ALPACAS: A Language for Parametric Assessment of Critical Architecture Safety

This paper introduces Alpacas, a domain-specific language and algorithms aimed at architecture modeling and safety assessment for critical systems. It allows to study the effects of random and systematic faults on complex critical systems and their reliability. The underlying semantic framework of the language is Stochastic Guarded Transition Systems, for which Alpacas provides a feature-rich declarative modeling language and algorithms for symbolic analysis and Monte-Carlo simulation, allowing to compute safety indicators such as minimal cutsets and reliability. Built as a domain-specific language deeply embedded in Scala 3, Alpacas offers generic modeling capabilities and type-safety unparalleled in other existing safety assessment frameworks. This improved expressive power allows to address complex system modeling tasks, such as formalizing the architectural design space of a critical function, and exploring it to identify the most reliable variant. The features and algorithms of Alpacas are illustrated on a case study of a thrust allocation and power dispatch system for an electric vertical takeoff and landing aircraft

ALPACAS: A Language for Parametric Assessment of Critical Architecture Safety (Artifact)

This artifact contains a virtual machine allowing to use ALPACAS, a domain-specific language and algorithms aimed at architecture modeling and safety assessment for critical systems. ALPACAS allows to study the effects of random and systematic faults on complex critical systems and their reliability. The underlying semantic framework of the language is Stochastic Guarded Transition Systems, for which ALPACAS provides a feature-rich declarative modeling language and algorithms for symbolic analysis and Monte-Carlo simulation, allowing to compute safety indicators such as minimal cutsets and reliability. Built as a domain-specific language deeply embedded in Scala 3, ALPACAS offers generic modeling capabilities and type-safety unparalleled in other existing safety assessment frameworks. This improved expressive power allows to address complex system modeling tasks, such as formalizing the architectural design space of a critical function, and exploring it to identify the most reliable variant. The features and algorithms of ALPACAS are illustrated on a case study of a thrust allocation and power dispatch system for an electric vertical takeoff and landing aircraft

A probabilistic data assimilation framework to reconstruct finite element error fields from sparse error estimates: Application to sub‐modeling

Abstract: The present work proposes a computational approach that recovers full finite element error fields from a small number of estimates of errors in scalar quantities of interest. The approach is weakly intrusive and is motivated by large scale industrial applications wherein modifying the finite element models is undesirable and multiple regions of interest may exist in a single model. Error estimates are developed using a Zhu‐Zienkiewicz estimator coupled with the adjoint methodology to deliver goal‐oriented results. A Bayesian probabilistic estimation framework is deployed for full field estimation. An adaptive, radial basis function based reduced order modeling strategy is implemented to reduce the cost of calculating the posterior. The Bayesian reconstruction approach, accelerated by the proposed model reduction technology, is shown to yield good probabilistic estimates of full error fields, with a computational complexity that is acceptable compared to the evaluation of the goal‐oriented error estimates. The novelty of the work is that a set of computed error estimates are considered as partial observations of an underlying error field, which is to be recovered. Future improvements of the method include the optimal selection of goal‐oriented error measures to be acquired prior to the error field reconstruction

An Abaqus plugin for efficient damage initiation hotspot identification in large-scale composite structures with repeated features

© 2021 Elsevier Ltd Identifying the hotspots for damage initiation in large-scale composite structure designs presents a significant challenge due to the high modelling cost. For most industrial applications, the finite element (FE) models are often coarsely meshed with shell elements and used to predict the global stiffness and internal loads. Because of the lack of detailed descriptions for the composite materials and 3D stress states, most of the established failure criteria are not applicable. In this work we present an Abaqus plugin tool which implements a framework to identify the hotspots by using a pre-computed database generated for specific, heavily-repeated feature types based on a given structural model. Developed with an object-oriented implementation in Python, this software is split into two main parts, specifically for feature generation and structural analysis. The pre-computed model presents a full 3D description for the considered feature and works as a submodel to the coarse structure model driven by a one-way transfer of the boundary conditions. The presented framework is an analysis tool for efficient sizing of large-scale composite structures, as it enables 3D damage analysis of the structures in critical zones with significant savings of the modelling and computational cost. The results are compared with conventional FE modelling and satisfactory agreement is observed. In addition, the software also enables the pre-computed database to be stored in an HDF5 data file for further reuse on new structures with the same feature

