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In this topic I will display a simple design of Artillery Module. The "directly" in the topic means the artillery support is created by vehicles' directly shooting and the trajectory of shell strictly follows OFP original rule (namely airFriction is -0.0005*v^2 and gravity is 9.80665m/s^2). The "shotShell" in the topic means the CfgAmmo objects applied in this module have same "simulation" value: "shotShell". The (physical) rule (simulated in OFP) of "shotShell" is simple and totally known by now (August 17th, 2020), while rules of "shotRocket" or "shotMissile" are more complex and still remain unknown parts. See for more information. By motion equations one can solve the final range basing on initial data (direction and length of velocity vector and difference in height. The topography should be taken into consider when necessary, remain to readers), but the ODEs don't have elementary function solution, and I failed to build an appriximately one. The problem is "Cauchy Problem", we solve the final data basing on initial ones. And it's hard to solve conversely problem, like that we've known the range and difference in height between shooting position and the being attacked position, it's hard for us to solve the initial velocity, especially when we're still lacking of analysis solution of the ODEs. (Actually we can solve converse problem as an "Cauchy Problem" as well. If we know the final velocity and position, we can take converse velocity as initial velocity, and solve the ODEs whose form is totally same as while the coefficient shoult take "minus airfriction", namely if airFirction is -0.0005, the coefficient in converse problem should be +0.0005. However, mostly the final velocity is unknown. We might wish to solve the initial velocity basing on known range, which, however, provides only position information but not velocity.) I thus applied the numerical method in 2 aspects: 1. Use RK4 (Runge-Kutta-4-order) method to solve the ODEs and obtain final position/velocity. The time step is set as 0.05. This value is accuracy-and-efficiency-balanced one, with few meters' error. If set 0.1 then error will reach more than 10 meters with only half time cost, and set 0.01 can obtain precise result with no more than 1 meter error, but cost 5 times computation. 2. The relationship between elevation difference and range difference is locally linear (see figure below, which is a graduation of a initSpeed 180m/s weapon on elevation 15°~30°), and "Newton method" can thus be applied. Given Proper initSpeed and elevation, one can obtain precise enough range with no more than 4 meters' error by at most 5 iterations. The Proper elevation is provided by OFP vehicles in my Artillery Module. Roughly speaking, I designed a model for "target" object, and ask AI vehicles aiming at it. OFP will provide a predicted elevation when aiming, this elevation is precise only when initSpeed high and distance low, but it's enough as a "initial value" in iterating. The "Newton method" calculates the range basing on this initial elevation, and plus 1° (or -1° , depending on whether the range is less or more than actual distance between the vehicle and target. Mostly the range given by initial elevation is less) then calculates another range. Due to the locally linear relationship, a new elevation can be calculated by these 2 elevations and ranges, following a new range obtained again by RK4, which ends the first iteration. The terminate condition can be either that error no more than a threhold (like 4 meters) or having iterated 5 times (5 times is enough if initSpeed and elevation is proper, at least in OFP for shotShell). The error is defined as the absolute value of difference between iterated range and actual range. 3 videos displaying its effects: See more details below.