scholarly journals Estimation of Nutation Time Constant Model Parameters for On-Axis Spinning Spacecraft

2008 ◽  
Author(s):  
Keith Schlee ◽  
James Sudermann
Keyword(s):  
Time Constant ◽  
Model Parameters

Panel conditional and multinomial logit with time-varying parameters

2015 ◽  
Vol 19 (3) ◽  
Author(s):  
Myoung-jae Lee
Keyword(s):  
Panel Data ◽  
Dynamic Model ◽  
Time Constant ◽  
Unobserved Heterogeneity ◽  
Multinomial Logit ◽  
Model Parameters ◽  
Time Varying ◽  
Varying Parameters ◽  
Conditional Logit ◽  
Time Varying Parameters

AbstractPanel conditional logit estimators (PCLE) in the literature use mostly time-constant parameters. If the panel periods are volatile or long, however, the model parameters can change much. Hence this paper generalizes PCLE with time-constant parameters to PCLE with time-varying parameters; both static and dynamic PCLE are considered for this. The main finding is that time-varying parameters are fully allowed for static PCLE and the dynamic “pseudo” PCLE of [Bartolucci, F. and V. Nigro. 2010. “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a

Observation Frequency Selection Based on Dynamic and Electric Field Excitation Method for Advanced Detection in Coal Mine Roadway

2014 ◽  
Vol 675-677 ◽  
pp. 1301-1307
Author(s):  
Zhi Min Liu ◽  
Xi Gao Liu ◽  
Jin Tao Zhang ◽  
Wei Jie Zhang ◽  
Miao Wu
Keyword(s):  
Electric Field ◽  
Time Constant ◽  
Coal Mine ◽  
Measurement Accuracy ◽  
Model Parameters ◽  
Geological Conditions ◽  
Mine Roadway ◽  
Field Excitation ◽  
Excitation Method ◽  
Electric Field Excitation

The dynamic and electric field excitation method is a new advanced detection method. In this method, the observation frequency and of emission electrode must be reasonably selected according to the actual geological conditions of coal mine roadway to obtain the apparent frequency with adequate Induced Polarization (IP) information of geological anomalies and improve the measurement accuracy of the instrument. This paper completed the simulation of apparent frequency varying with the observation frequency and Cole-Cole model parameters with MATLAB software. The results show that the key to selecting the observation frequency mainly depends on the charge and discharge time constant .Then the paper analyzed the effect of the measured apparent frequency on the measurement accuracy. According to time constant values​​ of geological conditions in coal mine roadway, considering the efficiency and electromagnetic coupling effects on the measurement, it is best to select the observation frequency within a frequency range of 0.05~0.1Hz and the frequency ratio coefficient of 13 or 15. Now apparent IP effect is obtained.

scholarly journals An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2057 ◽  
Author(s):  
Liang Zhang ◽  
Shunli Wang ◽  
Daniel-Ioan Stroe ◽  
Chuanyun Zou ◽  
Carlos Fernandez ◽  
...  
Keyword(s):  
Equivalent Circuit ◽  
Time Constant ◽  
Lithium Battery ◽  
Equivalent Circuit Model ◽  
Circuit Model ◽  
Parameter Determination ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Accurate Identification ◽  
Battery Model

An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model.

Optimization of Waterflooding Utilizing Data Driven Models: An Application of the Two-Phase Capacitance Resistance Model

2021 ◽  
Author(s):  
Ahmed Alghamdi Abdullah Ghamdi ◽  
Daniel Opoku ◽  
Abeeb Awotunde ◽  
Mohamed Mahmoud ◽  
Qinzhuo Liao
Keyword(s):  
Time Constant ◽  
Pore Volume ◽  
Injection Rate ◽  
Data Driven ◽  
Production Data ◽  
Model Parameters ◽  
Two Phase ◽  
Reservoir Characteristics ◽  
Resistance Model ◽  
Injection Rates

