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Статті в журналах з теми "Dynamic experimental data"

Автор: Grafiati

Опубліковано: 10 березня 2023

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1

SCHMID, PETER J. "Dynamic mode decomposition of numerical and experimental data." Journal of Fluid Mechanics 656 (July 1, 2010): 5–28. http://dx.doi.org/10.1017/s0022112010001217.

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The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment. The extracted dynamic modes, which can be interpreted as a generalization of global stability modes, can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system of significantly fewer degrees of freedom. The concentration on subdomains of the flow field where relevant dynamics is expected allows the dissection of a complex flow into regions of localized instability phenomena and further illustrates the flexibility of the method, as does the description of the dynamics within a spatial framework. Demonstrations of the method are presented consisting of a plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane and a jet passing between two cylinders.

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2

VALI, Rahil, and Takashi SAITO. "208 Model-Based Estimation about Mechanical Characters of Artificial Vessels Using Experimental Dynamic Response Data." Proceedings of the Dynamics & Design Conference 2011 (2011): _208–1_—_208–9_. http://dx.doi.org/10.1299/jsmedmc.2011._208-1_.

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3

Eek, Rob A., Sjoerd Dijkstra, and Gerda M. Van Rosmalen. "Dynamic modeling of suspension crystallizers, using experimental data." AIChE Journal 41, no. 3 (March 1995): 571–84. http://dx.doi.org/10.1002/aic.690410315.

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4

Yu, Misun, Seung-Min Park, Ingeol Chun, and Doo-Hwan Bae. "Experimental Performance Comparison of Dynamic Data Race Detection Techniques." ETRI Journal 39, no. 1 (February 1, 2017): 124–34. http://dx.doi.org/10.4218/etrij.17.0115.1027.

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5

McBride, Michael, Nils Persson, Elsa Reichmanis, and Martha Grover. "Solving Materials’ Small Data Problem with Dynamic Experimental Databases." Processes 6, no. 7 (June 27, 2018): 79. http://dx.doi.org/10.3390/pr6070079.

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6

Ba, Bocar A., John C. Ham, Robert J. LaLonde, and Xianghong Li. "Estimating (Easily Interpreted) Dynamic Training Effects from Experimental Data." Journal of Labor Economics 35, S1 (July 2017): S149—S200. http://dx.doi.org/10.1086/692710.

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7

Lemnitzer, Anne, Lohrasb Keykhosropour, Yohsuke Kawamata, and Ikuo Towhata. "Dynamic Response of Underground Structures in Sand: Experimental Data." Earthquake Spectra 33, no. 1 (February 2017): 347–72. http://dx.doi.org/10.1193/032816eqs048dp.

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A densely instrumented system of large-scale underground structures consisting of two vertical shafts connected through a cut-and-cover tunnel and two independent shield tunnels was installed in an 8 m-diameter laminar soil box at the E-Defense shake table in Miki, Japan. The system was subjected to step-sine sweeps and scaled ground motion records of the Kobe (1995) earthquake. The underground structures were embedded in Albany Silica Sand with an average relative density of 54%. System instrumentation consisted of over 800 sensors, including strain gauges, accelerometers, displacement transducers, bender elements and pressure sensors. A U.S.-Japanese research collaboration was established to instrument the vertical shaft elements and record seismic soil pressures. Data records are archived at the NHERI DesignSafe Data Depot_and can be used to analyze the structural response, soil-structure interaction and other response parameters of individual subsurface components as well as the entire system. The DOI for the data set is 10.17603/DS21C78.

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8

Saito, Akira, and Tomohiro Kuno. "Data-driven experimental modal analysis by Dynamic Mode Decomposition." Journal of Sound and Vibration 481 (September 2020): 115434. http://dx.doi.org/10.1016/j.jsv.2020.115434.

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9

Na, SangDo, JinSeok Jang, KwangSuk Kim, and WanSuk Yoo. "Dynamic vehicle model for handling performance using experimental data." Advances in Mechanical Engineering 7, no. 11 (November 5, 2015): 168781401561812. http://dx.doi.org/10.1177/1687814015618126.

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10

Schmid, Peter J. "Application of the dynamic mode decomposition to experimental data." Experiments in Fluids 50, no. 4 (February 2, 2011): 1123–30. http://dx.doi.org/10.1007/s00348-010-0911-3.

