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Готові списки джерел за темами / Grey-box modeling / Статті в журналах

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Статті в журналах з теми "Grey-box modeling"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 6 лютого 2022

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1

Bidarvatan, M., V. Thakkar, M. Shahbakhti, B. Bahri, and A. Abdul Aziz. "Grey-box modeling of HCCI engines." Applied Thermal Engineering 70, no. 1 (September 2014): 397–409. http://dx.doi.org/10.1016/j.applthermaleng.2014.05.031.

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2

Hasan, Md Moudud, Md Shariot Ullah, Ajoy Kumar Saha, and MG Mostofa Amin. "Comparing the performances of multiple rainfall-runoff models of a karst watershed." Asian-Australasian Journal of Bioscience and Biotechnology 6, no. 1 (July 18, 2021): 26–39. http://dx.doi.org/10.3329/aajbb.v6i1.54878.

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Анотація:

Different modeling concepts, a simple (black-box) to a fully distributed modeling (white-box), were used to develop a rainfall-runoff model based on the watershed characteristics to estimate runoff at the watershed outlet. A conceptual (grey-box) model is usually a balance between the black-box and white-box model. In this study, three grey-box models were developed by varying model structures for a karst watershed. The performance of the grey-box models was evaluated and compared with a semi-distributed type (white-box) model that was developed using the Soil and Water Assessment Tool in a previous study. The evaluation was carried out using goodness-of-fit statistics and extreme flow analysis using WETSPRO (Water Engineering Time Series Processing tool). Nash-Sutcliffe efficiencies (NSE) of the grey-box models were from 0.39 to 0.77 in the calibration period and from 0.30 to 0.61 in the validation period. However, the white-box model performed better in terms of NSE but has a higher bias. The best grey-box model performed better in simulating extreme flow, whereas the white-box (SWAT) model adequately simulated daily flows. Asian Australas. J. Biosci. Biotechnol. 2021, 6 (1), 26-39

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3

Green, Christy, and Srinivas Garimella. "Residential microgrid optimization using grey-box and black-box modeling methods." Energy and Buildings 235 (March 2021): 110705. http://dx.doi.org/10.1016/j.enbuild.2020.110705.

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4

Li, Kang, Steve Thompson, Gareth-Guan R. Duan, and Jian-xun Peng. "A CASE STUDY OF FUNDAMENTAL GREY-BOX MODELING." IFAC Proceedings Volumes 35, no. 1 (2002): 127–32. http://dx.doi.org/10.3182/20020721-6-es-1901.00432.

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5

Halmschlager, Verena, Stefan Müllner, and René Hofmann. "Mechanistic Grey-Box Modeling of a Packed-Bed Regenerator for Industrial Applications." Energies 14, no. 11 (May 28, 2021): 3174. http://dx.doi.org/10.3390/en14113174.

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Thermal energy storage is essential to compensate for energy peaks and troughs of renewable energy sources. However, to implement this storage in new or existing industries, robust and accurate component models are required. This work examines the development of a mechanistic grey-box model for a sensible thermal energy storage, a packed-bed regenerator. The mechanistic grey-box model consists of physical relations/equations and uses experimental data to optimize specific parameters of these equations. Using this approach, a basic model and two models with extensions I and II, which vary in their number from Equations (3) to (5) and parameters (3 to 6) to be fitted, are proposed. The three models’ results are analyzed and compared to existing models of the regenerator, a data-driven and a purely physical model. The results show that all developed grey-box models can extrapolate and approximate the physical behavior of the regenerator well. In particular, the extended model II shows excellent performance. While the existing data-driven model lacks robustness and the purely physical model lacks accuracy, the hybrid grey-box models do not show significant disadvantages. Compared to the data-driven and physical model, the grey-box models especially stands out due to their high accuracy, low computational effort, and high robustness.

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6

Hellsen, R. H. A., G. Z. Angelis, M. J. G. van de Molengraft, A. G. de Jager, and J. J. Kok. "Grey-box Modeling of Friction: An Experimental Case-study." European Journal of Control 6, no. 3 (January 2000): 258–67. http://dx.doi.org/10.1016/s0947-3580(00)71134-4.

