Temperature Estimation and Precision Control of RTP Systems by Multi-Zone Lamp Interference and Wafer Emissivity Compensation I: Model Identification

1997 ◽  
Vol 470 ◽  
Author(s):  
Sergey Belikov ◽  
Jalil Kamali ◽  
Yong Jin Lee ◽  
Mehrdad Moslehi
Keyword(s):  
Computational Algorithm ◽  
Model Identification ◽  
Model Parameters ◽  
Back Side ◽  
Heating Effect ◽  
Temperature Estimation ◽  
Precision Control ◽  
Temperature Dynamics ◽  
Window Chamber ◽  
Emissivity Compensation

ABSTRACTRTP temperature measurement using conventional pyrometric technique has serious limitations such as uncertainty of emissivity, lamp interference, and window/chamber heating effect. In this paper, we propose to compensate for these effects using a real-time computational algorithm based on physical model of pyrometric detectors and wafer temperature dynamics. We consider an RTP system for processing 200 mm wafers with five circular zones of heating lamps and ten pyrometric sensors, five of which measure the radiation from five optically isolated dummy lamps and the rest measure the radiation of the wafer back side at different radial positions. Thermocouple measurements are also used to identify the model parameters. Part I of the paper is concerned with modeling and parameter identification.

scholarly journals A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2890
Author(s):  
Alessio Giorgini ◽  
Rogemar S. Mamon ◽  
Marianito R. Rodrigo
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Harmonic Oscillator ◽  
Numerical Simulations ◽  
Model Parameters ◽  
Call Option ◽  
Temperature Model ◽  
Option Prices ◽  
Harmonic Oscillator Model ◽  
Temperature Dynamics

Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are presented. The temperature model is used to price a one-month call option and a sensitivity analysis is undertaken to examine how call option prices are affected when the model parameters are varied.

scholarly journals Black-Box Behavioral Modeling of Voltage and Frequency Response Characteristic for Islanded Microgrid

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2049 ◽  
Author(s):  
Yong Shi ◽  
Dong Xu ◽  
Jianhui Su ◽  
Ning Liu ◽  
Hongru Yu ◽  
...  
Keyword(s):  
Frequency Response ◽  
Least Squares Method ◽  
Model Identification ◽  
Response Characteristic ◽  
Black Box ◽  
System Stability ◽  
Model Parameters ◽  
Response Model ◽  
Modeling Process ◽  
Damped Least Squares

The voltage and frequency response model of microgrid is significant for its application in the design of secondary voltage frequency controller and system stability analysis. However, most models developed for this aspect are complex in structure due to the difficult mechanism modeling process and are only suitable for offline identification. To solve these problems, this paper proposes a black-box modeling method to identify the voltage and frequency response model of microgrid online. Firstly, the microgrid system is set as a two-input, two-output black-box system and can be modeled only by data sampled at the input and output ports. Therefore, the simplicity of modeling steps can be guaranteed. Meanwhile, the recursive damped least squares method is used to realize the online model identification of the microgrid system, so that the model parameters can be adjusted with the change of the microgrid operating structure, which makes the model more adaptable. The paper analyzes the black-box modeling process of the microgrid system in detail, and the microgrid platform, including 100 kW rated power inverters, is employed to validate the analysis and experimental results.

Dissolved Oxygen Dynamics of a Stream: Model Discrimination and Estimation of Parameter Variability Using an Extended Kalman Filter

1984 ◽  
Vol 16 (5-7) ◽  
pp. 561-569 ◽  
Author(s):  
B J Cosby
Keyword(s):  
Model Identification ◽  
Model Parameters ◽  
Low Light ◽  
Short Period ◽  
Kaiman Filter ◽  
Short And Long Term ◽  
Highly Correlated ◽  
Oxygen Dynamics ◽  
Stream Model

A model identification procedure is presented based on an extended Kaiman filter (EKF) applied to the oxygen mass balance equation of a stream. The procedure is used with data from a small Danish stream to discriminate among eight mathematical models of the photosynthesis-light (P-I) response of macrophytes in the stream. Each model was tested for adequacy using objective criteria based on the expected behavior of the innovations sequence (the time series of differences between predicted and observed oxygen concentrations) derived from the EKF. Temporal variation of the model parameters was also examined using the EKF. The maximum photosynthetic rate (Pm) varied slowly over the year but was essentially stationary within any three to six day period. The low light efficiency of photosynthesis (Eo) varied from day to day within any short period, but three to six day means of Eo were essentially stationary over the year. The ratio (Ik = Pm/Eo) was highly correlated with both short and long term variations in daily mean light intensity.

Identification of Certain Polynomial Nonlinear Structures by Adaptive Selectively-Sensitive Excitation

1993 ◽  
Vol 115 (3) ◽  
pp. 246-255 ◽  
Author(s):  
Y. Ben-Haim
Keyword(s):  
Vibrational Excitation ◽  
Model Identification ◽  
Elastic Beam ◽  
Estimation Procedure ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Nonlinear Part ◽  
Precise Knowledge ◽  
Low Dimensional ◽  
Selective Sensitivity

