This short report briefly illustrates the main ingredients required to perform Nonlinear Time History Analyses (NLTHAs) of a Single Degree of Freedom (SDF) system having rate-independent hysteretic behavior.
The Vaiana Rosati Model - Differential Formulation (VRM DF) is adopted to simulate the behavior of the rate-independent hysteretic element.
The second-order Ordinary Differential Equation (ODE) of motion is replaced by an equivalent system of three coupled first-order ODEs and numerically solved by using the MATLAB® ode45 solver that is based on an explicit fourth-fifth-order Runge Kutta Method (RKM).
This short report briefly illustrates the main ingredients required to perform Nonlinear Time History Analyses (NLTHAs) of a Single Degree of Freedom (SDF) system having rate-independent hysteretic behavior.
The Vaiana Rosati Model - Analytical Formulation (VRM AF) is adopted to simulate the behavior of the rate-independent hysteretic element.
The second-order Ordinary Differential Equation (ODE) of motion is numerically solved by using the Chang's Family of Explicit structure-dependent time integration Methods (CFEMs).
Identification of the Mathematical Models of Complex Relaxation Processes in ...Vladimir Bakhrushin
The approach to solving the problem of complex relaxation spectra is presented.
Presentation for the XI International Conference on Defect interaction and anelastic phenomena in solids. Tula, 2007.
On selection of periodic kernels parameters in time series predictioncsandit
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIREditor IJMTER
Suppose a composite system consisting of two subsystems designated as ‘P’ and
‘Q’ connected in series. Subsystem ‘P’ consists of N non-identical units in series, while the
subsystem ‘Q’ consists of three identical components in parallel redundancy.
We have implemented a multiple precision ODE solver based on high-order fully implicit Runge-Kutta(IRK) methods. This ODE solver uses any order Gauss type formulas, and can be accelerated by using (1) MPFR as multiple precision floating-point arithmetic library, (2) real tridiagonalization supported in SPARK3, of linear equations to be solved in simplified Newton method as inner iteration, (3) mixed precision iterative refinement method\cite{mixed_prec_iterative_ref}, (4) parallelization with OpenMP, and (5) embedded formulas for IRK methods. In this talk, we describe the reason why we adopt such accelerations, and show the efficiency of the ODE solver through numerical experiments such as Kuramoto-Sivashinsky equation.
A short report that briefly illustrates the analytical formulation of the Vaiana-Rosati Model of hysteresis. In such a report, you can also find the related matlab code.
This short report briefly illustrates the main ingredients required to perform Nonlinear Time History Analyses (NLTHAs) of a Single Degree of Freedom (SDF) system having rate-independent hysteretic behavior.
The Vaiana Rosati Model - Analytical Formulation (VRM AF) is adopted to simulate the behavior of the rate-independent hysteretic element.
The second-order Ordinary Differential Equation (ODE) of motion is numerically solved by using the Chang's Family of Explicit structure-dependent time integration Methods (CFEMs).
Identification of the Mathematical Models of Complex Relaxation Processes in ...Vladimir Bakhrushin
The approach to solving the problem of complex relaxation spectra is presented.
Presentation for the XI International Conference on Defect interaction and anelastic phenomena in solids. Tula, 2007.
On selection of periodic kernels parameters in time series predictioncsandit
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIREditor IJMTER
Suppose a composite system consisting of two subsystems designated as ‘P’ and
‘Q’ connected in series. Subsystem ‘P’ consists of N non-identical units in series, while the
subsystem ‘Q’ consists of three identical components in parallel redundancy.
We have implemented a multiple precision ODE solver based on high-order fully implicit Runge-Kutta(IRK) methods. This ODE solver uses any order Gauss type formulas, and can be accelerated by using (1) MPFR as multiple precision floating-point arithmetic library, (2) real tridiagonalization supported in SPARK3, of linear equations to be solved in simplified Newton method as inner iteration, (3) mixed precision iterative refinement method\cite{mixed_prec_iterative_ref}, (4) parallelization with OpenMP, and (5) embedded formulas for IRK methods. In this talk, we describe the reason why we adopt such accelerations, and show the efficiency of the ODE solver through numerical experiments such as Kuramoto-Sivashinsky equation.
A short report that briefly illustrates the analytical formulation of the Vaiana-Rosati Model of hysteresis. In such a report, you can also find the related matlab code.