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7

Tanaka, Hideyuki, and Yoshito Ohta. "Grey-box modeling for mechanical systems in frequency domain." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2014 (May 5, 2014): 149–54. http://dx.doi.org/10.5687/sss.2014.149.

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8

Aghababaei, A., and M. Hexamer. "Grey-box Modeling of Ex-vivo Isolated Perfused Kidney." IFAC-PapersOnLine 48, no. 20 (2015): 171–76. http://dx.doi.org/10.1016/j.ifacol.2015.10.134.

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9

Leifsson, Leifur Þ., Hildur Sævarsdóttir, Sven Þ. Sigurðsson, and Ari Vésteinsson. "Grey-box modeling of an ocean vessel for operational optimization." Simulation Modelling Practice and Theory 16, no. 8 (September 2008): 923–32. http://dx.doi.org/10.1016/j.simpat.2008.03.006.

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10

Özkan, Leyla, Reinout Romijn, Siep Weiland, Wolfgang Marquardt, and Jobert Ludlage. "MODEL REDUCTION OF NONLINEAR SYSTEMS: A GREY-BOX MODELING APPROACH1." IFAC Proceedings Volumes 40, no. 12 (2007): 366–71. http://dx.doi.org/10.3182/20070822-3-za-2920.00061.

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11

Romijn, Reinout, Leyla Özkan, Siep Weiland, Jobert Ludlage, and Wolfgang Marquardt. "A grey-box modeling approach for the reduction of nonlinear systems." Journal of Process Control 18, no. 9 (October 2008): 906–14. http://dx.doi.org/10.1016/j.jprocont.2008.06.007.

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12

Barzegari, Mohammad M., Ebrahim Alizadeh, and Amir H. Pahnabi. "Grey-box modeling and model predictive control for cascade-type PEMFC." Energy 127 (May 2017): 611–22. http://dx.doi.org/10.1016/j.energy.2017.03.160.

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13

Barszcz, Tomasz, and Piotr Czop. "Estimation of feedwater heater parameters based on a grey-box approach." International Journal of Applied Mathematics and Computer Science 21, no. 4 (December 1, 2011): 703–15. http://dx.doi.org/10.2478/v10006-011-0056-4.

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Estimation of feedwater heater parameters based on a grey-box approachThe first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There are two common problems associated with the modeling of dynamic systems. If an analytical model is chosen, it is very costly to determine all model parameters and that often prevents this approach from being used. If a data model is chosen, one does not have a clear interpretation of the model parameters. The paper uses the so-called grey-box approach, which combines first-principle and data-driven models. The model is represented by nonlinear state-space equations with geometrical and physical parameters deduced from the available documentation of a feedwater heater, as well as adjustable phenomenological parameters (i.e., heat transfer coefficients) that are estimated from measurement data. The paper presents the background of the method, its implementation in the Matlab/Simulink environment, the results of parameter estimation, and a discussion concerning the accuracy of the method.

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14

Buchaniec, Szymon, Marek Gnatowski, and Grzegorz Brus. "Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset." Energies 14, no. 16 (August 19, 2021): 5127. http://dx.doi.org/10.3390/en14165127.

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One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternatively, the prediction can be made by an artificial neural network (black-box) based on trends found in past data. Both approaches have their advantages and disadvantages. Mathematical models were seen as more trustworthy as their prediction is based on the laws of physics expressed in the form of mathematical equations. However, the majority of existing mathematical models include different empirical parameters, and both approaches inherit inevitable experimental errors. Simultaneously, the approximation of neural networks can reproduce the solution exceptionally well if fed sufficient data. The difference is that an artificial neural network requires big data to build its accurate approximation, whereas a typical mathematical model needs several data points to estimate an empirical constant. Therefore, the common problem that developers meet is the inaccuracy of mathematical models and artificial neural networks. Another common challenge is the mathematical models’ computational complexity or lack of data for a sufficient precision of the artificial neural networks. Here we analyze a grey-box solution in which an artificial neural network predicts just a part of the mathematical model, and its weights are adjusted based on the mathematical model’s output using the evolutionary approach to avoid overfitting. The performance of the grey-box model is statistically compared to a Dense Neural Network on benchmarking functions. With the use of Shaffer procedure, it was shown that the grey-box approach performs exceptionally well when the overall complexity of a problem is properly distributed with the mathematical model and the Artificial Neural Network. The obtained calculation results indicate that such an approach could increase precision and limit the dataset required for learning. To show the applicability of the presented approach, it was employed in modeling of the electrochemical reaction in the Solid Oxide Fuel Cell’s anode. Implementation of a grey-box model improved the prediction in comparison to the typically used methodology.

