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Enregistrement W2314918442 · doi:10.2514/6.2009-6523

Enhancing Human Spaceflight Safety Through Spacecraft Survivability Engineering

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

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueRocket and propulsion systems research
Établissements canadiensLockheed Martin (Canada)
Organismes subventionnairesnon disponible
Mots-clésSurvivabilityCrewSpacecraftHuman spaceflightEngineeringAeronauticsSystems engineeringVulnerability (computing)Reliability (semiconductor)Resilience (materials science)Adaptation (eye)Computer scienceReliability engineeringComputer securityAerospace engineering

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,332
Score d'incertitude au seuil0,823

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,271
Écart entre enseignants0,255 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2009
Routes d'admission1
Résumé présentoui

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