In my investigation of air-launched fixed-wing UAV drones, I focus on the critical problem of safe separation from a carrier aircraft. Unlike conventional store separation, fixed-wing UAV drones possess lifting surfaces that remain unfolded during certain test phases, which increases the risk of collision with the carrier. I therefore combine structured-grid numerical simulation, aerodynamic modeling, six-degree-of-freedom motion simulation, and full-scale flight testing to define safe deployment windows and control-law strategies. The central objective is to understand how Mach number, angle of attack, sideslip angle, center of gravity, and elevator pre-deflection influence the separation trajectory of UAV drones. Throughout this article, I use the term UAV drones to emphasize the unmanned aerial systems under study, and I show that wake effects, crosswind disturbances, and inertial properties dominate the early separation behavior.
Air deployment of UAV drones offers several advantages over ground launch or rocket-assisted takeoff. Because the UAV drones are released from a carrier aircraft, they do not require landing gear, structural reinforcement for high launch accelerations, or extremely low-speed aerodynamic performance. These benefits allow the UAV drones to be optimized for high-speed cruise and efficient loiter. However, when the wings of UAV drones are not folded, the exposed lifting surfaces create a large lateral area and a small roll moment of inertia, making the UAV drones highly sensitive to sideslip and wake-induced disturbances. My work addresses this gap by quantifying the separation safety envelope for non-folding fixed-wing UAV drones.
The research process I follow begins with the selection of an initial deployment window from the flight envelopes of both the carrier and the UAV drones. I then simulate the isolated aerodynamic characteristics of the UAV drones over a range of Mach numbers, altitudes, angles of attack, and sideslip angles. Next, I compute the steady aerodynamic interference caused by the carrier at several vertical separations. These data are used to build an aerodynamic model that feeds a six-degree-of-freedom simulation. The simulation evaluates multiple factors, including Mach number, angle of attack, sideslip, center of gravity, and control surface deflection. Based on the simulation results, I propose deployment-window adjustments and flight-control strategies, which are finally validated through flight tests. This integrated approach allows me to identify the dominant risks and to design effective mitigation measures for UAV drones.
The flow solver I employ is a three-dimensional compressible Reynolds-averaged Navier–Stokes solver based on multi-block structured grids. The solver uses a finite-volume discretization with an implicit time-integration method. Turbulence is modeled using the Spalart–Allmaras one-equation model, and the convective fluxes are discretized with Roe’s flux-difference splitting scheme. A Van Leer limiter is applied to maintain accuracy and stability. For unsteady simulations, I use a dual-time-stepping method and dynamic overset grids. The governing equations are written in conservation form as
$$ \frac{\partial W}{\partial t} + \frac{\partial}{\partial x_i}(f_i – f_{v i}) = 0 $$
where the state vector is
$$ W = [\rho, \rho u_1, \rho u_2, \rho u_3, e]^T $$
and the convective flux vector is
$$ f_i = [\rho u_i, \rho u_i u_1 + p \delta_{i1}, \rho u_i u_2 + p \delta_{i2}, \rho u_i u_3 + p \delta_{i3}, u_i(e+p)]^T $$
The viscous flux vector is
$$ f_{v i} = \frac{1}{Re_a} [0, \tau_{i1}, \tau_{i2}, \tau_{i3}, \tau_{ij}u_j – q_i]^T $$
Here, \(\rho\) is density, \(u_1, u_2, u_3\) are velocity components, \(p\) is pressure, \(e\) is total energy per unit mass, \(\delta_{ij}\) is the Kronecker delta, \(Re_a\) is the Reynolds number based on freestream sound speed, \(\tau_{ij}\) is the viscous stress tensor, and \(q_i\) is the heat flux component. I accelerate convergence using multigrid, residual averaging, and local time stepping. This solver has been extensively validated for store separation and aerodynamic interference problems.
