Nikiforov
Vladimir O.
D.Sc., Prof.
doi: 10.17586/2226-1494-2018-18-2-322-330
OPTIMAL MATHEMATICAL MODEL FOR DESCRIPTION OF PHYSICAL PHENOMENA AND TECHNOLOGICAL PROCESSES
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For citation: Menin B.M. Optimal mathematical model for description of physical phenomena and technological processes. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 2, pp. 322–330 (in Russian). doi: 10.17586/2226-1494-2018-18-2-322-330
Abstract
Subject of Research. The paper presents an approach that provides a numerical calculation of the absolute uncertainty of the measured physical quantity, which is capable of quantitatively estimating the minimum achievable discrepancy between the developed model and the object under study. The question of achieving a reasonable limit of measurement accuracy in engineering and science remains open, despite the use of powerful computers that take into account a huge number of quantities, and the latest mathematical methods of calculation. Since any physical and mathematical model contains a certain amount of information about the object under research, depending on the quantitative and qualitative set of the selected physical quantities, the optimal number of the chosen quantities must be found. Method. The principles of information theory are used to provide theoretical explanation and justification for the experimental results that determine the accuracy of various fundamental constants. We present the method of choosing a model with the optimal number of quantities considered and calculating the minimum achievable absolute and comparative uncertainties of the measured variable. The amount of information contained in the model is proposed as the criterion of optimality. Main Results. Within the framework of the information approach, the results show that the perfection of devices in physics and engineering is fundamentally limited. The limit of measurement accuracy calculated by the information method is much more stringent than is predicted by the Heisenberg uncertainty relation. The proposed metric provides numerical calculation of the absolute uncertainty, which is capable of quantitatively estimating the difference between the developed model and the object under study. The basic principles of the theory of measurements remain in force, and they can be used separately in the further stage of the specification and computerization of the model. Practical Relevance. The application of comparative uncertainty concept reduces the risk of selecting overestimated indicators of the equipment being designed, gives the possibility to reduce the time and cost of its development. Methodology application for calculating the relative uncertainty according to the information method will reduce significantly the financial costs of improving the International system of SI units.
Acknowledgements. The author expresses his deep gratitude and profound respect to Prof. А.А. Gukhman and Prof. E.I. Guigo (let their memory be blessed) for many years of their spiritual support and highly qualified advice, comments in developing the concept and detailed structure of the information approach, which took a total of 33 years to be developed.
References
-
Voskoglou M.G. Fuzzy logic and uncertainty in mathematics education. International Journal of Applications of Fuzzy Sets, 2011, vol. 1, pp. 45–64.
-
Kunes J. Similarity and Modelling in Science and Technology. Springer, 2012, 442 p.
-
NIST Special Publication 330. The International System of Units (SI). 2008, 77 p.
-
Guhman A.A. Introduction to the Theory of Similarity. Moscow, Vyshaya Shkola Publ., 1973, 296 p. (In Russian)
-
Menin B. Information measure approach for calculating model uncertainty of physical phenomena. American Journal of Computational and Applied Mathematics, 2017,vol. 7, no. 1, pp. 11–24. doi: 10.5923/j.ajcam.20170701.02
-
Brillouin L. Science and Information Theory. NY, Academic Press, 1956.
-
Beyer A., Maisenbacher L., Matveev A. et al. The Rydberg constant and proton size from atomic hydrogen. Science, 2017, vol. 358, no. 6359, pp. 79–85. doi: 10.1126/science.aah6677
-
Biraben F. Spectroscopy of atomic hydrogen. How is the Rydberg constant determined? European Physical Journal Special Topics, 2009, vol. 172, no. 1, pp. 109–119.doi: 10.1140/epjst/e2009-01045-3
-
Mohr P.J., Taylor B.N., Newell D.B. CODATA recommended values of the fundamental physical constants: 2006. Reviews of Modern Physics, 2008, vol. 80, no. 2, pp. 633–730. doi: 10.1103/RevModPhys.80.633
-
Mohr P.J., Taylor B.N., Newell D.B. CODATA recommended values of the fundamental physical constants: 2010. Journal of Physical and Chemical Reference Data, 2012, vol. 41, no. 4. doi: 10.1063/1.4724320
-
Mohr P.J., Taylor B.N., Newell D.B. The 2014 CODATA recommended values of the fundamental physical constants: 2014. Reviews of Modern Physics, 2016, vol. 88, no. 3. doi: 10.1103/RevModPhys.88.035009
-
Karshenboim S.G. Progress in the accuracy of the fundamental physical constants: 2010 CODATA recommended values. Physics-Uspekhi, 2013, vol. 56, no. 9, pp. 883–909. doi: 10.3367/UFNe.0183.201309d.0935
-
Taleb N.N. The Black Swan: The Impact of the Highly Improbable.NY, Random House, 2007, 182 p.
-
Kirakosyan G.S. The correlation of the fine structure constant with the redistribution of intensities in interference of the circularly polarized Compton’s wave. PACS numbers: 12.10.-g, 12.20.Ds, 11.15.Kc, 2010, 7 p.
-
Azuma Y., Barat P., Bartl G. et al. Improved measurement results for the Avogadro constant using a 28Si-enriched crystal. Metrologia, 2015, vol. 52, no. 2, pp. 360–375. doi: 10.1088/0026-1394/52/2/360
-
DodsonB. So you think YOU'RE confused about quantum mechanics? 2013. Available at: https://newatlas.com/confusion-basic-nature-quantum-mechanics/26216/ (accessed: 06.02.18).
-
Okun L.B. The theory of relativity and the Pythagorean theorem. Physics-Uspekhi, 2008, vol. 51, no. 6, pp. 622–631.doi: 10.1070/PU2008v051n06ABEH006552