This Presentation describes, in short, Introduction to Time Series and the overall procedure required for Time Series Modelling including general terminologies and algorithms. However the detailed Mathematics is excluded in the slides, this ppt means to give a start to understanding the Time Series Modelling before going into detailed Statistics.
An Exponential Observer Design for a Class of Chaotic Systems with Exponentia...ijtsrd
In this paper, a class of generalized chaotic systems with exponential nonlinearity is studied and the state observation problem of such systems is explored. Using differential inequality with time domain analysis, a practical state observer for such generalized chaotic systems is constructed to ensure the global exponential stability of the resulting error system. Besides, the guaranteed exponential decay rate can be correctly estimated. Finally, several numerical simulations are given to demonstrate the validity, effectiveness, and correctness of the obtained result. Yeong-Jeu Sun "An Exponential Observer Design for a Class of Chaotic Systems with Exponential Nonlinearity" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38233.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/38233/an-exponential-observer-design-for-a-class-of-chaotic-systems-with-exponential-nonlinearity/yeongjeu-sun
Investigation of auto-oscilational regimes of the system by dynamic nonlinear...IJECEIAES
The paper proposes a method for the analysis and synthesis of self-oscillations in the form of a finite, predetermined number of terms of the Fourier series in systems reduced to single-loop, with one element having a nonlinear static characteristic of an arbitrary shape and a dynamic part, which is the sum of the products of coordinates and their derivatives. In this case, the nonlinearity is divided into two parts: static and dynamic nonlinearity. The solution to the problem under consideration consists of two parts. First, the parameters of self-oscillations are determined, and then the parameters of the nonlinear dynamic part of the system are synthesized. When implementing this procedure, the calculation time depends on the number of harmonics considered in the first approximation, so it is recommended to choose the minimum number of them in calculations. An algorithm for determining the self-oscillating mode of a control system with elements that have dynamic nonlinearity is proposed. The developed method for calculating self-oscillations is suitable for solving various synthesis problems. The generated system of equations can be used to synthesize the parameters of both linear and nonlinear parts. The advantage is its versatility.
ELEG 421 Control Systems Transient and Steady State .docxtoltonkendal
ELEG 421
Control Systems
Transient and Steady State
Response Analyses
Dr. Ashraf A. Zaher
American University of Kuwait
College of Arts and Science
Department of Electrical and Computer Engineering
Layout
2
Objectives
This chapter introduces the analysis of the time response of different
control systems under different scenarios. Only first and second order
systems will be considered in details using analytical and numerical
methods. Extension to higher order systems will be developed. Both
transient and steady state responses will be evaluated. Stability analysis
will be analyzed for different kinds of feedback, while investigating the
effect of both proportional and derivative control actions on the
performance of the closed-loop system. Finally systems types and
steady state errors will be calculated for unity feedback.
Outcomes
By the end of this chapter, students will be able to:
evaluate both transient/steady state responses for control systems,
analyze the stability of closed-loop LTI systems,
investigate the effect of P and I control actions on performance, and
understand dominant dynamics of higher order systems.
Dr. Ashraf Zaher
Introduction
3
Test signals
Transient response
Steady state response
Analytical techniques, and
Numerical (simulation) techniques.
Stability (definition and analysis methods),
Relative stability, and
Effect of P/I control actions on stability and performance.
Summary of the used systems:
First order systems,
Second order systems, and
Higher order systems.
Dr. Ashraf Zaher
Test Signals
4 Dr. Ashraf Zaher
Impulse function:
Used to simulate shock inputs,
Laplace transform: 1.
Step function:
Used to simulate sudden disturbances,
Laplace transform: 1/s.
Ramp function:
Used to simulate gradually changing inputs,
Laplace transform: 1/s2.
Sinusoidal function(s):
Used to test response to a certain frequency,
Laplace transform: s/(s2+ω2) for cos(ωt) and ω/(s2+ω2) for sin(ωt).
White noise function:
Used to simulate random noise,
It is a stochastic signal that is easier to deal with in the time domain.
Total response:
C(s) = R(s)*TF(s) = Ctr(s) + Css(s) → c(t) = ctr(t) + css(t)
Fundamentals
5 Dr. Ashraf Zaher
Definitions:
Zeros (Z) of the TF
Poles (P) of the TF
Transient Response (Natural)
Steady State Response (Forced)
Total Response
Limits:
Initial values
Final values
Systems (?Zs):
First order (one P)
Second order (two Ps)
Higher order!
