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Fault tree analysis (FTA) is a well-established technique to analyze the safety risks of a system. Two specific prominent FTA methods, largely applied in the aerospace field, are the so-called minimal cut sets (MCS), which uses an approximate evaluation of the problem, and the direct evaluation (DE) of the fault tree, which uses a top-down recursive algorithm. The first approach is only valid for small values of basic event probabilities and has historically yielded faster results than exact solutions for complex fault trees. The second one means exact solutions at a higher computational cost. This article presents several improvements applied to both approaches in order to upgrade the computing performance. First, improvements to the MCS approach have been performed, where the main idea has been to optimize the number of required permutations and to take advantage of the available information from previous solved subsets. Second, improvements to the DE approach have been applied, which deal with a reduction of the number of recursive calls through a deep search for independent events in the fault tree. This could dramatically reduce the computation time for industrial fault trees with a high number of repeated events. Additional implementation improvements have been also applied regarding hash tables, and memory access and usage, but also implementing the so-called “virtual gates”, which enable limitless children on each gate. The results presented hereafter are promising, not only because they show that both approaches have been highly optimized compared to the literature, but also because a DE solution has been achieved, which can compete in time resources (and obviously in precision) with the MCS approach. These improvements are relevant when considering the industrial, and more specifically the aeronautical, implementation and application of both techniques. The author Jordi Pons‐Prats acknowledges the support from Serra Hunter programme, Generalitat de Catalunya, as well as the support through the Severo Ochoa Centre of Excellence (2019‐2023) under the grant CEX2018‐000797‐S funded by MCIN/AEI/10.13039/501100011033.

The Taxibot® is a hybrid electric vehicle that can tow medium to large aircraft in the movement area of an airport. Its use can lead to lower noise levels near terminal buildings and reduce fuel consumption and emissions; Aircraft emissions and cost of the aircraft ground operations are reduced significantly. The Taxibot®, which is the name that receives the vehicle developed by IAI (Israel Aerospace Industries), has been deployed in very few airports around the world, but the number is envisioned to increase in the following years. If the system goes fully autonomous, pilots will have to learn and accustom themselves to the differences when performing the procedure with the vehicle and airports need to make studies on the effect it could have on capacity and safety. The aim of this paper is to describe the development of a simulator of the operations of a remotecontrolled tow tractor for first person training. The objective of the simulator is evaluating and improving human factors in terms of required skills and ergonomics. The study takes into account a review of the current state of the art on available tools and their shortcomings to establish requirements of the proposed solution. The steps taken to create the virtual environment, physics simulation and other systems of the implementation are disclosed. Validation of the simulated experience was conducted with users of different expertise levels. Although the validation methodology was limited in scope, most users reportedly became more aware of the processes involved in the operation of a tow car. Additionally, the experience can be updated to test new vehicles and conditions.

In this work, a numerical model for the simulation of two-phase electrohydrodynamic (EHD) problems is proposed. It is characterized by a physically consistent treatment of surface tension as well as a jump in the electric material properties. The formulation is based on a finite element method enriched with special shape functions, capable of accurate capturing discontinuities both in the fluid pressure and the gradient of the electric potential. Phase interface is, thus, represented as a zero-thickness boundary. The proposed methodology allows modeling the electric force as an interfacial one, strictly abiding with the physics. The approach is tested using the droplet deformation benchmarks. Moreover, application of the method to study a three-dimensional (3D) case, not characterized by symmetry of revolution, is shown. The proposed methodology defines a basis for an enriched finite element method for a wide range of EHD problems. The authors acknowledge the financial support of the Ministerio de Ciencia, Innovaci on e Universidades of Spain via the “Severo Ochoa Programme” for Centres of Excellence in R&D (Referece No. CEX2018-000797-S) given to the International Centre for Numerical Methods in Engineering (CIMNE). The work of C. Narvaez-Mu~noz was supported by the “Severo Ochoa Ph.D. Scholarship” Reference No. PRE2020-096632. Parts of this work were done in the framework of DIDRO project (Toward establishing a Digital twin for manufacturing via drop-on-demand inkjet printing. Proyectos Estrat egicos Orientados a la Transici on Ecol ogica y a la Transici on Digital. Reference No. TED2921-130471B-I00) supported by the Ministerio de Ciencia, Innovaci on e Universidades of Spain. M. Hashemi acknowledges the funding received from European Union’s Horizon 2020 Research and Innovation Programme (European High-Performance Computing Joint Undertaking Grant Agreement No. 955558) as part of EFLOWS4HPC project. P. Ryzhakov and J. Pons-Prats are Serra Hunter fellows.

