Enhancing Human Spaceflight Safety Through Spacecraft Survivability Engineering
Bibliographic record
Abstract
These Lockheed Martin introduced an innovation entitled Spacecraft Survivability (SCS) Engineering to further the advancement of crew safety design techniques for implementation on the Orion CEV contract with NASA awarded in September of 2006. This innovation identified new potential for enhancing crew safety of the Orion vehicle through the adaptation of techniques pioneered for military aircraft survivability. The benefit of this approach became apparent in early applications as vehicle evolution trade studies were undertaken when new advantages of potential designs were identified through the study of design options through SCS and considered in the trade study decisions. Three years after the award, Spacecraft Vulnerability Reduction (SVR) has grown from a concept to an application. Where only System Reliability, Crew Survival and System Safety were applied, SVR brings further closure of gaps to prevent loss of life for potential mishap scenarios by complementing but not duplicating efforts in System Safety, Reliability, and Crew Survival and providing a more comprehensive design and assessment approach. This innovation has been embraced by the aircraft survivability world with recent developments for potential collaboration of efforts. These techniques must be developed and applied to space design, now, in order to support human missions to Mars. The example set by military aircraft programs teaches the road to developing and implementing a structured survivability program is long and could take decades to mature. Collaborative work has begun with the Naval Post Graduate College and NASA in efforts to gain expertise and expand what is traditionally done for spacecraft safety by applying new techniques to support design survivability decisions, drive designs through new survivability requirements, and measure the effectiveness of these techniques through a new system metric. This paper describes the program as envisioned and currently implemented, the achieved and projected benefits to the NASA project Orion, and insight into the future of Survivability and potential benefits beyond Orion to other human and uncrewed space applications where common concerns such as optimizing safety while minimizing weight are priority concerns. Work being performed now includes the Damage Modes and Effects Analysis (DMEA) and Methodology Document written for Spacecraft application, Emergency Return Mode application to affect design and operational scenario development, additional robustness to lowered fault tolerance systems, and program development. The DMEA follows the widely know Failure Modes and Effects Analysis (FMEA). Where the FMEA identifies the possible failures and hazards, the DMEA plays through the failures and analyzes the damage and its cascading events. To this day, safety requirements only look at the susceptibility: “how likely is it to happen?”, and design only to prevent occurrence to a certain level of reliability. SVR is the practice of assuming the hazard has occurred. What then? By identifying these vulnerabilities during the design phase, LM is able to create a safer spacecraft, while having positive impacts on budget and schedule. This paper will propose the potential future of survivability driven design to strengthen the synergy between aircraft and spacecraft as we prepare for the moon, Mars and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".