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11

Mazzei, R., and O. A. Bernaola. "Track experimental data related to post-irradiation dynamic processes." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 63, no. 3 (February 1992): 309–18. http://dx.doi.org/10.1016/0168-583x(92)95114-7.

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12

Cremers, J., and A. Hübler. "Construction of Differential Equations from Experimental Data." Zeitschrift für Naturforschung A 42, no. 8 (August 1, 1987): 797–802. http://dx.doi.org/10.1515/zna-1987-0805.

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Анотація:

A new algorithm to determine the number of degrees of freedom of dynamic systems is presented. To obtain a concise description of an observed chaotic time sequence, an approximation of the flow in a state space representation by series is shown to be useful.

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13

Chen, Tian-Yu, Yang Chen, Hu-Jiang Yang, Jing-Hua Xiao, and Gang Hu. "Reconstruction of dynamic structures of experimental setups based on measurable experimental data only." Chinese Physics B 27, no. 3 (March 2018): 030503. http://dx.doi.org/10.1088/1674-1056/27/3/030503.

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14

Le Clainche, Soledad, and José M. Vega. "Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods." Complexity 2018 (December 12, 2018): 1–21. http://dx.doi.org/10.1155/2018/6920783.

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This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition (which provides a purely temporal Fourier-like decomposition) and spatiotemporal Koopman decomposition (which gives a spatiotemporal Fourier-like decomposition). These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used to either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models.

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15

Lee, An Sung, and Itzhak Green. "Physical Modeling and Data Analysis of the Dynamic Response of a Flexibly Mounted Rotor Mechanical Seal." Journal of Tribology 117, no. 1 (January 1, 1995): 130–35. http://dx.doi.org/10.1115/1.2830587.

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The dynamic behavior of mechanical face seals has been an active area of research over the past three decades. Analytical and experimental investigations have exclusively been devoted to the flexibly mounted stator (FMS) seal. Recent theoretical work on the dynamics of the noncontacting flexibly mounted rotor (FMR) seal has proven that it excels in every aspect of dynamic behavior compared to the FMS seal. The advantages of the FMR seal, however, have to be experimentally verified. This work introduces a physical model (i.e., test rig) for an experimental investigation of the dynamic behavior of a noncontacting FMR seal. Features of the test rig, a new method of modeling and measuring the stiffness and damping of elastomeric O-ring secondary seals, and data analysis procedures will be introduced. Finally, experimental results will be compared with theory.

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16

Yin, Yunfei, Liufa Guan, and Chengen Zheng. "An Approach to Dynamic Sensing Data Fusion." Sensors 19, no. 17 (August 23, 2019): 3668. http://dx.doi.org/10.3390/s19173668.

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For the research and development of sensor systems, the collection and fusion of sensing data is the core. In order to make sensor data acquisition change with the change in environment, a dynamic data acquisition and fusion method based on feedback control is proposed in this paper. According to the sensing data acquisition and fusion model, the optimal acquisition of sensor data is achieved through real-time dynamic judgment of the collected data, decision-making of the next acquisition time interval, and adjustment. This model enables the sensor system to adapt to different environments. An experimental study of the proposed model was carried out on an experimental platform, and the results show that the proposed model can not only reflect the change in sensing data but also improve the transmission efficiency.

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17

INAMURA, Toyoshiro, Mitsuru OHMORI, Tamotsu MACHIDA, and Takeshi YASUI. "Improvement of a dynamic simulation model based on experimental data." Journal of the Japan Society of Precision Engineering 51, no. 8 (1985): 1600–1606. http://dx.doi.org/10.2493/jjspe1933.51.1600.

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18

Anishchenko, V. S., and M. A. Safonova. "Homoclinics in the Reconstruction of Dynamic Systems From Experimental Data." Applied Mechanics Reviews 46, no. 7 (July 1, 1993): 361–71. http://dx.doi.org/10.1115/1.3120365.

