Analysis of Operating Deflection Shapes Based on Subspace Method

1997 ◽  
Author(s):  
Nobutaka Tsujiuchi ◽  
Yuichi Matsumura ◽  
Takayuki Koizumi
Keyword(s):  
Experimental Data ◽  
Time Domain ◽  
State Space Model ◽  
Measurement Data ◽  
Time Varying ◽  
Subspace Method ◽  
Identification Scheme ◽  
Space Model ◽  
Time Varying Systems ◽  
Frequency Components

Abstract In this paper, we propose the new method to identify the Operating Deflection Shapes (ODSs) from the measurement data of time domain. At first, we present the identification scheme of ODSs based on a state-space model. Then the scheme is extended to identify the ODSs adaptively for the time-varying systems by using the URV Decomposition (URVD). Proposed scheme is able to decompose the deformation of a structure under operating condition into the underlying superposition of well excited frequency components. This paper introduces the algorithm and shows the effectiveness of our proposed scheme applyed for both synthesized and experimental data.

Estimating Time-Varying Networks With a State-Space Model

2020 ◽  
Author(s):  
Shaowen Liu ◽  
Massimiliano Caporin ◽  
Sandra Paterlini
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Space Model ◽  
Time Varying Networks

Analysis of a Hydrofoil Craft with a Suspension System

2020 ◽  
Author(s):  
Michel Touw ◽  
Jacob Lotz ◽  
Ido Akkerman
Keyword(s):  
Experimental Data ◽  
Control System ◽  
State Space ◽  
State Space Model ◽  
Suspension System ◽  
Future Research ◽  
Passive System ◽  
Height Control ◽  
Space Model ◽  
Front Wing

In this paper we investigate the efficacy of augmenting, or replacing, an active height control system for a submerged hydrofoil with a passive system based on springs and dampers. A state-space model for submerged hydrofoils is formulated and extended to allow for a suspension at the front wing, aft wing or both wings. The model is partially verified by obtaining results in the fixed-wing limit and comparing these with experimental data from the MARIN Foiling Future Demonstrator. In the current study we limit ourselves to translational springs, only allowing suspension motion in the heave direction. This results in unfavorable behavior: either the motions increased or the system becomes unstable. It is therefore recommended for future research to try rotational springs.

scholarly journals Time-Domain Substructure Transient Vibration Transfer Path Analysis Based on Time-Varying Frequency Response Functions under Operational Excitations

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Rong He ◽  
Hong Zhou
Keyword(s):  
Path Analysis ◽  
Frequency Response ◽  
Time Domain ◽  
Time Varying ◽  
Response Functions ◽  
Transient Vibration ◽  
Transfer Path ◽  
Time Varying Systems ◽  
Transfer Path Analysis ◽  
The Time Domain

The time-domain substructure inverse matrix method has become a popular method to detect and diagnose problems regarding vehicle noise, vibration, and harshness, especially for those impulse excitations caused by roads. However, owning to its reliance on frequency response functions (FRFs), the approach is effective only for time-invariable linear or weak nonlinear systems. This limitation prevents this method from being applied to a typical vehicle suspension substructure, which shows different nonlinear characteristics under different wheel transient loads. In this study, operational excitation was considered as a key factor and applied to calculate dynamic time-varying FRFs to perform accurate time-domain transient vibration transfer path analysis (TPA). The core idea of this novel method is to divide whole coupled substructural relationships into two parts: one involved time-invariable components; normal FRFs could be obtained through tests directly. The other involved numerical computations of the time-domain operational loads matrix and FRFs matrix in static conditions. This method focused on determining dynamic FRFs affected by operational loads, especially the severe transient ones; these loads are difficult to be considered in other classical TPA approaches, such as operational path analysis with exogenous inputs (OPAX) and operational transfer path analysis (OTPA). Experimental results showed that this new approach could overcome the limitations of the traditional time-domain substructure TPA in terms of its strict requirements within time-invariable systems. This is because in the new method, time-varying FRFs were calculated and used, which could make the FRFs at the system level directly adapt to time-varying systems from time to time. In summary, the modified method extends TPA objects studied in time-invariable systems to time-varying systems and, thus, makes a methodology and application innovation compared to traditional the time-domain substructure TPA.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP)

