Gap Analysis and Optimization of Rocket Fairing Locking Mechanisms

Background

       With the commercialization of space exploration, lightweight rocket structures and cost control have become key challenges in design. The payload fairing (see Figure 1) as a critical protective structure of a rocket, directly impacts payload safety and launch costs.
       This project focuses on an orbit-insertion rocket independently developed in Taiwan. The goal is to design a high-performance, lightweight fairing system and, through optimization techniques, reduce the fairing gap while ensuring structural stability and separation reliability.

 

Figure 1. Rocket payload fairing (Source: National Space Organization)

Results

       Through design parameter optimization with HyperStudy, the National Space Organization successfully reduced the fairing weight by 4 kg and controlled the gap within 0.21 mm. This result not only increased the rocket’s payload capability, but also indirectly saved up to USD 150,000 in launch costs, marking an important milestone for Taiwan’s independently developed launch vehicles.

 

Technical Features

End-to-End Analysis Workflow

• Aerodynamic pressure analysis: Using CFD (Computational Fluid Dynamics) to simulate the aerodynamic pressure on the fairing during launch, especially under Max Q (Maximum Dynamic Pressure) conditions.
   
• Internal and external pressure analysis: Simulating venting design to ensure that the internal–external pressure difference (e.g., design ΔP of 0.4 kPa) stays within a safe range.
   
• Thermal protection analysis: Evaluating heat conduction in high-temperature environments and designing effective thermal protection materials and structures.
   
• Rigid body motion analysis: Simulating rigid body motion during fairing separation to ensure a smooth process without interference (see Figure 2).

Figure 2. Fairing displacement contour (Source: National Space Organization)

Definition of Design Variables

• Seven design variables (DV1–DV7) were defined, covering latch locations, structural thicknesses, and material properties.

• Mathematical relations were used to handle symmetric dependent variables, ensuring the rationality and accuracy of model parameters.

Optimization Methods

• Using HyperStudy to perform DOE (Design of Experiments) and multi-objective optimization with the GRSM (Global Response Surface Method) algorithm.

• Applying the PB (Plackett–Burman) method and Taguchi method for sensitivity analysis, which confirmed that the outer shell stiffener (DV7) is the parameter with the highest weight sensitivity (see Figure 3).

Figure 3. Comparison of DOE results (Source: National Space Organization)

 

• Compared with ARSM (Adaptive Response Surface Method) and GA (Genetic Algorithm), GRSM achieved the best balance between weight reduction and gap control.

Conclusion

       By combining Altair Inspire analysis tools with HyperStudy optimization capabilities, the design efficiency and performance of the payload fairing were significantly improved. Lightweight design brought substantial cost benefits to the launch vehicle, while the integrated workflow—covering aerodynamic, structural, and thermal protection analyses—provided comprehensive protection for payload safety.
       Amid fierce international competition, Taiwan’s independent launch-vehicle R&D team has demonstrated outstanding technical capability and innovation, laying a solid foundation for more complex future aerospace missions.

 

Source: 2024 RTC Technical Highlights