Aerodynamic modeling is essential for the six-degree-of-freedom simulation of UAV drones. I model the lift coefficient as a linear combination of zero-angle-of-attack lift, angle-of-attack contribution, and elevator deflection contribution:
$$ C_L = C_{L,\alpha=0} + C_{L\alpha}\alpha + C_{L\delta_e}\delta_e $$
Each term depends on flight altitude \(H\), Mach number \(Ma\), vertical separation \(\Delta Z\), and sideslip angle \(\beta\). The expanded form is
$$ C_L = C_{L,\alpha=0}(H, Ma, \Delta Z, \beta) + C_{L\alpha}(H, Ma, \Delta Z, \beta)\alpha + C_{L\delta_e}(H, Ma)\delta_e $$
For the pitching moment, I use
$$ C_m = C_{m,\alpha=0} + C_{m\alpha}\alpha + C_{m\delta_e}\delta_e + C_{mq}q_{pitch} + C_{m\dot{\alpha}}\dot{\alpha} $$
where the first three terms represent static contributions, the fourth term represents pitch damping from the horizontal tail, and the fifth term represents downwash lag. The roll moment coefficient is modeled as
$$ C_l = C_{l\beta}\beta + C_{l\delta_a}\delta_a + C_{l\delta_r}\delta_r + C_{lp}p_{roll} + C_{lr}r $$
and the yaw moment coefficient is
$$ C_n = C_{n\beta}\beta + C_{n\delta_a}\delta_a + C_{n\delta_r}\delta_r + C_{np}p_{roll} + C_{nr}r $$
In these equations, \(p_{roll}\), \(q_{pitch}\), and \(r\) are the body-axis angular rates. Their nondimensional forms are defined by \(p_{roll} = p b / (2V)\), \(q_{pitch} = q c / (2V)\), and \(r = r b / (2V)\), where \(b\) is the reference span and \(c\) is the reference chord. The coefficients \(C_{l\beta}\), \(C_{n\beta}\), and the control derivatives are obtained from the numerical simulations. This modeling approach captures the dominant aerodynamic effects while remaining computationally efficient for long-duration simulations of UAV drones.
The six-degree-of-freedom dynamics of the UAV drones are governed by the rigid-body equations. The translational equations are
$$ \dot{u} = ur – w q_{pitch} – g\sin\theta + \frac{F_x}{m} $$
$$ \dot{v} = -ur + w q_{pitch} + g\cos\theta\sin\phi + \frac{F_y}{m} $$
$$ \dot{w} = u q_{pitch} – v p_{roll} + g\cos\theta\cos\phi + \frac{F_z}{m} $$
The kinematic equations for the center of gravity are
$$ \dot{x}_g = V\cos\mu\cos\varphi $$
$$ \dot{y}_g = V\cos\mu\sin\varphi $$
$$ \dot{z}_g = V\sin\mu $$
The rotational dynamics are
$$ \dot{p}_{roll} = (c_1 r + c_2 p_{roll})q_{pitch} + c_3 L + c_4 N $$
$$ \dot{q}_{pitch} = c_5 p_{roll} r – c_6(p_{roll}^2 – r^2) + c_7 M $$
$$ \dot{r} = (c_8 p_{roll} – c_2 r)q_{pitch} + c_4 L + c_9 N $$
and the attitude kinematics are
$$ \dot{\phi} = p_{roll} + (r\cos\phi + q_{pitch}\sin\phi)\tan\theta $$
$$ \dot{\theta} = q_{pitch}\cos\phi – r\sin\phi $$
$$ \dot{\psi} = \frac{1}{\cos\theta}(r\cos\phi + q_{pitch}\sin\phi) $$
Here, \(u, v, w\) are the body-axis velocity components, \(F_x, F_y, F_z\) are the body-axis forces, \(L, M, N\) are the body-axis moments, \(\phi, \theta, \psi\) are the roll, pitch, and yaw angles, and \(\mu, \varphi\) are the flight-path angles. The constants \(c_1\) through \(c_9\) depend on the mass moments of inertia of the UAV drones. I solve these equations numerically with a fourth-order Runge–Kutta scheme and a time step of \(0.001\) s. The aerodynamic database is interpolated at each time step using the current Mach number, altitude, angle of attack, and sideslip angle.
Before applying the methodology to the UAV drones, I validate the solver against a standard store separation experiment. The validation case is a wing/pylon/finned-store configuration tested in a wind tunnel. The carrier wing is a tapered 45-degree swept delta wing with a NACA 64-010 airfoil. The store is a cylindrical body with four fins. The freestream Mach number is \(0.95\), the altitude is \(7925\) m, and the initial angle of attack and sideslip are zero. Two ejection forces act on the store for \(0.055\) s. The key parameters are listed in Table 1.
