Artículo 2

The team based the ExtendSim model on the OV-5b (Figure 14). The ExtendSim model integrates the critical operational issues (COI) into a 72-hour SAG mission. UAV capacity for the SAG, system maintainability, reliability, UAV movement, and UAV loitering times are integrated together to simulate the effectiveness and availability of the UAV communications system with the SAG (Figure 34).

Figure 34. ExtendSim Model Simulating the Number of Operational Nodes Based on UAV type, TDL type, MDT, and Failure Rate.

Figure 35 depicts the deploy phase of the “deploy, launch, operate, recover”

cycle. It begins with the input of UAVs carried by each ship in the SAG and ends with a weather decision. SEA23 based UAV input on the number of UAVs from the analysis of SAG hanger capacity for each type of UAV as the input number of items for the simulation.

Figure 35. Deploy Phase Of The ExtendSim Model Depicting Sag Carrying Capacity, Maintenance, Pre-Flight Check, And Weather Check.

There are three strings, one for each SAG ship, for the deployment phase. The UAVs are in a queue prior to the pre-flight check. The pre-flight check activity takes five minutes and only allows one UAV to perform the activity on each ship. UAV reliability determined whether the pre-flight check was good or bad. Historical Predator UAV failure rates provide the pre-flight check results. SEA23 used the Predator as a basis for the failure rates. The team assumes that any UAV system used for the communication has 10 to 15 years of operational time, which is similar to the Predator. According to a 2012 article by the American Security Project, Predator failure rates were 7.6 per 100,000 flight hours (Boyle 2012), or λ=0.000076^failures_hour. Using equation 12.5 from Blanchard and Fabrycky (2011), the reliability of the UAV over a 72-hour period will be:

995

This reliability means that there is a 99.5 percent chance a pre-flight check is good and a 0.5 percent chance it is bad. A failed pre-flight check requires the UAV to get maintenance. The maintenance activity holds an unlimited number of UAVs and immediately services each UAV as it enters the queue. This reliability means that there is a 99.5percent chance a pre-flight check is good and a 0.5percent chance it is bad. A failed pre-flight check requires the UAV to get maintenance. The maintenance activity holds an unlimited number of UAVs and immediately services each UAV as it enters the

maintenance activity block. We assume there is unlimited manpower/materiel for maintenance. A triangular distribution simulates maintenance downtime (MDT). Ship personnel conduct maintenance after each successful UAV recovery. SEA23 used the triangular distribution to “lean” the results towards the minimum and average maintenance times, while still considering that some UAVs will require a long maintenance action at some point during the 72-hour operation. A good pre-flight check results in the UAV going through a weather check decision. The scenario assumes a good weather check. A successful weather check moves the UAV to the launch phase of cycle, while an unsuccessful check results in the UAV going back the “ready” pool.

Figure 36 depicts the launch phase of the deploy, launch, operate, recover cycle.

This phase is simply the launch of a single UAV from each SAG ship. Since the successful weather check is always successful, all UAVs enter a single item queue where they are “launched” to either a far, middle, or close node. The optimal flow of UAVs to the far, middle, and close destinations was difficult to model using ExtendSim. This difficulty resulted in a simplification of the launch sequence that limits its ability to allocate more than three UAVs to each far, middle, or close destination, for the first cycle. The model prioritizes UAV destination based on far destinations first, followed by the middle and close destinations, respectively.

Figure 36. Launch Phase of the ExtendSim Model Depicting Single UAV Launch per Ship and Destination Decision.

The next phase in the ExtendSim model is the operate phase. This phase splits the UAVs into destination strings (far, middle, close) (Figure 37).

Figure 37. Operate Phase of the ExtendSim Model Depicting Far, Middle, and Close Node Strings Each With “Fly-To” Time, Failure/Shot Decision,

Loiter Time, “Fly-From” Time, and Recovery Decision.

An activity that “delays” the UAV from entering the “fly-to” activity simulated constant coverage at each node point. The expected launch cycle for each UAV traveling to the far, middle, or close destinations forms the basis for the “delay” activity. Assuming each “string” of far, middle, or close nodes is equidistant from the SAG, the “delay” was calculated by subtracting the “fly-to” time from sum of the “loiter” and “fly-from” time for each node distance. The “delay” activity has a built in trigger that signals it to “delay”

UAVs once five UAVs have entered the “fly-to” activity. It also triggers the activity capacity to switch to zero, stopping launches of UAVs to that node position based on backlog in the “fly-to” activity. Figure 38 describes the ExtendSim statement that triggers the delay and Figure 39 describes the statement that triggers a stop.

Figure 38. ExtendSim Statement That Triggers a Delay of UAVs in order to Stagger Arrival Time On-Station for the Wasp UAVs Being Sent to the Far Nodes.

Figure 39. ExtendSim Statement That Triggers a Stop of UAVs Being Sent to the Far Node String.

Table 15 shows the resulting “halt” times. A flaw in the “halt” activity is that it does not know when the first backlog is going to occur in the “fly-to” activity. It is only capable of knowing that a backlog is occurring resulting in too many UAVs sent to the

“fly-to” activity, possibly creating a backlog in some simulations.