The use of Active Flow Control (AFC) technologies to modify the forces acting on streamlined bodies is one of the most active research fields in aerodynamics. For each particular application, finding the optimum set of AFC parameters which maximises lift, minimises drag or maximises lift-to-drag ratio (aerodynamic efficiency), has become a necessary design requirement. In the present paper, the AFC technology was applied to the Selig-Donovan 7003 (SD7003) airfoil at Reynolds number 6 × 104. Synthetic jets were employed to modify the lift and drag forces acting on the airfoil. Four angles of attack (AoA) of 4¿, 6¿, 8¿ and 14¿ were considered, alongside five AFC parameters: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. A multi objective optimisation based on genetic algorithms (GA) was performed for each angle of attack to find the optimum combination of AFC parameters. Each GA generation was simulated using Computational Fluid Dynamics (CFD). A home-made GA package was linked with a mesh generator and the CFD solver, and the results were automatically fed back to the GA code. Over 2200 CFD simulations were performed in two dimensions, using the SpalartAllmaras turbulent model. The motivation behind the current study is to understand the dependence of the optimum set of AFC parameters on the AoA. Results show that, as AoA is increased, the potential benefits of AFC become more pronounced, which allows for considerable improvement in aerodynamic efficiency. The physics involved in the interaction between the main flow and synthetic jet are clearly presented and clarifies that the physical phenomenon to obtain maximum efficiency is completely different at pre-stall and post-stall AoA. In particular, the aerodynamic efficiency was increased by 251% from baseline (no actuation) by using a moderate/finite momentum coefficient at AoA=14¿, while a mere 39% increase was obtained at AoA=8¿. In addition, the interaction between the incoming flow and the synthetic jet pulsating flow at different injection angles has been thoroughly investigated This work was supported by the Spanish and Catalan Governments under grants FIS2016-77849-R and 2017-SGR-00785, respectively. Part of the computations were done in the Red Española de Supercomputación (RES), Spanish supercomputer network, under the grants IM-2019-3-0002 and IM-2020-1-0001. Dr. Jordi Pons-Prats acknowledges the support of the Serra Hunter programme by the Catalan Government

The development of innovative engineered epoxy composites aiming to manufacture cost-efficient materials with reduced weight and enhanced physical properties remains as a current industrial challenge. In this work we report an original procedure for manufacturing glass-fiber epoxy reinforced nanocomposites (GFECs) by employing electrospun fiber-mats as a reinforcing phase. These fibers have been produced from polyacrylonitrile and multiwalled carbon nanotubes solutions. Optimal protocols are designed by combining Taguchi method with the morphological, structural and mechanical properties obtained by scanning electron microscopy, profilometry and tensile tests. It is demonstrated that GFECs fabricated using GF800 glass fiber show an improvement/enhancement of the mechanical properties with a fracture strain up to 500¿MPa (around 20% higher than the non-reinforced epoxy composite counterpart). It is also shown that GFECs fabricated using GF3M glass fiber exhibited a reduction of the roughness up to 56%, which corresponds with a roughness improvement from N8 to N7 following the guidelines provided by the ISO 1302. These results suggest that this type of nanocomposites would be suitable to be used in the aeronautics and automotive industries. This work was financially supported by the “Convocatoria de Ayudas a la Realización de Proyectos de Grupos de Investigación 2020-2021” of the Universidad Católica de Murcia (UCAM), Spain, Reference: PMFI-12/21. Pavel Ryzhakov, Jordi Pons-Prats, and Christian Narváez- Muñoz would also like to acknowledge the support of the Ministerio de Ciencia, Innovación y Universidades of Spain via the “Severo Ochoa Programme” for Centres of Excellence in R&D (reference: CEX2018-000797-S) given to the International Centre for Numerical Methods in Engineering (CIMNE). The work of Christian Narváez- Muñoz was financially supported by the “Severo Ochoa PhD Scholarship” Reference: PRE2020-096632. Pavel Ryzhakov and Jordi Pons-Prats are Serra Hunter fellows.