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15

ADACHI, Shuichi, and Tomoaki EDA. "Continuous-Time Grey-Box Modeling in Consideration of Deterministic a priori Knowledge." Transactions of the Society of Instrument and Control Engineers 32, no. 3 (1996): 417–19. http://dx.doi.org/10.9746/sicetr1965.32.417.

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16

Beghi, Alessandro, Marco Liberati, Sergio Mezzalira, and Stivi Peron. "Grey-box modeling of a motorcycle shock absorber for virtual prototyping applications." Simulation Modelling Practice and Theory 15, no. 8 (September 2007): 894–907. http://dx.doi.org/10.1016/j.simpat.2007.04.011.

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17

Mabrouk, M., M. A. Boujemaa, and F. Choubani. "Grey Box Non-Linearities Modeling and Characterization of a BandPass BAW Filter." Radioengineering 25, no. 2 (April 14, 2016): 338–44. http://dx.doi.org/10.13164/re.2016.0338.

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18

Tan, Xin, Hideyuki Tanaka, and Yoshito Ohta. "Grey-box Modeling of Rotary Type Pendulum System with Position-Variable Load*." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1263–68. http://dx.doi.org/10.3182/20120711-3-be-2027.00275.

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19

Halmschlager, V., M. Koller, F. Birkelbach, and R. Hofmann. "Grey Box Modeling of a Packed-Bed Regenerator Using Recurrent Neural Networks." IFAC-PapersOnLine 52, no. 16 (2019): 765–70. http://dx.doi.org/10.1016/j.ifacol.2019.12.055.

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20

Liu, Xin-yu, Yi-ping Li, Ya-xing Wang, and Xi-sheng Feng. "Hydrodynamic modeling with grey-box method of a foil-like underwater vehicle." China Ocean Engineering 31, no. 6 (December 2017): 773–80. http://dx.doi.org/10.1007/s13344-017-0088-0.

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21

Li, Yanfei, Zheng O'Neill, Liang Zhang, Jianli Chen, Piljae Im, and Jason DeGraw. "Grey-box modeling and application for building energy simulations - A critical review." Renewable and Sustainable Energy Reviews 146 (August 2021): 111174. http://dx.doi.org/10.1016/j.rser.2021.111174.

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22

Yu, Wen, and Francisco Vega. "Nonlinear system modeling using the takagi-sugeno fuzzy model and long-short term memory cells." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4547–56. http://dx.doi.org/10.3233/jifs-200491.

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Анотація:

The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvantages. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the long-short term memory cell. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to observe the advantages of the proposal.

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23

Mei, Bin, Licheng Sun, Guoyou Shi, and Xiaodong Liu. "Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder." Polish Maritime Research 26, no. 3 (September 1, 2019): 115–27. http://dx.doi.org/10.2478/pomr-2019-0052.

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Abstract A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation, and no overfitting. Testing and validation are conducted using the ship model test data. The test case reveals the practicability of the RF-SVM based modeling method, while the validation cases confirm the generalization ability of the grey box framework.

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24

Pitarch, José, Antonio Sala, and César de Prada. "A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression." Processes 7, no. 3 (March 23, 2019): 170. http://dx.doi.org/10.3390/pr7030170.

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Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data.

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25

Tanaka, Hideyuki, and Yoshito Ohta. "Grey-box Modeling of an Inverted Pendulum System Based on PD-LTI System." Transactions of the Institute of Systems, Control and Information Engineers 29, no. 12 (2016): 544–51. http://dx.doi.org/10.5687/iscie.29.544.