More:
Stability and relative stability
Steady state errors (unity feedback)
First Order Systems
6 Dr. Ashraf Zaher
TF:
T: time constant
Unit Step Response:
1
1
)(
)(
+
=
TssR
sC
)/1(
11
1
1
1
11
)(
TssTs
T
sTss
sC
+
−=
+
−=
+
=
Ttetc /1)( −−=
632.01)( 1 =−== −eTtc
T
e
Tdt
tdc Tt
t
11)( /
0
== −
=
01)0( 0 =−== etc
11)( =−=∞= −∞etc
First Order Systems.
CHAOS CONTROL VIA ADAPTIVE INTERVAL TYPE-2 FUZZY NONSINGULAR TERMINAL SLIDING...ijcsitcejournal
In this paper, a novel robust adaptive type-2 fuzzy nonsingular sliding mode controller is proposed to
stabilize the unstable periodic orbits of uncertain perturbed chaotic system with internal parameter
uncertainties and external disturbances. This letter is assumed to have an affine form with unknown
mathematical model, the type-2 fuzzy system is used to overcome this constraint. A global nonsingular
terminal sliding mode manifold is proposed to eliminate the singularity problem associated with normal
terminal sliding mode control. The proposed control law can drive system tracking error to converge to
zero in finite time. The adaptive type-2 fuzzy system used to model the unknown dynamic of system is
adjusted on-line by adaptation law deduced from the stability analysis in Lyapunov sense. Simulation
results show the good tracking performances, and the efficiently of the proposed approach.
Debabrata Pal, Aksum University, College of Engineering and Technology Department of Electrical and Computer Engineering Ethiopia, NE Africa, Email:debuoisi@gmail.com,website:www.ijrd.in
On the principle of optimality for linear stochastic dynamic systemijfcstjournal
In this work, processes represented by linear stochastic dynamic system are investigated and by
considering optimal control problem, principle of optimality is proven. Also, for existence of optimal
control and corresponding optimal trajectory, proofs of theorems of necessity and sufficiency condition are
attained.
In this work, we study H∞ control wind turbine fuzzy model for finite frequency(FF) interval. Less conservative results are obtained by using Finsler’s lemma technique, generalized Kalman Yakubovich Popov (gKYP), linear matrix inequality (LMI) approach and added several separate parameters, these conditions are given in terms of LMI which can be efficiently solved numerically for the problem that such fuzzy systems are admissible with H∞ disturbance attenuation level. The FF H∞ performance approach allows the state feedback command in a specific interval, the simulation example is given to validate our results.
A Novel Extended Adaptive Thresholding for Industrial Alarm SystemsKoorosh Aslansefat
Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system’s performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.
Stochastic augmentation by generalized minimum variance control with rst loop...UFPA
In this work we use the RST structure to shape the GMV optimization problem. The RST
controller is tuned by pole assignment based on a second order plant model and a second order desired closedloop model. The derived RST controller is then passed to the GMV generalized output weighting polynomials
in order to produce a stochastic equivalent controller. The result is the equivalence of the RST and the GMV
produced closed-loop dynamics whilst in ideal conditions (without noise or uncertainties), but a more economic
and efficient GMV closed-loop dynamics under an adverse stochastic scenario
Adaptive Projective Lag Synchronization of T and Lu Chaotic Systems IJECEIAES
In this paper, the synchronization problem of T chaotic system and Lu chaotic system is studied. The parameter of the drive T chaotic system is considered unknown. An adaptive projective lag control method and also parameter estimation law are designed to achieve chaos synchronization problem between two chaotic systems. Then Lyapunov stability theorem is utilized to prove the validity of the proposed control method. After that, some numerical simulations are performed to assess the performance of the proposed method. The results show high accuracy of the proposed method in control and synchronization of chaotic systems.
This Presentation describes, in short, Introduction to Time Series and the overall procedure required for Time Series Modelling including general terminologies and algorithms. However the detailed Mathematics is excluded in the slides, this ppt means to give a start to understanding the Time Series Modelling before going into detailed Statistics.
