Modeling Operational Variability for Robust Multidisciplinary Design Optimization

International audienceThe aim of this paper is to model and propagate operational uncertainties in view of its integration in a multidisciplinary optimization methodology for aircraft robust design. From databases relative to one specic type of long-range airplane, we analyze the variations of four ight parameters (altitude, speed, temperature and range), and build the associated statistical distributions. Then, using an uncertainty propagation methodology, we identify the distribution of operational costs

A Multidisciplinary Airplane Research Integrated Library With Applications To Partial Turboelectric Propulsion

International audienc

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

Peri-operative red blood cell transfusion in neonates and infants: NEonate and Children audiT of Anaesthesia pRactice IN Europe: A prospective European multicentre observational study

BACKGROUND: Little is known about current clinical practice concerning peri-operative red blood cell transfusion in neonates and small infants. Guidelines suggest transfusions based on haemoglobin thresholds ranging from 8.5 to 12 g dl-1, distinguishing between children from birth to day 7 (week 1), from day 8 to day 14 (week 2) or from day 15 (≥week 3) onwards. OBJECTIVE: To observe peri-operative red blood cell transfusion practice according to guidelines in relation to patient outcome. DESIGN: A multicentre observational study. SETTING: The NEonate-Children sTudy of Anaesthesia pRactice IN Europe (NECTARINE) trial recruited patients up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures from 165 centres in 31 European countries between March 2016 and January 2017. PATIENTS: The data included 5609 patients undergoing 6542 procedures. Inclusion criteria was a peri-operative red blood cell transfusion. MAIN OUTCOME MEASURES: The primary endpoint was the haemoglobin level triggering a transfusion for neonates in week 1, week 2 and week 3. Secondary endpoints were transfusion volumes, 'delta haemoglobin' (preprocedure - transfusion-triggering) and 30-day and 90-day morbidity and mortality. RESULTS: Peri-operative red blood cell transfusions were recorded during 447 procedures (6.9%). The median haemoglobin levels triggering a transfusion were 9.6 [IQR 8.7 to 10.9] g dl-1 for neonates in week 1, 9.6 [7.7 to 10.4] g dl-1 in week 2 and 8.0 [7.3 to 9.0] g dl-1 in week 3. The median transfusion volume was 17.1 [11.1 to 26.4] ml kg-1 with a median delta haemoglobin of 1.8 [0.0 to 3.6] g dl-1. Thirty-day morbidity was 47.8% with an overall mortality of 11.3%. CONCLUSIONS: Results indicate lower transfusion-triggering haemoglobin thresholds in clinical practice than suggested by current guidelines. The high morbidity and mortality of this NECTARINE sub-cohort calls for investigative action and evidence-based guidelines addressing peri-operative red blood cell transfusions strategies. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02350348

Système d'évitement d'obstacles biomimétique basé sur le flux optique. Application à un drone à voilure fixe en environnement urbain simulé.

National audienceLes drones destinés à opérer en milieu urbain auront nécessairement une taille réduite et une capacité d'emport limitée, surtout s'ils ont vocation à pénétrer à l'intérieur des bâtiments. C'est pourquoi, leurs capacités à éviter les obstacles ne peuvent dépendre de capteurs de proximité usuels tels que sonars et capteurs infrarouges, trop encombrants ou consommant trop d'énergie. Une caméra, au contraire, est un capteur léger, consommant peu d'énergie, et susceptible d'être utilisé par un système d'évitement d'obstacles. Cette tâche nécessite le calcul du flux optique qui est le champ de vecteurs des vitesses apparentes des objets de la scène sur le plan-image.Le principe de la stratégie d'évitement d'obstacles présentée dans cet article est inspiré du comportement de la mouche ou de l'abeille et vise à équilibrer les vitesses de défilement sur le plan-image d'objets vus à droite et à gauche du drone.Des expériences en simulation ont été menées pour comparer diverses stratégies de contrôle et divers algorithmes d'extraction du flux optique. Le moteur physique utilisé simule un drone à voilure fixe de manière réaliste. Un environnement graphique 3D reproduit un milieu urbain. Le calcul du flux optique est réalisé par un système qui fonctionne en temps réel.L'objectif à moyen terme est l'implémentation du système sur le drone Pégase fabriqué par l'Ensica

