An Efficient Preconditioner for Linear System Solution in Multi-Domain Modeling of the Circulatory System

2013 ◽  
Author(s):  
Mahdi Esmaily Moghadam ◽  
Yuri Bazilevs ◽  
Tain-Yen Hsia ◽  
Alison Marsden
Keyword(s):  
Strong Coupling ◽  
Large Scale ◽  
Computational Cost ◽  
Circulatory System ◽  
Flow Simulation ◽  
Global Dynamics ◽  
Domain Modeling ◽  
Computationally Efficient ◽  
Lumped Parameter ◽  
System Solution

A closed-loop lumped parameter network (LPN) coupled to a 3D domain is a powerful tool that can be used to model the global dynamics of the circulatory system. Coupling a 0D LPN to a 3D CFD domain is a numerically challenging problem, often associated with instabilities, extra computational cost, and loss of modularity. A computationally efficient finite element framework has been recently proposed that achieves numerical stability without sacrificing modularity [1]. This type of coupling introduces new challenges in the linear algebraic equation solver (LS), producing an strong coupling between flow and pressure that leads to an ill-conditioned tangent matrix. In this paper we exploit this strong coupling to obtain a novel and efficient algorithm for the linear solver (LS). We illustrate the efficiency of this method on several large-scale cardiovascular blood flow simulation problems.

A Computationally Efficient Finite Element Framework to Simulate Additive Manufacturing Processes

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Shiyan Jayanath ◽  
Ajit Achuthan
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Large Scale ◽  
Computational Cost ◽  
Cylindrical Part ◽  
Adaptive Meshing ◽  
Computationally Efficient ◽  
Thin Walled ◽  
Melt Pool Size ◽  
Simulation Time

Macroscale finite element (FE) models, with their ability to simulate additive manufacturing (AM) processes of metal parts and accurately predict residual stress distribution, are potentially powerful design tools. However, these simulations require enormous computational cost, even for a small part only a few orders larger than the melt pool size. The existing adaptive meshing techniques to reduce computational cost substantially by selectively coarsening are not well suited for AM process simulations due to the continuous modification of model geometry as material is added to the system. To address this limitation, a new FE framework is developed. The new FE framework is based on introducing updated discretized geometries at regular intervals during the simulation process, allowing greater flexibility to control the degree of mesh coarsening than a technique based on element merging recently reported in the literature. The new framework is evaluated by simulating direct metal deposition (DMD) of a thin-walled rectangular and a thin-walled cylindrical part, and comparing the computational speed and predicted results with those predicted by simulations using the conventional framework. The comparison shows excellent agreement in the predicted stress and plastic strain fields, with substantial savings in the simulation time. The method is then validated by comparing the predicted residual elastic strain with that measured experimentally by neutron diffraction of the thin-walled rectangular part. Finally, the new framework's capability to substantially reduce the simulation time for large-scale AM parts is demonstrated by simulating a one-half foot thin-walled cylindrical part.

scholarly journals Computational Efficiency of a Modular Reservoir Network for Image Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Yifan Dai ◽  
Hideaki Yamamoto ◽  
Masao Sakuraba ◽  
Shigeo Sato
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Complex Dynamics ◽  
Computational Cost ◽  
Image Data ◽  
Small World ◽  
Strong Relationship ◽  
Great Success ◽  
Computationally Efficient ◽  
Network Functionality

Liquid state machine (LSM) is a type of recurrent spiking network with a strong relationship to neurophysiology and has achieved great success in time series processing. However, the computational cost of simulations and complex dynamics with time dependency limit the size and functionality of LSMs. This paper presents a large-scale bioinspired LSM with modular topology. We integrate the findings on the visual cortex that specifically designed input synapses can fit the activation of the real cortex and perform the Hough transform, a feature extraction algorithm used in digital image processing, without additional cost. We experimentally verify that such a combination can significantly improve the network functionality. The network performance is evaluated using the MNIST dataset where the image data are encoded into spiking series by Poisson coding. We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size. We also show that the proposed structure has better robustness against system damage than the small-world and random structures. We believe that the proposed computationally efficient method can greatly contribute to future applications of reservoir computing.

Multiscale Interactions in an Idealized Walker Cell: Simulations with Sparse Space–Time Superparameterization

2015 ◽  
Vol 143 (2) ◽  
pp. 563-580 ◽  
Author(s):  
Joanna Slawinska ◽  
Olivier Pauluis ◽  
Andrew J. Majda ◽  
Wojciech W. Grabowski
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Low Frequency ◽  
Space Time ◽  
Scale Model ◽  
Moist Convection ◽  
Computationally Efficient ◽  
Convective Systems ◽  
Large Scale Model ◽  
Walker Cell

Abstract This paper discusses the sparse space–time superparameterization (SSTSP) algorithm and evaluates its ability to represent interactions between moist convection and the large-scale circulation in the context of a Walker cell flow over a planetary scale two-dimensional domain. The SSTSP represents convective motions in each column of the large-scale model by embedding a cloud-resolving model, and relies on a sparse sampling in both space and time to reduce computational cost of explicit simulation of convective processes. Simulations are performed varying the spatial compression and/or temporal acceleration, and results are compared to the cloud-resolving simulation reported previously. The algorithm is able to reproduce a broad range of circulation features for all temporal accelerations and spatial compressions, but significant biases are identified. Precipitation tends to be too intense and too localized over warm waters when compared to the cloud-resolving simulations. It is argued that this is because coherent propagation of organized convective systems from one large-scale model column to another is difficult when superparameterization is used, as noted in previous studies. The Walker cell in all simulations exhibits low-frequency variability on a time scale of about 20 days, characterized by four distinctive stages: suppressed, intensification, active, and weakening. The SSTSP algorithm captures spatial structure and temporal evolution of the variability. This reinforces the confidence that SSTSP preserves fundamental interactions between convection and the large-scale flow, and offers a computationally efficient alternative to traditional convective parameterizations.