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26

Oaki, Junji, and Shuichi Adachi. "Grey-box Modeling of Elastic-joint Robot with Harmonic Drive and Timing Belt." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1401–6. http://dx.doi.org/10.3182/20120711-3-be-2027.00168.

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27

Lindblom, E., H. Madsen, and P. S. Mikkelsen. "Comparative uncertainty analysis of copper loads in stormwater systems using GLUE and grey-box modeling." Water Science and Technology 56, no. 6 (September 1, 2007): 11–18. http://dx.doi.org/10.2166/wst.2007.585.

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In this paper two attempts to assess the uncertainty involved with model predictions of copper loads from stormwater systems are made. In the first attempt, the GLUE methodology is applied to derive model parameter sets that result in model outputs encompassing a significant number of the measurements. In the second attempt the conceptual model is reformulated to a grey-box model followed by parameter estimation. Given data from an extensive measurement campaign, the two methods suggest that the output of the stormwater pollution model is associated with significant uncertainty. With the proposed model and input data, the GLUE analysis show that the total sampled copper mass can be predicted within a range of ±50% of the median value (385 g), whereas the grey-box analysis showed a prediction uncertainty of less than ±30%. Future work will clarify the pros and cons of the two methods and furthermore explore to what extent the estimation can be improved by modifying the underlying accumulation-washout model.

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28

Jorgensen, S. Bay, and K. M. Hangos. "Grey-Box Modeling for Identification and Control: An Emerging Discipline or an Established Technology?" IFAC Proceedings Volumes 27, no. 8 (July 1994): 1193–98. http://dx.doi.org/10.1016/s1474-6670(17)47871-2.

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29

Bechmann, Henrik, Henrik Madsen, Niels Kj�lstad Poulsen, and Marinus K. Nielsen. "Grey box modeling of first flush and incoming wastewater at a wastewater treatment plant." Environmetrics 11, no. 1 (January 2000): 1–12. http://dx.doi.org/10.1002/(sici)1099-095x(200001/02)11:1<1::aid-env377>3.0.co;2-n.

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30

Qi, Chenkun, Feng Gao, Xianchao Zhao, and Yi Yue. "A Grey-Box Distributed Parameter Modeling Approach for a Flexible Manipulator with Nonlinear Dynamics." IFAC-PapersOnLine 48, no. 28 (2015): 544–49. http://dx.doi.org/10.1016/j.ifacol.2015.12.185.

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31

Farooq, Abdul Atisam, Abdul Afram, Nicola Schulz, and Farrokh Janabi-Sharifi. "Grey-box modeling of a low pressure electric boiler for domestic hot water system." Applied Thermal Engineering 84 (June 2015): 257–67. http://dx.doi.org/10.1016/j.applthermaleng.2015.03.050.

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32

Dong, James, and Ward Whitt. "Stochastic grey-box modeling of queueing systems: fitting birth-and-death processes to data." Queueing Systems 79, no. 3-4 (December 2, 2014): 391–426. http://dx.doi.org/10.1007/s11134-014-9429-3.

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33

Xue, Peng, Zhengtao Ai, Dongjin Cui, and Wei Wang. "A Grey Box Modeling Method for Fast Predicting Buoyancy-Driven Natural Ventilation Rates through Multi-Opening Atriums." Sustainability 11, no. 12 (June 12, 2019): 3239. http://dx.doi.org/10.3390/su11123239.

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The utilization of buoyancy-driven natural ventilation in atrium buildings during transitional seasons helps create a healthy and comfortable indoor environment by bringing fresh air indoors. Among other factors, the air flow rate is a key parameter determining the ventilation performance of an atrium. In this study, a grey box modeling method is proposed and a prediction model is built for calculating the buoyancy-driven ventilation rate using three openings. This model developed from Bruce’s neutral height-based formulation and conservation laws is supported with a theoretical structure and determined with 7 independent variables and 4 integrated parameters. The integrated parameters could be estimated from a set of simulated data and in the results, the error of the semi-empirical predictive equation derived from CFD (computational fluid dynamics) simulated data is controlled within 10%, which indicates that a reliable predictive equation could be established with a rather small dataset. This modeling method has been validated with CFD simulated data, and it can be applied extensively to similar buildings for designing an expected ventilation rate. The simplicity of this grey box modeling should save the evaluation time for new cases and help designers to estimate the ventilation performance and choose building optimal opening designs.