This paper presents a method for identification of certain polynomial nonlinear dynamic systems by adaptive vibrational excitation. The identification is based on the concept of selective sensitivity and is implemented by an adaptive multihypothesis estimation algorithm. The central problem addressed by this method is reduction of the dimensionality of the space in which the model identification is performed. The method of selective sensitivity allows one to design an excitation which causes the response to be selectively sensitive to a small set of model parameters and insensitive to all the remaining model parameters. The identification of the entire system thus becomes a sequence of low-dimensional estimation problems. The dynamical system is modelled as containing both a linear and a nonlinear part. The estimation procedure presumes precise knowledge of the linear model and knowledge of the structure, though not the parameter values, of the nonlinear part of the model. The theory is developed for three different polynomial forms of the nonlinear model: quadratic, cubic and hybrid polynomial nonlinearities. The estimation procedure is illustrated through simulated identification of quadratic nonlinearities in the small-angle vibrations of a uniform elastic beam.

scholarly journals Nodular Cast Iron ONERA Fatigue Model Fitting

2017 ◽  
Vol 50 (3) ◽  
pp. 179-181 ◽  
Author(s):  
Asko Kumpula ◽  
Joona Vaara ◽  
Anton Leppänen ◽  
Tero Frondelius
Keyword(s):  
Cast Iron ◽  
Software Package ◽  
Model Fitting ◽  
Model Identification ◽  
Nodular Cast Iron ◽  
Model Parameters ◽  
Stress Effect ◽  
Mean Stress Effect ◽  
Fatigue Model ◽  
Iron Material

ONERA fatigue model identification has been carried out for the nodular cast iron material. Selected fatigue model considers mean stress effect, temperature dependency, multiaxiality and non-linear damage cumulation due to variable amplitude loading. Fitting of model parameters was carried out using the Z-set software package. 

On the Nonlinear Modeling and Identification of Piezoelectric Inertial Actuators

2001 ◽  
Author(s):  
David W. Knowles ◽  
Nader Jalili ◽  
Taufiquar Khan
Keyword(s):  
Experimental Data ◽  
Vibration Control ◽  
Nonlinear Model ◽  
Vibration Suppression ◽  
Nonlinear Modeling ◽  
Model Identification ◽  
Polynomial Form ◽  
Structural Vibration ◽  
Model Parameters ◽  
Lyapunov’S Second Method

Abstract Active counter-force vibration control has significant advantages over the more traditional motion-based active vibration suppression schemes. A piezoelectric ceramic (PZT) inertial actuator is an efficient and inexpensive solution for this type of structural vibration control. In order to properly tune the control parameters of the absorber subsection, an accurate mathematical model is necessary. For this, a nonlinear model for the PZT inertial actuators is presented. In particular, a polynomial form of non-linearity in the dynamical model of the actuator is assumed. An inverse problem is then formed to identify the model parameters of the actuator (absorber). The model parameters consist of the effective mass, damping and stiffness of the actuator as well as the polynomial form of the non-linearity. Using Lyapunov’s second method, the stability conditions for the proposed nonlinear model are established. An experimental setup is developed to validate the proposed nonlinear model. The results of the model identification using the actual experimental data demonstrate that the nonlinear model would better fit the experimental data, when compared to the linear model.

Grey-box model identification of temperature dynamics in a photobioreactor

2017 ◽  
Vol 121 ◽  
pp. 125-133 ◽  
Author(s):  
A. Jiménez-González ◽  
M. Adam-Medina ◽  
M.A. Franco-Nava ◽  
G.V. Guerrero-Ramírez
Keyword(s):  
Model Identification ◽  
Box Model ◽  
Temperature Dynamics

scholarly journals Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains

2021 ◽  
Author(s):  
Farid K. Moghadam ◽  
Geraldo F. de S. Rebouças ◽  
Amir R. Nejad
Keyword(s):  
Dynamic Properties ◽  
Model Identification ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Model Parameters ◽  
Test Case ◽  
Digital Twin ◽  
Twin Model ◽  
Control And Monitoring System ◽  
Useful Lifetime

AbstractThis paper presents a multi-degree of freedom torsional model of drivetrain system as the digital twin model for monitoring the remaining useful lifetime of the drivetrain components. An algorithm is proposed for the model identification, which receives the torsional response and estimated values of rotor and generator torques, and calculates the drivetrain dynamic properties, e.g. eigenvalues, and torsional model parameters. The applications of this model in prediction of gearbox remaining useful lifetime is discussed. The proposed method is computationally fast, and can be implemented by integrating with the current turbine control and monitoring system without a need for a new system and sensors installation. A test case, using 5 MW reference drivetrain, has been demonstrated.

scholarly journals Parameter inference with analytical propagators for stochastic models of autoregulated gene expression

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Frits Veerman ◽  
Nikola Popović ◽  
Carsten Marr
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Model Identification ◽  
Simulated Data ◽  
Model Parameters ◽  
Stochastic Gene Expression ◽  
Parameter Inference ◽  
Efficient Approximation ◽  
Cell Data ◽  
Autoregulatory Mechanism

Abstract Stochastic gene expression in regulatory networks is conventionally modelled via the chemical master equation (CME). As explicit solutions to the CME, in the form of so-called propagators, are oftentimes not readily available, various approximations have been proposed. A recently developed analytical method is based on a separation of time scales that assumes significant differences in the lifetimes of mRNA and protein in the network, allowing for the efficient approximation of propagators from asymptotic expansions for the corresponding generating functions. Here, we showcase the applicability of that method to simulated data from a ‘telegraph’ model for gene expression that is extended with an autoregulatory mechanism. We demonstrate that the resulting approximate propagators can be applied successfully for parameter inference in the non-regulated model; moreover, we show that, in the extended autoregulated model, autoactivation or autorepression may be refuted under certain assumptions on the model parameters. These results indicate that our approach may allow for successful parameter inference and model identification from longitudinal single cell data.

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