UAM has the potential to disrupt air transportation, providing disruptive innovation not only to aviation but also to mobility systems and urban planning. Underpinned by technological advances in batteries, as well as in electric and distributed propulsion that facilitate the design of novel aircraft types with the capability for VTOL, the UAM is attracting the attention of an extensive list of stakeholders, institutions and companies. It also covers a broad range of different areas of interest that have to be considered by a holistic and multidisciplinary approach in order to derive its full potential. This paper aims to provide an in-depth qualitative analysis of relevant aspects of UAM development and implementation. The added value of this paper is in assessing the current status and prospects of the most important UAM areas by analysing them from the literature and practice point of views. The paper also deals with the main challenges and multidisciplinary constraints that might slow down the pace towards the successful application of the subject concept. The first author is a Serra Hunter Fellow. This research has been partially supported through the Severo Ochoa Centre of Excellence (2019–2023) under the grant CEX2018-000797-S funded by MCIN/AEI/10.13039/501100011033. The authors would like to acknowledge and thank the fruitful discussions and advise received from many colleagues. Special mention should be made to Dr. Enric Pastor, Dr. Xavier Prats, and Dr. Miquel Estrada, the three of them associate professors in the UPC-BarcelonaTech, as well as Prof. Nataša Bojković, full professor at the Faculty of Transport and Traffic Engineering of the University of Belgrade. Finally, the authors would like to thank the reviewers for the comments that help improving the quality of the manuscript.

In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. The error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time needed in order to minimize the initialization bias, as well as techniques to choose the effective time needed to keep the statistical error under control. This is accompanied by strategies to reduce simulation cost. The proposed method is assessed in application to the prediction of the drag force over high rise buildings and specifically in application to the CAARC building, a relevant benchmark for the wind engineering community. The authors thank Prof. Barbara Wohlmuth and Dr. Brendan Keith for their valuable discussions and contributions to the subject. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 800898 and from project RTI2018-096898-B-I00 from the FEDER/Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación. The authors also acknowledge the Severo Ochoa Centre of Excellence (2019–2023), which financially supported this work under the grant CEX2018-000797-S funded by MCIN/AEI/10.13039/501100011033. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (IM-2020-2-0030). This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140). Dr. Jordi Pons-Prats acknowledges the support of the Serra Hunter programme by the Catalan Government.

International audience; Since 1947, when Schubauer and Skramstad (J Res: 69–78, 1947 [1]) established the basis of the technology with its revolutionary work about steady state tools and mechanisms for the flow management, the progress of the flow control technology and the development of devices have progressed constantly. Anyway, the applicability of such devices is limited, and only few of them have arrived to the assembly workshop. The problem is that the range of actuation is still limited. Despite their operability limitations, flow control devices are of great interest for the aeronautical industry. The number of projects investigating this technology demonstrates the relevance of in the Fluid Dynamic field. The scientific interest focus not only on the industrial applications and the improvement of the technology, but also on the deep understanding of the physical phenomena associated to the flow separation, turbulence formation associated to the final drag reduction aim. A clear example of what has been mentioned is the EC MARS research project (MARS project, FP7 project number 266326, [2]). Its objectives are aimed to a better understanding of the Reynolds Stress and turbulent flow related to both drag reduction and flow control. The research was carried out through the analysis of several flow control devices and the optimization of the parameters for some of them was an important element of the research. When solving a traditional fluid dynamics optimisation problem numerical flow analysis are used instead of experimental ones due to their lower cost and shorter needed time for evaluation of candidate solutions. Nevertheless, in the particular case of the selected flow control plasma devices the experimental measurement of the performance of each candidate configuration has been much quicker than a numerical analysis. For this reason, the corresponding optimisation problem has been solved by coupling an evolutionary optimization algorithm with an experimental device. This paper discusses the design quality and efficiency gained by this innovative coupling.

