Probabilistic Characterization of Operational Uncertainties in Transport Aircraft using OpenSky

The aerodynamic design of transonic wings is already a mature field, and the use of aerodynamic shape optimization is a well-established discipline in industrial setting. Aircraft manufacturers design configurations by considering a representative but limited set of flight conditions. In practice, airlines do not always fly at the conditions they were designed to operate. Flight altitude, airspeed and aircraft weight are affected by operational requirements and environmental uncertainties. As a result, aircraft altitude, Mach number and lift coefficient, three of the most important parameters when performing aerodynamic design, can not be treated as single deterministic values in the design process. A full probabilistic approach is required to better characterize the real performance of the aircraft. However, there is a lack of aircraft operational data necessary to characterize uncertainty sources in flight. The objective of this paper is the characterization and quantification of operational uncertainty sources based on aircraft surveillance data. The definition of these uncertainties will be essential for the robust design of the next generation of commercial aircraft. To understand the variability in operating conditions of a representative aircraft fleet, surveillance data from the OpenSky network is gathered. The Mach number is directly obtained from the BDS-60 codes, while the altitude is provided by the ADS-B. The lift coefficient of the aircraft at each instant is roughly estimated according to the Breguet equation and the initial and final fuel weights. These are determined by the distance between departure and arriving airports. After the Mach, lift coefficient and altitude are obtained for each individual flight, they are filtered for cruise conditions (level flight). A Kernel Density Estimation is used to obtain the full probability distribution function. This methodology enables the accurate characterization of operational uncertainties that will be required for the aerodynamic robust design of the next generation of aircraft. The design will be tailored to the airliners operations. This framework can also be used by designers and operators to understand how aircraft are operated in reality

Development of Efficient Surrogate-Assisted Methods to Support Robust Design of Transonic Wings

Mit dem kontinuierlichen Anstieg der Anzahl kommerzieller Flüge sind ökologische und ökonomische Bedenken die Hauptantriebskräfte für die Reduzierung der Betriebskosten und der Emission von Treibhausgasen. Der Einsatz der aerodynamischen Formoptimierung spielt dabei eine Schlüsselrolle, um den Luftwiderstand und den gesamten CO2-Fußabdrucks von Flugzeugen zu reduzieren. Sie wurde bisher regelmäßig auf deterministische Weise durchgeführt und vernachlässigte Unsicherheiten. Die Sensitivität einer optimierten Konfiguration gegenüber betrieblichen, umweltbedingten und geometrischen Unsicherheiten kann jedoch die tatsächliche Leistung eines Flugzeugs beeinflussen. Die Suche nach einer robusten Konfiguration, die weniger empfindlich auf solche zufälligen Änderungen reagiert, ist in der Praxis ein attraktiveres Ziel. Aktuelle robuste Entwurfsansätze leiden aber unter einem großen Rechenaufwand, wenn es um aerodynamische Probleme im industriellen Maßstab geht und wurden hauptsächlich an akademischen und vereinfachten Testfällen demonstriert. Das Ziel dieser Dissertation ist die Entwicklung von effizienten robusten Entwurfsmethoden und deren Anwendung auf die robuste aerodynamische Formoptimierung von Tragflächen und Flügeln unter realistischen Unsicherheiten. Die erste Methodik führt eine robuste Entwurfsformulierung mit zwei Ebenen ein, die auf Gaussian Prozessen basiert. Die Kombination eines ersatzmodellbasierten Optimierungsalgorithmus mit einem ersatzmodellbasierten Ansatz zur Quantifizierung von Unsicherheiten macht die Methode unter einer geringen bis moderaten Anzahl von Entwurfsparametern und Unsicherheiten effektiv. Die zweite Methode etabliert ein effizientes gradientenbasiertes Ansatz für den robusten Entwurf, der unempfindlich gegenüber der Anzahl der Entwurfsparameter ist, indem eine adjungierte Formulierung verwendet wird. Der dritte Ansatz beinhaltet eine Bayes'sche Formulierung für robustes Design, die unempfindlich gegenüber der Anzahl der Unsicherheiten ist. Diese Methoden werden erfolgreich mit analytischen Testfunktionen und 2D-Problemen zur Tragflächenoptimierung validiert. In allen Fällen übertreffen die robust optimierten Konfigurationen diejenigen, die auf deterministische Weise optimiert wurden, wenn sie Unsicherheiten ausgesetzt sind. Die erste industrietaugliche Anwendung konzentriert sich auf den robusten Entwurf einer 3D-Anordnung von Stoßbeulen, die für ein modernes Transportflugzeug nachgerüstet werden können. Realistische Unsicherheiten in Machzahl, Auftriebsbeiwert und Flughöhe werden aus Flugbetriebsdaten extrahiert. Die robuste Konfiguration übertrifft nicht nur die mit traditionellen Einpunkt- und Mehrpunkt-Optimierungsverfahren erhaltenen Konfigurationen, sondern demonstriert auch das Potenzial des robusten Entwurfs nachrüstbarer Stoßbeulen. Die zweite Anwendung beschäftigt sich mit dem robusten Entwurf von 2.5D Flügeln mit natürlicher laminarer Strömung für eine Kurzstrecken-Zivilflugzeugkonfiguration unter Umwelt- und Betriebsunsicherheiten. Es wird gezeigt, dass die robusten Konfigurationen unter Verwendung der robusten Entwurfsformulierung mit zwei Ebenen in der Lage sind, die Laminarität unter Strömungsstörungen besser aufrechtzuerhalten und den Entwurfsbereich in Bezug auf Machzahl, Auftriebsbeiwert und kritische N-Faktoren zu erweitern. Die Berücksichtigung von Unsicherheiten im Optimierungsprozess erweist sich als äußerst vorteilhaft in Bezug auf Robustheit und Leistungsverbesserung beim Entwurf von transsonischen Profilen und Flügeln. Die in dieser Arbeit entwickelten, maßgeschneiderten Methoden sind die Voraussetzung für die Berücksichtigung von Unsicherheiten in die aerodynamische Formoptimierung und ermöglichen den Übergang von einer deterministischen zu einer probabilistischen Formulierung

Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

The use of robust design in aerodynamic shape optimization is increasing in popularity in order to come up with configurations less sensitive to operational conditions. However, the addition of uncertainties increases the computational cost as both design and stochastic spaces must be explored. The objective of this work is the development of an efficient framework for gradient-based robust design by using an adjoint formulation and a non-intrusive surrogate-based uncertainty quantification method. At each optimization iteration, the statistic of both the quantity of interest and its gradients are efficiently obtained through Gaussian Processes models. The framework is applied to the aerodynamic shape optimization of a 2D airfoil. With the presented approach it is possible to reduce both the mean and standard deviation of the drag compared to the deterministic optimum configuration. The robust solution is obtained at a reduced run time that is independent of the number of design parameters

Robust Design of Transonic Natural Laminar Flow Wings under Environmental and Operational Uncertainties

The introduction of laminar flow configurations is envisioned to provide new opportunities to further reduce aircraft fuel consumption. The robustness of laminar wings is critical, both against instabilities that can unexpectedly trigger transition and against off-design conditions outside the cruise point. However, current inverse design methodologies not only provide suboptimal configurations, but are unable to come up with robust configurations. The objective of this paper is the development and demonstration of a framework for the robust direct design of transonic natural laminar flow wings using state-of-the-art industrial tools such as computational fluid dynamics, linear stability theory and surrogate models. The deterministic optimization problem, which serves as a baseline, searches for the optimum shape that minimizes drag applying a surrogate based optimization strategy. In that case Cross-Flow and Tollmien-Schlichting critical N-Factors are fixed according to calibration data. For the robust approach, uncertainties in these critical N-Factors as well as operational conditions such as Mach number are considered to account for situations that could prematurely trigger transition and thus significantly decrease performance. The surrogate based optimizer is therefore coupled with a surrogate based uncertainty quantification methodology, following a bi-level approach. The objective function shifts towards the expectation of the drag to minimize average fuel consumption, or the 95% quantile to account for extreme events. The framework is able to come up with state-of-the-art natural laminar configurations for a short-haul civil aircraft configuration. The deterministic optimum is able to delay transition till 60% of the wing upper surface where the shock is present but is highly sensitive to small changes in the predefined critical N-Factors, as minor deviations will lead to fully turbulent configuration and hence an increase in drag. The robust configurations are more balanced, as the transition location smoothly moves upstream as the critical N-Factors are reduced. As a direct consequence, obtained pressure profiles are more resistant against instabilities, extending the current design envelope of natural laminar flow wings

