Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://elartu.tntu.edu.ua/handle/lib/35999
Назва: Methods mathematical modeling and identification of complex processes and systems on the basis of high-performance calculations
Інші назви: Methods mathematical modeling and identification of complex processes and systems on the basis of high-performance calculations (neuro- and nanoporous feedback cyber systems, models with sparse structure data, parallel computations)
Автори: Mykhaylo, Petryk
Tomasz, Gancarczyk
Oleksander, Khimich
Бібліографічний опис: Mykhaylo Petryk, Tomasz Gancarczyk, Oleksander Khimich (2021) Methods mathematical modeling and identification of complex processes and systems on the basis of high-performance calculations (neuro- and nanoporous feedback cyber systems, models with sparse structure data, parallel computations), Akademia Techniczno-Humanistyczna w Bielsku-Białej, Bielsko-Biała, p.195.
Дата публікації: 2021
Дата внесення: 26-лис-2021
Видавництво: Wydawnictwo Naukowe Akademii Techniczno-Humanistycznej w Bielsku-Białej
Країна (код): PL
Кількість сторінок: 195
Зміст: PREFACE ...9 INTRODUCTION ...15 Chapter1. High-performance methods of diagnostics and identification of the abnormal neurological state parameters caused by cognitive feedback influences of the cerebral cortex ...21 1.1 Problems of human neurological conditions ...21 1.2 Comprehensive methodology and analysis tools for the diagnosis of neurological conditions of T-objects based on the hybrid ANM model. Problems of human neurological conditions ...23 1.3 Hybrid mathematical model for the analysis of the ANM of the T-object based on feedback-connections and the effects of the neural nodes of the CC...26 1.4 Identification of AMM amplitude components. Inverse heterogeneous boundary value problem taking into account the cognitive feedback influences of the neuronodes of the CC ...32 1.5 Initial-boundary value problems accompanying algorithms for identifying parameters in the ANM ...35 1.6 Statement and methodology for the ANM conjugate boundary value problem solving ...36 1.7 Statement and methodology for solving conjugate initial-boundary value problems of functional identification of the ANM ...37 1.8 Expressions for gradient components and regularization expressions ...39 1.9 Modeling and identification of parameters of complex multicomponent non-biofeedback systems on multicore computers ...42 Chapter 2. High-performance methods of modeling and identification of feedback influences of competitive adsorption of gaseous air pollutants at micro- and macro-levels in nanoporous systems ...50 2.1. Analysis of research state ...50 2.2 Experimental setap ...52 2.3 Experimental results: Gaseous benzene and hexane competitive adsorption curves ...52 2.4 A mathematical model of competitive adsorption and competitive diffusion in microporous solids ...54 2.5 Numerical simulation and analysis: Competitive diffusion coefficients. Concentration profiles in inter- and intracrystallite spaces ...62 2.6 Iterative gradient method of the identification of competitive diffusion coefficients ...65 2.7 The linearization schema of the nonlinear competitive adsorption model. System of linearized problems and construction of solutions ...69 Chapter 3. High computational methods and simulation technology nanoporous systems with feedback adsorption for gas purification ...76 3.1 Nonlinear mathematical model of nonisothermal adsorption and desorption based on the generalized Langmuir adsorption equilibrium equation ...77 3.2 The methodology for constructing analytical solution systems to heterogeneous adsorption / desorption problems ...81 3.3 Computer simulation. Analysis of the distributions of the adsorbent concentration in the gas phase and nanopores of zeolite and temperatures ...86 Chapter 4. High-performance algorithms for solving systems of nonlinear equations on supercomputers with parallel organization of computations ...92 4.1 Layered parallel computing model ...93 4.2 Parallel algorithms for solving SNE with a sparse data structure ...97 4.3 Parallel algorithms for solving systems of linear equations with a sparse matrix ...99 4.4 Hybrid algorithms for solving linear systems with sparse matrices of irregular structure based on LLT-decomposition of block-diagonal matrices with framing .. 125 4.5 Experimental study of parallel algorithms ...131 Chapter 5. The methods of integral transformations for creation of hybrid ANM-models ...137 5.1. Finite integral Fourier transformation with spectral parameter for homogeneous media ...137 5.2 Finite hybrid integral Fourier transformation for bounded heterogeneous ncomponent media ...147 5.3 Integral Fourier transformation for semi-bounded heterogeneous n – component media ...169 Conclusions ...187 References ...189