| Parameter | Value |
|---|---|
| Store mass | \(907.8\) kg |
| Roll moment of inertia \(I_{xx}\) | \(27.12\) kg·m\(^2\) |
| Pitch and yaw moments of inertia \(I_{yy}, I_{zz}\) | \(488.1\) kg·m\(^2\) |
| Front ejection force \(F_1\) | \(10679.4\) N |
| Rear ejection force \(F_2\) | \(42717.5\) N |
| Ejection duration \(\Delta t_F\) | \(0.055\) s |
| Store center of gravity from nose | \(1.41732\) m |
| Front force application point \(\Delta x\) | \(-0.176\) m |
| Rear force application point \(\Delta x\) | \(0.334\) m |
| Freestream Mach number | \(0.95\) |
| Altitude | \(7925\) m |
| Time step | \(0.005\) s |
The computational grids consist of body-fitted structured grids for both the wing and the store. The wing grid contains approximately \(4.52\) million cells, and the store grid contains about \(1.58\) million cells. The store grid moves relative to the wing grid using an overset approach. I compare the computed store trajectories and attitude angles with the experimental data. The axial force coefficient is slightly overpredicted at the very beginning of the separation, which I attribute to differences in the base support geometry. However, the normal force, side force, pitch moment, roll moment, and yaw moment histories agree well with the measurements. The center-of-gravity displacement, velocity, attitude angles, and angular rates also match the experiment closely. In particular, the abrupt change in pitch rate at \(t = 0.055\) s, when the ejection forces are removed, is captured accurately. This validation gives me confidence that the solver can predict the separation behavior of UAV drones and carrier interference.
After validating the solver, I investigate the aerodynamic interference of the carrier aircraft on the UAV drones. The carrier aircraft in my study has a twin-fuselage, \(\pi\)-tail, four-engine layout. The UAV drones are mounted under the straight wing section between the fuselages. The carrier is approximately \(5.3\) m long, with a wing span of about \(5.8\) m and a height of about \(2\) m. I select a nominal deployment window at Mach \(0.45\) and altitude \(6\) km. This choice balances the low-speed boundary of the carrier with the need to keep the dynamic pressure low enough for the UAV drones to accelerate downward safely.
I compute the steady aerodynamic forces on the UAV drones at five vertical separations from the carrier: \(0\), \(0.25\), \(0.5\), \(0.75\), and \(1.0\) m. The computational domain extends \(50\) body lengths from the configuration, and the first cell height at the wall is \(5 \times 10^{-6}\) m to ensure \(y^+ \approx 1\). A half-model grid with about \(21\) million cells is used. Table 2 summarizes the effect of the carrier on the lift and pitching moment of the UAV drones.
| Vertical separation \(\Delta Z\) (m) | Lift-curve slope reduction relative to isolated UAV drones | Zero-angle-of-attack pitching moment shift (nose-up positive) |
|---|---|---|
| 0.00 | Strong reduction | Large nose-up moment |
| 0.25 | Moderate reduction | Moderate nose-up moment |
| 0.50 | Small reduction | Small nose-up moment |
| 0.75 | Very small reduction | Very small nose-up moment |
| 1.00 | Negligible | Negligible |
The results show that the carrier’s wake significantly reduces the lift-curve slope of the UAV drones and generates a nose-up pitching moment. The reduction in lift-curve slope is beneficial because it allows the UAV drones to accelerate downward more rapidly, which helps separation. However, the nose-up moment is detrimental because it increases the pitch attitude and can cause the UAV drones to pitch up excessively. I find that the primary cause of the lift-curve slope reduction is the downwash from the carrier’s central wing. The local angle of attack ahead of the UAV drones is significantly lower than the freestream angle of attack, while the local flow velocity is slightly higher. The effective angle of attack reduction is on the order of \(5\) degrees at zero vertical separation. This explains why the lift-curve slope decreases so strongly in the presence of the carrier.
To model these interference effects, I express the lift coefficient of the UAV drones as a function of vertical separation, angle of attack, and sideslip angle. The local angle of attack reduction can be represented as
$$ \alpha_{\text{eff}} = \alpha – \Delta\alpha_w(\Delta Z, Ma, H) $$
where \(\Delta\alpha_w\) is the wake-induced downwash angle. Similarly, the lift-curve slope is modeled as
$$ C_{L\alpha} = C_{L\alpha,0} \left[1 – k_w(\Delta Z, Ma, H)\right] $$
where \(k_w\) is a wake-interference factor that I obtain from the numerical simulations. For the pitching moment, I add a wake-induced term:
$$ C_{m,w} = C_{m,w0}(\Delta Z, Ma, H, \beta) + C_{m,w\alpha}(\Delta Z, Ma, H, \beta)\alpha $$
These terms are included in the six-degree-of-freedom simulation. The aerodynamic database covers Mach numbers from \(0.40\) to \(0.50\), altitudes from \(5.5\) km to \(6.5\) km, angles of attack from \(1.5\) to \(6.0\) degrees, and sideslip angles up to \(7.5\) degrees. This range reflects the uncertainties expected in actual flight tests of UAV drones.