Abstract The Capacitance-Resistance Model, commonly known as CRM, is a data-driven model derived from the material balance equation, and only requires production and injection data for history matching and prediction of reservoir performance. The CRM has two model parameters: The input and output are related the first parameter is the connectivity (also called gain, or weight), which is a dimensionless number that quantifies the connectivity between producers and injectors (i.e. how much of the input is supporting the output). The second parameter is the time delay (also called time constant) and is a function of pore volume, total compressibility, and productivity indices, and it represents the time it takes for the input (injection) to result in an output (production). Since the CRM inception in 2005, several authors have further developed it to increase its range of applications. When CRM was first introduced, it was suited most for single-phase reservoirs. A recent improvement of the CRM added two-phase capability. In this project, Two-phase CRM was utilized to test how this tool performed in waterflooding optimization. The main hypothesis in CRM is that the several reservoir characteristics can be inferred from analyzing production and injection data only. These reservoir characteristics are the connectivity, which can be thought of as an analog to permeability, and the time constant, which is a measure of the pore volume and compressibility. CRM does not require core data, logs, seismic, or any rock or fluids properties. This hypothesis, that reservoir characteristics can be inferred from injection and production data, can be challenged easily since most reservoirs have gradients of fluid properties, multi-porosity systems, and heterogeneous formations with different wettability presences. Regardless, several publications have shown that CRM can result in high certainty output. To test the two-phase CRM, three synthetic heterogeneous reservoirs were created. Model 1 was developed with nearly stabilized injection and production data. Model 2 had more fluctuations in the injection data than model 1. And model 3 had extreme fluctuations in injection data compared to model 2 with lower rock and fluid compressibilities. The results presented in this project show that the CRM ability to match field production depends largely on two aspects: first is the compressibility of the system. When the compressibility was lowered in model 3, the CRM achieved excellent results. The second aspect is the degree of the fluctuations in injection rate the CRM is developed upon. Model 2 with a higher degree of injection rate fluctuations than model 1 has achieved a better future prediction performance. CRM model 3 was used to optimize the field waterflooding injection rates subject to two constraints, The first constraint is a set value for maximum field injection rate at any time step while the second constraint limits each injector maximum injection rate. The optimization of the annual injection rates has added 290,000 bbls of oil produced.

Time constant spectra based fitting of thermal model parameters

2017 ◽  
Author(s):  
Tomasz Raszkowski ◽  
Agnieszka Samson ◽  
Tomasz Torzewicz ◽  
Piotr Zajac ◽  
Artur Sobczak ◽  
...  
Keyword(s):  
Time Constant ◽  
Thermal Model ◽  
Model Parameters

scholarly journals Mixed hidden Markov quantile regression models for longitudinal data with possibly incomplete sequences

2016 ◽  
Vol 27 (7) ◽  
pp. 2231-2246 ◽  
Author(s):  
Maria Francesca Marino ◽  
Nikos Tzavidis ◽  
Marco Alfò
Keyword(s):  
Quantile Regression ◽  
Time Constant ◽  
Large Scale ◽  
Real Life ◽  
Random Coefficients ◽  
Drop Out ◽  
Model Parameters ◽  
Time Varying ◽  
Response Dynamics ◽  
Real Life Data

Quantile regression provides a detailed and robust picture of the distribution of a response variable, conditional on a set of observed covariates. Recently, it has be been extended to the analysis of longitudinal continuous outcomes using either time-constant or time-varying random parameters. However, in real-life data, we frequently observe both temporal shocks in the overall trend and individual-specific heterogeneity in model parameters. A benchmark dataset on HIV progression gives a clear example. Here, the evolution of the CD4 log counts exhibits both sudden temporal changes in the overall trend and heterogeneity in the effect of the time since seroconversion on the response dynamics. To accommodate such situations, we propose a quantile regression model, where time-varying and time-constant random coefficients are jointly considered. Since observed data may be incomplete due to early drop-out, we also extend the proposed model in a pattern mixture perspective. We assess the performance of the proposals via a large-scale simulation study and the analysis of the CD4 count data.

scholarly journals Effects of data time-step on the accuracy of calibrated rainfall–streamflow model parameters: practical aspects of uncertainty reduction