The Particle Finite Element Method (PFEM) is used to develop a model to study two-phase flow in fuel cell gas channels. First, the PFEM is used to develop the model of free and sessile droplets. The droplet model is then coupled to an Eulerian, fixed-grid, model for the airflow. The resulting coupled PFEM-Eulerian algorithm is used to study droplet oscillations in an air flowand droplet growth in a lowtemperature fuel cell gas channel. Numerical results show good agreement with predicted frequencies of oscillation, contact angle, and deformation of injected droplets in gas channels. The PFEM-based approach provides a novel strategy to study droplet dynamics in fuel cells.

The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U 0 = 15 m/s, Re h = 30,000, Re θ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model.

The final publication is available at Springer via http://dx.doi.org/10.1007/s11431-016-9013-6 Turbulent boundary layer control (TBLC) for skin-friction drag reduction is a relatively new technology made possible through the advances in computational-simulation capabilities, which have improved the understanding of the flow structures of turbulence. Advances in micro-electronic technology have enabled the fabrication of active device systems able to manipulating these structures. The combination of simulation, understanding and micro-actuation technologies offers new opportunities to significantly decrease drag, and by doing so, to increase fuel efficiency of future aircraft. The literature review that follows shows that the application of active control turbulent skin-friction drag reduction is considered of prime importance by industry, even though it is still at a low technology readiness level (TRL). This review presents the state of the art of different technologies oriented to the active and passive control for turbulent skin-friction drag reduction and contributes to the improvement of these technologies.

This paper aims is to provide an overview of how new data collection methods and the various advances in urban travel demand modelling are improving the understanding of mobility. These new modelling applications and data allow for a study of both new disruptive transport services and changes in travel behaviours in the “Mobility as a Service” (MaaS) context that needs to be overcome in the future.

The present section will focus on the applicability issues of Monte Carlo-based methods, as well as those methods based on sampling techniques. Special focus will be put on the Multi-Level Monte Carlo method and the two implementations developed during the UMRIDA project, namely the Continuous MLMC and MLMC. All named methods have been described in the above sections of this book.

In this chapter, we present a general introduction to Monte Carlo (MC)-based methods, sampling methodologies, stratification methods, and variance reduction techniques. In the first part, we will discuss the theoretical basis and the convergence proprieties of MC methods. The next part is devoted to pseudorandom and quasi-random number generation, the generation of random variables and the application of stratification. It is followed by techniques for correlation and discrepancy control. The third part presents the concept of Latin Hypercube Sampling (LHS). The last part introduces the concept of Multi-Level Monte Carlo (MLMC).

Uncertainty quantification (UQ) is becoming a strategic step in the design phase. Robust Design Optimization (RDO) is the following step. The Technological Readiness Level (TRL) of intrusive and non-intrusive methodologies is increasing rapidly, although several limitations remain. Nowadays, UQ is a major trend in research, because there is a lot of room for improvement.

Uncertainty quantification has gained interest during the recent years. Two clear examples are NODESIM-CFD and, the just finished, UMRIDA projects.

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process. This work was partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502), by the AGAUR grant 2016-PROD-00047, the European Union Seventh Framework Program (FP7/2007-2013), Grant agreement 304857, HEAR-EU project and the QUAES Foundation Chair for Computational Technologies for Healthcare.

