Using Optimization to Exploit a Composable Satellite Product Line Architecture
Bibliographic record
Abstract
Lockheed Martin has implemented a composable design methodology in the update of its A2100 Satellite product line. The composable design methodology leverages model-based systems engineering language and tools to formalize allowable architectural choices within the product line, limiting the decision points and decision options available to system architects, as well as imposing constraints on how those decisions may be combined. Even in the context of those limitations, a complex system can have a large design space, required to support flexibility for widely varying missions, payloads, and customers. This can result in many valid architecture configurations that satisfy a given set of mission requirements. When faced with identifying the preferred satellite architecture for a given mission, a system architect may often resort to the familiar rather than evaluating all potential configurations for the optimal balance of cost, schedule, and performance. By leveraging optimization techniques with a Composable System Reference Architecture, Lockheed Martin enables a system architect to rapidly identify a subset of configurations that form an optimal pareto front based on performance and cost objectives. The system architect can choose a configuration that best achieves all identified objectives, while retaining flexibility to prioritize among competing objectives in a manner most appropriate for their program. This paper shows that the composable model suite developed by Lockheed Martin supports repeated analysis of optimal architecture configurations to satisfy varying mission and customer applications from program to program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".