Towards the Industrialization of New MDO Methodologies and Tools for Aircraft Design

An overall summary of the Institute of Technology IRT Saint Exupery MDA-MDO project (Multi-Disciplinary Analysis - Multidisciplinary Design Optimization) is presented. The aim of the project is to develop efficient capabilities (methods, tools and a software platform) to enable industrial deployment of MDO methods in industry. At IRT Saint Exupery, industrial and academic partners collaborate in a single place to the development of MDO methodologies; the advantage provided by this mixed organization is to directly benefit from both advanced methods at the cutting edge of research and deep knowledge of industrial needs and constraints. This paper presents the three main goals of the project: the elaboration of innovative MDO methodologies and formulations (also referred to as architectures in the literature 1) adapted to the resolution of industrial aircraft optimization design problems, the development of a MDO platform featuring scalable MDO capabilities for transfer to industry and the achievement of a simulation-based optimization of an aircraft engine pylon with industrial Computational Fluid Dynamics (CFD) and Computational Structural Mechanics (CSM) tools

Goal oriented error estimation in multi-scale shell element finite element problems

International audienceAbstract A major challenge with modern aircraft design is the occurrence of structural features of varied length scales. Structural stiffness can be accurately represented using homogenisation, however aspects such as the onset of failure may require information on more refined length scale for both metallic and composite components. This work considers the errors encountered in the coarse global models due to the mesh size and how these are propagated into detailed local sub-models. The error is calculated by a goal oriented error estimator, formulated by solving dual problems and Zienkiewicz-Zhu smooth field recovery. Specifically, the novel concept of this work is applying the goal oriented error estimator to shell elements and propagating this error field into the continuum sub-model. This methodology is tested on a simplified aluminium beam section with four different local feature designs, thereby illustrating the sensitivity to various local features with a common global setting. The simulations show that when the feature models only contained holes on the flange section, there was little sensitivity of the von Mises stress to the design modifications. However, when holes were added to the webbing section, there were large stress concentrations that predicted yielding. Despite this increase in nominal stress, the maximum error does not significantly change. However, the error field does change near the holes. A Monte Carlo simulation utilising marginal distributions is performed to show the robustness of the multi-scale analysis to uncertainty in the global error estimation as would be expected in experimental measurements. This shows a trade-off between Saint-Venant’s principle of the applied loading and stress concentrations on the feature model when investigating the response variance

Let us not underestimate the long-term risk of SPLC after surgical resection of NSCLC

International audienceObjectives: Several studies have reported that patients operated on for non-small cell lung cancer (NSCLC) are at high risk of second primary lung cancer (SPLC). However, widely varying estimates of this risk have been reported, with very few studies taking into account that these patients are at particularly high competing risk of death, due to recurrence of the initial disease and to comorbidities. Risk factor evaluation over time has significant repercussions on the post-surgery surveillance strategy offered for NSCLC. This study primarily sought to measure the risk of SPLC in a long-term follow-up series, using statistical methods considering competing risks of death.Materials and methods: The cumulative SPLC risk was estimated using the cumulative incidence of patients with completely resected Stage I-III NSCLC diagnosed between 2002 and 2015 based on the Doubs and Belfort cancer registry (France). A proportional sub-distribution hazard model (sdRH) was used to investigate factors associated with SPLC risk in the presence of competing risks.Results: Among the 522 patients, adenocarcinoma and Stage I or II disease accounted for 52.3% and 75.7% of patients, respectively. Overall, 84 patients developed SPLC (16.1%). The cumulative risk of SPLC was 20.2% at 10 years post-surgery (95% confidence interval [CI]: 15.3-23.2), and 25.2% (CI: 19.4-31.3) at 14 years post-surgery. On multivariate analysis, the SPLC risk was significantly higher in patients with postoperative thoracic radiotherapy (sdRH 2.79; 95% CI: 1.41-5.52; p = 0.003).Conclusion: This study using appropriate statistical methods to consider competing risks showed that after complete NSCLC resection, the cumulative incidence function of SPLC was high, with patients receiving postoperative thoracic radiotherapy at higher risk. These data support the need for life-long follow-up of patients who undergo NSCLC surgery, with the objective of screening for SPLC