Center for Composite Materials - University of Delaware

Research Summary

A Methodology to Reduce Variability in VARTM with Optimized Distribution Media

Authors: Hatice S. Sas (PhDME), Pavel Simacek, Suresh G. Advani

Research Goal

Find a permeability map along with the inlet and vents that will always fill the given mold cavity, despite: variations in fabric permeability values due to manufacturing defects, variations in permeability due to lay-up sequence and race-tracking effects around the edges



Pedagogical example:
For specified inlet and vent locations, three different race-tracking options are identified, the permutations of which could result in 23=8 different race-tracking scenarios (a). In the first step, all possible eight scenarios are simulated without the use of DM using the simulation engine LIMS and the worst case of filling is identified by the maximum percentage of unfilled region (dry spots or voids) by halting the simulation when the resin arrives at the selected vent. For this example “Case 8” resulted in largest unfilled region (14.1%- worst case) as shown in a. The methodology suggests finding the DM layout for the worst case “Case 8” first. DM layout solution is found by dividing the domain into six areas and conducting six separate simulations with DM placed in each of the six regions on top of the preform successively and the percentage of unfilled region is recorded for each placement as shown in b. As seen fromb, by placing the DM on the right bottom corner provides the best filling option (0.1% void) out of the six configurations and that DM lay-out results in a mold filling that is within the tolerance prescribed which is usually around 1 to 2% as allowing for bleeding may reduce that region to less than 1% and in some cases if the void is close to the vent, it will get flushed out with the resin. Next, this DM layout that provides successful fill for “Case 8” is applied to the remaining seven scenarios and it is found that the “Case 1” which has no race-tracking along any edge has one of the largest unfilled region (11.5%) in this case as shown in c and is the worst case out of the six.
In order to find a successful filling solution for “Case 1”, the DM design from the previous solution is retained and the approach to conduct six simulations with an additional DM patch placed in each of the six areas successively is repeated and the resulting unfilled area percentages are recorded. It should be noted that the 5th configuration represents placement of 2 layers of DM on the right bottom corner. The best filling solution for the “Case 1” results in 7.3% voids, which is greater than the tolerance (d). Therefore, the successive placement of DM in the six regions is repeated for “case 1” while retaining both the DM patches placed in the earlier trials (e). This trial results in zero voids and successful filling for the case in which the DM is placed in the middle of the top half while maintain the two DM layers from the previous trials as shown in e. Finally, the updated design, which resulted in no voids for “Case1”, is used to perform mold filling simulation for the remaining seven cases and the results are shown in f, which shows that the worst case scenario is “Case 8” with void region of 0.5% which is within the tolerance limit. Thus, this represents the final DM layout design which will provide successful resin impregnations for all 8 scenarios possibly expected during manufacturing with the specified locations for the gate and vent. For this example and for this gate and vent location we were successful in finding the DM layout, which worked for all 8 cases. However, if this was not the case, one would continue with this algorithm of adding a region and testing the cases until all six regions were covered with DM. After covering all the regions if still one could not find a solution, then the number of regions is increased from six to eight (or ten or twelve) and the algorithm is repeated. If the gate and vent location are changed, one would expect the DM design to change as well.


• The proposed methodology is tested with a complex geometry with corners and edges
• Filling is improved
• More uniform pressure distribution is obtained.


• A methodology is introduced to design an optimum distribution media layout that makes the process robust by successful filling for all possible disturbances caused by different race-tracking scenarios around inserts
• Experimental validation is performed
• Numerical analysis with complex geometries are investigated
• Mesh study is performed


This material is based upon work supported by the National Science Foundation under Grant No. (1138182), Dr. Mary Poats, Program Director, as well as the Air Force Office of Scientific Research (AFOSR) Young Investigator Grant (FA9550-09-1-0218), Dr. Byung-Lip Lee, Program Director.

302-831-8149 •