scholarly journals Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

2021 ◽  
Author(s):  
Majeed Mohamed
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Experimental Data ◽  
Neural Model ◽  
Model Parameters ◽  
Air Traffic Controller ◽  
Single Output ◽  
Estimated Parameters ◽  
Model Response ◽  
Relative Standard

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.

scholarly journals Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

2018 ◽  
Vol 9 (3) ◽  
pp. 696 ◽  
Author(s):  
Byamakesh Nayak ◽  
Sangeeta Sahu ◽  
Tanmoy Roy Choudhury
Keyword(s):  
Experimental Data ◽  
Transfer Function ◽  
Adaptive Method ◽  
Dc Motor ◽  
Model Parameters ◽  
Current Response ◽  
Adaptive Model ◽  
Unknown Parameters ◽  
Dc Motors ◽  
Estimated Parameters

<p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Method is used for adaption algorithm. To prove the method robustness and for students learning, the simple system of separately excited dc motor is considered in this paper. The experimental data of speed response and corresponding current response are taken and transfer function parameters of  dc motors are adapted based on Nelder-Mead optimisation to match with the experimental data. The effectiveness of estimated parameters with different objective functions are compared and validated with machine specification parameters.</p>

scholarly journals BAYESIAN INFERENCE OF HETEROGENEOUS VISCOPLASTIC MATERIAL PARAMETERS

2018 ◽  
Vol 15 ◽  
pp. 41-45
Author(s):  
Eliška Janouchová ◽  
Anna Kučerová
Keyword(s):  
Experimental Data ◽  
Bayesian Inference ◽  
Material Model ◽  
Isotropic Hardening ◽  
Model Parameters ◽  
Viscoplastic Material ◽  
Loading Test ◽  
Model Response ◽  
Probabilistic Description ◽  
Probabilistic Setting

<p>Modelling of heterogeneous materials based on randomness of model input parameters involves parameter identification which is focused on solving a stochastic inversion problem. It can be formulated as a search for probabilistic description of model parameters providing the distribution of the model response corresponding to the distribution of the observed data</p><p>In this contribution, a numerical model of kinematic and isotropic hardening for viscoplastic material is calibrated on a basis of experimental data from a cyclic loading test at a high temperature. Five material model parameters are identified in probabilistic setting. The core of the identification method is the Bayesian inference of uncertain statistical moments of a prescribed joint lognormal distribution of the parameters. At first, synthetic experimental data are used to verify the identification procedure, then the real experimental data are processed to calibrate the material model of copper alloy.</p>

Simulations of Bending Experiments of Concrete Beams by Stochastic Discrete Model

2014 ◽  
Vol 627 ◽  
pp. 457-460
Author(s):  
Jana Kaděrová ◽  
Jan Eliáš
Keyword(s):  
Experimental Data ◽  
Material Properties ◽  
Discrete Model ◽  
Concrete Beams ◽  
Particle Model ◽  
Model Parameters ◽  
Three Point Bending ◽  
Model Response ◽  
Different Types ◽  
Lattice Particle

The paper describes results of numerical simulations of experiments on concrete beams loaded in three-point bending. Stochastic lattice-particle model has been applied in which the material was represented by discrete particles of random size and location. Additional spatial variability of material properties was introduced by stationary autocorrelated random field. Three different types of geometrically similar beams were modeled: half-notched, fifth-notched and unnotched, each in four different sizes. The deterministic and stochastic model parameters were identified via automatic procedure based on comparison to a subset of experimental data, so that the adequacy of the model response could be validated by comparison with the remaining experimental data.

scholarly journals Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Metals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 876 ◽  
Author(s):  
Ehsan Adeli ◽  
Bojana Rosić ◽  
Hermann G. Matthies ◽  
Sven Reinstädler ◽  
Dieter Dinkler
Keyword(s):  
Experimental Data ◽  
Parameter Identification ◽  
Measurement Errors ◽  
Surface Displacement ◽  
Life Time ◽  
Material Model ◽  
Model Parameters ◽  
Bayesian Approaches ◽  
Model Response ◽  
The Difference

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

scholarly journals Problems of Modelling Toxic Compounds Emitted by a Marine Internal Combustion Engine in Unsteady States

2015 ◽  
Vol 21 (4) ◽  
pp. 57-65 ◽  
Author(s):  
Jacek Rudnicki ◽  
Ryszard Zadrąg
Keyword(s):  
Combustion Engine ◽  
Neural Model ◽  
Multiple Regression Model ◽  
Diagnostic Tools ◽  
Model Parameters ◽  
Single Output ◽  
Frequent Use ◽  
Measurement Results ◽  
Sample Application ◽  
Analytical Tools

Abstract Contemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous analysis of effects and interactions between many output variables. They can also be used as a tool in preparing experimental material for other advanced diagnostic tools, such as the models making use of neural networks which, when properly prepared, enable also analysing measurement results recorded during dynamic processes. The article presents advantages of the use of the abovementioned analytical tools and a sample application of the neural model developed based on the results of examination carried out on the engine research rig.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Parameter Determination for Chloride and Tritium Transport in Undisturbed Lysimeters during Steady Flow

1992 ◽  
Vol 23 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Ole H. Jacobsen ◽  
Feike J. Leij ◽  
Martinus Th. van Genuchten
Keyword(s):  
Experimental Data ◽  
Unsaturated Flow ◽  
Transport Model ◽  
Time Moment ◽  
Breakthrough Curves ◽  
Coarse Sand ◽  
Retardation Factor ◽  
Parameter Determination ◽  
Model Parameters ◽  
Water Fraction

Breakthrough curves of Cl and 3H2O were obtained during steady unsaturated flow in five lysimeters containing an undisturbed coarse sand (Orthic Haplohumod). The experimental data were analyzed in terms of the classical two-parameter convection-dispersion equation and a four-parameter two-region type physical nonequilibrium solute transport model. Model parameters were obtained by both curve fitting and time moment analysis. The four-parameter model provided a much better fit to the data for three soil columns, but performed only slightly better for the two remaining columns. The retardation factor for Cl was about 10 % less than for 3H2O, indicating some anion exclusion. For the four-parameter model the average immobile water fraction was 0.14 and the Peclet numbers of the mobile region varied between 50 and 200. Time moments analysis proved to be a useful tool for quantifying the break through curve (BTC) although the moments were found to be sensitive to experimental scattering in the measured data at larger times. Also, fitted parameters described the experimental data better than moment generated parameter values.

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