Eigenmode Analysis in Unsteady Aerodynamics: Reduced Order Models

1997 ◽  
Vol 50 (6) ◽  
pp. 371-386 ◽  
Author(s):  
Earl H. Dowell ◽  
Kenneth C. Hall ◽  
Michael C. Romanowski
Keyword(s):  
Unsteady Flow ◽  
Unsteady Aerodynamics ◽  
Reduced Order Modeling ◽  
Reduced Order Model ◽  
Full Potential ◽  
Order Model ◽  
Reduced Order ◽  
Unsteady Aerodynamic ◽  
Eigenmode Analysis ◽  
Wide Range

In this article, we review the status of reduced order modeling of unsteady aerodynamic systems. Reduced order modeling is a conceptually novel and computationally efficient technique for computing unsteady flow about isolated airfoils, wings, and turbomachinery cascades. Starting with either a time domain or frequency domain computational fluid dynamics (CFD) analysis of unsteady aerodynamic or aeroacoustic flows, a large, sparse eigenvalue problem is solved using the Lanczos algorithm. Then, using just a few of the resulting eigenmodes, a Reduced Order Model of the unsteady flow is constructed. With this model, one can rapidly and accurately predict the unsteady aerodynamic response of the system over a wide range of reduced frequencies. Moreover, the eigenmode information provides important insights into the physics of unsteady flows. Finally, the method is particularly well suited for use in the active control of aeroelastic and aeroacoustic phenomena as well as in standard aeroelastic analysis for flutter or gust response. Numerical results presented include: 1) comparison of the reduced order model to classical unsteady incompressible aerodynamic theory, 2) reduced order calculations of compressible unsteady aerodynamics based on the full potential equation, 3) reduced order calculations of unsteady flow about an isolated airfoil based on the Euler equations, and 4) reduced order calculations of unsteady viscous flows associated with cascade stall flutter, 5) flutter analysis using the Reduced Order Model. This review article includes 25 references.

A Reduced Order Model of Unsteady Flows in Turbomachinery

1994 ◽  
Author(s):  
Kenneth C. Hall ◽  
Răzvan Florea ◽  
Paul J. Lanzkron
Keyword(s):  
Fluid Motion ◽  
Unsteady Flows ◽  
Reduced Order Model ◽  
Lanczos Algorithm ◽  
Order Model ◽  
Reduced Order ◽  
Unsteady Aerodynamic ◽  
Natural Modes ◽  
Wide Range ◽  
Modes Of Vibration

A novel technique for computing unsteady flows about turbomachinery cascades is presented. Starting with a frequency domain CFD description of unsteady aerodynamic flows, we form a large, sparse, generalized, non-Hermitian eigenvalue problem which describes the natural modes and frequencies of fluid motion about the cascade. We compute the dominant left and right eigenmodes and corresponding eigenfrequencies using a Lanczos algorithm. Then, using just a few of the resulting eigenmodes, we construct a reduced order model of the unsteady flow field. With this model, one can rapidly and accurately predict the unsteady aerodynamic loads acting on the cascade over a wide range of reduced frequencies and arbitrary modes of vibration. Moreover, the eigenmode information provides insights into the physics of unsteady flows. Finally we note that the form of the reduced order model is well suited for use in active control of aeroelastic and aeroacoustic phenomena.

A Reduced Order Model of Unsteady Flows in Turbomachinery

1995 ◽  
Vol 117 (3) ◽  
pp. 375-383 ◽  
Author(s):  
K. C. Hall ◽  
R. Florea ◽  
P. J. Lanzkron
Keyword(s):  
Fluid Motion ◽  
Unsteady Flows ◽  
Reduced Order Model ◽  
Lanczos Algorithm ◽  
Order Model ◽  
Reduced Order ◽  
Unsteady Aerodynamic ◽  
Natural Modes ◽  
Wide Range ◽  
Modes Of Vibration

A novel technique for computing unsteady flows about turbomachinery cascades is presented. Starting with a frequency domain CFD description of unsteady aerodynamic flows, we form a large, sparse, generalized, non-Hermitian eigenvalue problem that describes the natural modes and frequencies of fluid motion about the cascade. We compute the dominant left and right eigenmodes and corresponding eigenfrequencies using a Lanczos algorithm. Then, using just a few of the resulting eigenmodes, we construct a reduced order model of the unsteady flow field. With this model, one can rapidly and accurately predict the unsteady aerodynamic loads acting on the cascade over a wide range of reduced frequencies and arbitrary modes of vibration. Moreover, the eigenmode information provides insights into the physics of unsteady flows. Finally we note that the form of the reduced order model is well suited for use in active control of aeroelastic and aeroacoustic phenomena.

