Design, analysis and optimization of a CubeSat structure

TEKREVOLUTION is a high-tech service and consulting company from Italy with huge experience in R&D activities. One of their main activities is to explore intelligent additive manufacturing to provide higher stress-to-weight ratio solutions for the market.


For the space sector, it is important to reduce the weight to the minimum possible, while maintaining excellent performance. That is why it is important to comprehend that it is more optimum to have different thicknesses and material properties through different zones with particular characteristics.
Moreover, it is interesting to understand which parts of the component are affecting more to the behavior when loaded. For this reason, in this case, the component was divided into 10 different parts whose properties would be studied individually. Three different aspects were examined, the displacement of the center of mass of the payload, the first natural mode and the maximum stress supported with the objective of minimizing the CubeSat weight.
Overall, we considered the thickness of each of the 10 parts that composed the model and the Young Modulus of the material. A high resolution study was made with only 143 Nastran simulations.

Fig. 1. Scheme of the CubeSat Structure


We coupled the UptimAI tool with the simulation software Nastran, without the need of interfering with its code or interface. Then the Nastran started running simulations for the sample points indicated by the UptimAI algorithm for creating a metamodel of the behavior of the component under load.

The UptimAI Sensitivity Analysis (Fig. 2.) helped to identify which parts of the component were the most crucial, and supported most of the load, measuring which input had more influence in the three outputs (displacement, first natural mode and stress). Also, it was very useful to determine which parts of the component weren’t supporting the load at all, so its thickness could be reduced without affecting the overall performance at all. In this case, only the thickness of the load area had an important effect on the maximum stress supported by the structure, being able from now on to focus on that two parts and knowing that it is possible to reduce the thickness on the other parts to reduce the weight without affecting the stress performance.

Fig. 2. Sensitivity Analysis of the Maximum Stress on the CubeSat

Details of specific zones of the domain can be easily seen in the UptimAI Increment plot (Fig. 3.). It shows the shape of increment functions illustrating the interactions and dependencies between the values of the input parameters and the displacement. That allows to easily identify high response zones, for avoiding in the design phase and reduce both the variability of the displacement and to reduce its average response. In this case it was necessary to have at least one of the loading areas with a thickness higher than 2.5 mm, to operate in a stable condition.

Fig. 3. Interaction between thicknesses of loading parts in the displacement of the payload

The histogram (Fig. 4.) is showing the position of the 1st Natural Mode. The Nastran computations were cut to 120 Hz as maximum because there is no interest in knowing the exact value of the first frequency if it is higher than this value, as it already surpasses the safety restraints (NOTE: the metamodel accurately caught this aspect). The histogram allows to check which is the average response (around 110 Hz), and to study in detail the input conditions of the left tail of the distribution that make appear undesired behavior of the structure.

Fig. 4. Statistical Modal Response of the CubeSat

Finally, the engineers used the UptimAI Preliminary Optimization tool to establish new input distributions. These proposed distributions show which zones of the domain carry an statistical improvement, and which zones should be avoided. That way it helps the engineering team on the decision making of which combination of input parameters are more interesting to explore. Using the thickness distributions for the loading parts suggested by the UptimAI algorithm, the average displacement was halved and the variance was largely decreased, leading to a better and more reliable performance.

Fig. 5. Statistical response of the displacement of the payload before and after improvements


  • Definition of influential parameters. The parameters of the component that were relevant to the results were reduced by more than 60%
  • Optimization of the results. Decreasing the average displacement of the payload to the half and its variance to input parameters while complying with modal restrictions.
  • Deeper insights into the structural behavior of the component under high loadings.