An Exponential Observer Design for a Class of Chaotic Systems with Exponentia...ijtsrd
In this paper, a class of generalized chaotic systems with exponential nonlinearity is studied and the state observation problem of such systems is explored. Using differential inequality with time domain analysis, a practical state observer for such generalized chaotic systems is constructed to ensure the global exponential stability of the resulting error system. Besides, the guaranteed exponential decay rate can be correctly estimated. Finally, several numerical simulations are given to demonstrate the validity, effectiveness, and correctness of the obtained result. Yeong-Jeu Sun "An Exponential Observer Design for a Class of Chaotic Systems with Exponential Nonlinearity" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38233.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/38233/an-exponential-observer-design-for-a-class-of-chaotic-systems-with-exponential-nonlinearity/yeongjeu-sun
Investigation of auto-oscilational regimes of the system by dynamic nonlinear...IJECEIAES
The paper proposes a method for the analysis and synthesis of self-oscillations in the form of a finite, predetermined number of terms of the Fourier series in systems reduced to single-loop, with one element having a nonlinear static characteristic of an arbitrary shape and a dynamic part, which is the sum of the products of coordinates and their derivatives. In this case, the nonlinearity is divided into two parts: static and dynamic nonlinearity. The solution to the problem under consideration consists of two parts. First, the parameters of self-oscillations are determined, and then the parameters of the nonlinear dynamic part of the system are synthesized. When implementing this procedure, the calculation time depends on the number of harmonics considered in the first approximation, so it is recommended to choose the minimum number of them in calculations. An algorithm for determining the self-oscillating mode of a control system with elements that have dynamic nonlinearity is proposed. The developed method for calculating self-oscillations is suitable for solving various synthesis problems. The generated system of equations can be used to synthesize the parameters of both linear and nonlinear parts. The advantage is its versatility.
ELEG 421 Control Systems Transient and Steady State .docxtoltonkendal
ELEG 421
Control Systems
Transient and Steady State
Response Analyses
Dr. Ashraf A. Zaher
American University of Kuwait
College of Arts and Science
Department of Electrical and Computer Engineering
Layout
2
Objectives
This chapter introduces the analysis of the time response of different
control systems under different scenarios. Only first and second order
systems will be considered in details using analytical and numerical
methods. Extension to higher order systems will be developed. Both
transient and steady state responses will be evaluated. Stability analysis
will be analyzed for different kinds of feedback, while investigating the
effect of both proportional and derivative control actions on the
performance of the closed-loop system. Finally systems types and
steady state errors will be calculated for unity feedback.
Outcomes
By the end of this chapter, students will be able to:
evaluate both transient/steady state responses for control systems,
analyze the stability of closed-loop LTI systems,
investigate the effect of P and I control actions on performance, and
understand dominant dynamics of higher order systems.
Dr. Ashraf Zaher
Introduction
3
Test signals
Transient response
Steady state response
Analytical techniques, and
Numerical (simulation) techniques.
Stability (definition and analysis methods),
Relative stability, and
Effect of P/I control actions on stability and performance.
Summary of the used systems:
First order systems,
Second order systems, and
Higher order systems.
Dr. Ashraf Zaher
Test Signals
4 Dr. Ashraf Zaher
Impulse function:
Used to simulate shock inputs,
Laplace transform: 1.
Step function:
Used to simulate sudden disturbances,
Laplace transform: 1/s.
Ramp function:
Used to simulate gradually changing inputs,
Laplace transform: 1/s2.
Sinusoidal function(s):
Used to test response to a certain frequency,
Laplace transform: s/(s2+ω2) for cos(ωt) and ω/(s2+ω2) for sin(ωt).
White noise function:
Used to simulate random noise,
It is a stochastic signal that is easier to deal with in the time domain.
Total response:
C(s) = R(s)*TF(s) = Ctr(s) + Css(s) → c(t) = ctr(t) + css(t)
Fundamentals
5 Dr. Ashraf Zaher
Definitions:
Zeros (Z) of the TF
Poles (P) of the TF
Transient Response (Natural)
Steady State Response (Forced)
Total Response
Limits:
Initial values
Final values
Systems (?Zs):
First order (one P)
Second order (two Ps)
Higher order!
More:
Stability and relative stability
Steady state errors (unity feedback)
First Order Systems
6 Dr. Ashraf Zaher
TF:
T: time constant
Unit Step Response:
1
1
)(
)(
+
=
TssR
sC
)/1(
11
1
1
1
11
)(
TssTs
T
sTss
sC
+
−=
+
−=
+
=
Ttetc /1)( −−=
632.01)( 1 =−== −eTtc
T
e
Tdt
tdc Tt
t
11)( /
0
== −
=
01)0( 0 =−== etc
11)( =−=∞= −∞etc
First Order Systems.