URI (Уніфікований ідентифікатор ресурсу): http://elartu.tntu.edu.ua/handle/lib/35999
ISBN: 978-83-66249-80-6
Власник авторського права: © Mykhaylo Petryk, Tomasz Gancarczyk, Oleksander Khimich, 2021
Перелік літератури: 1. Rajaraman V., Jack D., Adamovich S.V., Hening W., Sage J., Poizner H. A novel quantitative method for 3D measurement of Parkinsonian tremor. Clinical neurophysiology, 11(2), 187-369 (2000) 2. Haubenberger D, Kalowitz D, Nahab F B, Toro C, Ippolito D, Luckenbaugh DA, Wittevrongel L, Hallett M. Validation of Digital Spiral Analysis as Outcome Parameter for Clinical Trials in Essential Tremor. Movement Disorders 26 (11), 2073-2080, (2011) 3. Legrand A.P., Rivals I., Richard A., Apartis E., Roze E., Vidailhet M., Meunier S., Hainque E. New insight in spiral drawing analysis methods – Application to action tremor quantification. J Clinical Neurophysiology, 128 (10), 1823–1834. (2017) 4. Wang J.-S., Chuang F.-C. An Accelerometer-Based Digital Pen with a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition. IEEE Transactions on Industrial Electronics, 59(7), 2998-3007 (2012) 5. Louis, E. D., Gillman, A., Böschung, S., Hess, C. W., Yu, Q., & Pullman, S. L. High width variability during spiral drawing: Further evidence of cerebellar dysfunction in essential tremor. Cerebellum, 11, 872-879 (2012). 6. Unger N., Bond T.C., Wang J.S., Koch D.M., Menon S., Shindell D.T., Bauer S. Attribution of climate forcing to economic sectors, Proc. Natl. Acad. Sci., 107(8), 3382-7 (2010). 7. Puertolas B., Navarro M.V., Lopez J.M., Murillo R., Mastral A.M., Garcia T. Modelling the heat and mass transfers of propane onto a ZSM-5 zeolite / Separation and Puri􀂿cation Technology 86, 127–136 (2012) 8. Krisnha R., Van Baten J.M.I nvestigating the Non-idealities in Adsorption of CO 2 -bearing Mixtures in Cation-exchanged Zeolites. Separation and Purification Technology 2018, Volume 206, 208-217. 9. Ruthven D.M. Principles of Adsorption and Adsorption Processes, John Wiley, New York, 1984. 433 p 10. Kärger J., Ruthven D., Theodorou D. Diffusion in Nanoporous Materials. Hoboken, John Wiley & Sons, 2012, 660 p.
11. Leniuk M. P., Petryk M.R. The Methods of integral transformations in the problems of mathematical modeling of masstranser in heterogeneus media. Kyiv: Naukova Dumka. - 2000. - 372 p. 12. Khimich A.N., Petryk M.R., Mykhalyk D.N., Boyko I.V., Popov A.V., Sydoruk. V.A. Methods for mathematical modeling and identification of complex processes and systems based on visoproductive computing (neuro- and nanoporous cyber-physical systems with feedback, models with sparse structure data, parallel computing). Kiev: National Academy of Sciences of Ukraine. Glushkov Institute of Cybernetics. 2019. - 188 p. 13. Mykhalyk D., Mudryk I., Hoi A., Petryk M. Modern hardware and software solution for identification of abnormal neurological movements of patients with essential tremor. IEEE. Proceeding of 2019 9th International Conference on Advanced Computer Information Technologies (ACIT, Budejovice, Czech Republic), 183-186 (2019) 14. Petryk M.R., Mykhalyk D.M., Mudryk I.Ya. Method for digital measurement of parameters of abnormal neurological movements of upper extremities in patients with tremor. Utility model patent 􀊋130247, Bul. 􀊋22 􀉜􀉿􀉞 26.11.2018. 15. Lecler S., Petryk M., Canet D., Fraissard J. Competitive Diffusion of Gases in a Zeolite Using Proton NMR and Sclice Selection Procedure. Catalysis Today, Elsevier B.V., 187(1), 104-107 (2012) 16. Petryk M., Leclerc S., D. Canet, Sergienko I.V., Deineka V.S., Fraissard J. The Competitive Diffusion of Gases in a zeolite bed: NMR and Slice Procedure, Modelling and Identification of Parameters. The Journal of Physical Chemistry C. ACS, 119 (47), 26519-26525 (2015). 17. Petryk M.R., Khimich O.M., Boyko I.V, Mykhalyk D.M., Petryk M.M., Kovbashyn V.I. Mathematical modeling of heat transfer and adsorption of hydrocarbons in nanoporous media of exhaust gas neutralization systems. National Academy of Sciences of Ukraine. Kyiv, 2018, 280 p. 18. Petryk M., Khimitch A., Petryk M.M. Simulation of Adsorption and Desorption of Hydrocarbons in Nanoporous Catalysts of Neutralization Systems of Exhaust Gases Using Nonlinear Langmuir Isotherm. Journal of Automation and Information Sciences, Begell House USA, 50 (10), 18-33 (2018)