scholarly journals Performance of k-ω and k-ε Model for Blood Flow Simulation in Stenosed Artery

2021 ◽  
Vol 40 (2) ◽  
pp. 111-125
Author(s):  
Md Alamgir Kabir ◽  
Kausari Sultana ◽  
Md Ashraf Uddin
Keyword(s):  
Experimental Data ◽  
Blood Flow ◽  
Large Scale ◽  
Flow Behavior ◽  
Computational Cost ◽  
Flow Simulation ◽  
Turbulence Models ◽  
Arterial Stenosis ◽  
Blood Flow Simulation ◽  
Stenosed Artery

Blood flow through arterial stenosis can play a crucial role at the post stenotic flow regions. This produces a disturbance in the normal flow path. The intensity of the flow disturbance (i.e. laminar, transitional and turbulent flow characteristics) depends not only on the severity of the stenosis but also on the pattern of the geometrical model. In that case, the turbulence model plays vital role to measure these flow disturbances. However, it is very important to choose a proper flow simulation model that can predict the flow behavior of fluid accurately and efficiently with less computational cost and time. Thus, this study aims to analyze the results of two turbulence models i.e. k-ω and k-ε for blood flow simulation to compare their performance for the prediction of the flow behavior. Simulations have been performed with 75% area reductions in the arteries. The results of simulation show that, the flow parameters obtained from the k-ε model exhibits lack of fits with the experimental data. On the other hand, k-ω model can capture large scale gradient in the different parameters of blood flow and has a good agreement with the experimental data. This study suggests that, k-ω model has the better performance comparing to k-ε model to predict the behavior of blood flow in stenosed artery. GANIT J. Bangladesh Math. Soc. 40.2 (2020) 111-125

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

Near Wall Turbulence Effects on Particle Transport and Deposition in Human Tracheobronchial Multi-Level Bifurcation Model

2015 ◽  
Author(s):  
Amir A. Mofakham ◽  
Lin Tian ◽  
Goodarz Ahmadi
Keyword(s):  
Boundary Conditions ◽  
Particle Deposition ◽  
Flow Simulation ◽  
Tracheobronchial Tree ◽  
Lung Model ◽  
Wall Turbulence ◽  
Nano Particles ◽  
Turbulent Regime ◽  
Computationally Efficient ◽  
Multi Level

Transport and deposition of micro and nano-particles in the upper tracheobronchial tree were analyzed using a multi-level asymmetric lung bifurcation model. The multi-level lung model is flexible and computationally efficient by fusing sequence of individual bifurcations with proper boundary conditions. Trachea and the first two generations of the tracheobronchial airway were included in the analysis. In these regions, the airflow is in turbulent regime due to the disturbances induced by the laryngeal jet. Anisotropic Reynolds stress transport turbulence model (RSTM) was used for mean the flow simulation, together with the enhanced two-layer model boundary conditions. Particular attention is given to evaluate the importance of the “quadratic variation of the turbulent fluctuations perpendicular to the wall” on particle deposition in the upper tracheobroncial airways.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

scholarly journals Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

2021 ◽  
pp. 1-13
Author(s):  
Jonghyuk Kim ◽  
Jose Guivant ◽  
Martin L. Sollie ◽  
Torleiv H. Bryne ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computational Cost ◽  
Satellite System ◽  
Measurement Unit ◽  
Electronic Systems ◽  
Computationally Efficient ◽  
Global Correlation ◽  
Aerial Vehicle ◽  
Correlation Information ◽  
Global Navigation Satellite

Abstract This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

scholarly journals A modular microfluidic system based on a multilayered configuration to generate large-scale perfusable microvascular networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tao Yue ◽  
Da Zhao ◽  
Duc T. T. Phan ◽  
Xiaolin Wang ◽  
Joshua Jonghyun Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Circulatory System ◽  
Vertical Direction ◽  
Microfluidic System ◽  
Vital Role ◽  
Microvascular Networks ◽  
Biological Studies ◽  
Wide Range ◽  
High Flexibility

AbstractThe vascular network of the circulatory system plays a vital role in maintaining homeostasis in the human body. In this paper, a novel modular microfluidic system with a vertical two-layered configuration is developed to generate large-scale perfused microvascular networks in vitro. The two-layer polydimethylsiloxane (PDMS) configuration allows the tissue chambers and medium channels not only to be designed and fabricated independently but also to be aligned and bonded accordingly. This method can produce a modular microfluidic system that has high flexibility and scalability to design an integrated platform with multiple perfused vascularized tissues with high densities. The medium channel was designed with a rhombic shape and fabricated to be semiclosed to form a capillary burst valve in the vertical direction, serving as the interface between the medium channels and tissue chambers. Angiogenesis and anastomosis at the vertical interface were successfully achieved by using different combinations of tissue chambers and medium channels. Various large-scale microvascular networks were generated and quantified in terms of vessel length and density. Minimal leakage of the perfused 70-kDa FITC-dextran confirmed the lumenization of the microvascular networks and the formation of tight vertical interconnections between the microvascular networks and medium channels in different structural layers. This platform enables the culturing of interconnected, large-scale perfused vascularized tissue networks with high density and scalability for a wide range of multiorgan-on-a-chip applications, including basic biological studies and drug screening.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

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