CHAOS CONTROL VIA ADAPTIVE INTERVAL TYPE-2 FUZZY NONSINGULAR TERMINAL SLIDING...ijcsitcejournal
In this paper, a novel robust adaptive type-2 fuzzy nonsingular sliding mode controller is proposed to
stabilize the unstable periodic orbits of uncertain perturbed chaotic system with internal parameter
uncertainties and external disturbances. This letter is assumed to have an affine form with unknown
mathematical model, the type-2 fuzzy system is used to overcome this constraint. A global nonsingular
terminal sliding mode manifold is proposed to eliminate the singularity problem associated with normal
terminal sliding mode control. The proposed control law can drive system tracking error to converge to
zero in finite time. The adaptive type-2 fuzzy system used to model the unknown dynamic of system is
adjusted on-line by adaptation law deduced from the stability analysis in Lyapunov sense. Simulation
results show the good tracking performances, and the efficiently of the proposed approach.
Debabrata Pal, Aksum University, College of Engineering and Technology Department of Electrical and Computer Engineering Ethiopia, NE Africa, Email:debuoisi@gmail.com,website:www.ijrd.in
On the principle of optimality for linear stochastic dynamic systemijfcstjournal
In this work, processes represented by linear stochastic dynamic system are investigated and by
considering optimal control problem, principle of optimality is proven. Also, for existence of optimal
control and corresponding optimal trajectory, proofs of theorems of necessity and sufficiency condition are
attained.
In this work, we study H∞ control wind turbine fuzzy model for finite frequency(FF) interval. Less conservative results are obtained by using Finsler’s lemma technique, generalized Kalman Yakubovich Popov (gKYP), linear matrix inequality (LMI) approach and added several separate parameters, these conditions are given in terms of LMI which can be efficiently solved numerically for the problem that such fuzzy systems are admissible with H∞ disturbance attenuation level. The FF H∞ performance approach allows the state feedback command in a specific interval, the simulation example is given to validate our results.
A Novel Extended Adaptive Thresholding for Industrial Alarm SystemsKoorosh Aslansefat
Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system’s performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.
Stochastic augmentation by generalized minimum variance control with rst loop...UFPA
In this work we use the RST structure to shape the GMV optimization problem. The RST
controller is tuned by pole assignment based on a second order plant model and a second order desired closedloop model. The derived RST controller is then passed to the GMV generalized output weighting polynomials
in order to produce a stochastic equivalent controller. The result is the equivalence of the RST and the GMV
produced closed-loop dynamics whilst in ideal conditions (without noise or uncertainties), but a more economic
and efficient GMV closed-loop dynamics under an adverse stochastic scenario
Adaptive Projective Lag Synchronization of T and Lu Chaotic Systems IJECEIAES
In this paper, the synchronization problem of T chaotic system and Lu chaotic system is studied. The parameter of the drive T chaotic system is considered unknown. An adaptive projective lag control method and also parameter estimation law are designed to achieve chaos synchronization problem between two chaotic systems. Then Lyapunov stability theorem is utilized to prove the validity of the proposed control method. After that, some numerical simulations are performed to assess the performance of the proposed method. The results show high accuracy of the proposed method in control and synchronization of chaotic systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
SDF Hysteretic System 1 - Differential Vaiana Rosati Model
1. Hysteretic Mechanical Systems
and Materials
with Matlab Codes
Version 14 August 2023 Nicolò Vaiana, Ph.D.
University of Naples Federico II
Polytechnic and Basic Sciences School
Department of Structures for Engineering and Architecture
3. P21
Introduction
This short report briefly illustrates the main ingredients required to perform Nonlinear Time History Analyses
(NLTHAs) of a Single Degree of Freedom (SDF) system having rate-independent hysteretic behavior.
The Vaiana Rosati Model - Differential Formulation (VRM DF) is adopted to simulate the behavior of the rate-
independent hysteretic element.
The second-order Ordinary Differential Equation (ODE) of motion is replaced by an equivalent system of three
coupled first-order ODEs and numerically solved by using the MATLAB® ode45 solver that is based on an
explicit fourth-fifth-order Runge Kutta Method (RKM).
