Using Autoregressive with Exogenous Input Models to Study Pulsatile Flows

被引:1
|
作者
Duran-Hernandez, Carlos [1 ]
Ledesma-Alonso, Rene [2 ]
Etcheverry, Gibran [1 ]
机构
[1] Univ Americas Puebla, Dept Comp Elect & Mechatron, Cholula 72810, Puebla, Mexico
[2] Univ Americas Puebla, Dept Ind & Mech Engn, Cholula 72810, Puebla, Mexico
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 22期
关键词
autoregressive exogenous input; fluid-structure interaction; computational fluid dynamics; system identification; leaflets; hereditary computation; FLUID-STRUCTURE INTERACTION; ARX MODEL; MITRAL-VALVE; SYSTEM; PHYSIOLOGY;
D O I
10.3390/app10228228
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The content of this paper shows the first outcomes of a supplementary method to simulate the behavior of a simple design formed by two rectangular leaflets under a pulsatile flow condition. These problems are commonly handled by using Fluid-Structure Interaction (FSI) simulations; however, one of its main limitations are the high computational cost required to conduct short time simulations and the vast number of parameter adjustments to simulate different scenarios. In order to overcome these disadvantages, we propose a system identification method with hereditary computation-AutoRegressive with eXogenous (ARX) input method-to train a model with FSI simulation outcomes and then use this model to simulate the outputs that are commonly measured from this kind of simulation, such as the pressure difference and the opening area of the leaflets. Numerical results of the presented methodology show that our model is able to follow the trend with significant agreement with the FSI results, with an average correlation coefficient R of R-tr = 90.14% and R-tr = 92.27% in training; whereas for validation, the average R is R-val = 93.31% and R-val = 83.08% for opening area and pressure difference, respectively. The system identification model is efficiently capable of estimating the outputs of the FSI approach; however, it is not intended to substitute FSI simulations, but to complement them when the requirement is to conduct many repetitions of the phenomena with similar conditions.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 50 条
  • [41] Comparative Study of Elman Neural Network (ENN) and Neural Network Autoregressive With Exogenous Input (NARX) For Flood Forecasting
    Zainorzuli, Siti Maisarah
    Abdullah, Syahrul Afzal Che
    Adnan, Ramli
    Ruslan, Fazlina Ahmat
    2019 IEEE 9TH SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE), 2019, : 11 - 15
  • [42] Mutual nonlinear prediction of cardiovascular variability series: Comparison between exogenous and autoregressive exogenous models
    Faes, Luca
    Porta, Alberto
    Nollo, Giandomenico
    2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16, 2007, : 5955 - +
  • [43] Non-Linear Autoregressive with Exogenous Input (Narx) Chiller Plant Prediction Model
    Universiti Malaysia Pahang, Faculty of Computing, College of Computing and Applied Sciences, Pekan
    26600, Malaysia
    不详
    不详
    Proc. - Int. Conf. Softw. Eng. Comput. Syst. Int. Conf. Comput. Sci. Inf. Manag., ICSECS-ICOCSIM, 1600, (388-393):
  • [44] Parametric identification of linear systems using AutoRegressive with eXogenous input model expansion on Meixner-like orthonormal bases
    Maraoui, Safa
    Bouzrara, Kais
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2020, 42 (02) : 306 - 321
  • [45] Forecasting Gold and Oil Prices Considering US-China Trade War Using Vector Autoregressive with Exogenous Input
    Ulyah, Siti Maghfirotul
    Andreas, Christopher
    Rahmayanti, Ilma Amira
    INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020, 2021, 2329
  • [46] CLASSIFYING AUTOREGRESSIVE MODELS USING DISSIMILARITY MEASURES: A COMPARATIVE STUDY
    Magnant, Clement
    Grivel, Eric
    Giremus, Audrey
    Ratton, Laurent
    Joseph, Bernard
    2015 23RD EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2015, : 998 - 1002
  • [47] Testing for Serial Correlation in Autoregressive Exogenous Models with Possible GARCH Errors
    Li, Hanqing
    Liu, Xiaohui
    Chen, Yuting
    Fan, Yawen
    ENTROPY, 2022, 24 (08)
  • [48] HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows
    Zhou, Fan
    Wang, Zhiyuan
    Zhong, Ting
    Trajcevski, Goce
    Khokhar, Ashfaq
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (10) : 6833 - 6856
  • [50] REGIONAL FIRST ORDER PERIODIC AUTOREGRESSIVE MODELS FOR MONTHLY FLOWS
    Ozcelik, Ceyhun
    PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, 2008, 14 (01): : 11 - 21