Application of the theory of dimensions in research of floor materials dispensers In multifactor experiment

2023;
: pp. 13 - 20
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University, Ukraine
3
Lviv Polytechnic National University, Ukraine

Aim. The development of methods of the theory of similarity and dimensionality, criterion values, as an intermediate component between theory and experiment, which ensures a functional connection between entire sets of values that characterize the process at the level of a physical model and simplify the planned experiment. Method. Processes that have a single nature of the interaction of physical phenomena can be used to build mathematical models in the study of a continuous disk dispenser. That is, only those physical processes related to the mechanics of a dispersed body can serve as models for the processes occurring during dosing. In this case, the main processes occurring in the model and nature will have the same equations describing similar processes. Thus, geometric, kinematic and dynamic similarities can be used to model the dosing process. Results. The application of methods of the theory of similarity and dimensionality, criterion values, as an intermediate component between theory and experiment, ensures a functional connection between entire sets of values that characterize the process at the level of a physical model. Scientific novelty. The use of dimensionality theory in a factorial planned experiment allows to reduce the number of factors, simplifies the mathematical interpretation of the nature of the response criterion and provides a graphical representation in the form of a 3-D model. Access to the fundamental similarity numbers confirms the reliability of the model and expands the number of factors that characterize the physics of the process directly through the similarity numbers. Practical value. The method of transforming the factor space by the methods of the theory of dimensional similarity and enabling the formation of criterion values, as an intermediate component between theory and experiment, which provides a functional connection between entire sets of values that characterize the process at the level of a physical model and simplifies the conduct of a planned experiment for processes and systems, which are characterized by a significant number of factors.

 

  1. Dmytriv V.T., Dmytriv I.V., Horodetskyy I.M. et all. Adaptive cyber-physical system of the milk production   process.   INMATEH   -   Agricultural   Engineering.  –   2020.   –    Vol.    61(2).    –    P. 199–208, DOI: 10.35633/inmateh-61-22
  2. Kettaneha N., Berglund A., Wold S. PCA and PLS with very large data sets. Computational Statistics & Data Analysis. – 2005. – Vol. 48(1). – P. 69–85. https://doi.org/10.1016/j.csda.2003.11.027 ;
  3. Elgamal T., Hefeeda M. Analysis of PCA algorithms in distributed environments. Preprint. Available at : arXiv:1503.05214v2. – 2015.
  4. Bouveyron  C.,   Brunet-Saumard   C.,   Model-based   clustering   of   high-dimensional   data:   A review. Computational Statistics & Data Analysis. – 2014. – Vol. 71. – P. 52–78. https://doi.org/10.1016/ j.csda.2012.12.008
  5. Fan J., Lv J. Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical    Society:    Series    B    (Statistical    Methodology).     –     2008.     –     Vol.     70(5).     ––     P. 849– 911. https://doi.org/10.1111/j.1467-9868.2008.00674.x
  6. Fan J., Feng Y., Song R. Nonparametric independence screening in sparse ultra-high dimensional additive models. Journal of the American Statistical Association. – 2011. – Vol. 106(494). – P. 544–557. DOI: 10.1198/jasa.2011.tm09779
  7. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. The Annals of Statistics. – 2004. – Vol. 32 (2). – P. 407 – 499. https://doi.org/10.1214/009053604000000067
  8. Schifano E. D., Wu J., Wang C., Yan J., Chen M.-H. Online updating of statistical inference in the big data setting. Technometrics.2016. – Vol. 58(3). – P. 393–403. DOI: 10.1080/00401706.2016.1142900
  9. Liberty E. Simple and deterministic matrix sketching. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (August 2013). – 2013. – P. 581–588. ACM, New York. https://doi.org/10.1145/2487575.2487623
  10. Islam M.F., Lye L.M. Combined use of dimensional analysis and modern experimental design methodologies in hydrodynamics experiments. Ocean Engineering. –  2009.  ––Vol.  36(3-4).  –  P.  237-247. DOI: 10.1016/j.oceaneng.2008.11.004
  11. Woods D. C., Overstall A. M., Adamou M., Waite T. W. Bayesian design of experiments for generalized linear models and dimensional analysis with industrial and scientific application. Quality Engineering. – 2017. – Vol. 29(1). – P. 91-103. https://doi.org/10.1080/08982112.2016.1246045
  12. Hu P., Chang C.-kan. Research on optimize application of Buckingham Pi theorem to wind tunnel test and its aerodynamic simulation verification. Journal of Physics: Conference Series. – 2020. – Vol. 1507(8). https://doi.org/10.1088/1742-6596/1507/8/082047
  13. Wang Y., Willis S., Tsoutsouras V., Stanley-Marbell Ph. Deriving Equations from Sensor Data Using Dimensional Function Synthesis. ACM Transactions on Embedded Computing Systems. – 2019. – Vol. 18(5s), No. 84. - P. 1–22. https://doi.org/10.1145/3358218
  14. Sonin A.A. The Physical Basis of Dimensional Analysis. 2nd Edition, Department of Mechanical Engineering, MIT, Cambridge. – 2001. http://goo.gl/2BaQM6
  15. Shen W., Lin D. K. J. Statistical theories for dimensional analysis. Statistica Sinica. – 2019. – Vol. 29(2).– P. 527–550. https://www.jstor.org/stable/26705477
  16. Albrecht M. C., Albrecht T. A., Nachtsheim C. J., Cook R. D. Experimental Design for Engineering Dimensional Analysis. Technometrics. – 2013. – Vol. 55(3). – P. 257–270. http://www.jstor.org/stable/24587346
  17. Jónsson D. Dimensional Analysis: A Centenary Update. – 2014. arXiv: 1411.2798
  18. Dmytriv V., Dmytriv I., Dmytriv T. Recearch in thermoanemometric measuring device of pulse flow of two-phase medium. 17th International Scientific Conference: Engineering for Rural Development. – 2018. - Vol. 17. - P. 894-904. DOI: 10.22616/ERDev2018.17.
  19. Dmytriv V., Dmytriv I., Horodetskyy I., Dmytriv T. Analytical dynamic model of coefficient of friction of air pipeline under pressure. Diagnostyka. – 2019. – Vol. 20(4). – P. 89–94. DOI: 10.29354/diag/114334
  20. Tsoutsouras V., Willis S., Stanley-Marbell Ph. Deriving equations from sensor data using dimensional function synthesis. Communications of the ACM. – 2021. – Vol. 64(7). – P. 91–99. https://doi.org/10.1145/3465216