Recommendations on Increased Use of Modelling and Simulation for Certification / Qualification in Aerospace Industry
Notice bibliographique
Résumé
The perspective of the Aerospace industry, both civil and military, is constantly evolving in response to international crises, environmental footprint, sustainability expectations etc. The pace of this change is only increasing and constraints on budgets creates renewed pressure to do things differently to become far faster and more cost effective than ever before. One aspect of the traditional aerospace approach to aircraft development that is increasingly coming under scrutiny is the use of physical testing for development, qualification certification and through life changes to the type design and/or enhancements in capability. There is a transformation opportunity to increase the use of Modelling and Simulation (M&S) for Certification and Qualification (C&Q) techniques to support the showing of compliance with airworthiness (and performance/contractual) requirements. Regulator oversight must be proportionate considering that physical testing is accepted without further investigation even though it cannot fully replicate real world effects nor is it infallible. However, a reduction in scope or replacement of traditionally accepted physical testing with M&S for C&Q may not be appropriate or cost effective for all systems. The safety requirements that underpin current qualification and certification objectives within the aerospace industry are of paramount importance to all actors and authorities. However, the effort and cost expended by airframe, engine and component manufacturers alike in order to achieve these objectives are significant. Due to the extensive list of compliance regulations, certification efforts for a new aircraft programme can easily require over one year of total flow time, with the aggregate cost of the certification process approaching $1bn [1]. Even in the case of an incremental change, this certification cost can often be the deciding factor in a business case. Within an increasingly Volatile, Uncertain, Complex and Ambiguous (VUCA) world, the demand on the industry is to develop solutions faster and more costeffectively than ever before, whilst maintaining full compliance to regulatory requirements. With advancements in technology, computing power and data availability/analytics capabilities, there is an opportunity to replace some level of physical testing with digital model-based analysis or virtual testing, without compromising the regulatory process. Industry-wide surveys estimate that certification costs could be reduced by around 50% with a thorough embodiment of standardised M&S for C&Q methods [1]. It should be noted that efforts to progress and investigate Certification (& Qualification) by Analysis (C(Q)bA) is already a longstanding and worldwide initiative with a lot of focus from Industry, Airworthiness Regulators and Customers alike ([2] raised the idea of C(Q)bA back in 1976). In order to look at the possibility of increased C(Q)bA, it was important to look at the different perspectives of the major stakeholders involved. Increasing the use of M&S for the purpose of satisfying regulatory certification requirements has been a consistent goal across the aerospace industry for a number of decades. As far back as the 1970s, a key recommendation provided by [14] was to explore ways to increase the use of M&S for C(Q)bA [14]. The Multinational Team Project (MTP) “Modelling and Simulation for Certification/Qualification by Analysis (M&S/Q(C)bA)” of the European Consortium for Advanced Training in Aerospace (ECATA) addresses the increased use of digital models and technology through M&S/C(Q)bA to deliver time and cost saving across the entire product lifecycle of an Aircraft.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».