Lidozin

I had absolutely no intention of defending anyone — neither dissatisfied users, nor, certainly, the developers. My goal is to establish the facts, based on knowledge — as lofty as that may sound. If this model had shown significant deviations from the reference documentation, I would have gladly published those findings. This particular aircraft caught my attention because it's one of the most frequently criticized by users, and at the same time, one of the best-documented — both in terms of source data, which undoubtedly gave the developers a solid foundation to work with, and in terms of flight performance, which provides a solid basis for comparing the model with the real aircraft in detail.

Let me clarify a few points, since there seems to be a misunderstanding about what trajectory-based (or “point mass”) flight models actually include. First of all, the notion that a trajectory model ignores angle of attack is simply incorrect. In fact, angle of attack (AoA) is one of the key input parameters used to define the aerodynamic polar — the lift and drag coefficients are tabulated precisely as functions of AoA. When a simulation computes the net force on the aircraft at any moment, it determines AoA from the velocity vector and attitude, then looks up the corresponding L and D values from that polar. This is standard in both AI models and performance simulation tools used in real-world aerospace. So yes — energy loss due to drag is inherently tied to AoA in such models. It’s not being ignored; it’s the foundation for how excess power, turn rate, and climb performance are derived.

As far as I understand from press releases, the advantage of GFM is that it provides a much more natural simulation of aircraft behavior during short-period motion — that is, rotation around the center of mass — while preserving the accuracy of the older, trajectory-based model. The very same accuracy we’ve been seeing demonstrated throughout this discussion thread. And that’s exactly why we’re looking forward to the new model with such anticipation. https://www.digitalcombatsimulator.com/en/news/2021-12-03/

null You are fundamentally mistaken here. When we talk about AI aircraft — or even more advanced flight models — in the context of maneuvering characteristics (i.e. the ability to change trajectory), we're primarily discussing the motion of the aircraft in space as a point mass. Simulating this type of motion has been possible since long ago — even a ZX Spectrum or a PC XT was sufficient for solving such equations. And today, it's entirely feasible to simulate thousands of aircraft using an AI-level flight model in real time. A few lookup tables — lift and drag coefficients, thrust vs. altitude and speed — along with some auxiliary data, and voila: you have a highly accurate trajectory and maneuvering model.

If the induced drag component of the aircraft's L/D polar is known (and the polar itself is available from wind tunnel tests up to high values of CL, and the model's energy performance matches the real aircraft both at low lift coefficients (e.g. specific excess power in 1g flight) and at high CL (e.g. sustained turn at zero excess power), then it follows that energy loss during high-load, non-sustained maneuvers — where CL is even higher — will also closely match the real aircraft. I find it hard to believe that whoever tuned this flight model would have ignored such a rich dataset — especially given how thoroughly the design bureau compiled and published the aircraft’s aerodynamic characteristics in the official technical documentation. nullnull

This is easy to verify with a simple test setup: position yourself relative to the AI aircraft so that you're firmly within its rear blind cone — specifically, low-six o'clock, about 100–200 meters behind and below, matching its speed to maintain position. Maintain pursuit while staying within that no-visibility sector. If the AI still detects you and reacts — despite following its flight plan and having orders to engage the first contact it sees — then it truly does have 360-degree situational awareness, which would be unrealistic. Next, briefly move just outside the blind cone — into a zone where a real pilot could reasonably acquire a contact visually — and observe the difference in reaction time and behavior.

Thus, the casual claims that the AI aircraft MiG-15 possesses "supernatural" power can be put to rest. The core energy-related parameters — both in 1g flight (such as maximum speed and specific excess power) and in sustained turns at zero excess power — show very strong agreement with available reference data.

For turn time at 1,000 meters altitude, the documentation provides calculated reference data. This point has been plotted on the graph of the AI aircraft’s computed turn time. To keep the analysis concise, we'll limit the comparison to two altitude points — 1,000 meters and 10,000 meters. For 10,000 meters, the documentation includes a turn performance chart obtained from actual flight tests; those reference points will be overlaid on the AI's calculated curve. null null

The next parameter to be evaluated is rate of climb. Unlike with the flight envelope, the documentation does include charts for both maximum rate of climb and the indicated airspeed at which it is achieved, across different altitudes. By plotting the AI aircraft’s rate-of-climb versus IAS at various altitudes, we can determine both the peak climb rate and the corresponding airspeed at three key points: sea level, 5,000 meters, and 10,000 meters. Once again, we observe a very close match between the AI’s energy performance and the documented data — strong evidence that the flight model behaves realistically in this regard as well. All calculations were performed for a nominal aircraft weight of 5,000 kg.

I've seen many claims here suggesting that the AI (aircraft type) possesses "unrealistic" or "supernatural" flight physics. However, since all the aerodynamic and performance data used by the AI are available in the .lua file, it's possible to compute its actual characteristics and compare them directly with those from the real aircraft’s technical documentation. Let’s start with the flight envelope. The original manual doesn’t provide a complete envelope chart, so we'll have to compare individual performance points mentioned throughout the text. When these reference points are plotted over the AI’s calculated envelope, the result speaks for itself. At the very least, in this aspect, there’s no evidence of any "supernatural" behavior — the AI’s performance stays well within the bounds of what's expected based on real-world data.

That's a really interesting question. I have the ability to test MiG-15 bot, but unfortunately I can't test F4U bot because the module is missing. The data needed for the calculations is included in the file CoreMods\F4U-1D.lua .

One can check AI FM using data from F4U-1D.lua from CoreMods. Drag polars for given Mach numbers can be easily plotted using the data from the file.

Could somebody share a file F4U-1D.lua from CoreMods?