Multi-Fidelity Modeling and Adaptive Co-Kriging Based Optimization for All-Electric GEO Satellite Systems
Bibliographic record
Abstract
All-electric GEO satellite systems design is a challenging multidisciplinary design optimization (MDO) problem, which is computation-intensive due to the employment of expensive simulations. In this paper, the all-electric GEO satellite MDO problem with multi-fidelity models is investigated. The MDO problem involving six inter-coupled disciplines is formulated to minimize the total mass of the satellite system subject to a number of engineering constraints. To reduce the computational cost of the multidisciplinary analysis (MDA) process, multi-fidelity transfer dynamics models and finite element analysis (FEA) models are developed for the geosynchronous transfer orbit (GTO) and structure disciplines respectively. To effectively solve the all-electric GEO satellite MDO problem using multi-fidelity models, an adaptive Co-Kriging based optimization framework is proposed. In this framework, the samples from a high-fidelity MDA process are integrated with those from a low-fidelity MDA process to create a Co-Kriging metamodel with moderate computational cost for optimization. Besides, for refining the Co-Kriging metamodels, a multi-objective adaptive infill sampling approach is developed to produce the infill sample points in terms of expected improvement (EI) and probability of feasibility (PF) functions. Optimization results show that the proposed optimization framework can significantly reduce the total mass of satellite system with limited computational budget, which demonstrates the effectiveness and practicality of the multi-fidelity modeling and adaptive Co-Kriging based optimization framework for all-electric GEO satellite systems design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".