Newly emerging composite manufacturing processes, where there exist only limited industrial experience, demonstrate a definite need for process simulations to reduce the time and cost associated with the product and process developments. The physical and geometric complexity of the net-shape composite structural parts produced by resin transfer molding (RTM) lead to larger computational problem sizes and computational complexity demanding more memory and computational time which can be provided by high-performance and massively parallel computing platforms. The associated computational costs and times for large-scale simulations is a major factor for the effective use of massively parallel computing platforms. However, the limited availability of these massively parallel computing platforms and the demand for their computing resources make it imperative to have improved computational methodologies and algorithms for physical problems for effective use of the massively parallel computing platforms. An effective pure finite element methodology for the process simulation analysis of mold filling in RTM is employed for large-scale process simulation of composite structures and the computational effectiveness of the methodology is demonstrated for large-scale process simulations. The Connection Machine CM-5 is the massively parallel computing platform employed in this study. Issues regarding the implementation and software development of these manufacturing process simulations, behavior of the linear system solution methodologies during large-scale composite manufacturing process simulations, computation and communication loads, performance and scalability are specifically addressed. Demonstrative process simulation applications involving practical large-scale composite structures such as the Comanche helicopter keel beam and an aircraft composite wing box section are also presented.
Bibliographical noteFunding Information:
The authors from the University of Minnesota are very pleased to acknowledge the support from the US Army Research Office (ARO), Research Triangle Park, North Carolina, under grant number DAAH04-96-1-0172. Special thanks are due to Mr Walter Roy, Dr Shawn Walsh and Dr Dennis Viechnicki of the Materials Division of the US Army Research Laboratory, MD. Thanks are also due to Mr Bill Mermagen of the US Army Research Laboratory and the IMT activities at ARL for the encouragement and support. Special thanks are also due to Mr C. Nietubicz and ARL/MSRC computing facilities. Additional support in the form of computer grants from the Minnesota Supercomputer Institute (MSI), University of Minnesota, is also acknowledged.
Copyright 2018 Elsevier B.V., All rights reserved.
- Large-scale composite structures
- Massively parallel computing platforms
- Process modeling