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Record W4391301668 · doi:10.2514/6.2024-1625

Recommendations on Increased Use of Modelling and Simulation for Certification / Qualification in Aerospace Industry

2024· article· en· W4391301668 on OpenAlexaff
Fabio Vetrano, Paul Worton, Hichem Smaoui, Alexander Szukala, Santosh Kumar Thukaram, Ali Al-Logmani, Abdalmajeed Alyazidi, Steve Green, Mark Halsall, P. E. Montagnon, Loubna Sahbatou, Anna-Lena Simon, Francesca Pucci Donati, Elena Galli, Giuseppe Stilo, Pierre Herbillon, Benjamin Haberkorn, S Kremer, Andreas Bergström, Johan Hagelin, Boris Brault, Yann Delga, Anne-Caroline Lemine, Gaetan Mabboux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsAerospaceCertificationManufacturing engineeringAeronauticsComputer scienceEngineeringSystems engineeringAerospace engineeringManagementEconomics

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.528
GPT teacher head0.502
Teacher spread0.026 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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