Self-Regulating Multi-Agent System for Multi-Disciplinary Optimisation Process

International audienceThis article presents a multi-agent method to tackle multidisciplinary optimisation, based on the notions of cooperation and self-regulation. It is focused on the preliminary aircraft design, which is a complex compromise. In our approach several cooperative agents collectively act to achieve a common goal, i.e. optimising a multi-objective function, even if the environment of the system (the user’s requirements) changes during the solving process. In MASCODE, one agent encapsulates one discipline and is designed individually without considering the dependencies with the others. So the computation is conceptually distributed without central control. Experimental results including efficiency comparison with the classical FSQP method are presented, and show that the adaptive behaviour of MASCODE provides new capabilities to understand and manage the complexity of the preliminary aircraft design

Chance constrained business case of a three-engines hybrid aircraft

International audienceThe purpose of this article is to present a Chance Constrained Optimization of an unconventional configuration of hybrid-powered-aircraft. The Chance Constrained Methodology is applied using a method of uncertainty propagation based on error distribution moments. The hybrid configuration is compared to a conventional, pure thermodynamic one, both being designed according to the same set of requirements. Models are briefly described in the article, more details can be found in literature. We present the method of uncertainty propagation through the well-known Rosenbrock function and validate it in comparison to a Monte-Carlo method. Concerning the aircraft and engine models, the uncertainty brought by each design and simulation module has been assessed individually, according to its predictive performance versus existing database. The results show that under model uncertainties we would be able to reach an economically viable hybrid-powered-aircraft with a probability of 0.95 when energy and power management technologies become between 2 to 3 times better compared to today values. In addition, the efficiency of uncertainty propagation by the way of moments is really interesting in term of computation time. Improvements are currently done to make this method even more precise especially when the amount of uncertainty increases

Système d'évitement d'obstacles biomimétique basé sur le flux optique. Application à un drone à voilure fixe en environnement urbain simulé.

National audienceLes drones destinés à opérer en milieu urbain auront nécessairement une taille réduite et une capacité d'emport limitée, surtout s'ils ont vocation à pénétrer à l'intérieur des bâtiments. C'est pourquoi, leurs capacités à éviter les obstacles ne peuvent dépendre de capteurs de proximité usuels tels que sonars et capteurs infrarouges, trop encombrants ou consommant trop d'énergie. Une caméra, au contraire, est un capteur léger, consommant peu d'énergie, et susceptible d'être utilisé par un système d'évitement d'obstacles. Cette tâche nécessite le calcul du flux optique qui est le champ de vecteurs des vitesses apparentes des objets de la scène sur le plan-image.Le principe de la stratégie d'évitement d'obstacles présentée dans cet article est inspiré du comportement de la mouche ou de l'abeille et vise à équilibrer les vitesses de défilement sur le plan-image d'objets vus à droite et à gauche du drone.Des expériences en simulation ont été menées pour comparer diverses stratégies de contrôle et divers algorithmes d'extraction du flux optique. Le moteur physique utilisé simule un drone à voilure fixe de manière réaliste. Un environnement graphique 3D reproduit un milieu urbain. Le calcul du flux optique est réalisé par un système qui fonctionne en temps réel.L'objectif à moyen terme est l'implémentation du système sur le drone Pégase fabriqué par l'Ensica

Evolution of neuro-controllers for flapping-wing animats

National audienceThis article reports preliminary results obtained with an evolutionary approach to the design of neural controllers for flapping-wing animats. This approach involves a multi-objective evolutionary algorithm and continuous-time neural networks. It has been used to automatically generate controllers securing an energetically thrifty horizontal flight at constant speed in a simulated artificial bird