2012 ◽  
Vol 44 (3) ◽  
pp. 430-440 ◽  
Author(s):  
Ian G. Littlewood ◽  
Barry F. W. Croke
Keyword(s):  
Time Constant ◽  
Discrete Time ◽  
Decay Time ◽  
Decay Time Constant ◽  
Model Parameters ◽  
Effective Rainfall ◽  
Time Step ◽  
Loss Of Information ◽  
Hourly Data ◽  
Streamflow Data

The effects of data time-step on the accuracy of calibrated parameters in a discrete-time conceptual rainfall–streamflow model are reviewed and further investigated. A quick-flow decay time constant of 19.9 hr, calibrated for the 10.6 km2 Wye at Cefn Brwyn using daily data, massively overestimates a reference value of 3.76 hr calibrated using hourly data (an inaccuracy of 16.1 hr or 429%). About 42 and 58% of the inaccuracy are accounted for by loss of information in the effective rainfall and streamflow data, respectively. A slow-flow decay time constant is inaccurate by about +111%, of which about 94 and 17 percentage points (85 and 15% of the absolute inaccuracy) are due to loss of information in the effective rainfall and streamflow data, respectively. Discrete-time rainfall–streamflow model parameter inaccuracy caused by data time-step effects is discussed in terms of its implications for parameter regionalisation (including database aspects) and catchment-scale process studies.

Fuel Cell Frequency Response Function Modeling for Control Applications

2010 ◽  
Author(s):  
Thomas C. H. Roberts ◽  
Patrick J. Cunningham
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Transfer Function ◽  
Frequency Response ◽  
Response Function ◽  
Time Constant ◽  
Frequency Response Function ◽  
Model Parameters ◽  
Order Model ◽  
First Order

This paper provides the framework for first-order transfer function modeling of a fuel cell for controls use. It is shown that under specific conditions a fuel cell can be modeled as a first-order system. With a first order model, it is possible to determine how the fuel cell responds dynamically on a systems level before incorporating it into a larger more complex system. Current data sheets for fuel cells provide limited information of the output of the fuel cell, and a polarization curve based on static operation. This is vital information, but gives no insight into how the fuel cell responds under dynamic conditions. Dynamic responses are important when incorporating fuel cells as a power source in larger systems, such as automobiles, as loads and conditions are constantly changing. The modeling technique used in this research is the frequency response function. In this approach an experimental frequency response, or Bode plot, is computed from a frequency rich input signal and corresponding output signal. Here the controlled input is the Hydrogen flow and the output is the fuel cell voltage. During these tests, the fuel cell was connected to a constant resistance load. Using the frequency response function approach, a family of first-order transfer function models was created for a fuel cell at different operating temperatures and reactant relative humidity. These models are validated through comparison to experimental step responses. From this family of models the variations in the first-order model parameters of static gain and time constant are quantified. Static gain varied from 0.675 to 0.961 and the time constant ranged between 4.5 seconds and 10.5 seconds.

scholarly journals On the use of the Cole–Cole equations in spectral induced polarization

2013 ◽  
Vol 195 (1) ◽  
pp. 352-356 ◽  
Author(s):  
Andrey Tarasov ◽  
Konstantin Titov
Keyword(s):  
Time Constant ◽  
Angular Frequency ◽  
Induced Polarization ◽  
Model Parameters ◽  
Data Sets ◽  
Polarization Data ◽  
Spectral Induced Polarization ◽  
The Difference ◽  
Fitting Model ◽  
Fitting Parameters

Abstract Two different equations, both of which are often called ‘the Cole–Cole equation’, are widely used to fit experimental Spectral Induced Polarization data. The data are compared on the basis of fitting model parameters: the chargeability, the time constant and the exponent. The difference between the above two equations (the Cole–Cole equation proposed by the Cole brothers and Pelton's equation) is manifested in one of the fitting parameters, the time constant. The Cole–Cole time constant is an inverse of the peak angular frequency of the imaginary conductivity, while Pelton's time constant depends on the chargeability and exponent values. The difference between the time constant values corresponding to the above two equations grows with the increase of the chargeability value, and with the decrease of the Cole–Cole exponent value. This issue must be taken into consideration when comparing the experimental data sets for high polarizability media presented in terms of the Cole–Cole parameters.

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