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Classical D-optimal design is used to create experimental designs for situations in which an underlying system model is known or assumed known. The D-optimal strategy can also be used to add additional experimental runs to an existing design. This paper demonstrates a study of variable choices related to sequential D-optimal design and how those choices influence the D-efficiency of the resulting complete design. The variables studied are total sample size, initial experimental design size, step size, whether or not to include center points in the initial design, and complexity of initial model assumption. The results indicate that increasing total sample size improves the D-efficiency of the design, less effort should be placed in the initial design, especially when the true underlying system model isn't known, and it is better to start off with assuming a simpler model form, rather than a complex model, assuming that the experimenter can reach the true model form during the sequential experiments. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Today's military operating environments are more operationally diverse and technically challenging. Fielding relevant weapons systems to meet the demands of this environment is increasingly difficult, prompting policy shifts that mandate a focus on systems capable of combating a wide threat range. The capabilities-based test and evaluation construct is the Department of the Navy's effort to concentrate on integrated system design with the objective of satisfying a particular operational response (capability) under a robust range of operating conditions. One aspect of capabilities-based test and evaluation is the increased employment of advanced mathematical and statistical techniques in the test and evaluation (T&E) process. This case study illustrates advantages of incorporating these invaluable techniques, like design of experiments and modeling and simulation, within the T&E process. We found through statistical analysis that the application of design of experiment concepts to the System Under Test throughout three primary phases of T&E quantifiably improved the accomplishment of the selected response variable of interest. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]