Articles
Vol. 6 No. 4 (2019)
Minimal Realization of Linear Graph Models for Multi-physics Systems
-
Submitted
-
February 5, 2024
-
Published
-
2024-02-05
Abstract
An engineering system may consist of several different types of components, belonging to such physical “domains” as mechanical, electrical, fluid, and thermal. It is termed a multi-domain (or multi-physics) system. The present paper concerns the use of linear graphs (LGs) to generate a minimal model for a multi-physics system. A state-space model has to be a minimal realization. Specifically, the number of state variables in the model should be the minimum number that can completely represent the dynamic state of the system. This choice is not straightforward. Initially, state variables are assigned to all the energy-storage elements of the system. However, some of the energy storage elements may not be independent, and then some of the chosen state variables will be redundant. An approach is presented in the paper, with illustrative examples in the mixed fluid-mechanical domains, to illustrate a way to recognize dependent energy storage elements and thereby obtain a minimal state-space model. System analysis in the frequency domain is known to be more convenient than in the time domain, mainly because the relevant operations are algebraic rather than differential. For achieving this objective, the state space model has to be converted into a transfer function. The direct way is to first convert the state-space model into the input-output differential equation, and then substitute the time derivative by the Laplace variable. This approach is shown in the paper. The same result can be obtained through the transfer function linear graph (TF LG) of the system. In a multi-physics system, first the physical domains have to be converted into an equivalent single domain (preferably, the output domain of the system), when using the method of TFLG. This procedure is illustrated as well, in the present paper.
Downloads
Download data is not yet available.
-
Seyed Amir Saeedi-Sini,
Sedigheh Sina,
Mohammad Hossein Sadeghi,
Ebrahim Farajzadeh,
Development and characterization of a Fricke gel dosimeter for precise measurement in low-dose photon fields
,
Instrumentation: Vol. 11 No. 3 (2024)
-
Tingwei Zhao,
Juan Wang,
Jiangxuan Che,
Yingjie Bian,
Tianyu Chen,
Performance Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on CEEMDAN-KPCA and DA-GRU Networks
,
Instrumentation: Vol. 11 No. 1 (2024)
-
Shining TIAN ,
Jihua LU ,
Boyu GU ,
Hua WANG,
Medical Diagnosis System Based on Fast-weights Scheme
,
Instrumentation: Vol. 7 No. 1 (2020)
-
KARRAY Fakhri ,
AMARA Hassene Ben ,
End-to-End Multiview Gesture Recognition for Autonomous Car Parking System
,
Instrumentation: Vol. 6 No. 3 (2019)
-
Na Feng,
Fei Fan,
Guanglin Xu,
Lianqing Yu,
Deep Reinforcement Learning Based AGV Self-navigation Obstacle Avoidance Method
,
Instrumentation: Vol. 9 No. 4 (2022)
-
Sheng Ai,
Yitao Chen,
Fang Liu,
Aoxiang Zhu,
Pill Defect Detection Based on Improved YOLOv5s Network
,
Instrumentation: Vol. 9 No. 3 (2022)
-
Rongxin XING ,
Han WANG,
Yurong HU ,
Liang WEI ,
Xiaosong CHEN ,
Yongming WU ,
Envelop Tracking and Measurement
,
Instrumentation: Vol. 7 No. 1 (2020)
-
Weinan Li,
Saixin Shi,
Hongxia Tang,
Liang Chen,
Jiawei Zhang,
Hao Tang,
Jianhua Zhao,
Study on Sealing Characteristics of Sliding Seal Assembly of Aircraft Hydraulic Actuator
,
Instrumentation: Vol. 11 No. 1 (2024)
-
Weihao Xiao,
Xuhong Huang,
Time Symmetry Analysis of Nonlinear Parity Based on S-P Compensation Network Structure
,
Instrumentation: Vol. 9 No. 1 (2022)
-
Lichen Shi,
jiahang Guo,
Haitao Wang,
A Precision machining equipment fault diagnosis based on CWT and improved ResNeXt
,
Instrumentation: Vol. 11 No. 2 (2024): Instrumentation Volume 11 Issue 2
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.