With the invention of the aircraft, it has become much faster and larger than the original Wright Brothers aircraft. When the speed is high enough to cross the speed of sound, air conditions will be different than that in low speed due to the existence of shock wave. In this work, we introduce several numerical ways to analyze the performance of the airfoil when the speed is higher than the speed of sound. With these numerical methods, we analyzed the performance of diamond-shaped airfoil under different angles of attack and speed. With this data, engineers can choose a better airfoil to attain a lower drag coefficient as well as lift coefficient when designing a high-speed aircraft.

Retrieving the similar solutions from the historical case base for new design requirements is the first step in mechanical part redesign under the context of case-based reasoning. However, the manual retrieving method has the problem of low efficiency when the case base is large. Additionally, it is difficult for simple reasoning algorithms (e.g., rule-based reasoning, decision tree) to cover all the features in complicated design solutions. In this regard, a text2shape deep retrieval model is established in order to support text description-based mechanical part shapes retrieval, where the texts are for describing the structural features of the target mechanical parts. More specifically, feature engineering is applied to identify the key structural features of the target mechanical parts. Based on the identified key structural features, a training set of 1000 samples was constructed, where each sample consisted of a paragraph of text description of a group of structural features and the corresponding 3D shape of the structural features. RNN and 3D CNN algorithms were customized to build the text2shape deep retrieval model. Orthogonal experiments were used for modeling turning. Eventually, the highest accuracy of the model was 0.98; therefore, the model can be effective for retrieving initial cases for mechanical part redesign.