Using the aerodynamic model, I perform open-loop six-degree-of-freedom simulations for the nominal deployment window. The baseline conditions are Mach \(0.45\), altitude \(6\) km, initial pitch angle \(2.5\) degrees, and zero sideslip. I simulate \(2\) seconds of free flight after release. The simulation matrix is shown in Table 3.
| Factor | Baseline value | Variation range |
|---|---|---|
| Mach number | \(0.45\) | \(0.40\) to \(0.50\) |
| Altitude | \(6\) km | \(5.5\) to \(6.5\) km |
| Angle of attack | \(2.5^\circ\) | \(1.5^\circ\) to \(6.0^\circ\) |
| Sideslip angle | \(0^\circ\) | \(0^\circ\) to \(7.5^\circ\) |
| Center of gravity shift | \(0\) cm | \(-2\) cm to \(+2\) cm |
| Elevator deflection | \(0^\circ\) | \(0^\circ\) to \(5^\circ\) |
I first examine the effect of Mach number. As Mach number increases, the dynamic pressure increases, which amplifies both the lift and the nose-up pitching moment. Table 4 shows the maximum pitch angle and the vertical drop after \(1\) second for three Mach numbers.
| Mach number | Maximum pitch angle (deg) | Vertical drop after 1 s (m) |
|---|---|---|
| \(0.40\) | \(7.5\) | \(2.5\) |
| \(0.45\) | \(8.0\) | \(1.8\) |
| \(0.50\) | \(8.5\) | \(1.0\) |
Higher Mach numbers produce a larger nose-up moment and a smaller vertical drop, which reduces separation safety. The larger dynamic pressure increases the aerodynamic forces on the UAV drones, making the nose-up moment more severe. However, even at Mach \(0.50\), the pitch attitude remains below \(9\) degrees, and the UAV drones eventually stabilize. The vertical drop decreases because the increased lift partially offsets the gravitational acceleration. This trade-off means that the deployment window should avoid the upper end of the Mach range if possible.
Next, I investigate the effect of initial angle of attack. The initial angle of attack depends on the carrier’s flight attitude and the mounting angle of the UAV drones. I vary the initial angle of attack from \(1.5\) to \(6.0\) degrees. Table 5 presents selected results.
| Initial angle of attack (deg) | Maximum pitch angle (deg) | Vertical drop after 1 s (m) |
|---|---|---|
| \(1.5\) | \(9.0\) | \(2.2\) |
| \(2.5\) | \(8.5\) | \(2.0\) |
| \(4.0\) | \(8.2\) | \(1.9\) |
| \(6.0\) | \(8.0\) | \(1.8\) |
Lower initial angles of attack produce a larger nose-up angular rate and a larger maximum pitch angle. The reason is that at low angles of attack, the initial nose-up moment from the carrier wake is larger relative to the lift. The vertical drop also increases at lower angles of attack because the lift is smaller. Therefore, a lower initial angle of attack is beneficial for vertical separation but detrimental for pitch attitude. I find that an initial angle of attack between \(2.5\) and \(4.0\) degrees offers a good compromise for UAV drones.
The effect of sideslip angle is more dramatic. Because the UAV drones have unfolded wings and a relatively small roll moment of inertia, even a modest sideslip can induce large roll angles. Table 6 shows the maximum roll angle and lateral displacement after \(1\) second for several sideslip angles.
| Sideslip angle (deg) | Maximum roll angle (deg) | Lateral displacement after 1 s (m) |
|---|---|---|
| \(-1.5\) | \(10\) | \(2\) |
| \(-3.5\) | \(30\) | \(6\) |
| \(-5.5\) | \(45\) | \(10\) |
| \(-7.5\) | \(60\) | \(13\) |
At a sideslip angle of \(7.5\) degrees, the UAV drones reach a roll angle of nearly \(60\) degrees within \(0.7\) seconds, and the lateral displacement exceeds \(13\) m after \(1\) second. This large roll and lateral motion significantly increase the risk of collision with the carrier. The roll is driven by the dihedral effect and the side force acting on the vertical tail and fuselage. Because the roll moment of inertia is small, the angular acceleration is high. I conclude that active roll control is essential for safe separation of UAV drones. Without flight control intervention, even small sideslip disturbances can lead to dangerous attitudes.
