In this work, research the essence of the wood drying process in a periodic wood drying chamber. This paper provides a mathematical model of a wood drying chamber, which describes the general essence of physical drying processes using the equipment available in the wood drying chamber. This approach allows to take into account the physical parameters of the necessary equipment, such as heaters, fans, humidifying nozzles or other. This approach also allows to ignore some design characteristics that may differ depending on the type of wood drying chamber. Considering this, the main task in this work is to determine the temperature and humidity of the drying agent and lumber in the stack, as well as the temperature of the main components of the wood drying chamber. Taking into account such a large number of input parameters and describing a complex non-stationary process of heat transfer, there is a need to create complicated mathematical models. The presence of such mathematical models greatly complicates their application and requires significant computer resources for their calculation. In this way, the mathematical description is reduced to the description of non-linear partial differential equations. To simplify and speed up the calculations of this mathematical model, the use of cellular automata is suggested. To do this, the 3D model of the wood drying chamber is represented as a cell-automatic field, which consists of cells of the same size but different types. As a result, neighboring cells contain local relationships that describe their general behavior. This behavior depends on the type of tangent cells and is described by transition rules based on a mathematical model. Through the use of the developed cell-automatic model and transition rules, it is possible to obtain the values of the temperature and moisture content of the wood in the stack, the drying agent in the chamber, as well as the temperature of the main components of the chamber. The work also shows the corresponding graphs of changes in temperature and moisture content. To check the adequacy and reliability, the obtained results were compared with the results of other authors' experiments. As a result of the verification, the values of the average absolute error aren't high, which confirms the adequacy of the mathematical model and the prospects of using the developed cell-automatic model.
- Volkhonov M., Jabbarov I., Soldatov V., & Smirnov I. (2018). Development of the method of exposure control of grain drying in hightemperature dryers. Eastern-European journal of enterprise technologies., 3, 22–29. https://doi.org/10.15587/1729-4061.2018.133607
- Ivanova D., Valov N., Valova I., & Stefanova D. (2017). Optimization of Convective Drying. Tem journal., 6.3, 572-577. https://doi.org/10.18421/TEM63-19
- Solomon A., Claudiu V., & Bogoi A. (2022). Some practical remarks in solving partial differential equations using reduced order schemes obtained through the POD method. Incas Bulletin., 14.1, 187-196. https://doi.org/10.13111/2066-8201.2022.14.1.15
- Gibson, M. J., Keedwell, E. C., & Savi'c, D. A. (2015). An investigation of the efficient implementation of cellular automata on multi-core CPU and GPU hardware. Parallel Distrib. Comput., 77, 11–25. https://doi.org/10.1016/j.jpdc.2014.10.011
- Sitko, M., Chao, Q., Wang, J., Perzynski, K., Muszka, K., & Madej, L. (2020). A parallel version of the cellular automata static recrystallization model dedicated for high performance computing platforms – Development and verification. Comput. Mater. Sci., 172, 109–283.https://doi.org/10.1016/j.commatsci.2019.109283
- Shumylyak, L., Zhikharevich, V., & Ostapov, S. (2016). Modeling of impurities segregation phenomenon in the melt crystallization process by the continuous cellular automata technique. Applied Mathematics and Computation, 290, 336–354. https://doi.org/10.1016/j.amc.2016.06.012
- Salehi, M. S., & Serajzadeh, S. (2012). Simulation of static recrystallization in non-isothermal annealing using a coupled cellular automata and finite element model. Comput. Mater. Sci., 53, 145–152. https://doi.org/10.1016/j.commatsci.2011.09.026
- Zaitsev, D. A. (2018). Simulating Cellular Automata by Infinite Petri Nets. Journal of Cellular Automata, 13(1–2), 121–144.
