behaviour has a large effect on simulation results. This paper will demonstrate the importance of stall and post-stall data handling in the performance estimation of Darrieus VAWTs. Using validated CFD simulations as a baseline, comparisons are provided for a blade in VAWT-like motion based on a DMS and a LLT code employing three sets of post-stall data obtained from a wind tunnel campaign, XFoil predictions extrapolated with the Viterna-Corrigan model and a combination of them. The polar extrapolation influence on quasi-steady operating conditions is shown and azimuthal variations of thrust and torque are compared for exemplary tip-speed ratios (TSRs). In addition, the major relevance of a proper dynamic stall model into both simulation methods is highlighted and discussed.

scholarly journals Numerical investigation of unsteady flow past a circular cylinder using 2-D finite volume method

1970 ◽  
Vol 4 (1) ◽  
pp. 27-42 ◽  
Author(s):  
Md Mahbubar Rahman ◽  
Md. Mashud Karim ◽  
Md Abdul Alim
Keyword(s):  
Circular Cylinder ◽  
Unsteady Flow ◽  
Turbulent Flow ◽  
Finite Volume Method ◽  
Finite Volume ◽  
Turbulence Models ◽  
Drag Coefficients ◽  
Pressure Formulation ◽  
Lift And Drag ◽  
Volume Method

The dynamic characteristics of the pressure and velocity fields of unsteady incompressible laminar and turbulent wakes behind a circular cylinder are investigated numerically and analyzed physically. The governing equations, written in the velocity pressure formulation are solved using 2-D finite volume method. The initial mechanism for vortex shedding is demonstrated and unsteady body forces are evaluated. The turbulent flow for Re = 1000 & 3900 are simulated using k-? standard, k-? Realizable and k-? SST turbulence models. The capabilities of these turbulence models to compute lift and drag coefficients are also verified. The frequencies of the drag and lift oscillations obtained theoretically agree well with the experimental results. The pressure and drag coefficients for different Reynolds numbers were also computed and compared with experimental and other numerical results. Due to faster convergence, 2-D finite volume method is found very much prospective for turbulent flow as well as laminar flow.Keywords: Viscous unsteady flow, laminar & turbulent flow, finite volume method, circular cylinder.DOI: 10.3329/jname.v4i1.914Journal of Naval Architecture and Marine Engineering 4(2007) 27-42

Extended Physics-Based Reduced-Order Capacity Fade Model for Lithium Ion Battery Cells

2021 ◽  
pp. 1-12
Author(s):  
Zachary Salyer ◽  
Matilde D'Arpino ◽  
Marcello Canova
Keyword(s):  
Lithium Ion Battery ◽  
Cell Model ◽  
Active Material ◽  
Lithium Ion ◽  
Calibration Procedure ◽  
Layer Growth ◽  
Operating Conditions ◽  
Capacity Fade ◽  
Computationally Efficient ◽  
Reduced Order

Abstract Aging models are necessary to accurately predict the SOH evolution in lithium ion battery systems when performing durability studies under realistic operatings, specifically considering time-varying storage, cycling, and environmental conditions, while being computationally efficient. This paper extends existing physics-based reduced-order capacity fade models that predict degradation resulting from the solid electrolyte interface (SEI) layer growth and loss of active material (LAM) in the graphite anode. Specifically, the physics of the degradation mechanisms and aging campaigns for various cell chemistries are reviewed to improve the model fidelity. Additionally, a new calibration procedure is established relying solely on capacity fade data and results are presented including extrapolation/validation for multiple chemistries. Finally, a condition is integrated to predict the onset of lithium plating. This allows the complete cell model to predict the incremental degradation under various operating conditions, including fast charging.