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The role of homoclinic effects in solution of a reconstruction problem of system attractors and model equations from experimental observable in the presence of external noise is investigated numerically. It is shown that the possibility of reconstruction essentially depends on character of origin system homoclinic trajectories and noise intensity. If the homoclinic structure belongs to the attractor, then the reconstruction results in restoration origin system attractors. A small noise influence causes in this case a small perturbation of attractors probability measure and practically disappears due to filtering properties of the reconstruction algorithm. The homoclinic structure does not belong to the attractor, then in the absence of noise the probability measure concentrates at the attractor, the structure of which is not defined by the homoclinics. The noise perturbation induces new regimes. Then the attractor structure essentially depends on the homoclinics structure and noise level. In this case the model system attractor of which reproduces “invisible” homoclinic structure, is obtained as a result of reconstruction.

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19

Afra, Hamid, and Safia Bedaoui. "Identification and calibration of structural dynamic models from experimental data." Revue Française de Génie Civil 8, no. 8 (November 2004): 953–73. http://dx.doi.org/10.1080/12795119.2004.9692636.

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20

Ramli, M. D., J. B. Roberts, and J. Ellis. "Determination of Squeeze-Film Dynamic Coefficients From Experimental Transient Data." Journal of Tribology 109, no. 1 (January 1, 1987): 155–63. http://dx.doi.org/10.1115/1.3261308.

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A technique for obtaining estimates of the direct damping and inertial coefficients of a squeeze-film bearing is described. This involves applying parametric identification and optimization techniques to digitized, free-decay experimental displacement records. The experimentally obtained coefficients, derived by this technique, were found to be significantly higher in magnitude (in some cases by a factor of about 10) than the corresponding values derived from conventional short-bearing theory, and to be virtually independent of the frequency of vibration.

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21

Chierichetti, M., C. Grappasonni, G. Coppotelli, and C. McColl. "A modal approach for dynamic response monitoring from experimental data." Mechanical Systems and Signal Processing 48, no. 1-2 (October 2014): 199–217. http://dx.doi.org/10.1016/j.ymssp.2014.04.003.

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22

Sheintuch, Moshe, and Dan Luss. "Identification of observed dynamic centres for analysis of experimental data." Chemical Engineering Science 42, no. 2 (1987): 233–43. http://dx.doi.org/10.1016/0009-2509(87)85053-4.

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23

KIM, HEUNG-SIK, and YOUNG-SOO CHUN. "Structural damage assessment of building structures using dynamic experimental data." Structural Design of Tall and Special Buildings 13, no. 1 (March 2004): 1–8. http://dx.doi.org/10.1002/tal.227.

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24

Shevchuk, Yury Vladimirovich. "Memory-efficient sensor data compression." Program Systems: Theory and Applications 13, no. 2 (April 4, 2022): 35–63. http://dx.doi.org/10.25209/2079-3316-2022-13-2-35-63.

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We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tests are about 1.5/4/1000000 (min/med/max), with compression context size about 10 bytes.

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25

Dong, Guanhua, Wei Wu, and Jianhui Zhou. "Joints dynamic identification and modeling based on FRFs data." MATEC Web of Conferences 189 (2018): 01010. http://dx.doi.org/10.1051/matecconf/201818901010.

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The problem of joints dynamic identification and modeling is discussed in this paper. The theoretical dynamic model of joints is established by FRFs (frequency response functions) data, and formulas for identifying the joints dynamic properties is deduced. The equivalent value of dynamic stiffness is extracted by solving the inconsistent equation using the least square method. The experimental example is provided to validate the feasibility and accuracy of the proposed method, the predicted result showing good fitting with experimental results.

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26

Li, Hui, and Chunmei Liu. "A Dynamic Data-Driven Framework for Biological Data Using 2D Barcodes." Computational and Mathematical Methods in Medicine 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/892098.

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Biology data is increasing exponentially from biological laboratories. It is a complicated problem for further processing the data. Processing computational data and data from biological laboratories manually may lead to potential errors in further analysis. In this paper, we proposed an efficient data-driven framework to inspect laboratory equipment and reduce impending failures. Our method takes advantage of the 2D barcode technology which can be installed on the specimen as a trigger for the data-driven system. For this end, we proposed a series of algorithms to speed up the data processing. The results show that the proposed system increases the system's scalability and flexibility. Also, it demonstrates the ability of linking a physical object with digital information to reduce the manual work related to experimental specimen. The characteristics such as high capacity of storage and data management of the 2D barcode technology provide a solution to collect experimental laboratory data in a quick and accurate fashion.