- Bandini, S., & Magagnini, M. (2001). Parallel Processing Simulation of Dynamic Properties of Filled Rubber Compounds Based On Cellular Automata. Parallel Comput., 27, 643–661.https://doi.org/10.1016/S0167-8191(00)00082-X
- Svyetlichnyy, D. S. (2010). Modeling of the microstructure: From classical cellular automata approach to the frontal one. Comput. Mater. Sci., 50, 92–97. https://doi.org/10.1016/j.commatsci.2010.07.011
- Sokolovskyy, Ya., Sinkevych, O., & Voliansky R. (2019). Software for Studying Wood Drying Chambers Based on SolidWorks Flow Simulation Experiment. Experience of Designing and Application of CAD Systems, 2019, 24-27. https://doi.org/10.1109/ACITT.2019.8780040
- Lee J.-Y., Seid E.R., & Majozi M. (2015). Heat Integration of Material Transfer Streams in Batch Processing Plants. Chemical engineering transactions, 45, 127-132.https://doi.org/10.3303/CET1545022
- Benthien J.T., Riegler M., Engehausen N., & Nopens M. (2020). Specific Dimensional Change Behavior of Laminated Beech Veneer Lumber in Terms of Moisture Absorption and Desorption. Fibers, 8.47, 47. https://doi.org/10.3390/fib8070047
- Sokolovskyy, Ya., Sinkevych, O., & Volianskyi, R. (2020). The study of cellular automata method when used in the problem of capillary-porous material thermal conductivity. Advances in Intelligent Systems and Computing V: Springer Computer Science, 1293, 714–729. https://doi.org/10.1007/978-3-030- 63270-0_49
- Faiza M., Kamilia A., Mohammed E., Rachid B., & Slimane G. (2017). Numerical analysis of heat, air, and moisture transfers in a wooden building material. Thermal science, 21.2, 785-795.https://doi.org/10.2298/TSCI160421248M
- Sokolovskyy, Ya., & Sinkevych, O. (2021). The use of cellular automata in modeling the processes of wood drying in a stack. Ukrainian Journal of Information Technology, 3.2, 39-44.https://doi.org/10.23939/ujit2021.02.039
- Sychevsky V.A., Chorny A.D., & Baranova T.A. (2016). Optimization of aerodynamic conditions of the chamber drier operation. Izvestiâ vysših učebnyh zavedenij i ènergetičeskih ob edinennij sng. Ènergetika, 59.3, 260-271. https://doi.org/10.21122/1029-7448-2016-59-3-260-271
- Sokolovskyy, Ya., & Sinkevych, O. (2018). Software and algorithmic support for represettation of 3D models in 2D von Neumann neighborhood. CEUR Workshop Proceedings, 2300, 215–218. https://ceur-ws.org/Vol-2300/Paper52.pdf
- Sokolovskyy, Ya., Sinkevych, O., & Voliansky, R. (2019). Development the software for simulation of physical fields in wood drying chambers by using cellular automata. Materials of the XV International Conference CADSM'2019, 24–27. https://doi.org/10.1109/CADSM.2019.8779262
- Sokolovskyy, Ya., Shymanskyi, V., Levkovych, M., & Yarkun V. (2017). Mathematical and software providing of research of deformation and relaxation processes in environments with fractal structure. Computer Sciences and Information Technologies CSIT 2017, 24-27. https://doi.org/10.1109/STC- CSIT.2017.8098728
- Sokolovskyy, Ya., & Shymanskyi, V. (2014). Mathematical Modelling of Non-Isothermal Moisture Transfer and Rheological Behavior in Cappilary-Porous Materials with Fractal Structure During Drying. Canadian Center of Science and Education, 7.4, 111-122. https://doi.org/10.5539/cis.v7n4p111
- Sokolovskyy, Ya., Shymanskyi, V., & Levkovych, M. (2016). Mathematical modeling of non- isothermal moisture transfer and visco-elastic deformation in the materials with fractal structure. Computer science and information technologies CSIT 2016, 91-95. https://doi.org/10.1109/STC-CSIT.2016.7589877