ARMA Model for Turbine and Compressor Clearance Forecasting

2010 ◽  
Author(s):  
Huageng Luo ◽  
George Ghanime ◽  
Liping Wang
Keyword(s):  
Arma Model ◽  
Design Parameters ◽  
Computationally Efficient ◽  
Detailed Model ◽  
Reduced Order ◽  
Operational Conditions ◽  
Turbine Efficiency ◽  
Modeling And Prediction ◽  
Efficiency And Effectiveness ◽  
Arma Modeling

In turbo machinery, clearance (the distance between the turbine or compressor blade tip to the casing) at high-pressure stages is one of the key design parameters to measure the turbine efficiency and effectiveness. Thus, appropriate modeling and prediction of the clearance under operational conditions is very important. If the clearance can be actively controlled, the turbine manufacturers get even more competitive advantages. For turbine design purpose, detailed physics based model is usually available. However, this kind of detailed model is not suitable for on-line prediction due to heavy computational requirements. Instead, a reduced order model based on the first order physics is used. Usually, the available reduced order models are computationally efficient, but they can hardly reach the accuracy desired by control engineers. In this paper, we applied an ARMA modeling technique for the reduced order clearance modeling and prediction. Typical turbine cycle operation data were used to build the ARMA model first. The built model is then used to predict other operations of the same unit, as well as other units of the same family.

A Study on the Use of Reduced Order Models for Unsteady Fluid Dynamic Analysis of Flexible Structures

2012 ◽  
Author(s):  
L. Ebrahimnejad ◽  
H. Yadollahi Farsani ◽  
D. T. Valentine ◽  
K. D. Janoyan ◽  
P. Marzocca
Keyword(s):  
Dynamic Analysis ◽  
Cross Sections ◽  
Fluid Dynamic ◽  
Flexible Structures ◽  
Reduced Order Models ◽  
Computationally Efficient ◽  
Reduced Order ◽  
Long Span ◽  
Long Span Bridge ◽  
Unsteady Fluid

Reduced order models (ROMs) are computationally efficient techniques, which have been widely used for predicting unsteady aerodynamic response of airfoils and wings. However, they have not been applied extensively to perform unsteady fluid dynamic analysis of flexible structures in civil engineering. This paper discusses the application of reduced order computational fluid dynamics (CFD) model based on the eigensystem realization algorithm (ERA) in the aerodynamic analysis of flexible structures with arbitrary shaped cross sections. As an example of a civil structure we examine the GBB long-span bridge for which there are published experimental data. The aerodynamic impulse responses of the GBB Bridge are used to construct the ROM, and then the aerodynamic forces due to arbitrary inputs are evaluated and compared to those of the model coupled with an advanced CFD code. Results demonstrate reasonable prediction power and high computational efficiency of the technique that can serve for preliminary design, optimization and control purposes. The methodology described in this paper has wide application in many offshore engineering problems where flexible structures interact with unsteady fluid mechanical phenomena.

scholarly journals A Multimoment Constrained Finite-Volume Model for Nonhydrostatic Atmospheric Dynamics

2013 ◽  
Vol 141 (4) ◽  
pp. 1216-1240 ◽  
Author(s):  
Xingliang Li ◽  
Chungang Chen ◽  
Xueshun Shen ◽  
Feng Xiao
Keyword(s):  
Finite Volume ◽  
Evolution Equations ◽  
Complex Geometry ◽  
High Order ◽  
Computationally Efficient ◽  
Dynamical Core ◽  
Benchmark Tests ◽  
Volume Method ◽  
Further Development ◽  
Mesh Cell

Abstract The two-dimensional nonhydrostatic compressible dynamical core for the atmosphere has been developed by using a new nodal-type high-order conservative method, the so-called multimoment constrained finite-volume (MCV) method. Different from the conventional finite-volume method, the predicted variables (unknowns) in an MCV scheme are the values at the solution points distributed within each mesh cell. The time evolution equations to update the unknown point values are derived from a set of constraint conditions based on the multimoment concept, where the constraint on the volume-integrated average (VIA) for each mesh cell is cast into a flux form and thus guarantees rigorously the numerical conservation. Two important features make the MCV method particularly attractive as an accurate and practical numerical framework for atmospheric and oceanic modeling. 1) The predicted variables are the nodal values at the solution points that can be flexibly located within a mesh cell (equidistant solution points are used in the present model). It is computationally efficient and provides great convenience in dealing with complex geometry and source terms. 2) High-order and physically consistent formulations can be built by choosing proper constraints in view of not only numerical accuracy and efficiency but also underlying physics. In this paper the authors present a dynamical core that uses the third- and the fourth-order MCV schemes. They have verified the numerical outputs of both schemes by widely used standard benchmark tests and obtained competitive results. The present numerical core provides a promising and practical framework for further development of nonhydrostatic compressible atmospheric models.

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