Hysteretic Mechanical Systems and Materials
P2
NONLINEAR TIME HYSTORY ANALYSIS
4. P31
Nonlinear Equilibrium Equation
The nonlinear equilibrium equation of the SDF rate-independent hysteretic system is:
𝑚 ሷ
𝑢(𝑡) + 𝑓(𝑡) = 𝑝 𝑡 ,
where ሷ
𝑢(𝑡) is the acceleration of the mass 𝑚, 𝑓(𝑡) represents the rate-independent hysteretic generalized
force, and 𝑝 𝑡 is the external generalized force.
Such a second-order ODE can be replaced by an equivalent system of coupled first-order ODEs. To this end,
the following state variables are first introduced:
𝑥1 𝑡 = 𝑢 𝑡 ,
𝑥2 𝑡 = ሶ
𝑢 𝑡 ,
𝑥3 𝑡 = 𝑓 𝑡 .
Subsequently, they are differentiated with respect to time 𝑡 thus obtaining:
ሶ
𝑥1 𝑡 = ሶ
𝑢 𝑡 = 𝑥2 𝑡 ,
ሶ
𝑥2 𝑡 = ሷ
𝑢 𝑡 = 𝑚−1
𝑝 𝑡 − 𝑥3(𝑡) ,
ሶ
𝑥3 𝑡 = ሶ
𝑓 𝑡 .
Hysteretic Mechanical Systems and Materials
P2
NONLINEAR TIME HYSTORY ANALYSIS
5. P41
Rate-Independent Hysteretic Generalized Force
The expression of ሶ
𝑓 𝑡 is provided by the Vaiana Rosati Model - Differential Formulation (VRM DF):
ሶ
𝑓 𝑡 = 𝑘𝑒 𝑡 + 𝑘𝑏 + sgn ሶ
𝑢 𝑡 𝛼 𝑓
𝑒 𝑡 + 𝑘𝑏𝑢 𝑡 + sgn ሶ
𝑢 𝑡 𝑓0 − 𝑓 𝑡 ሶ
𝑢 𝑡 ,
where:
𝑘𝑒 𝑡 = 𝛽1𝛽2𝑒𝛽2𝑢(𝑡)
+
4𝛾1𝛾2 𝑒−𝛾2 𝑢(𝑡)−𝛾3
1+𝑒−𝛾2 𝑢(𝑡)−𝛾3
2 ,
𝑓
𝑒 𝑡 = 𝛽1𝑒𝛽2𝑢(𝑡)
− 𝛽1 +
4𝛾1
1+𝑒−𝛾2 𝑢(𝑡)−𝛾3
− 2𝛾1.
The solution of the differential equation must satisfy the following initial condition:
𝑓 𝑢(𝑡𝑃) = 𝑓 𝑡𝑃 .
During the generic loading phase ( ሶ
𝑢(𝑡) > 0), the model parameters are:
𝑘𝑏 = 𝑘𝑏
+
, 𝑓0 = 𝑓0
+
, 𝛼 = 𝛼+
, 𝛽1 = 𝛽1
+
, 𝛽2 = 𝛽2
+
, 𝛾1 = 𝛾1
+
, 𝛾2 = 𝛾2
+
, 𝛾3 = 𝛾3
+
,
whereas, during the generic unloading one ( ሶ
𝑢(𝑡) < 0), they are:
𝑘𝑏 = 𝑘𝑏
−
, 𝑓0 = 𝑓0
−
, 𝛼 = 𝛼−
, 𝛽1 = 𝛽1
−
, 𝛽2 = 𝛽2
−
, 𝛾1 = 𝛾1
−
, 𝛾2 = 𝛾2
−
, 𝛾3 = 𝛾3
−
.
Note that the only conditions to be fulfilled are:
𝛼+
> 0, 𝛼−
> 0, 𝑓0
+
> 𝑓0
−
,
since the other parameters can be arbitrary real numbers.
Hysteretic Mechanical Systems and Materials
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NONLINEAR TIME HYSTORY ANALYSIS
6. P51
External Generalized Force
In the case of a sinusoidal harmonic generalized force (left), the expression of 𝑝 𝑡 is:
𝑝 𝑡 = 𝑝0 sin 2𝜋𝑓𝑝𝑡 ,
whereas, in the case of a cosine harmonic generalized force (right), it becomes:
𝑝 𝑡 = 𝑝0 cos 2𝜋𝑓𝑝𝑡 ,
where 𝑝0 and 𝑓𝑝 represent the force amplitude and frequency, respectively.
