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Record W2314918442 · doi:10.2514/6.2009-6523

Enhancing Human Spaceflight Safety Through Spacecraft Survivability Engineering

2009· article· en· W2314918442 on OpenAlexaff
Meghan Buchanan, Michael K. Saemisch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsSurvivabilityCrewSpacecraftHuman spaceflightEngineeringAeronauticsSystems engineeringVulnerability (computing)Reliability (semiconductor)Resilience (materials science)Adaptation (eye)Computer scienceReliability engineeringComputer securityAerospace engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.271
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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