In the field of unmanned aerial vehicles (UAVs), the fixed-wing drone has become a cornerstone for both military and civilian applications due to its long endurance, high payload capacity, and superior aerodynamic efficiency. Among the critical structural components of a fixed-wing drone, the main load-bearing frame (also referred to as the bulkhead or rib) plays a vital role in transmitting wing loads to the fuselage, supporting the skin, and forming sealed compartments. The performance of this frame directly influences the overall structural integrity, weight, and operational capability of the fixed-wing drone. Lightweight design of the main load-bearing frame is therefore essential to enhance the payload-to-weight ratio and extend the flight endurance. This study focuses on a specific type of fixed-wing drone that is required to perform long-duration reconnaissance missions in complex environments, imposing stringent demands on both lightweight construction and structural reliability.
Topology optimization, a mathematical approach that optimally distributes material within a given design space under specified loads, constraints, and performance criteria, offers a powerful tool for achieving significant mass reduction while maintaining or even improving mechanical performance. Compared with traditional shape or size optimization, topology optimization provides greater design freedom and has been widely applied in aerospace structural design. In this work, we adopt the solid isotropic microstructure with penalization (SIMP) method, combined with the Altair OptiStruct solver, to perform topology optimization on the front main load-bearing frame of a fixed-wing drone. Our objective is to achieve a lightweight structure that satisfies all mechanical requirements under a 3g overload condition. The optimization minimizes structural compliance subject to a volume fraction constraint of less than 0.25. The resulting material distribution reveals that the main load-bearing material concentrates in the wing attachment regions and connecting zones. After reconstructing the model based on the topology optimization results, the mass of the frame is reduced by 10.87%, while the maximum stress increases from 43 MPa to 56 MPa, indicating a significant improvement in material utilization efficiency. Although the maximum displacement of the fuselage increases slightly, it remains within acceptable limits. This study demonstrates that topology optimization can effectively guide the lightweight redesign of the fixed-wing drone’s main load-bearing frame, alleviate stress concentration, and enable material substitution for further cost reduction.
Introduction
Fixed-wing drones are increasingly deployed in a wide range of applications, including aerial surveillance, mapping, agricultural monitoring, and military reconnaissance. The structural design of these aircraft must balance weight reduction with strength and stiffness to ensure safe operation under various loading conditions. The main load-bearing frame is a critical component that connects the wings to the fuselage and transfers aerodynamic and inertial loads. In the specific fixed-wing drone considered in this study, the fuselage contains nine bulkheads: frames 1–4 form the equipment bay, frames 5–8 form the fuel tank bay, with frame 6 serving as the front main load-bearing frame that connects directly to the wing. Frames 8–9 constitute the aft equipment bay, and frame 9 supports the engine. The skin material is glass fiber reinforced plastic (GFRP), while all other structural components are made of aluminum alloy 7075-T651. The material properties are summarized in Table 1.
| Property | GFRP | 7075-T651 |
|---|---|---|
| Density (kg/m³) | 1700 | 2810 |
| Longitudinal Elastic Modulus (Pa) | 8.5E9 | 71.7E9 |
| Transverse Elastic Modulus (Pa) | 1.7E10 | 71.7E9 |
| Poisson’s Ratio (longitudinal uniaxial) | 0.1 | 0.33 |
| Shear Modulus (Pa) | — | 26.9E9 |
The main load-bearing frame initially weighs 3.652 kg. Its structural configuration is a web-type reinforced frame, comprising flanges (caps), a web panel, and stiffeners. An initial finite element analysis (FEA) under the 3g overload condition reveals that the maximum displacement of the frame occurs at its lower edge, reaching 0.3422 mm. The maximum von Mises stress is 43 MPa, located at the root of the lugs. The overall fuselage experiences a maximum displacement of 3.220 mm. While these values satisfy the design requirements, there is room for weight reduction through structural optimization, which is the primary motivation of this work.
Problem Description and Baseline Performance
The fixed-wing drone’s front main load-bearing frame is subjected to combined loads from the wing, landing gear, and internal payloads. In this study, we consider a 3g symmetric pull-up maneuver as the critical load case. The loads are applied as distributed forces at the wing attachment points and inertial relief from the structural mass. The finite element model includes the entire fuselage section to capture the interaction between the frame and adjacent components. The baseline model is analyzed using Altair OptiStruct with a linear static solver. The key performance metrics for the original frame are summarized in Table 2.
| Component | Mass (kg) | Maximum Displacement (mm) | Maximum Stress (MPa) |
|---|---|---|---|
| Front Main Load-Bearing Frame | 3.652 | 0.3422 | 43 |
| Fuselage (overall) | — | 3.220 | — |
The original design, though functional, exhibits conservative material distribution. The maximum stress of 43 MPa is significantly lower than the yield strength of 7075-T651 (approximately 503 MPa), indicating inefficient material usage. This motivates the application of topology optimization to redistribute material and reduce mass without compromising structural integrity.
Topology Optimization Formulation
We employ the SIMP method for topology optimization. In this approach, the design domain is discretized into finite elements, and each element is assigned a continuous density variable $\rho_e$ between 0 (void) and 1 (solid). The Young’s modulus of each element is penalized using a power law:
$$
E_e(\rho_e) = \rho_e^p E_0
$$
where $p$ is the penalization factor (typically $p = 3$), and $E_0$ is the Young’s modulus of the solid material. The optimization problem is formulated as follows:
$$
\begin{aligned}
\text{Find} \quad & \rho_e, \quad e = 1,2,\dots,n \\
\text{Minimize} \quad & C = \mathbf{U}^T \mathbf{K} \mathbf{U} = \sum_{e=1}^{n} \rho_e^p \mathbf{u}_e^T \mathbf{k}_0 \mathbf{u}_e \\
\text{Subject to} \quad & \frac{\sum_{e=1}^{n} \rho_e v_e}{V_0} \leq f \\
& \mathbf{K} \mathbf{U} = \mathbf{F} \\
& 0 \leq \rho_e \leq 1
\end{aligned}
$$
Here, $C$ is the structural compliance (inverse of stiffness), $\mathbf{U}$ is the global displacement vector, $\mathbf{K}$ is the global stiffness matrix, $\mathbf{F}$ is the load vector, $v_e$ is the element volume, $V_0$ is the total design domain volume, and $f$ is the prescribed volume fraction (0.25 in this work). The constraint ensures that the final design uses no more than 25% of the original design domain volume.
The design domain for the front main load-bearing frame is defined as the web region between the inner and outer flanges. The flanges themselves are designated as non-design regions to maintain connectivity with adjacent components. The optimization is performed under the 3g overload condition using Altair OptiStruct. The solver uses a gradient-based algorithm (method of moving asymptotes) to update the density variables. Convergence is achieved when the change in objective function is less than 0.1% over five consecutive iterations.
Optimization Results and Model Reconstruction
The topology optimization yields a density contour plot showing the preferred material distribution. To extract a clear load path, we filter elements with a relative density greater than 0.8. The material concentrates primarily in two regions: on the front face, material forms a spider-like pattern branching from the flanges toward the wing attachment points; on the rear face, material accumulates along the upper portion of the frame and the upper flanges. This distribution indicates that the primary load paths follow the wing root attachments and the upper flanges, with diagonal bracing providing shear resistance.
Based on these results, we reconstruct the geometry using computer-aided design (CAD) software. The reconstructed model retains the central web but introduces “X”-shaped stiffeners in the wing attachment regions, along with additional rib-like stiffeners in the upper zone. The total mass of the reconstructed frame is 3.255 kg, representing a reduction of 0.397 kg (10.87%) compared to the original. Table 3 compares the key parameters of the original and optimized frames.
| Parameter | Original | Optimized | Change (%) |
|---|---|---|---|
| Mass (kg) | 3.652 | 3.255 | -10.87 |
| Maximum Stress (MPa) | 43 | 56 | +30.23 |
| Maximum Displacement (frame) (mm) | 0.3422 | 0.3948 | +13.32 |
| Maximum Displacement (fuselage) (mm) | 3.220 | 3.390 | +5.01 |
It is evident that the optimized frame exhibits higher stress (56 MPa) but still remains well within the material’s yield strength. The stress concentration previously observed at the lug root has been alleviated, as the optimized geometry distributes loads more uniformly. The small increase in displacement (0.0526 mm for the frame) is negligible compared to the weight savings, and the overall fuselage displacement of 3.39 mm remains acceptable for the intended flight envelope.
Static Verification and Discussion
We reassemble the fuselage finite element model with the optimized frame and perform a full static analysis under the same 3g load case. The results confirm that the reconstructed frame satisfies all strength and stiffness requirements. The maximum von Mises stress occurs at the junction of the X-shaped stiffener with the flange, reaching 56 MPa. This is a 30% increase over the original but still far below the allowable stress of 7075-T651. The maximum displacement of the fuselage increases slightly from 3.220 mm to 3.390 mm, corresponding to a 5.01% increase. This rise is attributable to the reduced material in the frame, yet it remains within the design tolerance of 5 mm set for the fixed-wing drone.
The topology optimization not only reduces mass but also improves material utilization efficiency. The original design had an average stress of approximately 12 MPa across the frame, while the optimized design raises the average stress to around 20 MPa, indicating a more uniform load-bearing state. This improvement opens the possibility of substituting the current aluminum alloy with a lighter material, such as a high-strength magnesium alloy or advanced composite, to achieve further weight reduction and cost savings. Future work could explore multi-load case optimization and manufacturing constraints to enhance the practicality of the design.
In summary, this study successfully demonstrates the application of SIMP-based topology optimization to the front main load-bearing frame of a fixed-wing drone. The method effectively identifies optimal material paths, leading to a 10.87% mass reduction while maintaining structural performance within acceptable limits. The results highlight the potential of topology optimization as a systematic tool for lightweight design of aerospace structures, particularly for fixed-wing drones where every gram of weight saved translates into extended endurance or increased payload.
Conclusion
We have presented a comprehensive topology optimization study for the front main load-bearing frame of a fixed-wing drone. Using the SIMP method with a volume fraction constraint of 0.25 and the 3g overload condition, we obtained a clear material distribution that concentrates in the wing attachment zones and upper flanges. The reconstructed model reduces the frame mass by 10.87% (from 3.652 kg to 3.255 kg), while the maximum stress increases from 43 MPa to 56 MPa, indicating enhanced material utilization. The slight increase in displacement (0.0526 mm for the frame, 0.170 mm for the fuselage) is negligible relative to the weight savings. The stress concentration problem in the original design is effectively mitigated. This work validates the effectiveness of topology optimization in the structural design of fixed-wing drones and provides a reference for lightweight optimization of other key components. Future studies will incorporate fatigue life assessment and manufacturability constraints to further advance the practical application of this approach.