scholarly journals Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition

Fluids ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 111
Author(s):  
Milad Habibi ◽  
Scott T. M. Dawson ◽  
Amirhossein Arzani
Keyword(s):  
Blood Flow ◽  
Flow Rate ◽  
Coherent Structures ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Patient Specific ◽  
Dynamic Mode ◽  
Flow Data ◽  
Reduced Order ◽  
Mode Decomposition

Dynamic mode decomposition (DMD) is a purely data-driven and equation-free technique for reduced-order modeling of dynamical systems and fluid flow. DMD finds a best fit linear reduced-order model that represents any given spatiotemporal data. In DMD, each mode evolves with a fixed frequency and therefore DMD modes represent physically meaningful structures that are ranked based on their dynamics. The application of DMD to patient-specific cardiovascular flow data is challenging. First, the input flow rate is unsteady and pulsatile. Second, the flow topology can change significantly in different phases of the cardiac cycle. Finally, blood flow in patient-specific diseased arteries is complex and often chaotic. The objective of this study was to overcome these challenges using our proposed multistage dynamic mode decomposition with control (mDMDc) method and use this technique to study patient-specific blood flow physics. The inlet flow rate was considered as the controller input to the systems. Blood flow data were divided into different stages based on the inlet flow waveform and DMD with control was applied to each stage. The system was augmented to consider both velocity and wall shear stress (WSS) vector data, and therefore study the interaction between the coherent structures in velocity and near-wall coherent structures in WSS. First, it was shown that DMD modes can exactly represent the analytical Womersley solution for incompressible pulsatile flow in tubes. Next, our method was applied to image-based coronary artery stenosis and cerebral aneurysm models where complex blood flow patterns are anticipated. The flow patterns were studied using the mDMDc modes and the reconstruction errors were reported. Our augmented mDMDc framework could capture coherent structures in velocity and WSS with a fewer number of modes compared to the traditional DMD approach and demonstrated a close connection between the velocity and WSS modes.

scholarly journals Data-driven cardiovascular flow modelling: examples and opportunities

2021 ◽  
Vol 18 (175) ◽  
pp. 20200802
Author(s):  
Amirhossein Arzani ◽  
Scott T. M. Dawson
Keyword(s):  
Blood Flow ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Low Rank ◽  
Patient Specific ◽  
Dynamic Mode ◽  
Cardiovascular Research ◽  
Flow Modelling ◽  
Modelling Techniques ◽  
Cardiovascular Flow

High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

scholarly journals Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Soledad Le Clainche ◽  
José M. Vega
Keyword(s):  
Nonlinear Dynamics ◽  
Growth Rates ◽  
Transient Behavior ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Mode Decomposition ◽  
The Given

This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition (which provides a purely temporal Fourier-like decomposition) and spatiotemporal Koopman decomposition (which gives a spatiotemporal Fourier-like decomposition). These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used to either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models.

Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder

2020 ◽  
Author(s):  
Christian Amor ◽  
José M Pérez ◽  
Philipp Schlatter ◽  
Ricardo Vinuesa ◽  
Soledad Le Clainche
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Turbulent Wake ◽  
Orthogonal Decomposition ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Square Cylinder ◽  
Complex Flow ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Proper Orthogonal

Abstract This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.

scholarly journals An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

2020 ◽  
Author(s):  
Nicola Demo ◽  
Giulio Ortali ◽  
Gianluca Gustin ◽  
Gianluigi Rozza ◽  
Gianpiero Lavini
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Free Form ◽  
Dynamic Mode ◽  
Two Phase ◽  
Initial Shape ◽  
Reduced Order

Abstract This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

Dynamic mode decomposition analysis of the flow characteristics in a centrifugal compressor with vaned diffuser

2020 ◽  
pp. 095765092091346
Author(s):  
Xiaojian Li ◽  
Yijia Zhao ◽  
Zhengxian Liu ◽  
Ming Zhao
Keyword(s):  
Flow Rate ◽  
Centrifugal Compressor ◽  
Flow Characteristics ◽  
Leading Edge ◽  
Dynamic Mode Decomposition ◽  
Flow Structures ◽  
Dynamic Mode ◽  
Tip Leakage ◽  
Mode Decomposition ◽  
Dynamic Structures

To understand the flow dynamic characteristics of a centrifugal compressor, the dynamic mode decomposition (DMD) method is introduced to decompose the complex three-dimensional flow field. Three operating conditions, peak efficiency (OP1), peak pressure ratio (OP2), and small mass flow rate (near stall, OP3) conditions, are analyzed. First, the physical interpretations of main dynamic modes at OP1 are identified. As a result, the dynamic structures captured by DMD method are closely associated with the flow characteristics. In detail, the BPF/2BPF (blade passing frequency) corresponds to the impeller–diffuser interaction, the rotor frequency (RF) represents the tip leakage flow (TLF) from leading edge, and the 4RF is related to the interaction among the downstream TLF, the secondary flow, and the wake vortex. Then, the evolution of the dynamic structures is discussed when the compressor mass flow rate consistently declines. In the impeller, the tip leakage vortex near leading edge gradually breaks down due to the high backpressure, resulting in multi-frequency vortices. The broken vortices further propagate downstream along streamwise direction and then interact with the flow structures of 4RF. As a result, the 8RF mode can be observed in the whole impeller, this mode is transformed from upstream RF and 4RF modes, respectively. On the other hand, the broken vortices show broadband peak spectrum, which is correlated to the stall inception. Therefore, the sudden boost of energy ratio of 14RF mode could be regarded as a type of earlier signal for compressor instability. In the diffuser, the flow structures are affected by the perturbation from the impeller. However, the flow in diffuser is more stable than that in impeller at OP1–OP3, since the leading modes are stable patterns of BPF/2BPF.

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