2016 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
I PUTU GEDE DIAN GERRY SUWEDAYANA ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Percentage Error ◽  
Time Varying ◽  
Mean Square ◽  
Explanatory Variables ◽  
Space Model ◽  
A Value ◽  
Time Varying Parameter ◽  
Kalman Filter Algorithm

The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP) model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE) is equal to dan root mean square percentage error (RMSPE) is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016).

scholarly journals Finite-TimeH∞Filtering for Linear Continuous Time-Varying Systems with Uncertain Observations

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Huihong Zhao ◽  
Chenghui Zhang
Keyword(s):  
Quadratic Form ◽  
Continuous Time ◽  
Finite Time ◽  
Krein Space ◽  
State Space Model ◽  
Time Varying ◽  
Bounded Noise ◽  
Time Varying Systems ◽  
Kreĭn Space ◽  
Filtering Problem

This paper is concerned with the finite-timeH∞filtering problem for linear continuous time-varying systems with uncertain observations andℒ2-norm bounded noise. The design of finite-timeH∞filter is equivalent to the problem that a certain indefinite quadratic form has a minimum and the filter is such that the minimum is positive. The quadratic form is related to a Krein state-space model according to the Krein space linear estimation theory. By using the projection theory in Krein space, the finite-timeH∞filtering problem is solved. A numerical example is given to illustrate the performance of theH∞filter.

Path Flow Estimation Using Time Varying Coefficient State Space Model

2009 ◽  
Author(s):  
Yow-Jen Jou ◽  
Chien-Lun Lan ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Flow Estimation ◽  
Space Model ◽  
Varying Coefficient ◽  
Path Flow Estimation

scholarly journals Extended Application Of Time-Varying Observer For Rigid Rotors Unbalance Estimation During Acceleration

2018 ◽  
Vol 167 ◽  
pp. 02015
Author(s):  
Xunxing Yu ◽  
Kuanmin Mao ◽  
Yaming Zhu
Keyword(s):  
State Space ◽  
Control Strategy ◽  
State Space Model ◽  
Time Varying ◽  
Rigid Rotor ◽  
Space Model ◽  
Active Balancing ◽  
Extended Application ◽  
Balancing Control ◽  
Rigid Rotors

Unbalance is one of essential problems for modern rotating machines. In this work, an improved time-varying observer is proposed to estimate the unbalance of rigid rotor during acceleration. In order to fitting different speed acceleration laws, the unbalance forces have been included in an new designed augmented states, meanwhile the state space model of rigid rotor has been also developed. The developed state space model is transformed to a canonical transformation and a new designed time-varying observer can be obtained. The estimated unbalances can be directly obtained by using this time-varying observer. This method would be very helpful for active balancing control strategy during acceleration.

A State Space Thermal Model for HEV/EV Non-Linear and Time-Varying Battery Thermal Systems

2011 ◽  
Author(s):  
Xiao Hu ◽  
Shaohua Lin ◽  
Scott Stanton ◽  
Wenyu Lian
Keyword(s):  
State Space ◽  
Thermal Management ◽  
State Space Model ◽  
Time Varying ◽  
Cfd Model ◽  
Thermal Systems ◽  
Space Model ◽  
Battery Thermal Management ◽  
Non Linear ◽  
Electrical Vehicle

Battery thermal management for high power applications such as electrical vehicle (EV) or hybrid electrical vehicle (HEV) is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. While computational fluid dynamics (CFD) has been used quite successfully for battery thermal management, CFD models can be too large and too slow for repeated transient thermal analysis, especially for a battery module or pack. A state space model based on CFD results can be used to replace the original CFD model. The state space model runs approximately two orders of magnitude faster and yet under some conditions obtains equivalent results as the original CFD model. The state space model is based on linear and time-invariant (LTI) system theory. The main limitation of the method is that the method applies strictly speaking to systems that satisfy both linearity and time invariance conditions. General battery cooling problems unfortunately do not strictly satisfy those two conditions. This paper examines quantitatively the amount of error involved if these two conditions are not met. It turns out that these conditions can be relaxed in some ways while preserving satisfactory results for non-linear and time-varying battery thermal systems. This paper also discusses non-linear curve fitting needed for the method.

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