19. Petryk M., Khimitch A., Petryk M.M., Fraissard J. Experimental and computer simulation studies of dehydration on microporous adsorbent of natural gas used as motor fuel. Fuel. Vol. 239, 1324–1330 (2019) 20. Sergienko I.V., Petryk M.R., Leclerc S., Frassard J. mathematical modeling of masstranser in media of nanoporous structure particles. — Kyiv: National Academy of Ukraine. V.M. Glushkov Institut cybernetics. — 2014. — 210 p. 21. I.V. Sergienko, V.S. Deineka, Optimal Control of Distributed Systems with Conjugation Conditions, New York: Kluwer A􀉫ademic Publishers 2005. 22. A.N. Tikhonov, V.Y. Arsenin. Solutions of Ill-Posed Problems, Washington D.C.: V.H. Winston; New York: J. Wiley 1977. 23. J.-L. Lions, Perturbations Singulières dans les Problèmes aux Limites et en Contrôle Optimal, New York: Springer. Lecture Notes in Math. Ser. 2008. 24. Sergienko. I.V., Petryk M.R, Leclerc S., Fraissard J. Highly Efficient Methods of the Identification of Competitive Diffusion Parameters in Heterogeneous Media of Nanoporous Particles. Cybernetics and Systems Analysis. Springer, 51(4), 529-546 (2015). 25. Ivanchov M. Inverse Problems for Equations of Parabolic Type. Mathematical Studies. Monograph Series. Vol. 10, Lviv: VNTL Publishers 2003. 26. Petryk M., Ivanchov M., Leclerc S., Canet D., Fraissard J. Competitive Adsorption and Diffusion of Gases in a Microporous Solid.In the book "Zeolites – New Challenges”. IntecOpen London, UK. P.1-23. (2019) https://www.intechopen.com/online-first/competitive-adsorption-and-diffusionof- gases-in-a-microporous-solid 27. Landau L. To the theory of phase transitions. I.ZPhys. Ztshr Sow, 1937, Bd. 7. S. 19. 28. Prudnikov A.P., Brichkov Yu.A. Marychev O.I. Integrals and series. Additional chapters, Nauka, Moscow, 1986, 800 p. 29. Lavrentiev M.A., Shabat B.V. Methods of theory of functions of a complex variable. M.: Nauka, 1973, 736 p. 30. Petryk M., Khimich A., Mykhalylk M., Boyko I., Kovbachun V. Highperformance computing technologies of modeling and identification of adsorption in nanoporous systems with feedbacks for gas purification. Vistyk of TNTU. Vol. 3, 139-145 (2019)
31. Petryk M., Khimitch A., Petryk M.M. Simulation of Adsorption and Desorption of Hydrocarbons in Nanoporous Catalysts of Neutralization Systems of Exhaust Gases Using Nonlinear Langmuir Isotherm. Journal of Automation and Information Sciences 2018. Volume 50 (10), 18-33 32. Information website Top500 The List // Access the resource: http://www.top500.org 33. Gorodetsky A.S., Evzerov I.D. Computer models of constructions. 􀉄yiv: Fakt, 2007. 394 p. 34. Khimich A.N., Molchanov I.N., Popov A.V., Chistyakova T.V., Yakovlev M.F. Parallel algorithms for solving computational mathematics problems. Kyiv: Naukova Dumka, 2008.- 248 p. 35. Nesterenko A.N., Khimich A.N., Yakovlev M.F. Some problems of solving systems of nonlinear equations on multiprocessor computing systems with distributed memory. Bulletin of computer and information technologies. 􀉆oskow: 2006, Vol. 10, 54 – 56. 36. Nesterenko A.N., Popov A.V., Rudich A.V. Solving systems of nonlinear equations on computers with parallel organization of calculations. Mathematical and computer modeling. Series: Physical and Mathematical Sciences. Coll. of Scient. work. 2019, Vol. 19, 85–91 37. E.A. Velikoivanenko, A.S. Milenin, A.V. Popov, V.A. Sidoruk, A.N. Khimich. Methods of Numerical Forecasting of Serviceability of Welded Structures on Computers of Hybrid Architecture. Cybernetics and Systems Analysis, Vol. 53(1), January, 2019, 117-127 38. Khimich A.N., Popov A.V., Chistyakov A.V. Hybrid algorithms for solving the algebraic problem of eigenvalues with sparse matrices. Cybernetics and systems analysis. 2017, Vol. 53(6), 132 – 146. 39. George A., Liu J. Numerical solution of large sparse systems of equations. Moskow: Mir, 1984, 334p. 40. http://software.intel.com/en-us/intel-mkl. [Electronic resource] 41. http://developer.download.nvidia.com/CUBLAS Library.pdf. . [Electronic resource] CUBLAS Linear Algebra
