Synergistic wood drying modeling based on multiple-scale anatomical and physical attributes

我们的重点是了解木材中水分吸附和传输机制的复杂性，以及在对流和介电加热下的热特性。这些知识将有助于严格推导可靠的数学和计算模型，以预测对流和介电 因此，通过分析各种木材产品的加热/干燥历史，为行业提供了一个决策工具，有助于最大限度地减少加工时间、能源消耗、成本和产品缺陷。 我们的下一个5年的目标是调查的特征尺寸，可以物理描述的年轮吸附性能，确定流行的分形维数与他们的标度行为，并开发粗粒木材干燥预测模型，利用木材-水系统的通用标度关系和人工神经网络计算工具。从年轮的物理解剖特性出发，通过分析、识别和描述分形维数，将分形维数单独或组合起来形成特征长度，建立规格材干燥参数预测模型。 这项研究将包括两个阶段，即实验和分析建模。第一阶段将涉及密度分布测量、径向、切向和横截面表面的微观表征、年轮水平的水分吸附研究、流体流动和木材多孔结构研究、耦合传热和传质实验、单分形和多重分形年轮图像和吸附特性缩放分析。在第二阶段，将识别，分析和确定的主要特征长度的分形维数之后，随后推导和发展的木材干燥标度参数和基于分形的升级，将开发人工神经网络干燥参数预测模型。该模型将使基于分形的智能优化木材干燥操作

Our over all focus is to understand the complex nature of water sorption and transport mechanisms in wood as well as its thermal characteristics under convective and dielectric heating. This knowledge will help with the rigorous derivation of reliable mathematical and computational models to predict the convective and dielectric heating/drying history of various wood products thus, providing the industry with a decision tool that will help to minimize process times, energy consumption, costs and product defects. Our next 5-year objective is to investigate the characteristic dimensions that can physically describe sorption properties of annual rings, determine the prevailing fractal dimensions with their scaling behaviors and develop coarse grained wood drying prediction model using universal scaling relations in the wood-water system and artificial neural networks computational tools. Drying parameters prediction model will be developed for dimension lumber from physical and anatomical properties of annual rings by analysing, identifying and describing fractal dimensions that will form characteristic lengths individually or in combination. This study will be comprised of two phases, namely experimental and analytical modeling. The first phase will involve density profile measurement, microscopic characterization of radial, tangential and cross sectional surfaces, moisture sorption study at annual ring level, fluid flow and wood porous structure studies, coupled heat and mass transfer experiments, mono and multifractal annual rings image and sorption properties scaling analysis. In the second phase, the major characteristic lengths will be identified, analyzed and fractal dimensions determined after which follows the derivation and development of wood drying scaling parameters and fractal based upscaling that will be concluded by development artificial neural networks drying parameters prediction model. The model will enable fractal based intelligent optimization of the wood drying operations