A Reduced-Order Model for the Preliminary Design of Small-Scale Radial Inflow Turbines

2021 ◽  
Author(s):  
Marco Manfredi ◽  
Marco Alberio ◽  
Marco Astolfi ◽  
Andrea Spinelli
Keyword(s):  
Heat Recovery ◽  
Internal Combustion Engines ◽  
Reduced Order Model ◽  
Pollutant Emissions ◽  
Small Scale ◽  
Preliminary Design ◽  
Order Model ◽  
Reduced Order ◽  
Wide Range ◽  
Loss Models

Abstract Power production from waste heat recovery represents an attractive and viable solution to contribute to the reduction of pollutant emissions generated by industrial plants and automotive sector. For transport applications, a promising technology can be identified in bottoming mini-organic Rankine cycles (ORCs), devoted to heat recovery from internal combustion engines (ICE). While commercial ORCs exploiting turbo-expanders in the power range of hundreds kW to several MW are a mature technology, well-established design guidelines are not yet available for turbines targeting small power outputs (below 50 kW). The present work develops a reduced-order model for the preliminary design of mini-ORC radial inflow turbines (RITs) for high-pressure ratio applications, suitable to be integrated in a comprehensive cycle optimization. An exhaustive review of existing loss models, whose development pattern is retraced up to the original approaches, is proposed. This investigation is finalized in a loss models effectiveness analysis performed by testing several correlations over six existing geometries. These test case turbines, operating with different fluids and covering a wide range of target expansion ratio, size, and gross power output, are then employed to carry out the validation procedure, whose results prove the robustness and prediction capability of the proposed reduced-order model.

Linear Reduced-Order Model for Unsteady Aerodynamics of an L-Shaped Gurney Flap

2015 ◽  
Vol 52 (6) ◽  
pp. 1887-1904 ◽  
Author(s):  
Valentina Motta ◽  
Giuseppe Quaranta
Keyword(s):  
Unsteady Aerodynamics ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Gurney Flap

scholarly journals Evolve Then Filter Regularization for Stochastic Reduced Order Modeling

2018 ◽  
Author(s):  
Xuping Xie ◽  
Feng Bao ◽  
Clayton G. Webster
Keyword(s):  
Stochastic Systems ◽  
Spatial Filter ◽  
Reduced Order Modeling ◽  
Reduced Order Model ◽  
Galerkin Projection ◽  
Order Model ◽  
Reduced Order ◽  
Stochastic Burgers Equation ◽  
Numerical Stabilization ◽  
Convection Dominated

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 based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better results than G-ROM.

scholarly journals A reduced order model of the spine to study pediatric scoliosis

2020 ◽  
Author(s):  
Sunder Neelakantan ◽  
Prashant K. Purohit ◽  
Saba Pasha
Keyword(s):  
Idiopathic Scoliosis ◽  
Inflection Point ◽  
Reduced Order Model ◽  
The Other ◽  
Sagittal Profile ◽  
Deformation Pattern ◽  
Elastic Rod ◽  
Order Model ◽  
Reduced Order ◽  
Wide Range

AbstractThe S-shaped curvature of the spine has been hypothesized as the underlying mechanical cause of adolescent idiopathic scoliosis. In earlier work we proposed a reduced order model in which the spine was viewed as an S-shaped elastic rod under torsion and bending. Here, we simulate the deformation of S-shaped rods of a wide range of curvatures and inflection points under a fixed mechanical loading. Our analysis determines three distinct axial projection patterns of these S-shaped rods: two loop (in opposite directions) patterns and one lemniscate pattern. We further identify the curve characteristics associated with each deformation pattern showing that for rods deforming in a loop 1 shape the position of the inflection point is the highest and the curvature of the rod is smaller compared to the other two types. For rods deforming in the loop 2 shape the position of the inflection point is the lowest (closer to the fixed base) and the curvatures are higher than the other two types. These patterns matched the common clinically observed scoliotic curves - Lenke 1 and Lenke 5. Our elastic rod model predicts deformations that are similar to those of a pediatric spine and it can differentiate between the clinically observed deformation patterns. This provides validation to the hypothesis that changes in the sagittal profile of the spine can be a mechanical factor in parthenogenesis of pediatric idiopathic scoliosis.

