Reduced Order Model Analysis for Two Dimensional Molecular Dynamic Chain Structure Attached to an Atomic Force Microscope

2003 ◽  
Author(s):  
Deman Tang ◽  
Earl H. Dowell
Keyword(s):  
Atomic Force Microscope ◽  
Excitation Frequency ◽  
Chain Structure ◽  
Reduced Order Model ◽  
Two Dimensional ◽  
Order Model ◽  
Global Mode ◽  
Reduced Order ◽  
Atomic Force ◽  
Global Modes

Dynamic analysis and numerical simulation of a protein-ligand chain structure connected to a moving atomic force microscope (AFM) has been conducted. The elements of the chain are free to extend and rotate relative to each other in a two-dimensional plane. Sinusoidal base excitation of the cantilevered beam of the AFM is considered in some detail. Reduced order (dynamic) models are constructed using global modes for both linear and nonlinear dynamic systems with and without the “nearest neighbor assumption”. The agreement between the original and reduced order models (ROM) is very good even when only one global mode is included in the ROM for either the linear case or for the nonlinear case, provided the excitation frequency is lower than the fundamental natural frequency of the linear system. For higher excitation frequencies, more global modes are required. The computational advantage of the reduced order model is clear from the results presented.

Reduced Order Model Analysis for Two-Dimensional Molecular Dynamic Chain Structure Attached to an Atomic Force Microscope

2004 ◽  
Vol 126 (3) ◽  
pp. 531-546 ◽  
Author(s):  
Deman Tang ◽  
Earl H. Dowell
Keyword(s):  
Atomic Force Microscope ◽  
Excitation Frequency ◽  
Chain Structure ◽  
Reduced Order Model ◽  
Two Dimensional ◽  
Order Model ◽  
Global Mode ◽  
Reduced Order ◽  
Atomic Force ◽  
Global Modes

Dynamic analysis and numerical simulation of a protein-ligand chain structure connected to a moving atomic force microscope (AFM) has been conducted. The elements of the chain are free to extend and rotate relative to each other in a two-dimensional plane. Sinusoidal base excitation of the cantilevered beam of the AFM is considered in some detail. Reduced order (dynamic) models are constructed using global modes for both linear and nonlinear dynamic systems with and without the “nearest neighbor assumption.” The agreement between the original and reduced order models (ROM) is very good even when only one global mode is included in the ROM for either the linear case or for the nonlinear case, provided the excitation frequency is lower than the fundamental natural frequency of the linear system. For higher excitation frequencies, more global modes are required. The computational advantage of the reduced order model is clear from the results presented.

Dynamic Analysis of a Protein-Ligand Molecular Chain Attached to an Atomic Force Microscope

2004 ◽  
Vol 126 (4) ◽  
pp. 496-513 ◽  
Author(s):  
Deman Tang ◽  
Earl H. Dowell
Keyword(s):  
Nonlinear Systems ◽  
Atomic Force Microscope ◽  
Reduced Order Model ◽  
Molecular Chain ◽  
Static Equilibrium ◽  
Reduced Order Models ◽  
Base Excitation ◽  
Order Model ◽  
Reduced Order ◽  
Atomic Force

Dynamic numerical simulation of a protein-ligand molecular chain connected to a moving atomic force microscope (AFM) has been studied. A sinusoidal base excitation of the cantilevered beam of the AFM is considered in some detail. A comparison between results for a single molecule and those for multiple molecules has been made. For a small number of molecules, multiple stable static equilibrium positions are observed and chaotic behavior may be generated via a period-doubling cascade for harmonic base excitation of the AFM. For many molecules in the chain, only a single static equilibrium position exists. To enable these calculations, reduced-order (dynamic) models are constructed for fully linear, combined linear/nonlinear and fully nonlinear systems. Several distinct reduced-order models have been developed that offer the option of increased computational efficiency at the price of greater effort to construct the particular reduced-order model. The agreement between the original and reduced-order models (ROM) is very good even when only one mode is included in the ROM for either the fully linear or combined linear/nonlinear systems provided the excitation frequency is lower than the fundamental natural frequency of the linear system. The computational advantage of the reduced-order model is clear from the results presented.