Aircraft emission targets worldwide and their climatic effects have put pressure in government agencies, aircraft manufacturers and airlines to reduce water vapour, carbon dioxide (\(CO_{2}\)) and oxides of nitrogen (\(NO_{x}\)) resulting from aircraft emissions. The difficulty of reducing emissions including water vapor, carbon dioxide (\(CO_{2}\)) and oxides of nitrogen (\(NO_{x}\)) is mainly due to the fact that a commercial aircraft is usually designed for a particular optimal cruise altitude but may be requested or required to operate and deviate at different altitudes and speeds to archive a desired or commanded flight plan, resulting in increased emissions. This is a multi- disciplinary problem with multiple trade-offs such as optimizing engine efficiency, minimizing fuel burnt and emissions while maintaining prescribed aircraft trajectories, altitude profiles and air safety. There are possible attempts to solve such problems by designing new wing/aircraft shape, new efficient engine, ATM technology, or modifying the aircraft flight plan. Based on the rough data provided by an air carrier company, who was willing to assess the methodology, this paper will present the coupling of an advanced optimization technique with mathematical models and algorithms for aircraft emission, and fuel burnt reduction through flight plan optimization. Two different approaches are presented; the first one describes a deterministic optimization of the flight plan and altitude profile in order to reduce the fuel consumption while reducing time and distance. The second approach presents the robust design optimization of the previous case considering uncertainties on several parameters. Numerical results will show that the methods are able to capture a set of useful trade-offs solutions between aircraft range and fuel consumption, as well as fuel consumption and flight time.

This book contains thirty-five selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2017). This was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS). Topics treated in the various chapters reflect the state of the art in theoretical and numerical methods and tools for optimization, and engineering design and societal applications. The volume focuses particularly on intelligent systems for multidisciplinary design optimization (mdo) problems based on multi-hybridized software, adjoint-based and one-shot methods, uncertainty quantification and optimization, multidisciplinary design optimization, applications of game theory to industrial optimization problems, applications in structural and civil engineering optimum design and surrogate models based optimization methods in aerodynamic design.

Uncertainties are a daily issue to deal with in aerospace engineering and applications. Robust optimization methods commonly use a random generation of the inputs and take advantage of multi-point criteria to look for robust solutions accounting with uncertainty definition. From the computational point of view, the application to coupled problems, like computational fluid dynamics (CFD) or fluid–structure interaction (FSI), can be extremely expensive. This study presents a coupling between stochastic analysis techniques and evolutionary optimization algorithms for the definition of a stochastic robust optimization procedure. At first, a stochastic procedure is proposed to be applied into optimization problems. The proposed method has been applied to both CFD and FSI problems for the reduction of drag and flutter, respectively.

The practical interest of flow control approaches is no more debated as flow control provides an effective mean for considerably increasing the performances of ground or air transport systems, among many others applications. Here a fundamental configuration is investigated by using non-thermal surface plasma discharge. A dielectric barrier discharge is installed at the step corner of a backward-facing step (Reh=30000, Re?=1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step. The primary objective of this paper is the coupling of a numerical optimizer with an experiment. More specifically, optimization by genetic algorithm is implemented experimentally in order to minimize the reattachment point downstream of the step model. Validation through inverse problem is firstly demonstrated. When coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm finds the optimum forcing conditions with a good convergence rate, the best control design variables being in agreement with the literature that uses other types of control devices than plasma. Indeed, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing phenomena. At the best forcing conditions, the mean flow reattachment is reduced by 20%. This article, with its experiment-based approach, demonstrates the robustness of a single-objective multi-design optimization method, and its feasibility for wind tunnel experiments.

The practical interest of flow control approaches is no more debated as flow control provides an effective mean for considerably increasing the performances of ground or air transport systems, among many others applications. Here a fundamental configuration is investigated by using non-thermal surface plasma discharge. A dielectric barrier discharge is installed at the step corner of a backward-facing step (Reh=30000, Re¿=1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step. The primary objective of this paper is the coupling of a numerical optimizer with an experiment. More specifically, optimization by genetic algorithm is implemented experimentally in order to minimize the reattachment point downstream of the step model. Validation through inverse problem is firstly demonstrated. When coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm finds the optimum forcing conditions with a good convergence rate, the best control design variables being in agreement with the literature that uses other types of control devices than plasma. Indeed, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing phenomena. At the best forcing conditions, the mean flow reattachment is reduced by 20%. This article, with its experiment-based approach, demonstrates the robustness of a single-objective multi-design optimization method, and its feasibility for wind tunnel experiments.