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27

Eppinger, S. D., D. N. O’Connor, W. P. Seering, and D. N. Wormley. "Modeling and Experimental Evaluation of Asymmetric Pantograph Dynamics." Journal of Dynamic Systems, Measurement, and Control 110, no. 2 (June 1, 1988): 168–74. http://dx.doi.org/10.1115/1.3152667.

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High-performance pantograph design requires control of pantograph dynamic performance. Many pantograph dynamic models developed to aid in the design process have employed two degrees of freedom, one for the head mass and one for the frame. In this paper, the applicability of these models to symmetric and asymmetric pantograph designs is reviewed. Two degree-of-freedom models have been shown to be appropriate to represent a number of symmetric pantograph designs. To represent the asymmetric designs considered in this paper, an additional degree of freedom representing frame dynamics has been introduced to yield a three degree-of-freedom nonlinear dynamic performance model. The model has been evaluated with experimental data obtained from laboratory dynamic testing of an asymmetric pantograph.

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28

Chunxia Wang, Chunxia Wang, Qiuyu Zhang Chunxia Wang, and Yan Yan Qiuyu Zhang. "Differentially Private Feature Selection Based on Dynamic Relevance for Correlated Data." 電腦學刊 34, no. 1 (February 2023): 157–73. http://dx.doi.org/10.53106/199115992023023401012.

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<p>Traditional feature selection methods are only concerned with high relevance between selected features and classes and low redundancy among features, ignoring their interrelations which partly weak classification performance. This paper developed a dynamic relevance strategy to measure the dependency among them, where the relevance of each candidate feature is updated dynamically when a new feature is selected. Protecting sensitive information has become an important issue when executing feature selection. However, existing differentially private machine learning algorithms have seldom considered the impact of data correlation, which may cause more privacy leakage than expected. Therefore, the paper proposed a differentially private feature selection based on dynamic relevance measure, namely DPFSDR. Firstly, as a correlation analysis technique, the weighted undirected graph model is constructed via the correlated degree, which can reduce the dataset’s dimension and correlated sensitivity. Secondly, as a feature selection criterion, F-score with differential privacy is adopted to measure the feature importance of each feature. Finally, to evaluate the effectiveness of feature selection, differentially private SVM combined with dynamic relevance measure is utilized to choose features. Experimental results show that the proposed DPFSDR algorithm can effectively obtain the optimal feature subset, and improve data utility while preserving data privacy.</p> <p> </p>

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29

Xu, Chuanbo, Maoru Chi, Shulin Liang, Liangcheng Dai, and Yiping Jiang. "Research on Non-hyperelastic Mechanical Model of EMU Rubber Spring Based on Experimental Data." Journal of Strain Analysis for Engineering Design 57, no. 4 (November 3, 2021): 255–65. http://dx.doi.org/10.1177/03093247211055950.

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The dynamic mechanical properties of rubber spring have great influence on the vehicle dynamic performance, so the accurate description of the mechanical properties of rubber spring has always been the focus of the train dynamics. Among the mechanical properties of rubber springs, the study of non-hyperelastic properties are the most difficult and complex. Therefore, this paper mainly studies non-hyperelastic forces. Based on the experimental data of rubber springs, an elliptic analysis model is derived to describe the non-hyperelastic properties of rubber springs. On the basis of this model, a modified model based on time change and a modified model based on displacement change are also proposed. The results show that the ellipse analysis model is simple, but the error of calculation is large; the calculation precision of time correction model is high, but the calculation process is complex; the displacement correction model is between the previous two models, with both accuracy and convenience. Compared with other models, the displacement correction model has great advantages, which can improve the accuracy of the calculation of train dynamics. It is suggested to adopt the rubber spring displacement correction model in engineering application.

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30

France, J., S. Lopez, E. Kebreab, and J. Dijkstra. "Interpreting experimental data on egg production—Applications of dynamic differential equations." Poultry Science 92, no. 9 (September 2013): 2498–508. http://dx.doi.org/10.3382/ps.2012-02622.

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31

Di Piazza, Maria Carmela, Antonella Ragusa, Massimiliano Luna, and Gianpaolo Vitale. "A Dynamic Model of a Photovoltaic Generator Based on Experimental Data." Renewable Energy and Power Quality Journal 1, no. 08 (April 2010): 1410–16. http://dx.doi.org/10.24084/repqj08.679.