Numerical Investigation of Heat Transfer in the Wake of a Single Highly Confined Bubble in a Horizontal Minichannel

2018 ◽  
Author(s):  
John R. Willard ◽  
D. Keith Hollingsworth
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Enhancement ◽  
Numerical Investigation ◽  
Channel Flow ◽  
Heat Generation ◽  
Reduced Order Model ◽  
Two Dimensional ◽  
Transfer Enhancement ◽  
Order Model ◽  
Reduced Order

Confined bubbly flows in millimeter-scale channels produce significant heat transfer enhancement when compared to single-phase flows. Experimental studies support the hypothesis that the enhancement is driven by a convective phenomenon in the liquid phase as opposed to sourcing from microlayer evaporation or active nucleation. A numerical investigation of flow structure and heat transfer produced by a single bubble moving through a millimeter-scale channel was performed in order to document the details of this convective mechanism. The simulation includes thermal boundary conditions emulating those of the experiments, and phase change was omitted in order to focus only on the convective mechanism. The channel is horizontal with a uniform-heat-generation upper wall and an adiabatic lower surface. A Lagrangian framework was adopted such that the computational domain surrounds the bubble and moves at the nominal bubble speed. The liquid around the bubble moves as a low-Reynolds-number unsteady laminar flow. The volume-of-fluid method was used to track the liquid/gas interface. This paper reviews the central results of this simulation regarding wake heat transfer. It then compares the findings regarding Nusselt number enhancement to a reduced-order model on a two-dimensional domain in the wake of the bubble. The model solves the advective-diffusion equation assuming a velocity field consistent with fully developed channel flow in the absence of the bubble. The response of the uniform-heat-generation upper wall is included. The model assumes a temperature profile directly behind the bubble which represents a well-mixed region produced by the passage of the bubble. The significant wake heat transfer enhancement and its decay with distance from the bubble documented by the simulation were captured by the reduced-order model. However, the channel surface temperature recovered in a much shorter distance in the simulation compared to the reduced-order model. This difference is attributed to the omission of transverse conduction within the heated surface in the two-dimensional model. Beyond approximately one bubble diameter into the bubble wake, the complex flow structures are replaced by the momentum field of the precursor channel flow. However, the properties and thickness of the heated upper channel wall govern the heat transfer for many bubble diameters behind the bubble.

scholarly journals Stochastic Reduced-Order Model in Low-Frequency Dynamics in Presence of Numerous Local Elastic Modes

2011 ◽  
Vol 78 (6) ◽  
Author(s):  
C. Soize ◽  
A. Batou
Keyword(s):  
Structural Dynamics ◽  
Complex Structure ◽  
Probabilistic Approach ◽  
Low Frequency ◽  
Reduced Order Model ◽  
Frequency Range ◽  
Order Model ◽  
Reduced Order ◽  
Global Modes ◽  
Elastic Modes

This paper deals with the nonusual case in structural dynamics for which a complex structure exhibits both the usual global elastic modes and numerous local elastic modes in the low-frequency range. Despite the presence of these local elastic modes, we are interested in constructing a stochastic reduced-order model using only the global modes and in taking into account the local elastic modes with a probabilistic approach. In the first part, a formulation and an algorithm, which allow the “global elastic modes” and the “local elastic modes” to be calculated, are presented. The second part is devoted to the construction of the stochastic reduced-order model with the global elastic modes and in taking into account the uncertainties on the effects of the local elastic modes by the nonparametric probabilistic approach. Finally, an application, which validates the proposed theory is presented.