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34

Tanaka, Hideyuki, and Yoshito Ohta. "Grey-box modeling of an inverted pendulum system via identification of a PD-LTI system." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2016 (2016): 112–17. http://dx.doi.org/10.5687/sss.2016.112.

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35

Jiang, Mian, Xuejun Li, and Tiandong Peng. "A Grey-box Modeling Approach for the Reduction of Spatially Distributed Processes Using New Basis Functions." Information Technology Journal 12, no. 22 (November 1, 2013): 7019–23. http://dx.doi.org/10.3923/itj.2013.7019.7023.

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36

Sadegh, P., H. Melgaard, H. Madsen, and J. Holst. "On the Usefulness of Grey-box Information when doing Experiment Design for System Modeling and Identification." IFAC Proceedings Volumes 27, no. 8 (July 1994): 1181–86. http://dx.doi.org/10.1016/s1474-6670(17)47869-4.

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37

Abdelazim, Tamer, and O. P. Malik. "Identification of nonlinear systems by Takagi–Sugeno fuzzy logic grey box modeling for real-time control." Control Engineering Practice 13, no. 12 (December 2005): 1489–98. http://dx.doi.org/10.1016/j.conengprac.2005.03.009.

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38

Barrios, José Ángel, Gerardo Maximiliano Méndez, and Alberto Cavazos. "Hybrid-Learning Type-2 Takagi–Sugeno–Kang Fuzzy Systems for Temperature Estimation in Hot-Rolling." Metals 10, no. 6 (June 6, 2020): 758. http://dx.doi.org/10.3390/met10060758.

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Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-2 Sugeno fuzzy systems are evaluated and compared with the results presented earlier. Systems with both empirically and fuzzy c-means-generated rules as well as purely fuzzy systems and grey-box models are tested. Experimental data were collected from a real-life mill; datasets for rule-generation, training, and validation were randomly drawn. Two of the grey-box models presented here reach 100% of bars with 20 °C or less prediction error, while two of the purely fuzzy systems improved performance with respect to purely fuzzy systems presented elsewhere, however it was only a slight improvement.

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39

Bouaswaig, Ala E. F., Keivan Rahimi-Adli, Matthias Roth, Alireza Hosseini, Hugo Vale, Sebastian Engell, and Joachim Birk. "Application of a grey-box modelling approach for the online monitoring of batch production in the chemical industry." at - Automatisierungstechnik 68, no. 7 (July 26, 2020): 582–98. http://dx.doi.org/10.1515/auto-2020-0038.

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AbstractModel-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.

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40

McCallum, Hamish. "Risk assessment in conservation biology." Pacific Conservation Biology 1, no. 4 (1994): 372. http://dx.doi.org/10.1071/pc940372.

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Population viability analysis (PVA) has become one of the standard tools of conservation biology. Unfortunately, few examples have entered the refereed literature. Most remain in the "grey" world of internal government reports, where the results of "what-if" scenarios become transformed into the firm basis for policy settings. The problem is that rough guesses of population parameters enter the black box of a modeling package, to emerge as attractive and apparently precise graphs of extinction probability as a function of population size. Somewhere in the process, it is often forgotten that the quantitative predictions cannot be better than the quality of the parameters which went into them.

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Millie, David F., Gary R. Weckman, William A. Young, James E. Ivey, Hunter J. Carrick, and Gary L. Fahnenstiel. "Modeling microalgal abundance with artificial neural networks: Demonstration of a heuristic ‘Grey-Box’ to deconvolve and quantify environmental influences." Environmental Modelling & Software 38 (December 2012): 27–39. http://dx.doi.org/10.1016/j.envsoft.2012.04.009.