The movement of the UAVs uses a triangular distribution based on the time it takes a specific UAV to move to the closest, middle, and farthest node. Travel time evaluation, based on TDL types, used maximum and cruise speed of each UAV type and distance to each node position. In order to remain consistent on each UAVs remaining operational time, the model adds a “fly-to” attribute value to each UAV. This attribute value is equal to the value given by the triangular distribution and is carried through the

“loitering” and “fly-from” activities. For example, if a UAV is randomly assigned a three hour “fly-to” time, based on the triangular distribution, it will have a two hour “loitering”

time and a two hour “fly-from” time. Figure 40 illustrates the equations used to distribute the random “fly-to” times.

Figure 40. ExtendSim Equations That Assign “Fly-To” Time Attributes to Each UAV.

Table 16 shows the travel and time on station for each UAV, TDL combination, and altitude, while moving at maximum speed. For example, the maximum speed of a DP-5X Wasp is 120 knots (DPI 2016a). The max distance between nodes for Wasp carrying Link-16 is 95 NM. This means it will take

hours

for the Wasp to get to its closest position. It will take hours

for the Wasp to get to its middle position, 250Nm being the center of the “moving deadly half-circle.” To get to its furthest position the Wasp will take

hours

with 500 NM being the location of the sensor and 95Nm being the max distance Link-16 can communicate with the sensor. These values confirm the results of the calculations used in assigning the “fly-to” attribute time to each UAV.

Table 16. Altitude, Travel Time, Time on Station, and Launch Frequency Values for Each UAV and TDL Combination.

Smallest Type of UAV DP-5X Wasp DP-5X Wasp DP-5X Wasp (no helos) DP-5X Wasp (no helos) DP-5X Wasp (no helos) DP-14 Hawk (no helos) MQ-8B Fire Scout

Altitude (ft) 5000 10000 2000 5000 10000 2000 2000

Travel Distance (Nm)

Close 21.4 237.4 170.2 21.4 237.4 170.2 170.2

Medium 152.4 8.4 280.1 152.4 8.4 280.1 280.1

Far 326.2 254.2 390.1 326.2 254.2 390.1 390.1

Speed, max (knots) 120 120 120 120 120 105 85

Close Travel Time 0.2 N/A 1 1.5 2.1 1.1 1.3

Medium Travel Time 1.3 0.1 2.1 2.1 2.1 2.4 3

Far Travel Time 2.8 2.2 3.3 2.8 2.2 3.8 4.6

Operating Time* (hrs) 8 8 8 8 8 8 8

Close Time on Station 7.6 N/A 6 5 3.8 5.8 5.4

Medium Time on Station 5.4 7.8 3.8 3.8 3.8 3.2 2

Far Time on Station 2.4 3.6 1.4 2.4 3.6 0.4 -1.2

Launch Frequency* (hrs)

Close Delay 7.4 N/A 5.0 3.6 2.4 4.7 4.1

Medium Delay 4.1 7.7 1.7 1.9 2.4 0.8 1.0

Far Delay 2.5 0.7 0.5 1.1 0.5 0.3 0.2

Link-16 CEC

SEA23 based the operating times at each location on the time remaining after travel time to and from the UAV’s loitering location. The far time on station for the MQ-8B Fire Scout is 1.6 hours, which signifies the Fire Scout’s inability to achieve the “Fire Web” communications network. This is mainly due to the slow speed and operating time of the Fire Scout. The operate phase utilizes a selector that integrates the probability of failure and survival into the model. SEA23 did not considered the failure rates based on the reliability used for the pre-flight check and the survivability of the UAVs, but they are easily introduced into the model to analyze the effects enemy actions can have on the availability of operational nodes. The operate phase ends with the UAV finishing its

“time-from” activity. Each UAV then goes through a decision on whether the UAV can be recovered or not, and then sequentially being sent to Ship 1, 2, or 3 for recovery. The team also did not consider, at this time, the effects a recovery failure has on the availability of UAV nodes.

Recovery phase simulation occurs last in the ExtendSim model (Figure 41). This phase consists of each assigned UAV arriving at their designated ship to be “recovered.”

SEA23 assumes that recovery of a UAV will take five minutes based on the ease of landing for a vertical takeoff and landing (VTOL) UAV and subsequent movement into a hanger bay. The recovered UAVs return to maintenance, to be sent through the deploy, launch, operate, recover cycle again.

Figure 41. Recover Phase of the ExtendSim Model Depicting the Recovery Activity Time.

5.

The outputs of the ExtendSim model are a discrete event plotter and “lost” UAV exit (Figure 42). The discrete event plotter shows the average number of UAVs on-station and the average over 30 replications number of UAVs in maintenance at any time during a 72-hour SAG mission. This data determines if the minimum average number of UAVs on-station exceeds the minimum number of nodes needed to create the “Fire Web”

(Figure 43). It is also useful in analyzing the effects sustained operations have on the maintenance for the UAV fleet. The “lost” UAV exit shows the total number of UAVs that failed (this simulation is assuming no UAVs are shot down; all UAVs are recovered and all flight times and times on station are completed). We are assuming that UAV failures only affect the maintenance repair time on the ship. This determines the impact reliability and survivability have on availability. The team also analyzed the effects of a failed recovery using this value.

Figure 42. Outputs of the ExtendSim Model: Discrete Event Plotter and “Lost”

UAVs.

“Lost” UAV Exit

Figure 43. Discrete Event Plotter Results for a Link-16 Equipped DP-5X Wasp and SAG with Organic MH-60R Seahawk Helicopters (Average).