Compelling new electrocardiographic markers for automatic diagnosis

Producción CientíficaBackground and Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided. Methods: In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMM delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms. Results: High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal. Conclusions: The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrocardiographic interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.Ministerio de Ciencia, Innovación y Universidades (grant PID2019-106363RB-I00

Efficient Bilevel Surrogate Approach for Optimization Under Uncertainty of Shock Control Bumps

The assessment of uncertainties is essential in aerodynamic shape optimization problems to come up with configurations that are more robust against operational and geometrical uncertainties. However, exploring the stochastic design space significantly increases the computational cost. The aim of this paper is to develop a framework for efficient optimization under uncertainty by means of a bilevel surrogate approach and to apply it to the robust design of a retrofitted shock control bump over an airfoil. The framework combines a surrogate-based optimizer with an efficient surrogate-based approach for uncertainty quantification. The optimizer efficiently finds the global optimum of a given quantile of the quantity of interest through the combination of adaptive sampling and a moving trust region. At each iteration of the optimization, the surrogate-based uncertainty quantification uses an active infill criterion to accurately quantify the quantile requiring a reduced number of samples. Two different quantiles of the drag are chosen for the design of the shock control bump: the 95% to increase the robustness at off-design conditions, and the 50% for a configuration that is preferred for day-to-day operations. In both cases, the optimum bumps are more robust, compared to the one obtained through classical deterministic optimization

A Bayesian Approach for Quantile Optimization Problems with High-Dimensional Uncertainty Sources

International audienceRobust optimization strategies typically aim at minimizing some statistics of the uncertain objective function and can be expensive to solve when the statistic is costly to estimate at each design point. Surrogate models of the uncertain objective function can be used to reduce this computational cost. However, such surrogate approaches classically require a low-dimensional parametrization of the uncertainties, limiting their applicability. This work concentrates on the minimization of the quantile and the direct construction of a quantile regression model over the design space, from a limited number of training samples. A Bayesian quantile regression procedure is employed to construct the full posterior distribution of the quantile model. Sampling this distribution, we can assess the estimation error and adjust the complexity of the regression model to the available data. The Bayesian regression is embedded in a Bayesian optimization procedure, which generates sequentially new samples to improve the determination of the minimum of the quantile. Specifically, the sample infill strategy uses optimal points of a sample set of the quantile estimator. The optimization method is tested on simple analytical functions to demonstrate its convergence to the global optimum. The robust design of an airfoil’s shock control bump under high-dimensional geometrical and operational uncertainties serves to demonstrate the capability of the method to handle problems with industrial relevance. Finally, we provide recommendations for future developments and improvements of the method

The CLIMALERT project: Climate alert smart system for sustainable water and agriculture

The vulnerability of sensitive European regions to hydro-meteorological extremes has increased dramatically over the past few decades. Extreme weather and climate events are increasingly happening worldwide due to ongoing climate change. As a consequence, hydro- meteorological disaster events are affecting the European economy, environment and society, with impacts on food production, food distribution infrastructure, livelihood assets and human health, in both rural and urban areas. Meanwhile, climate services have started to be developed to further anticipate the impacts of climate variability and to apply climate forecasts in different sectors, such as agriculture and water management. However, connections between climate information users and providers are still weak. The CLIMALERT project emerges to provide climate information in a format that prospective users find it easy to understand and/or incorporate into decision-making. The project main goals are: i) strengthen the link between climate research, water resources and the agriculture sector to assist the management of natural resources, enhance agricultural livelihoods and reduce underlying causes of vulnerability, ii) improve the techniques and tools currently used to incorporate weather and climate information into risk assessment and decision making in agriculture and water sectors, and, iii) contribute to assist decision- makers in the implementation of adaptation and mitigation strategies. In this talk, we will present the project framework, the study areas, the engagement with stakeholders, the selection of climate and hydrological indicators, and the development of an alert system platform that aims to contribute to reduce the risks and vulnerabilities for the agriculture and water management sectors, providing economically valuable services and long-term benefits to farmers and societyERA4CS/0004/2016 - CLIMALER

LGI1 antibodies alter Kv1.1 and AMPA receptors changing synaptic excitability, plasticity and memory

