Measuring the effectiveness of weapons systems in terms of system attributes

In this thesis the relationship between the characteristics or attributes of a military weapon system (e.g., speed, reliability, survivability) and the effectiveness of that system is thoroughly examined. Success in system acquisition relies on (1) the early identification and successful incorporation of those system attributes that are critical to system effectiveness, and (2) the specification of numerical values for the system attributes (the system requirements) that maximizes system effectiveness at an acceptable cost. New definitions for system, system attributes, and system effectiveness, as well as relevant DoDI 5000.2 guidance are provided. In addition to the currently- mandated battle level at which system effectiveness should be measured (in terms of engagement or battle outcomes), the author uses a wide spectrum of system acquisition-related literature to advocate that system effectiveness should also be measured at the mission level (in terms of mission outcome). Several existing mathematical models which combine a few key system attribute measurements into single-number measures of system effectiveness in accomplishing a particular mission are described. Then, the author proposes a hierarchy or tree which relates many system attributes to the four key attributes, Availability, Reliability, Survivability, and Capability, and hence to system effectiveness in accomplishing a specified mission. (MM)http://archive.org/details/measuringeffecti1094531282NANAU.S. Marine Corps (U.S.M.C.) author

A systematic approach to design for lifelong aircraft evolution

Modern aerospace systems rely heavily on legacy platforms and their derivatives. Historical examples show that after a vehicle design is frozen and delivered to a customer, successive upgrades are often made to fulfill changing requirements. Current practices of adapting to emerging needs with derivative designs, retrofits, and upgrades are often reactive and ad-hoc, resulting in performance and cost penalties. Recent DoD acquisition policies have addressed this problem by establishing a general paradigm for design for lifelong evolution. However, there is a need for a unified, practical design approach that considers the lifetime evolution of an aircraft concept by incorporating future requirements and technologies. This research proposes a systematic approach with which the decision makers can evaluate the value and risk of a new aircraft development program, including potential derivative development opportunities. The proposed Evaluation of Lifelong Vehicle Evolution (EvoLVE) method is a two- or multi-stage representation of the aircraft design process that accommodates initial development phases as well as follow-on phases. One of the key elements of this method is the Stochastic Programming with Recourse (SPR) technique, which accounts for uncertainties associated with future requirements. The remedial approach of SPR in its two distinctive problem-solving steps is well suited to aircraft design problems where derivatives, retrofits, and upgrades have been used to fix designs that were once but no longer optimal. The solution approach of SPR is complemented by the Risk-Averse Strategy Selection (RASS) technique to gauge risk associated with vehicle evolution options. In the absence of a full description of the random space, a scenario-based approach captures the randomness with a few probable scenarios and reveals implications of different future events. Last, an interactive framework for decision-making support allows simultaneous navigation of the current and future design space with a greater degree of freedom. A cantilevered beam design problem was set up and solved using the SPR technique to showcase its application to an engineering design setting. The full EvoLVE method was conducted on a notional multi-role fighter based on the F/A-18 Hornet.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Bishop, Carlee; Committee Member: Costello, Mark; Committee Member: Nam, Taewoo; Committee Member: Schrage, Danie

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp