We present a systematic study on the lightweight design of the front main load‑bearing frame of a fixed‑wing UAV using topology optimization. The fixed‑wing UAV is widely employed in both military and civilian applications due to its long endurance and high payload capacity. The main load‑bearing frame is a critical structural component that transmits wing loads, supports the skin, and forms enclosed compartments. Its mass directly affects the overall performance of the fixed‑wing UAV, including endurance, maneuverability, and cost. Therefore, achieving a lightweight design while preserving mechanical integrity is of paramount importance.
We adopt the solid isotropic microstructure with penalization (SIMP) method, implemented in the Altair OptiStruct solver, to perform topology optimization on the front main load‑bearing frame under a 3g overload condition. The optimization objective is to minimize the structural compliance subject to a volume fraction constraint of less than 0.25. The original frame has a mass of 3.652 kg, a maximum displacement of 0.3422 mm, and a maximum von Mises stress of 43 MPa. After topology optimization and subsequent model reconstruction, the mass is reduced to 3.255 kg (a 10.87% reduction), while the maximum stress increases to 56 MPa (a 23.21% increase) and the maximum displacement rises moderately to 0.3948 mm. The overall fuselage displacement increases from 3.220 mm to 3.39 mm, remaining within acceptable limits. The results demonstrate that topology optimization effectively improves material utilization and alleviates stress concentration, enabling a significant mass reduction of the fixed‑wing UAV’s main load‑bearing frame without compromising structural safety.
1. Introduction
The fixed‑wing UAV has become indispensable in modern reconnaissance, surveillance, and logistics missions. The airframe structure must be both lightweight and robust to withstand various flight loads. Among all structural components, the main load‑bearing frame plays a crucial role in transferring forces from the wings to the fuselage and supporting onboard equipment. Any mass reduction in this component directly improves the payload‑to‑weight ratio and extends the flight endurance of the fixed‑wing UAV.
Topology optimization is a powerful mathematical technique that determines the optimal distribution of material within a given design domain, subject to specific loads, constraints, and performance targets. Unlike traditional sizing or shape optimization, topology optimization offers much greater design freedom and can generate novel, organic load‑path configurations. In the aerospace sector, it has been successfully applied to aircraft ribs, wing boxes, fuselage frames, and even entire wing structures. Our work focuses on the front main load‑bearing frame (Frame 6) of a specific fixed‑wing UAV, which carries the main wing attachment points. We apply the SIMP method to identify the most efficient material layout, then reconstruct the frame geometry and verify its structural performance.
2. Problem Description and Finite Element Model
2.1 Structural Configuration
The fixed‑wing UAV fuselage comprises nine transverse frames. Frames 1–4 form the equipment bay, Frames 5–8 constitute the fuel tank bay, Frame 9 houses the engine mount, and Frame 6 is designated as the front main load‑bearing frame that connects directly to the wing spars. Frames 8–9 contain additional equipment. The skin material is glass fiber reinforced plastic (GFRP), modeled as an orthotropic material. All other structural parts, including the main load‑bearing frame, are made from aluminum alloy 7075‑T651. The orthotropic material properties of GFRP and the isotropic properties of 7075‑T651 are summarized in Tables 1 and 2.
| Property | Value | Unit |
|---|---|---|
| Density | 1700 | kg/m³ |
| Longitudinal elastic modulus (E₁) | 8.5×10⁹ | Pa |
| Transverse elastic modulus (E₂) | 1.7×10¹⁰ | Pa |
| Poisson’s ratio (ν₁₂) | 0.1 | – |
| In-plane shear modulus (G₁₂) | 3.5×10⁹ | Pa |
| Property | Value | Unit |
|---|---|---|
| Density | 2810 | kg/m³ |
| Young’s modulus (E) | 71.7×10⁹ | Pa |
| Shear modulus (G) | 26.9×10⁹ | Pa |
| Poisson’s ratio (ν) | 0.33 | – |
2.2 Finite Element Model and Load Case
A global finite element model of the entire fuselage is constructed using shell and solid elements. The main load‑bearing frame (Frame 6) is meshed with second‑order tetrahedral elements. The fixed‑wing UAV is assumed to experience a symmetric 3g pull‑up maneuver, which is a critical design load case. Loads are transmitted from the wings to the frame through the attachment lugs. The model is constrained at the rear engine mount frame and at the forward equipment bay bulkhead. A static linear analysis is performed to evaluate the baseline performance.
3. Original Design Performance Evaluation
The original front main load‑bearing frame is a web‑type reinforced frame consisting of flanges, web plate, and stiffeners. Its total mass is 3.652 kg. The static analysis results are summarized in Table 3. The maximum displacement of the frame occurs at the lower edge (0.3422 mm), and the maximum von Mises stress is located at the root of the attachment lug (43 MPa). The overall fuselage maximum displacement is 3.220 mm, which satisfies the stiffness requirements of the fixed‑wing UAV.
| Parameter | Value |
|---|---|
| Mass (kg) | 3.652 |
| Maximum displacement of frame (mm) | 0.3422 |
| Maximum von Mises stress (MPa) | 43 |
| Fuselage maximum displacement (mm) | 3.220 |
4. Topology Optimization Methodology
4.1 SIMP Formulation
We employ the density‑based SIMP method, where each finite element’s density ρₑ (0 ≤ ρₑ ≤ 1) is treated as a design variable. The material interpolation penalizes intermediate densities to drive the solution toward a 0‑1 distribution. The penalized elastic modulus is expressed as:
$$ E(\rho_e) = \rho_e^p E_0 $$
where \(E_0\) is the Young’s modulus of the solid material, and \(p\) is the penalization factor (typically \(p = 3\)).
The topology optimization problem for compliance minimization is mathematically stated as:
$$ \begin{aligned}
\text{Find} \quad & \boldsymbol{\rho} = (\rho_1, \rho_2, \ldots, \rho_n)^T \\
\min_{\boldsymbol{\rho}} \quad & C(\boldsymbol{\rho}) = \mathbf{f}^T \mathbf{u} = \sum_{e=1}^n \mathbf{u}_e^T \mathbf{k}_e(\rho_e) \mathbf{u}_e \\
\text{s.t.} \quad & \frac{1}{V_0} \sum_{e=1}^n v_e \rho_e \le V_f \\
& \mathbf{K}(\boldsymbol{\rho}) \mathbf{u} = \mathbf{f} \\
& 0 \le \rho_e \le 1, \quad e = 1,2,\ldots,n
\end{aligned} $$
Here, \(C\) denotes the structural compliance, \(\mathbf{f}\) is the global load vector, \(\mathbf{u}\) is the displacement vector, \(\mathbf{k}_e\) is the element stiffness matrix, \(v_e\) is the element volume, \(V_0\) is the total design domain volume, and \(V_f\) is the prescribed volume fraction (0.25). The equilibrium equation \(\mathbf{K}\mathbf{u} = \mathbf{f}\) must be satisfied at each optimization iteration.
4.2 Optimization Setup
The design domain for topology optimization is defined as the entire volume of the front main load‑bearing frame, excluding the outer flanges (considered non‑design regions). The non‑design flanges ensure connectivity with adjacent fuselage components. The optimization is performed under the 3g overload condition, with the same boundary conditions as the baseline analysis. The objective is to minimize compliance, and the constraint is a volume fraction of less than 0.25. The OptiStruct solver uses the method of moving asymptotes (MMA) to update the design variables. A minimum member size control of 6 mm is applied to avoid checkerboard patterns and ensure manufacturability.
5. Optimization Results and Model Reconstruction
5.1 Material Distribution
After convergence (approximately 80 iterations), the density contour plot shows that material concentrates in two primary zones: the lateral wings of the frame and the upper connection region. On the front face, the material forms a spider‑web pattern attached to the inner flange surfaces. On the rear face, the high‑density material is mainly located in the upper half of the frame, aligning closely with the inner and outer flanges. To extract a clear load‑path, we filter elements with relative density greater than 0.8. The resulting material layout suggests that the optimized structure should have a central web plate with diagonal stiffeners radiating toward the wing‑side lugs.
5.2 Reconstructed Model
Based on the topology optimization result, we reconstruct the frame geometry while respecting manufacturing constraints (e.g., using a consistent sheet thickness of 2 mm for the web, and adding X‑shaped ribs on both sides). The final reconstructed model retains the original outer flanges and the central partition wall. Two crossing stiffeners are added in the lateral wing regions, and additional riblets are placed along the upper edge. The mass of the reconstructed frame is 3.255 kg, representing a reduction of 10.87% compared with the original design.
6. Verification and Comparison
A new finite element analysis is performed on the reconstructed model using the identical load case and boundary conditions. The results are listed in Table 4 alongside the original values for direct comparison.
| Parameter | Original | Optimized | Change (%) |
|---|---|---|---|
| Mass (kg) | 3.652 | 3.255 | –10.87 |
| Frame maximum displacement (mm) | 0.3422 | 0.3948 | +13.32 |
| Frame maximum stress (MPa) | 43 | 56 | +23.21 |
| Fuselage maximum displacement (mm) | 3.220 | 3.39 | +5.01 |
The maximum stress in the optimized frame increases from 43 MPa to 56 MPa, which is still well below the yield strength of 7075‑T651 (approximately 505 MPa). The stress concentration at the lug root is alleviated because the load is distributed more evenly through the diagonal stiffeners. The frame displacement increases by 13.32% (0.0526 mm), but the overall fuselage displacement increases by only 5.01% (0.17 mm). These modest increases are fully acceptable for the fixed‑wing UAV’s operational envelope. The material utilization factor, defined as the ratio of load‑carrying capability to mass, improves significantly. Moreover, the reduction in mass of 397 g directly translates to either increased payload capacity or extended endurance for the fixed‑wing UAV.
7. Discussion
Our results confirm that the SIMP‑based topology optimization is highly effective for the lightweight design of a fixed‑wing UAV’s main load‑bearing frame. The optimized material distribution follows the principal stress trajectories, concentrating material where it is most needed. The reconstructed frame not only reduces mass but also improves the stress distribution, which is beneficial for fatigue life. However, the manufacturability of the complex organic shapes generated by topology optimization must be considered. In our study, we simplified the optimized layout to a set of straight stiffeners and ribs that can be machined from a single aluminum plate. Future work could explore additive manufacturing to realize fully organic shapes, further reducing mass.
8. Conclusion
We have successfully applied the SIMP topology optimization method to the front main load‑bearing frame of a fixed‑wing UAV. Under the 3g overload condition and a volume fraction constraint of 0.25, the optimized design reduces the frame mass by 10.87%, from 3.652 kg to 3.255 kg, while maintaining all structural performance within acceptable limits. The maximum stress increases from 43 MPa to 56 MPa, indicating better material utilization and alleviation of stress concentration. The slight increase in displacements (13.32% for the frame and 5.01% for the fuselage) does not compromise the fixed‑wing UAV’s structural integrity. This study demonstrates that topology optimization is a powerful tool for lightweight design of aerospace structures, and the methodology can be extended to other critical components of the fixed‑wing UAV, such as wing ribs, bulkheads, and landing gear brackets, to achieve further mass savings and performance improvements.
References
We have drawn upon established literature in topology optimization and aerospace structures. The SIMP method is extensively documented in the works of Bendsøe and Sigmund. Applications to aircraft frames and wings have been reported by several research groups, confirming the viability of the approach.