Reduced Order Model for Unsteady Fluid Flows via Recurrent Neural Networks

2019 ◽  
Author(s):  
Sandeep B. Reddy ◽  
Allan Ross Magee ◽  
Rajeev K. Jaiman ◽  
J. Liu ◽  
W. Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reynolds Number ◽  
Flow Field ◽  
Unsteady Flow ◽  
Recurrent Neural Networks ◽  
Reduced Order Model ◽  
Data Driven ◽  
Order Model ◽  
Reduced Order ◽  
Low Dimensional

Abstract In this paper, we present a data-driven approach to construct a reduced-order model (ROM) for the unsteady flow field and fluid-structure interaction. This proposed approach relies on (i) a projection of the high-dimensional data from the Navier-Stokes equations to a low-dimensional subspace using the proper orthogonal decomposition (POD) and (ii) integration of the low-dimensional model with the recurrent neural networks. For the hybrid ROM formulation, we consider long short term memory networks with encoder-decoder architecture, which is a special variant of recurrent neural networks. The mathematical structure of recurrent neural networks embodies a non-linear state space form of the underlying dynamical behavior. This particular attribute of an RNN makes it suitable for non-linear unsteady flow problems. In the proposed hybrid RNN method, the spatial and temporal features of the unsteady flow system are captured separately. Time-invariant modes obtained by low-order projection embodies the spatial features of the flow field, while the temporal behavior of the corresponding modal coefficients is learned via recurrent neural networks. The effectiveness of the proposed method is first demonstrated on a canonical problem of flow past a cylinder at low Reynolds number. With regard to a practical marine/offshore engineering demonstration, we have applied and examined the reliability of the proposed data-driven framework for the predictions of vortex-induced vibrations of a flexible offshore riser at high Reynolds number.

Reduced Order Modeling of Entrained Flow Solid Fuel Gasification

2009 ◽  
Author(s):  
Rory F. D. Monaghan ◽  
Mayank Kumar ◽  
Simcha L. Singer ◽  
Cheng Zhang ◽  
Ahmed F. Ghoniem
Keyword(s):  
Fluid Dynamics ◽  
Reduced Order Modeling ◽  
Combined Cycle ◽  
Reduced Order Model ◽  
Temperature Profiles ◽  
Order Model ◽  
External Temperature ◽  
Reduced Order ◽  
Entrained Flow ◽  
Entrained Flow Gasifiers

Reduced order models that accurately predict the operation of entrained flow gasifiers as components within integrated gasification combined cycle (IGCC) or polygeneration plants are essential for greater commercialization of gasification-based energy systems. A reduced order model, implemented in Aspen Custom Modeler, for entrained flow gasifiers that incorporates mixing and recirculation, rigorously calculated char properties, drying and devolatilization, chemical kinetics, simplified fluid dynamics, heat transfer, slag behavior and syngas cooling is presented. The model structure and submodels are described. Results are presented for the steady-state simulation of a two-metric-tonne-per-day (2 tpd) laboratory-scale Mitsubishi Heavy Industries (MHI) gasifier, fed by two different types of coal. Improvements over the state-of-the-art for reduced order modeling include the ability to incorporate realistic flow conditions and hence predict the gasifier internal and external temperature profiles, the ability to easily interface the model with plant-wide flowsheet models, and the flexibility to apply the same model to a variety of entrained flow gasifier designs. Model validation shows satisfactory agreement with measured values and computational fluid dynamics (CFD) results for syngas temperature profiles, syngas composition, carbon conversion, char flow rate, syngas heating value and cold gas efficiency. Analysis of the results shows the accuracy of the reduced order model to be similar to that of more detailed models that incorporate CFD. Next steps include the activation of pollutant chemistry and slag submodels, application of the reduced order model to other gasifier designs, parameter studies and uncertainty analysis of unknown and/or assumed physical and modeling parameters, and activation of dynamic simulation capability.

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