Reduced Order Model for a Power-Law Fluid

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
M. Ocana ◽  
D. Alonso ◽  
A. Velazquez
Keyword(s):  
Newtonian Fluid ◽  
Power Law ◽  
Reduced Order Model ◽  
Processing Unit ◽  
Order Model ◽  
Power Law Fluid ◽  
Reduced Order ◽  
Polymeric Fluid ◽  
Central Processing ◽  
Highly Nonlinear

This article describes the development of a reduced order model (ROM) based on residual minimization for a generic power-law fluid. The objective of the work is to generate a methodology that allows for the fast and accurate computation of polymeric flow fields in a multiparameter space. It is shown that the ROM allows for the computation of the flow field in a few seconds, as compared with the use of computational fluid dynamics (CFD) methods in which the central processing unit (CPU) time is on the order of hours. The model fluid used in the study is a polymeric fluid characterized by both its power-law consistency index m and its power-law index n. Regarding the ROM development, the main difference between this case and the case of a Newtonian fluid is the order of the nonlinear terms in the viscous stress tensor: In the case of the polymeric fluid these terms are highly nonlinear while they are linear when a Newtonian fluid is considered. After the method is validated and its robustness studied with regard to several parameters, an application case is presented that could be representative of some industrial situations.

Reinforcement Learning for Control of a Shape Memory Alloy Based Self-Folding Sheet

2015 ◽  
Author(s):  
Peyman Moghadas ◽  
Richard Malak ◽  
Darren Hartl
Keyword(s):  
Reinforcement Learning ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Laminate Plate ◽  
Reduced Order Model ◽  
Hysteretic Behavior ◽  
Order Model ◽  
Control Policies ◽  
Reduced Order ◽  
Highly Nonlinear

Origami-inspired engineering provides engineers with new means for creating complicated three-dimensional structures through use of folding and fold-like operations. Motivated by the vision of origami engineering, we have created and modeled a reconfigurable self-folding sheet based on a laminate structure of shape memory alloy (SMA) surrounding a layer of elastomer. Folding behavior is achieved by activating an SMA layer through localized heating. In prior work, we demonstrated localized control of such a sheet using PID and On/Off type feedback controllers. The implementation of these control strategies requires several workarounds to deal with the highly nonlinear and hysteretic behavior of the SMA-based laminate sheet. In the current work, we use a reinforcement learning algorithm to learn control policies that better handle these aspects of the sheet behavior. We perform learning on a reduced order model of the sheet developed based on classical laminate plate theory. This significantly reduces computational costs compared to more complicated finite element modeling options. We demonstrate the effectiveness of the learned control policies in several folding scenarios on the reduced order model. Our results show that reinforcement learning can be a useful tool in feedback control of SMA-based structures.

A Non-Newtonian Fluid Flow Model for the Slip Condition on Blood Flow through a Stenosed Artery using Power-law Fluid

2018 ◽  
Vol 9 (7) ◽  
pp. 871-879
Author(s):  
Rajesh Shrivastava ◽  
R. S. Chandel ◽  
Ajay Kumar ◽  
Keerty Shrivastava and Sanjeet Kumar
Keyword(s):  
Blood Flow ◽  
Fluid Flow ◽  
Newtonian Fluid ◽  
Power Law ◽  
Flow Model ◽  
Slip Condition ◽  
Power Law Fluid ◽  
Stenosed Artery ◽  
Flow Through

Model Order Reduction of Transmission Line Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Transmission Line ◽  
Large Scale ◽  
Original System ◽  
Reduced Order Model ◽  
Benchmark Problems ◽  
Transmission Line Model ◽  
Order Model ◽  
Line Model ◽  
Reduced Order ◽  
Numerical Effort

Transmission Line model are an important role in the electrical power supply. Modeling of such system remains a challenge for simulations are necessary for designing and controlling modern power systems.In order to analyze the numerical approach for a benchmark collection Comprehensive of some needful real-world examples, which can be utilized to evaluate and compare mathematical approaches for model reduction. The approach is based on retaining the dominant modes of the system and truncation comparatively the less significant once.as the reduced order model has been derived from retaining the dominate modes of the large-scale stable system, the reduction preserves the stability. The strong demerit of the many MOR methods is that, the steady state values of the reduced order model does not match with the higher order systems. This drawback has been try to eliminated through the Different MOR method using sssMOR tools. This makes it possible for a new assessment of the error system Offered that the Observability Gramian of the original system has as soon as been thought about, an H∞ and H2 error bound can be calculated with minimal numerical effort for any minimized model attributable to The reduced order model (ROM) of a large-scale dynamical system is essential to effortlessness the study of the system utilizing approximation Algorithms. The response evaluation is considered in terms of response constraints and graphical assessments. the application of Approximation methods is offered for arising ROM of the large-scale LTI systems which consist of benchmark problems. The time response of approximated system, assessed by the proposed method, is also shown which is excellent matching of the response of original system when compared to the response of other existing approaches .

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