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Pislaru, Marius, Silvia Curteanu, and Maria Cazacu. "Fuzzy modeling applied to optical and surface properties of a ferrocenylsiloxane polyamide solution." Open Chemistry 10, no. 1 (February 1, 2012): 194–204. http://dx.doi.org/10.2478/s11532-011-0126-3.

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AbstractA fuzzy model was designed to predict changes in surface tension and maximum absorbance due to self-assembly in a DMF solution of poly{1,1′-ferrocene-diamide-[1,3-bis(propylene) tetramethyl-disiloxane} as a function of temperature and concentration. The building of fuzzy rule-based inference systems appears as a grey-box because it allows interpretation of the knowledge contained in the model as well as its improvement with a-priori knowledge. The method provides accurate results and increases the efficiency of utilizing the available information in the model. Small mean squared errors (0.0064 for absorbance and 0.79 for surface tension) and strong correlations between experiment and simulated results (0.93 and 0.97, respectively) were found during model validation. The results showed that it is feasible to apply a Mamdani fuzzy inference system to the estimation of optical and surface properties of a ferrocenylsiloxane polyamide solution.

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Chu, Yuan, Hu, Pan, and Pan. "Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine." Energies 12, no. 18 (September 5, 2019): 3429. http://dx.doi.org/10.3390/en12183429.

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With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

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Kamel, Mohamed A., Amr Y. Elbanhawy, and M. Abo El-Nasr. "Quantification of deviations between grey-box and constant efficiency modeling and optimization of trigeneration systems using a data-driven RMSD indicator." Sustainable Energy Technologies and Assessments 45 (June 2021): 101195. http://dx.doi.org/10.1016/j.seta.2021.101195.

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Ralph, Benjamin James, Karin Hartl, Marcel Sorger, Andreas Schwarz-Gsaxner, and Martin Stockinger. "Machine Learning Driven Prediction of Residual Stresses for the Shot Peening Process Using a Finite Element Based Grey-Box Model Approach." Journal of Manufacturing and Materials Processing 5, no. 2 (April 21, 2021): 39. http://dx.doi.org/10.3390/jmmp5020039.

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The shot peening process is a common procedure to enhance fatigue strength on load-bearing components in the metal processing environment. The determination of optimal process parameters is often carried out by costly practical experiments. An efficient method to predict the resulting residual stress profile using different parameters is finite element analysis. However, it is not possible to include all influencing factors of the materials’ physical behavior and the process conditions in a reasonable simulation. Therefore, data-driven models in combination with experimental data tend to generate a significant advantage for the accuracy of the resulting process model. For this reason, this paper describes the development of a grey-box model, using a two-dimensional geometry finite element modeling approach. Based on this model, a Python framework was developed, which is capable of predicting residual stresses for common shot peening scenarios. This white-box-based model serves as an initial state for the machine learning technique introduced in this work. The resulting algorithm is able to add input data from practical residual stress experiments by adapting the initial model, resulting in a steady increase of accuracy. To demonstrate the practical usage, a corresponding Graphical User Interface capable of recommending shot peening parameters based on user-required residual stresses was developed.

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46

Trần, Ngọc Thạch, Thanh Phương Nguyễn, Trọng Huy Nguyễn, and Đình Anh Khôi Phạm. "A new method in determination of electrical parameters and geometrical structure of a power transformer applicable to failure diagnosis." Science & Technology Development Journal - Engineering and Technology 3, no. 4 (December 27, 2020): first. http://dx.doi.org/10.32508/stdjet.v3i4.744.