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32

Chadeev, V. M., and S. S. Gusev. "Constructing dynamic model for thermal flow forecasting based on experimental data." Automation and Remote Control 73, no. 5 (May 2012): 893–99. http://dx.doi.org/10.1134/s000511791205013x.

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33

Smeulders, D. M. J., R. L. G. M. Eggels, and M. E. H. Van Dongen. "Dynamic permeability: reformulation of theory and new experimental and numerical data." Journal of Fluid Mechanics 245, no. -1 (December 1992): 211. http://dx.doi.org/10.1017/s0022112092000429.

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34

Bartoli, G., M. Betti, L. Facchini, A. M. Marra, and S. Monchetti. "Bayesian model updating of historic masonry towers through dynamic experimental data." Procedia Engineering 199 (2017): 1258–63. http://dx.doi.org/10.1016/j.proeng.2017.09.267.

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35

Altwicker, Elmar R. "Some laboratory experimental designs for obtaining dynamic property data on dioxins." Science of The Total Environment 104, no. 1-2 (May 1991): 47–72. http://dx.doi.org/10.1016/0048-9697(91)90007-2.

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36

Swiontkowski, M., S. Agnew, and D. Senft. "Low Contact Dynamic Compression Plate Experimental Data and Early Clinical Experience." Journal of Orthopaedic Trauma 5, no. 2 (1991): 215. http://dx.doi.org/10.1097/00005131-199105020-00024.

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37

Dias, J. N., G. O. Violato, and C. A. Martins. "Dynamic model of a two-stroke glow engine from experimental data." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 226, no. 12 (December 5, 2011): 1502–12. http://dx.doi.org/10.1177/0954410011426371.

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38

Valeriano-Medina, Y., A. Martínez, L. Hernández, H. Sahli, Y. Rodríguez, and J. R. Cañizares. "Dynamic model for an autonomous underwater vehicle based on experimental data." Mathematical and Computer Modelling of Dynamical Systems 19, no. 2 (April 2013): 175–200. http://dx.doi.org/10.1080/13873954.2012.717226.

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39

Mazhar, Farrukh, Mohammad A. Choudhry, and Muhammad Shehryar. "Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 7 (June 18, 2018): 2549–69. http://dx.doi.org/10.1177/0954410018783131.

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Анотація:

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

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40

Ames, F. E., and L. A. Dvorak. "Turbulent Transport in Pin Fin Arrays: Experimental Data and Predictions." Journal of Turbomachinery 128, no. 1 (February 1, 2005): 71–81. http://dx.doi.org/10.1115/1.2098792.

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Анотація:

The objective of this research has been to experimentally investigate the fluid dynamics of pin fin arrays in order to clarify the physics of heat transfer enhancement and uncover problems in conventional turbulence models. The fluid dynamics of a staggered pin fin array has been studied using hot wire anemometry with both single- and x-wire probes at array Reynolds numbers of 3000, 10,000, and 30,000. Velocity distributions off the endwall and pin surface have been acquired and analyzed to investigate turbulent transport in pin fin arrays. Well resolved 3D calculations have been performed using a commercial code with conventional two-equation turbulence models. Predictive comparisons have been made with fluid dynamic data. In early rows where turbulence is low, the strength of shedding increases dramatically with increasing Reynolds numbers. The laminar velocity profiles off the surface of pins show evidence of unsteady separation in early rows. In row three and beyond, laminar boundary layers off pins are quite similar. Velocity profiles off endwalls are strongly affected by the proximity of pins and turbulent transport. At the low Reynolds numbers, the turbulent transport and acceleration keep boundary layers thin. Endwall boundary layers at higher Reynolds numbers exhibit very high levels of skin friction enhancement. Well-resolved 3D steady calculations were made with several two-equation turbulence models and compared with experimental fluid mechanic and heat transfer data. The quality of the predictive comparison was substantially affected by the turbulence model and near-wall methodology.

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41

Hanhela, Matti, Olli Gröhn, Mikko Kettunen, Kati Niinimäki, Marko Vauhkonen, and Ville Kolehmainen. "Data-Driven Regularization Parameter Selection in Dynamic MRI." Journal of Imaging 7, no. 2 (February 20, 2021): 38. http://dx.doi.org/10.3390/jimaging7020038.