Center-of-gravity shifts also affect separation safety. I consider shifts along the longitudinal axis. A shift toward the tail reduces static longitudinal stability and increases the nose-up moment. Table 7 summarizes the effect.
| Center-of-gravity shift (cm) | Maximum pitch angle (deg) | Vertical drop after 1 s (m) |
|---|---|---|
| \(-2\) (toward tail) | \(9.0\) | \(1.5\) |
| \(-1\) | \(8.5\) | \(1.7\) |
| \(0\) (baseline) | \(8.0\) | \(2.0\) |
| \(+1\) | \(7.5\) | \(2.2\) |
| \(+2\) (toward nose) | \(7.0\) | \(2.4\) |
A rearward center-of-gravity shift of \(2\) cm increases the maximum pitch angle by about \(1\) degree and reduces the vertical drop by about \(0.5\) m. This occurs because the nose-up moment arm increases. A forward shift has the opposite effect. The sensitivity of the pitch attitude to center-of-gravity position is moderate but should be accounted for in the deployment window. Manufacturing tolerances and fuel slosh can cause such shifts in UAV drones, so I recommend that the center of gravity be kept within \(\pm 1\) cm of the nominal position.
To suppress the nose-up tendency, I investigate the use of elevator pre-deflection. By setting the elevator to a positive deflection before release, I generate a nose-down pitching moment that counteracts the carrier wake. Table 8 shows the maximum pitch angle for different elevator pre-deflections.
| Elevator deflection \(\delta_e\) (deg) | Maximum pitch angle (deg) | Minimum pitch angle (deg) |
|---|---|---|
| \(0\) | \(8\) | \(2.5\) |
| \(1\) | \(2\) | \(0\) |
| \(2\) | \(-2\) | \(-3\) |
| \(3\) | \(-6\) | \(-7\) |
| \(4\) | \(-9\) | \(-10\) |
| \(5\) | \(-12\) | \(-13\) |
A positive elevator deflection of \(5\) degrees reduces the maximum pitch angle from \(8\) degrees to \(-12\) degrees, which means the UAV drones pitch down instead of up. This is effective for preventing a nose-up collision with the carrier. However, excessive nose-down pitch can cause the tail to rise and potentially strike the carrier or create an unsafe descent attitude. Based on my simulations, an elevator pre-deflection of \(1\) to \(2\) degrees provides the best balance. This range keeps the pitch attitude close to the horizontal while providing enough nose-down moment to counteract the wake-induced nose-up tendency.
To further validate the aerodynamic model and the six-degree-of-freedom simulation, I compare the open-loop simulation results with unsteady numerical simulations using the same solver. I select two representative conditions: Mach \(0.45\), altitude \(6\) km, angle of attack \(4.5\) degrees, and sideslip angles of \(0\) and \(-7.5\) degrees. The unsteady simulations use dynamic overset grids with a time step of \(0.005\) s. Table 9 shows the comparison of pitch and roll angles at \(0.5\) seconds after release.
| Condition | Pitch angle from 6-DOF (deg) | Pitch angle from unsteady CFD (deg) | Roll angle from 6-DOF (deg) | Roll angle from unsteady CFD (deg) |
|---|---|---|---|---|
| \(\beta = 0^\circ\) | \(6.5\) | \(6.0\) | \(0\) | \(0\) |
| \(\beta = -7.5^\circ\) | \(5.0\) | \(4.5\) | \(65\) | \(60\) |
The open-loop simulation captures the qualitative trends accurately: the nose-up tendency at zero sideslip and the large roll at negative sideslip. However, there is a cumulative error in the absolute values of the attitude angles. The unsteady CFD predicts slightly lower pitch angles and roll angles because it resolves the time-dependent wake and vortex shedding more accurately. The differences are within \(5\) to \(10\) percent. For the purpose of control-law design, the open-loop simulation is adequate because it is conservative and highlights the worst-case trends. I therefore use it to define the control strategy for the flight test.
Based on the simulation results, I design a flight-control strategy for the UAV drones. The strategy consists of two elements: an elevator pre-deflection of \(1\) degree before release, and a zero-delay flight-control activation immediately after separation. The elevator pre-deflection generates a mild nose-down moment to counteract the carrier wake. The zero-delay activation ensures that the flight controller begins correcting roll and pitch as soon as the separation signal is received. The controller maintains a roll angle of \(0\) degrees and a pitch attitude of \(1\) degree. The control law uses a proportional-integral-derivative structure with rate feedback. The aileron and elevator commands are limited to \(20\) degrees and \(15\) degrees, respectively, to avoid excessive control deflections that could cause structural loads or stall.
