Currently, private enterprises such as Space Exploration Technologies Corp. (SpaceX), Virgin Galactic, and Blue Origin are developing launch vehicles or spacecraft. This implies that space development shifts form the public to commercial areas. This is based on increasing profits with the reduction of expenditures by reusable spacecraft and diversifying the purposes of the flights. However, designing spacecraft is challenging because it requires many disciplinary analyses that are difficult to be solved owing to their nonlinearity and multiple constraints. Additionally, correlations between each analysis or variable make it more complex.

The final goal of this research is to develop high performance reusable unmanned spacecraft. To achieve this goal, we are developing

1. Multidisciplinary optimization for evaluating the performance of the spacecraft

2. Efficient method for heat-flux calculation

3. The adaptive range method to find better design solutions

To evaluate the performance of the spacecraft, multidisciplinary optimization (MDO) is developed. The MDO consists of geometry definition, weight analysis, propulsion analysis, aerothermodynamic analysis, trajectory analysis, and multi-objective genetic algorithms.

To reduce the computational cost of the evaluation for the spacecraft, an efficient method for the heat-flux calculation is developed using approximate-convective-heating equations and adaptive time step for heat analysis.

To find better solution, a method for adaptive range of variables is developed. Genetic algorithms (GA) have higher performance with this method because the ranges of variables move to the region for better solution and the efficiency of GA is enhanced by reducing the range where the probability of solutions is low.