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In transmission and distribution networks throughout the world and in Vietnam nowadays, power transformers that are operating in the networks often are in black-box condition, i.e. there is no internal information available in terms of geometrical structure and material parameters. Geometrical structure of power transformers includes mainly winding structure and additional parts such as a static end ring or a would-in shield coil, if any whereas main materials in power transformers consists of conductive, insulating and magnetic materials… This makes difficulties in faults diagnosis that is based on the approach of physical modeling in general and the so-called electrical equivalentcircuit based modeling in particular since the physical approach requires internal information of power transformers for calculating electrical parameters. In case the electrical equivalent-circuit approach is used, the diagnosis is then conducted based on the change of values of electrical parameters in the circuit before and after an alarm or a suspicious fault that happens when power transformers are in operation. Relevant international investigations conducted recently have mainly focused on test objects as power transformers in grey- or white-box condition, i.e. during manufacturing phase, since they have available geometrical structure and material properties. To show a possibility that blackbox power transformers could be investigated in a physical manner, this article introduces a new method in determining electrical parameters and geometrical structure applied on a black-box power transformer. The research is based on the Frequency Response Analysis technique and has developed recent relevant investigations of the authors. This enables investigations of the value change of electrical equivalent parameters of this transformer on its simulated frequency responses for the purpose of physical fault diagnosis of power transformers later on.

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Kulkarni, Preeti, and Shreenivas N. Londhe. "Concrete strength prediction using artificial neural network and genetic programming." Challenge Journal of Concrete Research Letters 9, no. 3 (September 28, 2018): 75. http://dx.doi.org/10.20528/cjcrl.2018.03.002.

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Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

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Navid, Qamar, and Ahmed Hassan. "An Accurate and Precise Grey Box Model of a Low-Power Lithium-Ion Battery and Capacitor/Supercapacitor for Accurate Estimation of State-of-Charge." Batteries 5, no. 3 (July 1, 2019): 50. http://dx.doi.org/10.3390/batteries5030050.

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The fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. To curb the imbalance, energy storage systems comprising batteries and supercapacitors are widely employed. However, due to the variety of operational conditions, the performance prediction of the energy storage systems entails a substantial complexity that leads to capacity utilization issues. The current article attempts to precisely predict the performance of a lithium-ion battery and capacitor/supercapacitor under dynamic conditions to utilize the storage capacity to a fuller extent. The grey box modeling approach involving the chemical and electrical energy transfers/interactions governed by ordinary differential equations was developed in MATLAB. The model parameters were extracted from experimental data employing regression techniques. The state-of-charge (SoC) of the battery was predicted by employing the extended Kalman (EK) estimator and the unscented Kalman (UK) estimator. The model was eventually validated via loading profile tests. As a performance indicator, the extended Kalman estimator indicated the strong competitiveness of the developed model with regard to tracking of the internal states (e.g., SoC) which have first-order nonlinearities.

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Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Si Zhao. "Predictive Efficiency Comparison of ARIMA-Time-Series and the Grey System GM(1,1) Forecast Model on Forecasting the Incidence Rate of Hepatitis B." Advanced Materials Research 709 (June 2013): 836–39. http://dx.doi.org/10.4028/www.scientific.net/amr.709.836.

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To compare the stochastic autoregressive integrated moving average (ARIMA) model and the grey system GM(1,1) model to predict the hepatitis B incidence in Qianan. Considering the Box-Jenkins modeling and GM(1,1) model approach, hepatitis B incidence was collected monthly from 2004 to 2011, a SARIMA model and a gray system GM(1,1) model were fit. Then, these models were used for calculating hepatitis B incidence for the last 6 observations compared with observed data. The constructed models were performed to predict the monthly incidence rate in 2013. The model SARIMA(0,1,1)(0,1,1)12 and was established finally and the residual sequence was a white noise sequence. Using Excel 2003 to establish the gray system GM(1,1) model of hepatitis B incidence and evaluating the accuracy of the mode as well as forecasting. By posterior-error-test (C=0.435, p=0.821) and residual test, the model accuracy was qualified. It was necessary and practical to apply the approach of ARIMA model in fitting time series to predict hepatitis within a short lead time. The prediction results showed that the hepatitis B incidence in 2013 had a slight upward trend.

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Schwarz, Andreas, Benjamin James Ralph, and Martin Stockinger. "Planning and implementation of a digital shadow for the friction factor quantification of the ECAP process using a grey box modeling approach and finite element analysis." Procedia CIRP 99 (2021): 237–41. http://dx.doi.org/10.1016/j.procir.2021.03.035.

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