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NONLINEAR TIME HYSTORY ANALYSIS
7. The system of coupled first-order ODEs to be numerically solved is:
ሶ
𝑥1 𝑡 = 𝑥2 𝑡 ,
ሶ
𝑥2 𝑡 = 𝑚−1
𝑝 𝑡 − 𝑥3(𝑡) ,
ሶ
𝑥3 𝑡 = 𝑘𝑒 𝑡 + 𝑘𝑏 + sgn 𝑥2 𝑡 𝛼 𝑓
𝑒 𝑡 + 𝑘𝑏𝑥1 𝑡 + sgn 𝑥2 𝑡 𝑓0 − 𝑥3 𝑡 𝑥2 𝑡 ,
where:
𝑘𝑒 𝑡 = 𝛽1𝛽2𝑒𝛽2𝑥1 𝑡
+
4𝛾1𝛾2 𝑒−𝛾2 𝑥1 𝑡 −𝛾3
1+𝑒−𝛾2 𝑥1 𝑡 −𝛾3
2 ,
𝑓
𝑒 𝑡 = 𝛽1𝑒𝛽2𝑥1 𝑡
− 𝛽1 +
4𝛾1
1+𝑒−𝛾2 𝑥1 𝑡 −𝛾3
− 2𝛾1,
and:
𝑘𝑏 = 𝑘𝑏
+
, 𝑓0 = 𝑓0
+
, 𝛼 = 𝛼+
, 𝛽1 = 𝛽1
+
, 𝛽2 = 𝛽2
+
, 𝛾1 = 𝛾1
+
, 𝛾2 = 𝛾2
+
, 𝛾3 = 𝛾3
+
, if 𝑥2 𝑡 > 0,
𝑘𝑏 = 𝑘𝑏
−
, 𝑓0 = 𝑓0
−
, 𝛼 = 𝛼−
, 𝛽1 = 𝛽1
−
, 𝛽2 = 𝛽2
−
, 𝛾1 = 𝛾1
−
, 𝛾2 = 𝛾2
−
, 𝛾3 = 𝛾3
−
, if 𝑥2 𝑡 < 0.
To this end, it is adopted the MATLAB® ode45 solver that, being based on an explicit fourth-fifth-order Runge
Kutta formula, allows for the evaluation of the solution at time 𝑡 by adopting the solution at the preceding
time 𝑡𝑃 = 𝑡 − ∆𝑡.
P61
Numerical Method
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NONLINEAR TIME HYSTORY ANALYSIS
8. P71
Results – Sinusoidal Generalized Force
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NONLINEAR TIME HYSTORY ANALYSIS
mass applied force VRM parameters
𝑚 𝑝0 𝑓𝑝 𝑘𝑏 𝑓0 𝛼 𝛽1 𝛽2 𝛾1 𝛾2 𝛾3
Ns2m−1
N Hz Nm−1
N m−1
N m−1
N m−1
m
10 14 1 + 0 1.2 80 0.01 35 2 80 0.006
− 0 1.2 80 - 0.01 - 35 2 80 - 0.006
9. P81
Results – Cosine Generalized Force
Hysteretic Mechanical Systems and Materials
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NONLINEAR TIME HYSTORY ANALYSIS
mass applied force VRM parameters
𝑚 𝑝0 𝑓𝑝 𝑘𝑏 𝑓0 𝛼 𝛽1 𝛽2 𝛾1 𝛾2 𝛾3
Ns2m−1
N Hz Nm−1
N m−1
N m−1
N m−1
m
10 14 1 + 0 1.2 80 0.01 35 2 80 0.006
− 0 1.2 80 - 0.01 - 35 2 80 - 0.006
10. 9
Matlab Code - NLTHA_SYSTEM_1_VRM_DF_RKM.m
% =========================================================================================
% August 2023
% Nonlinear Time History Analysis of SDF Rate-Independent Hysteretic Systems
% Nicolo' Vaiana, Assistant Professor in Structural Mechanics and Dynamics
% Department of Structures for Engineering and Architecture
% University of Naples Federico II
% via Claudio 21, 80125, Napoli, Italy
% e-mail: nicolo.vaiana@unina.it, nicolovaiana@outlook.it
% =========================================================================================
clc; clear all; close all;
%% SDF RATE-INDEPEDENT HYSTERETIC SYSTEM MASS
m = 10; % Ns^2/m
%% VAIANA ROSATI MODEL PARAMETERS
kbp = 0; kbm = 0; % N/m
f0p = 1.2; f0m = 1.2; % N
alfap = 80; alfam = 80; % 1/m
beta1p = 0.01; beta1m = -0.01; % N
beta2p = 35; beta2m = -35; % 1/m
gamma1p = 2; gamma1m = 2; % N
gamma2p = 80; gamma2m = 80; % 1/m
gamma3p = 0.006; gamma3m = -0.006; % m
parp = [kbp f0p alfap beta1p beta2p gamma1p gamma2p gamma3p]; % -
parm = [kbm f0m alfam beta1m beta2m gamma1m gamma2m gamma3m]; % -
%% EXTERNAL GENERALIZED FORCE
tv = 0:0.001:10; % s
fp = 1; % Hz
p0 = 14; % N
p = p0*sin(2*pi*fp*tv(1:length(tv))); % N
%% RUNGE-KUTTA METHOD
%% INITIAL SETTING
