Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on the MBSE modeling in space science mission phase 0, during which the mission requirements are collected, and the corresponding dataset is constructed. The dataset is utilized to fine-tune the BERT pre-training model for the classification of requirements pertaining to space science missions. This process supports the subsequent automated creation of the MBSE requirement model, which aims to facilitate scientific objective analysis and enhances the overall efficiency of the space science mission design process. Based on the characteristics of space science missions, this paper categorized the requirements into four categories: scientific objectives, performance, payload, and engineering requirements, and constructed a requirements dataset for space science missions. Then, utilizing this dataset, the BERT model is fine-tuned to obtain a space science mission requirements classification model (SSMBERT). Finally, SSMBERT is compared with other models, including TextCNN, TextRNN, and GPT-2, in the context of the space science mission requirement classification task. The results indicate that SSMBERT performs effectively under Few-Shot conditions, achieving a precision of 95%, which is at least 10% higher than other models, demonstrating superior performance and generalization capabilities.

In deep-space exploration, Pickup Ion Analyzers (PUIAs) operate under varying thermal environments in orbit, where thermally induced stress–deformation coupling may severely degrade their performance and long-term stability. To address temperature field analysis for in-orbit PUIAs, in this study, we propose a coupled simulation framework integrating external heat flux, parallel temperature field calculation, and thermoelastic deformation analysis, establishing a systematic link from thermal inputs to performance analysis. Based on external heat flux results, a parallel LU decomposition algorithm reduced the computational time from 11.8 h to 2.9 h for rapid temperature field solutions. At 38 astronomical units (AUs), the instrument’s temperature distribution ranged from −45 °C to 51.13 °C, with simulation errors compared to COMSOL simulations meeting engineering accuracy requirements. Maximum thermoelastic deformation induced by thermal gradients reached 0.110 mm. Performance degradation due to deformation in key metrics—including ion energy resolution, angular resolution, detection field-of-view, geometric factor, and mass resolution—was below 7.2%. This research improves the computational efficiency of the temperature field and systematically quantifies temperature effects on PUIA performance in deep-space environments, and the proposed methodology could provide technical support for optimizing on-orbit thermal management strategies.

In recent years, the enthusiasm for deep space missions has remained unabated, resulting in continuous advancements in the research field of space environment and particles. Many instruments carried on these missions have conducted detection of pickup ions (PUIs) in the solar system. For those instruments, simulation is an effective means and a crucial step for their performance optimization and future operation in-orbit. It holds great significance for the instrument’s in-orbit performance assessment, science operation optimization, and detection efficiency enhancement. In this paper, the traditional probability model and the Vasyliunas and Siscoe (V–S) model are used to generate the PUIs, which are the input for the simulation of the PUI detector. For further analysis, the numerical results of the simulation are processed to calculate the instrument’s geometric factor, mass resolution, and count rates. Then, two sets of experiments are carried out for the comparison of the traditional probability model and the V–S model. The results show that, for the simulation of the instrument in the design stage, the simulation results of the traditional probability model and the V–S model are not much different. However, for the simulation of the instrument performance in-orbit, the PUI data generated based on the V–S model gave a better result than those of the traditional probability model. This conclusion is of great significance for evaluating the detection ability of the PUI detector in future deep space explorations.

Based upon the conjugate-descent (CD) method in conjugate gradient methods (CGMs), we first propose a modified conjugate-descent (MCD) scheme, which adjusts the denominator terms of original CD scheme. Next, we apply q-calculus to the MCD method to derive its q-variant. Regardless of whether the line search technique is exact or not, both methods guarantee descent directions at every iteration. Under appropriate assumptions and the standard Wolfe line search technique, the presented methods are proven to be globally convergent. Numerical experiments demonstrate that the offered methods are efficient.