Developing a Reduced-Order Model of Nonsynchronous Vibration in Turbomachinery Using Proper-Orthogonal Decomposition Methods

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Stephen T. Clark ◽  
Fanny M. Besem ◽  
Robert E. Kielb ◽  
Jeffrey P. Thomas
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Cross Flow ◽  
Decomposition Methods ◽  
Initial Study ◽  
Orthogonal Decomposition ◽  
Reduced Order Model ◽  
Two Dimensional ◽  
Order Model ◽  
Reduced Order ◽  
Proper Orthogonal

The paper develops a reduced-order model of nonsynchronous vibration (NSV) using proper orthogonal decomposition (POD) methods. The approach was successfully developed and implemented, requiring between two and six POD modes to accurately predict computational fluid dynamics (CFD) solutions that are experiencing NSV. This POD method was first developed and demonstrated for a transversely moving, two-dimensional cylinder in cross-flow. Later, the method was used for the prediction of CFD solutions for a two-dimensional compressor blade. This research is the first to offer a POD approach to the reduced-order modeling of NSV in turbomachinery. Modeling NSV is especially challenging because NSV is caused by complicated, unsteady flow dynamics; this initial study helps researchers understand the causes of NSV, and aids in the future development of predictive tools for aeromechanical design engineers.

Developing a Reduced-Order Model of Non-Synchronous Vibration in Turbomachinery Using Proper Orthogonal Decomposition Methods

2014 ◽  
Author(s):  
Stephen T. Clark ◽  
Fanny M. Besem ◽  
Robert E. Kielb ◽  
Jeffrey P. Thomas
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Cross Flow ◽  
Initial Study ◽  
Orthogonal Decomposition ◽  
Reduced Order Model ◽  
Two Dimensional ◽  
Order Model ◽  
Decomposition Approach ◽  
Reduced Order ◽  
Proper Orthogonal

The paper develops a reduced-order model of non-synchronous vibration (NSV) using proper orthogonal decomposition (POD) methods. The approach was successfully developed and implemented, requiring between two and six POD modes to accurately predict CFD solutions that are experiencing non-synchronous vibration. This POD method was first developed and demonstrated for a transversely-moving, two-dimensional cylinder in cross-flow. Later, the method was used for the prediction of CFD solutions for a two-dimensional compressor blade. This research is the first to offer a proper orthogonal decomposition approach to the reduced-order modeling of non-synchronous vibration in turbomachinery. Modeling non-synchronous vibration is especially challenging because NSV is caused by complicated, unsteady flow dynamics; this initial study helps researchers understand the causes of NSV, and aids in the future development of predictive tools for aeromechanical design engineers.

Data-Driven Reduced Order Modeling of Flows Around Two-Dimensional Bluff Bodies of Various Shapes

2019 ◽  
Author(s):  
Kazuto Hasegawa ◽  
Kai Fukami ◽  
Takaaki Murata ◽  
Koji Fukagata
Keyword(s):  
Number Fields ◽  
Flow Fields ◽  
Reduced Order Modeling ◽  
Reduced Order Model ◽  
Data Driven ◽  
Bluff Bodies ◽  
Two Dimensional ◽  
Order Model ◽  
Reduced Order ◽  
Low Dimensional

Abstract We propose a reduced order model for predicting unsteady flows using a data-driven method. As preliminary tests, we use two-dimensional unsteady flow around bluff bodies with different shapes as the training datasets obtained by direct numerical simulation (DNS). Our machine-learned architecture consists of two parts: Convolutional Neural Network-based AutoEncoder (CNN-AE) and Long Short Term Memory (LSTM), respectively. First, CNN-AE is used to map into a low-dimensional space from the flow field data. Then, LSTM is employed to predict the temporal evolution of the low-dimensional data generated by CNN-AE. Proposed machine-learned reduced order model is applied to two-dimensional circular cylinder flows at various Reynolds numbers and flows around bluff bodies of various shapes. The flow fields reconstructed by the machine-learned architecture show reasonable agreement with the reference DNS data. Furthermore, it can be seen that our machine-learned reduced order model can successfully map the high-dimensional flow data into low-dimensional field and predict the flow fields against unknown Reynolds number fields and shapes of bluff body. As concluding remarks, we discuss the extension study of machine-learned reduced order modeling for various applications in experimental and computational fluid dynamics.

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