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In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for temporal regularization and from a reference image for spatial regularization. Two formulations are proposed. Simultaneous search for a parameter pair yielding expected sparsity in both domains (S-surface), and a sequential parameter selection using the S-curve method (Sequential S-curve). The approaches are evaluated using simulated and experimental DCE-MRI. In the simulated test case, both methods produce a parameter pair and reconstruction that is close to the root mean square error (RMSE) optimal pair and reconstruction. In the experimental test case, the methods produce almost equal parameter selection, and the reconstructions are of high perceived quality. Both methods lead to a highly feasible selection of the regularization parameters in both test cases while the sequential method is computationally more efficient.

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42

Ren, Y., T. M. Lim, and M. K. Lim. "Identification of Properties of Nonlinear Joints Using Dynamic Test Data." Journal of Vibration and Acoustics 120, no. 2 (April 1, 1998): 324–30. http://dx.doi.org/10.1115/1.2893834.

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This paper presents a general purpose technique for identifying (extracting) the dynamic properties of nonlinear joints using dynamic test data. No theoretical model is required, only experimental data are needed for the identification. Because no assumption on the properties of the nonlinear joints is made, the method is applicable for identifying almost any type of nonlinear joints. In the identification method, the nonlinear force at a joint is treated as an external force and the principle of multi-harmonic balance is employed. The force-response relationship of a nonlinear joint is then obtained. Various measurement errors in experimental data can affect the accuracy of identification. In this paper, efforts are also devoted to understanding how the accuracy of identification is affected by measurement errors. Techniques for improving the accuracy of identification are also discussed. The method is verified by experimental studies. The nonlinear properties of an impact joint and a friction joint have been successfully identified.

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43

Cummins, Breschine, Francis C. Motta, Robert C. Moseley, Anastasia Deckard, Sophia Campione, Marcio Gameiro, Tomáš Gedeon, Konstantin Mischaikow, and Steven B. Haase. "Experimental guidance for discovering genetic networks through hypothesis reduction on time series." PLOS Computational Biology 18, no. 10 (October 10, 2022): e1010145. http://dx.doi.org/10.1371/journal.pcbi.1010145.

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Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small “core” network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.

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Xu, Hao, Yue Zhao, Li-Ning Xing, and You Zhou. "The Novel Heuristic for Data Transmission Dynamic Scheduling Problems." Journal of Applied Mathematics 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/837659.

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The data transmission dynamic scheduling is a process that allocates the ground stations and available time windows to the data transmission tasks dynamically for improving the resource utilization. A novel heuristic is proposed to solve the data transmission dynamic scheduling problem. The characteristic of this heuristic is the dynamic hybridization of simple rules. Experimental results suggest that the proposed algorithm is correct, feasible, and available. The dynamic hybridization of simple rules can largely improve the efficiency of scheduling.

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Đokić, Radomir, Jovan Vladić, Dragan Živanić, Tanasije Jojić, Vesna Jovanović, and Nebojša Zdravković. "Analysis of elevators dynamic parameters with solution of summary data acquisition during experimental research." IMK-14 - Istrazivanje i razvoj 27, no. 4 (2021): 141–48. http://dx.doi.org/10.5937/imk2104125n.

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Modelling of the dynamic behaviour of elevators with high lifting velocities (contemporary elevators in building construction and mine elevators) is a complex task and an important step in the design process and creating conditions for safe and reliable exploitation of these machines. Due to high heights and lifting velocities, the standard procedures for dynamic exploitation are not adequate. With the purpose of identifying the basic parameters of the dynamic model (stiffness and damping), a unique method was applied, based on experimental measures for a concrete elevator. Due to the verification of this procedure, the experiment was conducted on a mine elevator in RTB Bor, Serbia. Simulations of the dynamic behaviour of an empty and loaded cage are presented using the obtained computational-experimental results

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Cafeo, John A., Martin W. Trethewey, and H. Joseph Sommer. "Beam Element Structural Dynamics Modification Using Experimental Modal Rotational Data." Journal of Vibration and Acoustics 117, no. 3A (July 1, 1995): 265–71. http://dx.doi.org/10.1115/1.2874446.

