ANSYS Case Studies Enhancing Wood Heat Treatment Processes: A Case Study of U.S.D.A. Forest Products Laboratory

	Enhancing Wood Heat Treatment Processes: A Case Study of U.S.D.A. Forest Products Laboratory ANSYS

Enhancing Wood Heat Treatment Processes: A Case Study of U.S.D.A. Forest Products Laboratory

ANSYS

Technology Category	Sensors - Thermal Conductivity Sensors Sensors - Utility Meters
Applicable Industries	Agriculture Buildings
Applicable Functions	Product Research & Development
Use Cases	Structural Health Monitoring
Challenge	The U.S.D.A. Forest Products Laboratory, a leading wood research institute, was facing challenges in accurately determining the thermal conductivity of wood, a critical factor in the wood drying process. The conventional equations used for this purpose, developed over 50 years ago, only provided a rough guideline for certain types of wood. This led to lumber mills and wood processing companies having to perform costly and time-consuming trial-and-error tests to determine the proper temperatures and drying times, often resulting in high scrap rates. The heat transfer coefficients of wood depend on many variables including ring density, tree age, initial moisture content, and cell orientation. These characteristics are usually not uniform across all sections of the same tree, with wood structure affected by seasonal weather differences. Furthermore, ring density in small versus large-diameter trees varies widely depending on growth rates for different conditions such as surrounding vegetation and climate. Read More
About Customer	The customer in this case study is the U.S.D.A. Forest Products Laboratory, established in 1910 by the U.S. Department of Agriculture Forests Service. Located in Madison, Wisconsin, it serves the public as the nation’s leading wood research institute and is internationally recognized as an unbiased technical authority on wood science and utilization. The lab plays a crucial role in the public-private partnership needed to create technology for the long-term sustainability of forests. One of its key areas of research is the thermal conductivity of wood, which is critical in the drying process. Read More
Solution	To address this challenge, the laboratory developed models using ANSYS Parametric Design Language (APDL) to simulate the structural variation of cell porosity and alignment in determining effective heat transfer coefficients. On a macro level, boards cut from different locations in a typical log were modeled, solved, and plotted to examine the effects of wood structure on the transient heat transfer process using thermo conductivity values obtained from the micro-level analyses. Dozens of simulations were required to determine the heat transfer rate for a range of wood geometries and structural conditions. APDL enabled repetitive analyses of these varying parameters by allowing different values to be inserted and multiple analysis re-run without manually rebuilding multiple simulations. Read More Log in to view content
Contents

Technology Category

Sensors - Thermal Conductivity Sensors

Sensors - Utility Meters

Applicable Industries

Agriculture

Buildings

Applicable Functions

Product Research & Development

Use Cases

Structural Health Monitoring

Challenge

The U.S.D.A. Forest Products Laboratory, a leading wood research institute, was facing challenges in accurately determining the thermal conductivity of wood, a critical factor in the wood drying process. The conventional equations used for this purpose, developed over 50 years ago, only provided a rough guideline for certain types of wood. This led to lumber mills and wood processing companies having to perform costly and time-consuming trial-and-error tests to determine the proper temperatures and drying times, often resulting in high scrap rates. The heat transfer coefficients of wood depend on many variables including ring density, tree age, initial moisture content, and cell orientation. These characteristics are usually not uniform across all sections of the same tree, with wood structure affected by seasonal weather differences. Furthermore, ring density in small versus large-diameter trees varies widely depending on growth rates for different conditions such as surrounding vegetation and climate.

About Customer

The customer in this case study is the U.S.D.A. Forest Products Laboratory, established in 1910 by the U.S. Department of Agriculture Forests Service. Located in Madison, Wisconsin, it serves the public as the nation’s leading wood research institute and is internationally recognized as an unbiased technical authority on wood science and utilization. The lab plays a crucial role in the public-private partnership needed to create technology for the long-term sustainability of forests. One of its key areas of research is the thermal conductivity of wood, which is critical in the drying process.

Solution

To address this challenge, the laboratory developed models using ANSYS Parametric Design Language (APDL) to simulate the structural variation of cell porosity and alignment in determining effective heat transfer coefficients. On a macro level, boards cut from different locations in a typical log were modeled, solved, and plotted to examine the effects of wood structure on the transient heat transfer process using thermo conductivity values obtained from the micro-level analyses. Dozens of simulations were required to determine the heat transfer rate for a range of wood geometries and structural conditions. APDL enabled repetitive analyses of these varying parameters by allowing different values to be inserted and multiple analysis re-run without manually rebuilding multiple simulations.

Impact #1	The use of ANSYS Parametric Design Language (APDL) programmed models and simulations led to significant operational improvements. The study concluded that porosity of wood as well as the growth rate of the tree play major roles in determining effective thermal conductivities. It also found that heat transfer in a piece of wood is significantly affected by ring density and orientation, and quarter-sawn boards have higher heat transfer rates than flat-sawn boards due to the shorter pathway through latewood cells. These insights will enable more effective wood heat treating processes and help better utilize this critical natural resource, thereby contributing to the long-term sustainability of forests.

Impact #1

The use of ANSYS Parametric Design Language (APDL) programmed models and simulations led to significant operational improvements. The study concluded that porosity of wood as well as the growth rate of the tree play major roles in determining effective thermal conductivities. It also found that heat transfer in a piece of wood is significantly affected by ring density and orientation, and quarter-sawn boards have higher heat transfer rates than flat-sawn boards due to the shorter pathway through latewood cells. These insights will enable more effective wood heat treating processes and help better utilize this critical natural resource, thereby contributing to the long-term sustainability of forests.

Benefit #1	Reduced the time and cost associated with trial-and-error tests for determining proper temperatures and drying times.
Benefit #2	Enabled the development of equations or a look-up table for effective thermal conductivities, improving the accuracy of thermal conductivity determination.
Benefit #3	Provided insights into the significant effects of cell alignment, cell density, ring width, and ring orientation on thermal conductivity of wood.

Benefit #1

Reduced the time and cost associated with trial-and-error tests for determining proper temperatures and drying times.

Benefit #2

Enabled the development of equations or a look-up table for effective thermal conductivities, improving the accuracy of thermal conductivity determination.

Benefit #3

Provided insights into the significant effects of cell alignment, cell density, ring width, and ring orientation on thermal conductivity of wood.

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Overview

Enhancing Wood Heat Treatment Processes: A Case Study of U.S.D.A. Forest Products Laboratory

Operational Impact

Quantitative Benefit