The goal of the quantitative analysis was to determine the SSC characteristics required to both maximize the survivability and combat effectiveness of the units.
1. Model Requirements
The analysis approach shall be capable of accepting various inputs (within a prescribed set of limits) for offensive and defensive capabilities. It was beneficial to possess the capability to quickly change these input values, as a range of input parameters to analyze the effectiveness of the system through modeling were required. The model shall be capable of varying the following input parameters: speed, sensor range, missile range, radar cross section, offensive weapon battery size, offensive weapon fire rate, evasion capability, and force size.
The analysis shall generate the desired outputs listed in Table 6. The most significant output required was the number of units killed during each combat engagement. A breakdown of the individual unit types lost and the timeline of casualties was included.
(1) Required Inputs (2) Required Outputs
(3) Individual Unit Entities (4) Adjustable Model Stopping Conditions (5) Variable Speed
(6) Graphical Representation of Individual Units and Actions
(7) Variable Sensor Range (8) Duration of Engagement Output (9) Variable Weapons
Range (10) Number of Friendly Force Casualties
(11) Variable Probability of
Hit (12) Number of Enemy Force Casualties (13) Variable Salvo Size (14) Time Casualties Occur
(15) Variable Fire Rate
Table 6. Summary list of model requirements.
The main analysis tool shall provide a visual depiction of events unfolding.
Observing the events as they unfold will provide valuable insight on the engagement dynamics as the scenario develops.
2. Analysis Options
After reviewing the requirements and comparing the options, it was determined that discrete event simulation (DES) met all analysis requirements. DES has great generality and has potential to capture the desired details of naval combat (e.g., duration of engagement, own and adversary unit casualties, and time of detection). The main shortcoming of using computer simulation was that particular instances of simulation models can be large, time-consuming to construct, and require significant computer runtimes to achieve desired statistical accuracies.
Several DES tools exist that meet the desired analysis criteria. The two selected are described below, along with their particular strengths and weaknesses.
a. Map Aware Non-Uniform Automata
The first step in the modeling process was to determine the capabilities necessary to achieve the requirements defined during the SE process. These capabilities can be identified by performing multiple simulation runs, where many possible combinations of parameterized capabilities are evaluated to determine those parameters that are the most significant. One modeling tool that can be used to aid in the DOE analysis is Map Aware Non-Uniform Automata (MANA). MANA is an agent-based, time stepped, stochastic, map aware modeling tool (McIntosh, et al. 2007).
The first benefit of the MANA model is map awareness, meaning that the agents in the model move and react according to a specific preset decision process. Some modeling tools allow individual agents to move from point to point following specific waypoint guidance. MANA allows for similar guidance, yet the capability to alter individual squads based on the environment adds a touch of realism to the outcome. More specifically, each squad follows the predetermined path with a varying level of randomness, which simulates the friction of real life units in the battle space.
Additionally, units can only travel over specific environments, allowing the designer of the scenario to add realistic land masses and shoal waters that inhibit travel. Units can also be programmed to maintain minimum and maximum distances from other units allowing the designer to evaluate the concept of operations and standard operating procedures that may be employed in the future fleet.
Another feature of MANA is the model’s non-uniform aspect. Each individual unit or squad can be programmed with specific characteristics and limitations. This ability allows the user to alter each squad’s capabilities and specific features independently of the group, which will later become invaluable in the analysis.
Finally, MANA operates each individual unit as a separate and individually complex entity. This attribute means that each unit will operate according to a specific set of loosely defined guidelines, but slight differences in the environment or conditions of two identical units can result in drastically different behavioral actions. Again, this
variability accounts for some friction in the battle space, and provides a more realistic behavioral outcome for individual units in combat.
There are many advantages of using MANA as the major modeling tool for this project. The user interface is intuitive (changing specific capabilities does not require advance programming knowledge), and significant resources are available to aid the user when specific knowledge gaps occur. Another advantage is the ease with which accurate and realistic threat regions can be constructed. Overlaying global satellite data images and manipulating the terrain to reflect these images can be completed relatively quickly and intuitively. This rapid manipulation capability is particularly important if the user intends on evaluating specific forces in multiple threat regions. Finally, the model is time-stepped, which allows the user to expeditiously run multiple scenarios in a given time frame.
There are some disadvantages to using MANA. Most specifically, targeting is limiting to agent versus agent. This limitation means that offensive units must target specific enemy units to engage rather than shooting in the general direction of an incoming fleet. Changes in technology have made this limitation more significant, as advanced missile systems under consideration utilize individual targeting technologies (shoot a missile down a general bearing and allow the missile to conduct the targeting based on priority characterization). While this capability would increase the effectiveness of an offensive unit, the inability to achieve this targeting capability level does not invalidate the model results because current weapon systems do not possess this capability. The other major disadvantage to this modeling program is the absence of robust command and control capability. Although the units are capable of communicating with each other to prevent fratricide, no capability exists to control the individual unit actions from a central command unit. As the concept of operations shows, this capability is central to the swarming concept of a small flotilla force.
Despite its shortfalls, MANA remains the model of choice to model the operational scenarios in this study as the modeling program meets all requirements listed in Chapter V Section B.1.
b. COMBAT XXI
COMBAT XXI is a simulation tool for evaluating weapons systems and tactics. It is a modeling program designed for high-resolution, entity level combat simulations, producing stochastic results based on discrete events. COMBAT XXI is capable of providing high-fidelity results of campaign-level engagements (TRADOC Analysis Center 2011). Designed for combat simulation in a joint battle space with ground forces, air mobile forces, future forces, logistics and casualty handling, considerable effort was required to modify COMBAT XXI’s capability to model naval engagements.
Programming skills are required in order to build a model. Because of the time investment required to make COMBAT XXI capable of modeling a naval engagement, only the force composition that produced the best results using the MANA model were tested. The preliminary work with the COMBAT XXI model produced results similar to those of the MANA model as shown in Appendix C.
3. Input and Output Parameters
As discussed previously, multiple simulation runs are required to determine the most significant factors affecting the battle outcome. Many variables can change, and determining which variables to evaluate is critical to scoping the model to one that can be achieved in the project’s time horizon. Some of these factors include, but are not limited to, speed, missile capacity and range, sensor range, ship draft, fuel capacity, endurance, crew size, and armada group size. Based on the simulation program chosen for this analysis (MANA) and the desire to identify the armada system’s key capabilities, the primary input parameters were force structure, unit network capability, missile range, sensor range, and salvo size. These parameters are discussed in more detail in the Chapter V Section C.3.
The MANA simulation program is capable of providing thousands of outputs including battle duration, engagement time, number of shots fired and hit, and attrition rates and times. Ultimately, the armada force structure’s goal is to provide a credible (if not necessarily survivable) deterrence capability. In other words, if the chosen armada force structure is capable of inflicting substantial casualties to the adversary, the
adversary may avoid the conflict. The primary output used for most of the analysis is specific unit attrition rates. To provide a single factor of analysis, a comparison of the two side’s attrition can be determined using the exchange rate in the following formula:
US .
Some discussion is included that addresses the battle duration primarily for logistics rather than combat purposes.