Mathematical modeling of moisture transfer in wood drying for the two-dimensional case

Abstract

Mathematical modeling of moisture transfer in lumber during drying is a relevant and complex task with significant practical importance in the forestry and wood processing industries. Moisture transfer in lumber is a primary physical process during drying, and it directly affects the quality and efficiency of wood processing for various applications. Improving this process can lead to cost reduction and higher quality of the final product. However, predicting moisture release in lumber under different conditions and parameters remains a challenging task due to the complex nature of the process and various physical and mechanical factors that influence it. In total, this work dedicated to the development and utilization of mathematical models for the analysis and simulation of moisture transfer in lumber during drying. Special attention given to the use of finite element methods and cellular automata for modeling this process in a two-dimensional context. In this study, the cellular automata method employed as a potentially efficient tool for simulating moisture dynamics in lumber during drying. The fundamental idea of this method is to divide lumber into small entities or cells, each of which assigned its physical properties and state. Subsequently, the transfer of moisture between these cells simulated based on their states and surrounding conditions. This approach allows for a more detailed examination of moisture transfer processes and considers the influence of various factors, such as temperature, air humidity, lumber geometry, and its physical properties, on each individual cell. The results of the conducted research indicate that the cellular automata method proves to be an effective tool for modeling the dynamics of moisture transfer in lumber during drying. When comparing cellular automata with finite element methods, it is evident that cellular automata provide quicker results with fewer computational costs. This makes them an attractive choice for modeling complex processes like lumber drying and opens up possibilities for further research and innovations in this field.