Flow examination in abdominal aortic aneurysms: Reduced-order models driven by in vitro data and spectral proper orthogonal decomposition

2021 ◽  
Vol 33 (11) ◽  
pp. 111708
Author(s):  
Shahrzad Norouzi ◽  
Arnaud Le Floc'h ◽  
Giuseppe Di Labbio ◽  
Lyes Kadem
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Orthogonal Decomposition ◽  
Aortic Aneurysms ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Abdominal Aortic ◽  
Proper Orthogonal ◽  
Vitro Data ◽  
In Vitro Data

Abstract 116: Simvastatin Affects Monocyte Adhesion and Infiltrative Activity in Patients With Abdominal Aortic Aneurysms

2014 ◽  
Vol 34 (suppl_1) ◽  
Author(s):  
Kiana M Samadzadeh ◽  
Anthony Nguyen ◽  
Kevin C Chun ◽  
Eugene S Lee
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Statin Treatment ◽  
Aortic Aneurysms ◽  
Monocyte Adhesion ◽  
Abdominal Aortic ◽  
Monocyte Cell ◽  
High Concentrations ◽  
Statin Drugs ◽  
Monocyte Adherence

Purpose: The pleiotropic effects of statin drugs on reducing inflammation have been well regarded in decreasing AAA expansion. We hypothesize that increased monocyte activity plays a central role in AAA formation and expansion. This study examines whether statins can prevent monocyte cell adhesion, transmigration, and matrix metalloproteinase (MMP) and inhibitor (TIMP) concentrations in AAA patients compared to non-AAA patients. Methods: Peripheral blood was collected for monocyte and serum isolation from control (n=4) and AAA (n=8) patients. Monocyte adhesion and transmigration were assessed under untreated, statin treated, and statin + mevalonate (statin inhibitor) treated conditions in vitro. Luminex assays determined MMP and TIMP concentrations from cell culture and patient serum. Results: Untreated AAA patient monocytes showed higher levels of adhesion (p=0.05) and transmigration (p=0.04) compared to control subjects (Figure 1A & 1B). Statin treatment caused a decrease in AAA monocyte adherence to the endothelium (p=0.03) and high concentrations of mevalonate reversed statin treatment effects (p=0.04) (Figure 1A). A similar trend was noted in monocyte transmigration (Figure 1B). Higher concentrations of MMP-9 were found in AAA patient serum compared to controls (p=0.01) (Figure 1C). TIMP-4 concentration were decreased in AAA patients compared to controls (p=0.02) (Figure 1D). Conclusions: Statins reduce monocyte interaction with the endothelium in vitro, leading to decreased levels of MMP-9 and increased levels of TIMP-4, implying a possible mechanism by which statins reduce AAA expansion.

A Review of the In Vivo and In Vitro Biomechanical Behavior and Performance of Postoperative Abdominal Aortic Aneurysms and Implanted Stent-Grafts

2008 ◽  
Vol 15 (4) ◽  
pp. 468-484 ◽  
Author(s):  
Timothy J. Corbett ◽  
Anthony Callanan ◽  
Liam G. Morris ◽  
Barry J. Doyle ◽  
Pierce A. Grace ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Stent Grafts ◽  
Biomechanical Behavior ◽  
Abdominal Aortic ◽  
And Performance

Comparison of two model reduction approaches of an industrial drying process

2021 ◽  
Vol 69 (8) ◽  
pp. 667-682
Author(s):  
Marc Oliver Berner ◽  
Martin Mönnigmann
Keyword(s):  
Dynamic Models ◽  
Orthogonal Decomposition ◽  
Wood Chips ◽  
Residual Water ◽  
Balanced Truncation ◽  
Reduced Order Models ◽  
Drying Process ◽  
Reduced Order ◽  
Proper Orthogonal ◽  
Many Particles

Abstract Dynamic models have proven to be helpful for determining the residual water content in combustible biomass. However, these models often require partial differential equations, which render simulations impracticable when several thousand particles need to be considered, such as in the drying of wood chips. Reduced-order models help to overcome this problem. We compare proper orthogonal decomposition (POD) based to balanced truncation based reduced-order models. Both reduced models are lean enough for an application to systems with many particles, but the model based on balanced truncation shows more accurate results.

scholarly journals Constrained reduced-order models based on proper orthogonal decomposition

2017 ◽  
Vol 321 ◽  
pp. 18-34 ◽  
Author(s):  
Sohail R. Reddy ◽  
Brian A. Freno ◽  
Paul G.A. Cizmas ◽  
Seckin Gokaltun ◽  
Dwayne McDaniel ◽  
...  
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Proper Orthogonal

scholarly journals Construction of reduced-order models for fluid flows using deep feedforward neural networks

2019 ◽  
Vol 872 ◽  
pp. 963-994 ◽  
Author(s):  
Hugo F. S. Lui ◽  
William R. Wolf
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Fluid Flows ◽  
Leading Edge ◽  
Feedforward Neural Networks ◽  
Orthogonal Decomposition ◽  
Reduced Order Models ◽  
Order Model ◽  
Sparse Regression ◽  
Reduced Order ◽  
Proper Orthogonal

We present a numerical methodology for construction of reduced-order models (ROMs) of fluid flows through the combination of flow modal decomposition and regression analysis. Spectral proper orthogonal decomposition is applied to reduce the dimensionality of the model and, at the same time, filter the proper orthogonal decomposition temporal modes. The regression step is performed by a deep feedforward neural network (DNN), and the current framework is implemented in a context similar to the sparse identification of nonlinear dynamics algorithm. A discussion on the optimization of the DNN hyperparameters is provided for obtaining the best ROMs and an assessment of these models is presented for a canonical nonlinear oscillator and the compressible flow past a cylinder. Then the method is tested on the reconstruction of a turbulent flow computed by a large eddy simulation of a plunging airfoil under dynamic stall. The reduced-order model is able to capture the dynamics of the leading edge stall vortex and the subsequent trailing edge vortex. For the cases analysed, the numerical framework allows the prediction of the flow field beyond the training window using larger time increments than those employed by the full-order model. We also demonstrate the robustness of the current ROMs constructed via DNNs through a comparison with sparse regression. The DNN approach is able to learn transient features of the flow and presents more accurate and stable long-term predictions compared to sparse regression.

The Development of Physiological Compliant Abdominal Aortic Aneurysm Models for In Vitro Flow Studies

2009 ◽  
Author(s):  
Timothy J. Corbett ◽  
Barry J. Doyle ◽  
Anthony Callanan ◽  
Tim M. McGloughlin
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Material Properties ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Flow Loop ◽  
The Past ◽  
Abdominal Aortic

A vast amount of experimental research has been undertaken in the past decade to investigate different aspects of preoperative and postoperative abdominal aortic aneurysms (AAAs). Much of this research has been based on the use of mock arteries in an in vitro flow loop to mimic the behaviour of the abdominal aorta in vivo [1]. These models should be reproducible, have consistent material properties, consistent thickness and be physiological in behaviour.

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