42. Popov A.V. Parallel algorithms for solving linear systems with sparse symmetric matrices. Problems of programming, 32-3, 2008. Special. output. Proceedings of the Sixth International Scientific and Practical Conference. on programming UkrProg’2008, 111-118. 43. Khimich A.N., Popov A.V., Polyanko V.V. Algorithms of parallel calculations for problems of linear algebra with matrices of irregular structure. Cybernetics and systems analysis. 2011. Vol. 6, 159-174. 44. Khimich A.N., Popov A.V., Polyanko V.V. Problems of parallel and distributed computations in the study of mathematical models with sparse data structures. Proceedings of the International Scientific Conference "Computational Optimization (VO-XL)". Kyiv: V.M. Glushkov Institute of Cybernetics. NAS of Ukraine. 2013, 267-268. 45. Khimich A.N., Baranov A.Yu. Hybrid algorithm for solving linear systems with tape matrices by direct methods. Computer Mathematics 2013, Vol. 2, 80-87. 46. Popov O.V. On parallel algorithms for factorization of sparse matrices. Computer Mathematics. 2013, Vol. 2, 115-124. 47. Velykoivanenko 􀈿.􀈺., Milenin 􀈺.S., Popov 􀈺.V., Sydoruk V.A., Khimich A.N. Methods and Technologies of Parallel Computing for Mathematical Modeling of Stress-Strain State of Constructions Taking into Account Ductile Fracture. Journal of Automation and Information Sciences 2014. Volume 46 (11), 23-35 48. Baranov A.Yu. Hybrid algorithm for factorization of tape asymmetric matrices. Theory of optimal solutions. 2015, 22-28. 49. Khimich A.N., Sydoruk V.A. Tiled algorithm for factorization of a sparse matrix. Computer Mathematics, 2015. Vol. 2, 109-116. 50. Popov A.V. Rudich O.V. Research of parallel algorithm for solving linear systems with tape asymmetric matrix. Proceedings of the All-Ukrainian scientific-practical conference with international participation "Informatics and systems sciences (CCI-2015)", Poltava, March 19-21, 2015. 51. Baranov A. Yu., Slobodyan Y. E., Popov A.B., Khimich A.N. Mathematical Modeling of Building Constructions Using Hybrid Computing Systems. Journal of Automation and Information Sciences 2017. Volume 50 (7), 18-32
52. Popov A.V., Rudich A.V. Before solving systems of linear equations on computers of hybrid architecture. Mathematical and computer modeling. Series: Physical and Mathematical Sciences. Coll. of scientific works. 2017, Vol. 15, 158-164. 53. Wilkinson J. H., Reinsch K. Handbook of algorithms in Algol. Linear algebra. - Moscow: Mechanical Engineering. 1976, 389 p. 54. Popov A.V., Khimich A.N. Parallel algorithm for solving a system of linear algebraic equations with a tape symmetric matrix. Computer Mathematics. 2005, Vol.2, 52-59. 55. Khimich A.N., Sydoruk V.A. Fine-tile hybrid algorithm for factorization of a sparse matrix. Proceedings of the VII All-Ukrainian scientific-practical conference with international participation "Informatics and systems sciences (􀈱􀉋􀉇 – 2016) ". Poltav􀉚, 2016, 326-328. 56. Khimich A.N., Sydoruk V.A. Tile hybrid algorithm for factorization of sparse block-diagonal matrices with a frame. Computer Mathematics – 2016. – Vol. 1, 72-79. 57. Khimich A.N.., Sydoruk V.A. Tile hybrid algorithm for factorization of structurally symmetric matrices. Theory of optimal solutions. 2017, 125-132. 58. Buttari A., Langou J., Kurzak J., Dongarra J. A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures. Parallel Computing. 2009, Vol. 355(1), 8–53. 59. Supercomputer complex SKIT [Electronic resurs􀉫], access mode: http://icybcluster.org.ua/ 60. Boreskov A.B., Harlamov A.A. 􀀐 The Basis of work with technology CUDA. Moskov: Press. 2010, 232 p. 61. cuSparse Library. URL: http://docs.nvidia.com/cuda/cuSPARSE/ 62. Mykhalyk D., Petryk M., Petryk M., Petryk O., Mudryk I., Mathematical Modeling of Hydrocarbons Adsorption in Nanoporous Catalyst Media using Nonlinear Langmuir’s Isotherm using Activation Energy. IEEE. Proceeding of 2019 9th International Conference on Advanced Computer Information Technologies (ACIT, Budejovice, Czech Republic), 72 -75 (2019)
Тип вмісту: Monograph
Розташовується у зібраннях:Зібрання книг

Файли цього матеріалу:
Файл Опис РозмірФормат 
Monograph_23.11 84 PeGaKh.pdf4,94 MBAdobe PDFПереглянути/відкрити


Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.