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Structural dynamic modification (SDM) of a fixed-free (cantilever) beam to convert it into a fixed-fixed beam with experimental modal data is presented. The SDM focuses on incorporating experimental rotational degrees-of-freedom (DOF) measured with a novel laser measurement technique. A cantilever beam is tested to develop the experimental modal database including rotational degrees of freedom. A modal database from a finite-element model also is developed for comparison. A structural dynamic modification, with both databases, is performed using a Bernoulli-Euler beam to ground the free end of the cantilever beam. The hardware is then modified and a second experimental modal analysis of the resulting fixed-fixed beam performed. A finite-element model of the fixed-fixed beam also was created. Comparison of results from these four tests are used to assess the effectiveness of SDM using experimental modal rotational data. The evaluation shows that provided high quality experimental rotational modal data can be acquired, SDM work with beam elements can be effective in yielding accurate results.

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47

Zhu, Zi-wen, Xiaojun Zhu, and Yong-bo Pan. "Research and Application of Data-Driven Modeling Method for Gear System Based on Time-Varying Characteristics." Mathematical Problems in Engineering 2022 (September 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/1881361.

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The time-varying characteristics of the gear system have an essential influence on its vibration and stability characteristics. Aiming at this characteristic and taking the generalized force caused by load torque as input and dynamic transmission error as output, a data-driven modeling method for gear time-varying system is studied using the periodic time-varying system identification theory. The effectiveness of the proposed method is verified by the lumped mass model of the gear system. This method has high modeling accuracy and can accurately characterize the time-varying characteristics of the gear system. Then, the virtual experimental platform of gear system dynamics is established based on the finite element theory. The method successfully extends to the virtual experimental platform, laying a foundation for analyzing the gear system’s dynamic characteristics.

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Maslen, E. H., J. A. Va´zquez, and C. K. Sortore. "Reconciliation of Rotordynamic Models With Experimental Data." Journal of Engineering for Gas Turbines and Power 124, no. 2 (March 26, 2002): 351–56. http://dx.doi.org/10.1115/1.1416692.

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A computationally efficient strategy is presented for adjusting analytic rotordynamic models to make them consistent with experimental data. The approach permits use of conventional rotordynamic models derived using finite element methods in conjunction with conventional plant identification models derived from impact or sine sweep testing in a transfer function or influence coefficient format. The underlying assumption is that the predominant uncertainties in engineered models occur at discrete points as effects like shrink fits, seal coefficients or foundation interactions. Further, it is assumed that these unmodeled or poorly modeled effects are essentially linear (at least within the testing and expected operating domains). Matching is accomplished by deriving a dynamic model for these uncertain effects such that the resulting composite model has a transfer function which matches that obtained experimentally. The derived augmentations are computationally compatible with the original rotor model and valid for stability or forced response predictions. Further, computation of this augmentation is accomplished using well developed and widely disseminated tools for modern control. Background theory and a complete recipe for the solution are supported by a number of examples.

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Chen, Hongli, and Yuzheng Gao. "Behavior Data Analysis of English Learners Based on Discrete Dynamic System Modeling Method." Computational Intelligence and Neuroscience 2022 (September 2, 2022): 1–8. http://dx.doi.org/10.1155/2022/5409571.

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With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.

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Silva, Marcelo A., and Reyolando M. L. R. F. Brasil. "Nonlinear dynamic analysis based on experimental data of RC telecommunication towers subjected to wind loading." Mathematical Problems in Engineering 2006 (2006): 1–10. http://dx.doi.org/10.1155/mpe/2006/46815.

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The goal of this paper is to propose a nonlinear dynamic model based on experimental data and NBR-6123-87 to accomplish a nonlinear dynamic analysis of slender structures subjected to wind loading. At first we compute the static answer given by the mean wind speed. In this part of the problem we consider the concept of effective stiffness to represent the physical nonlinearity of material and aP-Delta method to represent the geometrical nonlinearity. Considering the final stiffness obtained in thatP-Delta method, we compute the dynamic answer given by the floating wind speed, according to the discrete dynamic model given by NBR-6123-87. A 40 m RC telecommunication tower was analyzed, and the results obtained were compared with those given by linear static and dynamic models.

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