neq = 3; % - number of equations
IC = [0 0 0]; % - initial conditions [x1 x2 x3]
%% CALCULATIONS AT EACH TIME STEP
options = odeset('RelTol',1e-10,'AbsTol',1e-10);
[t,x] = ode45(@(t,x) ODEs(t, x, neq, m, parp, parm, p, tv), tv, IC, options);
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NONLINEAR TIME HYSTORY ANALYSIS
12. 11
Matlab Code - ODEs.m
function xd = ODEs(t, x, neq, m, parp, parm, pv, tv)
%% EXTERNAL GENERALIZED FORCE
p = interp1(tv,pv,t); % N
%% STATE VARIABLES
u = x(1); % m displacement
ud = x(2); % m/s velocity
f = x(3); % N hysteretic force
%% VAIANA ROSATI MODEL PARAMETERS
if ud > 0
kb = parp(1); f0 = parp(2); alfa = parp(3); beta1 = parp(4);
beta2 = parp(5); gamma1 = parp(6); gamma2 = parp(7); gamma3 = parp(8);
else
kb = parm(1); f0 = parm(2); alfa = parm(3); beta1 = parm(4);
beta2 = parm(5); gamma1 = parm(6); gamma2 = parm(7); gamma3 = parm(8);
end
%% SYSTEM OF ORDINARY DIFFERENTIAL EQUATIONS
xd = zeros(neq,1);
xd(1) = ud;
xd(2) = (p-f)/m;
fe = beta1*exp(beta2*u)-beta1+(4*gamma1/(1+exp(-gamma2*(u-gamma3))))-2*gamma1;
ke = beta1*beta2*exp(beta2*u)+(4*gamma1*gamma2*exp(-gamma2*(u-gamma3)))/(1+exp(-gamma2*(u-gamma3)))^2;
xd(3) = (ke+kb+sign(ud)*alfa*(fe+kb*u+sign(ud)*f0-f))*ud;
end
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NONLINEAR TIME HYSTORY ANALYSIS
13. 12
References
[1] Vaiana N, Sessa S, Marmo F, Rosati L (2018) A class of uniaxial phenomenological models for simulating hysteretic
phenomena in rate-independent mechanical systems and materials. Nonlinear Dynamics 93(3): 1647-1669.
[2] Vaiana N, Sessa S, Marmo F, Rosati L (2019) An accurate and computationally efficient uniaxial phenomenological model for
steel and fiber reinforced elastomeric bearings. Composite Structures 211: 196-212.
[3] Vaiana N, Sessa S, Marmo F, Rosati L (2019) Nonlinear dynamic analysis of hysteretic mechanical systems by combining a
novel rate-independent model and an explicit time integration method. Nonlinear Dynamics 98(4): 2879-2901.
[4] Vaiana N, Sessa S, Rosati L (2021) A generalized class of uniaxial rate-independent models for simulating asymmetric
mechanical hysteresis phenomena. Mechanical Systems and Signal Processing 146: 106984.
[5] Vaiana N, Rosati L (2023) Classification and unified phenomenological modeling of complex uniaxial rate-independent
hysteretic responses. Mechanical Systems and Signal Processing 182: 109539.
[6] Vaiana N, Capuano R, Rosati L (2023) Evaluation of path-dependent work and internal energy change for hysteretic
mechanical systems. Mechanical Systems and Signal Processing 186: 109862.
[7] Vaiana N, Rosati L (2023) Analytical and differential reformulations of the Vaiana–Rosati model for complex rate-independent
mechanical hysteresis phenomena. Mechanical Systems and Signal Processing 199: 110448.
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