Water accumulation in fuel cell anode and cathode channels can lead to hydrogen starvation and therefore, severe electrode degradation, and significantly fuel cell performance deterioration. For this reason, liquid water transport in micro-channels remains an active area of research in fuel cells as well as many other research areas [1, 2]. Two-phase flow in micro-channels is governed by surface tension and viscous effects and involves the interaction of air, water and the solid substrate. To date most numerical studies have been performed using volume of fluid (VOF) [3] or level set (LS) [4] implementations in commercial software, however these methods are usually explicit thereby limiting the maximum time step that can be used. Investigations on new methods to solve two-phase flows in micro-channels is key to develop alternative methods that allow for faster simulation time and allow to study physical process that remain a challenge, such as interface conditions between the channel and porous media in fuel cells [5]. In this work, a novel formulation based on a Lagrangian-Eulerian formulation is presented and experimentally validated [6]. The governing equations for both air and water are the Navier-Stokes equations. Air is represented using a fixed mesh, whereas a moving mesh is used to discretize the water domain. This formulation is particularly advantageous to the problem at hand, since it allows for exact tracking the air-water interface. An implicit term is used to represent the surface tension effects, allowing us to use time steps greater than those from explicit formulations [7]. For the validation of the model, several experiments have been performed in a transparent microchannel. Droplet deformation and shedding on three substrates, i.e, Kapton, PTFE and a gas diffusion layer (Toray H60 10%PTFE), is studied both numerically and experimentally. The goal of the experiments is to reproduce different conditions for injected water in a microchannel. Kapton and PTFE are smooth surfaces, the former being hydrophilic and the latter hydrophobic, whereas the GDL is a hydrophobic rough substrate and is used to emulate the conditions in a fuel cell channel. Two cameras are used to capture the emergence of water into the channel. The first camera is used to obtain images of the droplet’s deformation from a lateral point of view, allowing us to measure the advancing and receding contact angles. The second camera obtains images along the channel, and therefore deformation effects of the droplet on the direction perpendicular to the airflow can also be quantified. The presented model can predict droplet emergence, deformation and posterior detachment. Numerical results are consistent with the experimental data. For instance, the advancing contact angle remains approximately constant in rough surfaces, whereas the receding contact angle decreases, showing a slight increase prior to droplet detachment. Results obtained with the current model are also compared to VOF results previously reported in literature and large discrepancies with the evolution of droplet deformation are observed. The model is shown to be able to predict the conditions that lead to droplet, slug and film flow in fuel cell channels. References [1] M. Wörner, Microfluid Nanofluid, 2012, 12, 841–886. [2] R.B. Ferreira et al., J. Power Sources, 2015, 277, 329–342. [3] X. Zhu et al., Microfluid Nanofluid, 20 08, 12, 841–886. [4] N. Akhtar et al., Int. J. Hydrogen Energy, 20 09, 34, 3104–3111. [5] A.Z. Weber et al., J. Electrochem. Soc, 2014, 161 (12), F1254-F1299. [6] P. B. Ryzhakov and A. Jarauta, Int. J. Num. Methods in Fluids, 2015, 81, 357-376; A. Jarauta et al., J. Power Sources, 2016, 323, 201-212; P. B. Ryzhakov et al., Comp. Particle Mechanics, 2016, 1-11. [7] M. Sussman and M. Ohta, SIAM Journal on Scientific Computing, 20 09, 31, 2447–2471. Figure 1

The paper investigates two advanced optimisation methods for solving active flow control device shape design problem and also compares their optimisation efficiency in terms of computational cost and design quality. The first optimisation method uses Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithm (HAPMOEA) and the second uses Hybridized EA with Nash-Game strategies. Both optimisation method are based on a canonical evolution strategy and incorporates the concepts of parallel computing and asynchronous evaluation. For the practical test case, one of active flow control devices named Shock Control Bump (SCB) is considered and it is applied to Natural Laminar Flow (NLF) aerofoil. The concept of SCB is to decelerate supersonic flow on upper/lower surface of transonic aerofoil that leads delay of shock occurrence. Such active flow technique reduces a total drag at transonic speeds. Numerical results clearly show that Hybrid-Game helps EA to accelerate optimisation process, and also applying SCB on the suction and pressure sides significantly reduces transonic wave drag and improves lift on drag (L/D) value when compared to the baseline design.

