Modeling and simulation in graduate military education

Military organizations use modeling and simulation (M&S) in support of planning and operations for a wide variety of purposes. Typically, one or more physical, mathematical, or logical representations are used to model real-world systems, processes, or phenomena in support of tactical, operational, or strategic decision making. These technical models are parameterized, to the detail necessary, to enable the study of a systems dynamics under a wide variety of different conditions. A primary benefit of simulation is that it enables the detailed study of complex systems without costly and time-consuming experiments. M&S leverages computational power to study and solve real-world problems inexpensively and in a time-efficient manner. M&S facilitates the understanding of a system’s behavior without the need for experimentation in the real world and provides several distinct advantages such as reduced costs, increased quality of developed systems, the ability to investigate and mimic system failures, improved operational documentation, and the ability to develop educational and illustrative representations of lessons learned during system operation. However, practical models are simple ideal abstractions of a real system and, as such, it has been said that “All models are wrong, but some are useful.” M&S users must recognize that the results generated by simulation are only as good as the underlying models, assumptions, conceptualizations, and constraints.

This special issue is composed of four papers that demonstrate the value of M&S to explore real-world phenomena in military graduate education. Each of these papers employ M&S in support of the development and analysis of technologies for defense applications across multiple domains, including neural network accuracy quantification, decision making in war gaming, material quality analysis, and data processing in reconnaissance and surveillance.

In the first paper, Llewellyn et al. present their research focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is first necessary to quantify the amount of NL present in the complex-valued function. The authors introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k-dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between 0 and 1. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.

In the second paper, DeBerry et al. present a novel approach to assist war game commanders in developing and analyzing courses of action (COAs) through semi-automation of the Military Decision Making Process (MDMP). The Wargaming Commodity Course of Action Automated Analysis Method (WCCAAM) receives the MDMP’s Mission Analysis phase as input, converts the war game into a directed graph, processes a multi-commodity flow algorithm on the nodes and edges, where the commodities represent units, and the nodes represent blue bases and red threats, and then programmatically processes the MDMP steps to output the recommended COA. To demonstrate the utility of the method, a military scenario developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) is implemented, which processes the various factors through WCCAAM and produces an optimal, minimal risk COA. The research shows through M&S that WCCAAM can reduce the time required to process the MDMP and improve decision quality.

In the third paper, Wing et al. analyze the heat transfer characteristics of Vascomax C300, a vacuum induction melted and vacuum arc re-melted, low-carbon, nickel-cobalt-molybdenum high-temperature nickel alloy, during high-speed sliding. The research is intended to help predict the wear rate of connecting shoes for a hypersonic rail system at Holloman Air Force Base to prevent critical failure of the system. Solutions were generated using finite element analysis and spectral methods. The frictional heat generated by the pin-on-disk is assumed to flow uniformly and normal to the face of the pin and the pin is assumed to be a perfect cylinder resulting in two-dimensional heat flow. Displacement data obtained from the experiment is used to define the moving boundary. The distribution of temperature resulting from transient finite element analysis is used to justify a one-dimensional model. Spectral methods are then employed to calculate the spatial derivatives improving the approximation of the function which represents the data. It is concluded that a one-dimensional approach with constant heat transfer parameters sufficiently models the high-speed pin-on-disk experiment.

In the fourth paper, Arnold et al. propose a new algorithm which was designed to enable real-time image processing of scenes imaged by aerial drones. The new algorithm exhibits a significant speed-up when compared to existing methods and has the additional benefit of presenting the original two-dimensional (2D) video data as a three-dimensional (3D) virtual model. The generated 3D reconstruction provides the added benefit of being able to generate a spatially accurate representation of a live environment that is precise enough to generate global positioning system (GPS) coordinates from any given point on an imaged structure, even in a GPS-denied environment. The research enables many potential applications for real-time image processing that could make autonomous vision–based navigation possible by completely replacing the need for a traditional live video feed.