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The Role of Engineering Technologies: Virtual Prototyping
By: Brian Bell, Peter Spicka and Aniruddha Mukhopadhyay
Posted: October 14, 2008, from the May 2006 issue of GCI Magazine.
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Simulation data can be used to determine pressure drop, power requirements and the minimum number of elements needed to attain the required mixing uniformity.5 Time and money are saved through the use of CFD to determine these parameters before construction of an experimental apparatus.
Making a Case for the Return on Investment
The use of CFD to understand the performance of equipment and products at a level of detail that would be difficult or impossible to gather through physical experimentation is gaining momentum in the industry. Further use caters to the optimization of process lines and unit operations. By strategic deployment of this tool in these areas, product innovation is enhanced through reduced time-to-market, allowing for more time to be spent on the creation of new or re-designed products—leading to the reliable production of more optimal or “perfect” products. Despite its obvious potential, an eye-opener in the form of demonstrated cost and effort savings is needed to justify the use of the tool on an ROI basis.
For example, the process line for web coating at Armstrong operated at 40 feet per minute with air pressures of 6 psi, while the production line needed to be operated at 125 feet per minute. However, no reliable scale-up laws existed to determine the nominal blower pressure needed for the production line or the maximum blower pressure required for the entire range of expected operating conditions.
To answer these questions, Armstrong zoomed into the process line and identified the area where analysis was needed the most. First, a CFD simulation of the pilot line was performed to establish the validity of the numerical modeling results. A comparison of the CFD results with the plant measurements indicated the predictions were accurate to within 10% of the plant data. Thus, engineers at Armstrong were able to confidently use CFD to perform parametric studies of the production line behavior over 175 different combinations of operating parameters. The amount of testing time needed for the production line was reduced from 35 weeks to two weeks, and the final design was completed ahead of schedule with a significant reduction in costs.
Armstrong was able to derive additional benefits in product quality from the results by developing a PC-based controller based on curve fits from the simulation and pilot plant data that allowed operators on the line to key in changes in feed condition, line speed and air pressure and allowing them to instantly know the effect of these parameters on the resulting product.