Las incertidumbres son un problema cotidiano en la ingeniería aeroespacial y en sus aplicaciones. Los métodos de optimización robusta utilizan, normalmente, y para asegurar la robustez de las soluciones, la generación aleatoria de los valores con incertidumbres así como criterios de selección multi-punto para la determinación del óptimo. Desde un punto de vista computacional, la aplicación a problemas de fluido-dinámica (CFD) o interacción fluido-estructura (FSI) puede ser extremadamente cara. Este trabajo presenta el acoplamiento entre el cálculo estocástico y los algoritmos evolutivos para la definición de un procedimiento de optimización robusta. Se propone, en primer lugar, una metodología para el cálculo estocástico, que a continuación se aplica a la solución de problemas de optimización. Estos métodos propuestos se han aplicado a dos tipos de problemas; un problema de CFD y otro de FSI orientados a la reducción de la resistencia aerodinámica y del fenómeno de estabilidad estructural conocido por «flutter», respectivamente. Uncertainties are a daily issue to deal with in aerospace engineering and applications. Robust optimization methods commonly use a random generation of the inputs and take advantage of multi-point criteria to look for robust solutions accounting with uncertainty definition. From the computational point of view, the application to coupled problems, like fluid-dynamics (CFD) or fluid-structure interaction (FSI), can be extremely expensive. This work presents a coupling between stochastic analysis techniques and evolutionary optimization algorithms for the definition of a stochastic robust optimization procedure. At first, a stochastic procedure is proposed to be applied into optimization problems. The proposed method has been applied to both CFD and FSI problems for the reduction of drag and flutter, respectively.

A Lagrangian incompressible fluid flow model is extended by including an implicit surface tension term in order to analyze droplet dynamics. The Lagrangian framework is adopted to model the fluid and track its boundary, and the implicit surface tension term is used to introduce the appropriate forces at the domain boundary. The introduction of the tangent matrix corresponding to the surface tension force term ensures enhanced stability of the derived model. Static, dynamic and sessile droplet examples are simulated to validate the model and evaluate its performance. Numerical results are capable of reproducing the pressure distribution in droplets, and the advancing and receding contact angles evolution for droplets in varying substrates and inclined planes. The model is stable even at time steps up to 20 times larger than previously reported in literature and achieves first and second order convergence in time and space, respectively. The present implicit surface tension implementation is applicable to any model where the interface is represented by a moving boundary mesh.

International audience; An Uncertainty Quantification analysis of a swirled stabilized combustor experiment is performed. Theobjective is to estimate the modal risk factor of the system, i.e. the probability of a thermoacoustic mode tobe unstable, which may facilitate the development and optimization of suitable control methods. Topropagate uncertainties, a Monte Carlo method is initially used based on 4000 Helmholtz-basedthermoacoustic simulations with random perturbations on the flame input parameters. The analysis of theMonte Carlo database suggests that a reduced two-step Uncertainty Quantification strategy may beefficient to deal with thermoacoustic systems. First, three bilinear surrogate models are tuned from amoderate number of Helmholtz solutions (a few tens). Then, these algebraic models are used to perform aMonte Carlo analysis at reduced cost and approximate the risk factor of the mode. Good agreements areobtained when comparing the risk factor from the full Monte Carlo database and the risk factor fromsurrogate models.

International audience; While the computational power is still increasing, thus arousing theinterest for high-fidelity simulations, the need of low-order models is also feltto both predict and understand combustion instabilities at low costs. Historicallyapplied to simple systems like longitudinal Rijke tubes to unveil the driven mechanismsleading to instability, they have recently been adapted to more complexconfigurations such as annular combustors. A network model is presented here topredict thermo-acoustic modes in an annular combustion chamber fed by burnersconnected to an annular plenum, typical of modern combustor designs. Explicitexpressions of the growth rate are derived in several cases showing key parameterscontrolling the stability. In more general situations, no explicit solution canbe obtained. Nevertheless, such an analytical model can be solved numerically atlow-cost compared with 3D acoustic tools and high-fidelity simulations. In thisframework, efficient sensitivity techniques and UQ methods can be developed totackle the UQ problem: “How can we assess the risk of instability in industrialcombustors at the predesign stage?”.