Leucine-rich glioma-inactivated 1 (LGI1) is a secreted neuronal protein that forms a trans-synaptic complex that includes the presynaptic disintegrin and metalloproteinase domain-containing protein 23 (ADAM23), which interacts with voltage-gated potassium channels Kv1.1, and the postsynaptic ADAM22, which interacts with AMPA receptors. Human autoantibodies against LGI1 associate with a form of autoimmune limbic encephalitis characterized by severe but treatable memory impairment and frequent faciobrachial dystonic seizures. Although there is evidence that this disease is immune-mediated, the underlying LGI1 antibody-mediated mechanisms are unknown. Here, we used patient-derived immunoglobulin G (IgG) antibodies to determine the main epitope regions of LGI1 and whether the antibodies disrupt the interaction of LGI1 with ADAM23 and ADAM22. In addition, we assessed the effects of patient-derived antibodies on Kv1.1, AMPA receptors, and memory in a mouse model based on cerebroventricular transfer of patient-derived IgG. We found that IgG from all patients (n = 25), but not from healthy participants (n = 20), prevented the binding of LGI1 to ADAM23 and ADAM22. Using full-length LGI1, LGI3, and LGI1 constructs containing the LRR1 domain (EPTP1-deleted) or EPTP1 domain (LRR3-EPTP1), IgG from all patients reacted with epitope regions contained in the LRR1 and EPTP1 domains. Confocal analysis of hippocampal slices of mice infused with pooled IgG from eight patients, but not pooled IgG from controls, showed a decrease of total and synaptic levels of Kv1.1 and AMPA receptors. The effects on Kv1.1 preceded those involving the AMPA receptors. In acute slice preparations of hippocampus, patch-clamp analysis from dentate gyrus granule cells and CA1 pyramidal neurons showed neuronal hyperexcitability with increased glutamatergic transmission, higher presynaptic release probability, and reduced synaptic failure rate upon minimal stimulation, all likely caused by the decreased expression of Kv1.1. Analysis of synaptic plasticity by recording field potentials in the CA1 region of the hippocampus showed a severe impairment of long-term potentiation. This defect in synaptic plasticity was independent from Kv1 blockade and was possibly mediated by ineffective recruitment of postsynaptic AMPA receptors. In parallel with these findings, mice infused with patient-derived IgG showed severe memory deficits in the novel object recognition test that progressively improved after stopping the infusion of patient-derived IgG. Different from genetic models of LGI1 deficiency, we did not observe aberrant dendritic sprouting or defective synaptic pruning as potential cause of the symptoms. Overall, these findings demonstrate that patient-derived IgG disrupt presynaptic and postsynaptic LGI1 signalling, causing neuronal hyperexcitability, decreased plasticity, and reversible memory deficits

Protecting and restoring Europe's waters:an analysis of the future development needs of the Water Framework Directive

The Water Framework Directive (WFD) is a pioneering piece of legislation that aims to protect and enhance aquatic ecosystems and promote sustainable water use across Europe. There is growing concern that the objective of good status, or higher, in all EU waters by 2027 is a long way from being achieved in many countries. Through questionnaire analysis of almost 100 experts, we provide recommendations to enhance WFD monitoring and assessment systems, improve programmes of measures and further integrate with other sectoral policies. Our analysis highlights that there is great potential to enhance assessment schemes through strategic design of monitoring networks and innovation, such as earth observation. New diagnostic tools that use existing WFD monitoring data, but incorporate novel statistical and trait-based approaches could be used more widely to diagnose the cause of deterioration under conditions of multiple pressures and deliver a hierarchy of solutions for more evidence-driven decisions in river basin management. There is also a growing recognition that measures undertaken in river basin management should deliver multiple benefits across sectors, such as reduced flood risk, and there needs to be robust demonstration studies that evaluate these. Continued efforts in ‘mainstreaming’ water policy into other policy sectors is clearly needed to deliver wider success with WFD goals, particularly with agricultural policy. Other key policy areas where a need for stronger integration with water policy was recognised included urban planning (waste water treatment), flooding, climate and energy (hydropower). Having a deadline for attaining the policy objective of good status is important, but even more essential is to have a permanent framework for river basin management that addresses the delays in implementation of measures. This requires a long-term perspective, far beyond the current deadline of 2027