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Flow Distribution in Entire Headbox Flow Variation in Headbox Plenum Flow Variations in the Contraction Region

Advanced Headbox Modeling

PSL provides detailed analysis of headboxes that can be used to identify potential sheet-forming problems. Working with the mill, we find ways to improve headbox performance using modeling tools which predict flow and turbulence characteristics throughout the headbox. Results from the analyses are used to:

Headbox Performance

Property variations in the paper sheet often originate in the headbox. Controlling basis weight variations in the cross-machine direction and reducing the MD/CD ratio can significantly improve profit margins. Traditionally, headbox designs and modifications have been based on experience and simple physical modeling. Because of the complexity of the geometry and the three dimensional nature of the flow field, such an approach is often inadequate to properly identify problems and improve headbox performance. PSL simulates three dimensional flow throughout the entire headbox and predicts in advance the performance of proposed headbox modifications. Management decisions can then be supported by reviewing the best available information.

Service Delivery Method

PSL works closely with the mill to collect the information necessary to build a three dimensional headbox model with parameters spanning the range of flow conditions used for production. Problems that limit production and performance are reviewed with the mill. PSL then uses its process-modeling tools to simulate the flow field and provide a physical picture of the flow phenomena inside the headbox. The results of the computer simulation are presented in the form of graphs and movies showing flow variations and turbulence characteristics inside the headbox. Injections of particles and simple fibre tracking can also be performed. The simulation results provide a comprehensive understanding of the headbox and provide a reliable basis for optimization. Any proposed changes can be evaluated in advance using the model, so that risks in the decision-making process can be minimized.