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240 CHAPTER 7 CONCLUSIONS Computational analysis tools and capabilities have expanded rapidly in the last decade making analyses that were considered too large, costly and complex only a few years ago to be economically feasible. While crash test costs have continued to increase, computational analysis costs have remained more stable while the fidelity and complexity that can be addressed in numerical solutions has increased many fold. Computational analyses are expected to continue to be a valuable tool in assessing roadside hardware designs; in fact, the use of computational analyses is today a standard feature of most roadside hardware design and crash testing activities. The same trend is taking place all across the mechanical design field from fields as diverse as automotive and aircraft design to the design of consumer products like cell phones, razor blades and packaging. As computational analyses have become more integrated into the design process so too is the importance of establishing objective, quantifiable validation methods and criteria. While analysts have always compared numerical and experimental results, the techniques used have generally been visual and subjective. For the results of computational analysis to be used in acceptance decisions, it is necessary to use methods that are objective, numerical and quantifiable in order that decisions may be fair, impartial and consistent. The objective of this project was to develop objective procedures for assessing the validity of computational analyses in the area of roadside hardware performance and, in particular, for cases where incremental improvements are being assessed for possible acceptance. Chapters 1 and 2 reviewed the literature regarding verification and validation in the general computational mechanics literature as well as a detailed description of the roadside safety computer simulation literature. Validation and verification processes were discussed and compared and metrics for validation and verification were described, compared and contrasted. A survey was performed to elicit the opinions of practitioners and researchers with experience in the use of LS-DYNA in roadside safety research and the results were presented as a representation of best practices currently used in computational roadside safety in Chapter 3. Chapter 4 presented general procedures that can be used to perform these types of acceptance assessments, Chapter 5 discussed the development and use of a computer tool, RSVVP, which largely automates much of the assessment work. Examples of how the procedures would work using real-life practical cases were presented in Chapter 6. The findings in this report are a starting point for establishing a way to talk about validation of computational models in roadside safety and the repeatability of full-scale crash testing. The findings in this report and the RSVVP software will help to encourage analysts to examine and present their analysis results in this standardized format. Crash testing procedures have evolved over the past 50 years resulting in five or six potential procedures for performing and analyzing full-scale crash tests. As testing and sensor technology improved and the range of activities requiring testing expanded, changes to the crash testing guidelines were made accordingly. The same will undoubtedly occur with these validation procedures as the roadside safety computational mechanics community gains experience and confidence in analyzing roadside hardware impacts. Likewise, decision makers will gain insight and experience that will likely improve these procedures further. What is important, however, is to start the process by using these procedures so that community experience can be captured, refined and incorporated in roadside hardware acceptance decisions.
