RESUMEN
Aluminum (Al) is usually added to solid propellants to improve the combustion performance, however the condensed combustion products (CCPs) especially the large agglomerates generated from aluminum combustion can reduce the specific impulse of the engine, and result in two-phase loss, residue accumulation and throat liner ablation. Al and ammonium perchlorate (AP), as important components of NEPE propellants, can affect the formation process of the CCPs of aluminized NEPE propellants. To clarify the effect of Al and AP particle sizes on the properties of the CCPs of aluminized NEPE propellants, a constant-pressure quench vessel was adopted to collect the combustion products of four different formulations of NEPE propellants. It was found that the condensed combustion products are mainly divided into aluminum agglomerates and oxide particles, the diameter of the aluminum agglomerates of these four different formulations of NEPE propellants at 7 MPa was smaller than that in 3 MPa, and the shells of the aluminum agglomerates were smoother and the spherical shape was more perfect. X-ray diffraction analysis of the CCPs of the four NEPE propellants under 3 MPa revealed the presence of both Al and Al2O3. With the increase of the particle size of Al and AP, the oxidation degree of aluminum particles decreases. The particle size of the CCPs of the four different formulations of NEPE propellants under 1 and 3 MPa was analyzed by using a laser particle size analyzer, it is found that the increase of AP particle size is helpful to reduce the size of condensate combustion products. Based on the classical pocket theory, establishing a new agglomeration size prediction model, which can be used to predict the agglomeration size on the burning surface. Compared with the empirical model, the new agglomeration size prediction model is in good agreement with the experimental results.
RESUMEN
Objectives: To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI). Materials and Methods: Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI. Results: Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data. Discussion and Conclusion: Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.