I conduct a full-scale flight test to validate the simulation results and the control strategy. The carrier aircraft flies to the designated altitude and speed window. At \(581\) seconds, the ground station sends the release command. The UAV drones maintain a roll angle of \(0\) degrees and a pitch attitude of \(1\) degree for \(10\) seconds before release. At the moment of separation, the UAV drones experience a pronounced right-roll tendency due to a crosswind disturbance. Table 10 summarizes the key telemetry data.
| Event | Time (s) | Value |
|---|---|---|
| Release command | \(581\) | — |
| Pre-release stabilization | \(581\)–\(591\) | Roll \(0^\circ\), pitch \(1^\circ\) |
| Separation instant | \(591\) | Right roll begins |
| Maximum roll rate | \(591.2\) | Near \(40\) deg/s |
| Maximum pitch angle | \(591.5\) | Approximately \(6^\circ\) |
| Roll stabilization | \(593\) | Roll angle returns to near \(0^\circ\) |
| Pitch stabilization | \(594\) | Pitch angle returns to \(1^\circ\) |
The flight test data show that the maximum roll rate reaches nearly \(40\) deg/s, which is consistent with the simulation prediction of a large roll tendency under sideslip. The maximum pitch angle is approximately \(6\) degrees, which is lower than the open-loop prediction because the elevator pre-deflection and the flight controller actively suppress the nose-up motion. After about \(2\) seconds, the roll and pitch attitudes stabilize, and the UAV drones enter controlled flight. The separation is successful, and no collision occurs. This outcome validates the simulation analysis and the flight-control strategy. The elevator pre-deflection of \(1\) degree and the zero-delay control activation were sufficient to manage the crosswind disturbance and the carrier wake.
From my analysis, I draw several conclusions about the deployment safety of fixed-wing UAV drones. First, the carrier’s wake reduces the lift-curve slope of the UAV drones and generates a nose-up pitching moment. The lift reduction aids separation, but the nose-up moment must be controlled. Second, higher Mach numbers increase the nose-up moment and reduce the vertical drop, making separation less safe. Third, lower initial angles of attack produce a larger nose-up angular rate and a larger vertical drop. Fourth, sideslip disturbances cause large roll angles because of the small roll moment of inertia and the unfolded wings; active roll control is essential. Fifth, a rearward center-of-gravity shift increases the nose-up tendency, while a forward shift reduces it. Sixth, elevator pre-deflection of \(1\) to \(2\) degrees effectively suppresses the nose-up motion without causing excessive nose-down pitch. Seventh, the flight test confirms that a combination of elevator pre-deflection and zero-delay flight control achieves safe separation of UAV drones.
In terms of deployment window design, I recommend that the carrier release the UAV drones at Mach numbers between \(0.40\) and \(0.45\), altitudes between \(5.5\) and \(6.5\) km, and initial angles of attack between \(2.5\) and \(4.0\) degrees. The sideslip angle should be kept as small as possible, ideally below \(3\) degrees. The center of gravity of the UAV drones should be maintained within \(\pm 1\) cm of the nominal position. The elevator should be pre-deflected by \(1\) to \(2\) degrees, and the flight controller should be activated with no delay. These conditions provide a robust safety margin for the separation of UAV drones.
For future work, I plan to extend the aerodynamic database to include aeroelastic effects, since the unfolded wings of UAV drones may flex under the dynamic pressure of separation. I also intend to investigate the use of differential thrust or drag devices to improve roll control during the critical first second after release. Additionally, I will explore adaptive control laws that can accommodate larger sideslip disturbances and center-of-gravity shifts. The goal is to make the deployment of UAV drones from carrier aircraft as safe and reliable as possible, enabling a wider range of operational scenarios.
In summary, my study demonstrates that the safe deployment of fixed-wing UAV drones requires a careful integration of aerodynamic analysis, six-degree-of-freedom simulation, and flight-control design. The carrier wake, sideslip disturbances, and inertial characteristics of the UAV drones are the dominant factors. By using structured-grid numerical simulation and validated aerodynamic models, I can predict the separation behavior and design effective mitigation strategies. The successful flight test confirms the validity of my approach. The methods and findings presented here can be applied to other UAV drones with unfolded wings or similar store separation problems. I believe that continued research in this area will enhance the operational capabilities of air-launched UAV drones and reduce the risks associated with their deployment.