Scipedia: Jordi Pons-Prats' publications

An implicit surface tension model for the analysis of droplet dynamics

María Jesús Samper — Fri, 14 Feb 2020 12:15:40 +0100

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.

Computational evaluation of cochlear implant surgery outcomes accounting for uncertainty and parameter variability

María Jesús Samper — Fri, 14 Feb 2020 12:11:34 +0100

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.

Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation

María Jesús Samper — Fri, 14 Feb 2020 12:00:50 +0100

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

Ram-air parachute simulation with panel methods and staggered coupling

María Jesús Samper — Fri, 14 Feb 2020 11:49:12 +0100

Numerical study of droplet dynamics in a polymer electrolyte fuel cell gas channel using an embedded Eulerian-Lagrangian approach

María Jesús Samper — Fri, 14 Feb 2020 11:45:06 +0100

An embedded Eulerian-Lagrangian formulation for the simulation of droplet dynamics within a polymer electrolyte fuel cell (PEFC) channel is presented. Air is modeled using an Eulerian formulation, whereas water is described with a Lagrangian framework. Using this framework, the gas-liquid interface can be accurately identified. The surface tension force is computed using the curvature defined by the boundary of the Lagrangian mesh. The method naturally accounts for material property changes across the interface and accurately represents the pressure discontinuity. A sessile drop in a horizontal surface, a sessile drop in an inclined plane and droplets in a PEFC channel are solved for as numerical examples and compared to experimental data. Numerical results are in excellent agreement with experimental data. Numerical results are also compared to results obtained with the semi-analytical model previously developed by the authors in order to discuss the limitations of the semi-analytical approach.

On the application of the PFEM to droplet dynamics modeling in fuel cells

María Jesús Samper — Fri, 14 Feb 2020 11:02:37 +0100

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 flow and droplet growth in a low-temperature 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.

Industrial Application of Genetic Algorithms to Cost Reduction of a Wind Turbine Equipped with a Tuned Mass Damper

María Jesús Samper — Thu, 13 Feb 2020 14:53:06 +0100

Design optimization has already become an important tool in industry. The benefits are clear, but several drawbacks are still present, being the main one the computational cost. The numerical simulation involved in the solution of each evaluation is usually costly, but time and computational resources are limited. Time is key in industry. The present communication focuses on the methodology applied to optimize the installation and design of a Tuned Mass Damper. It is a structural device installed within the tower of a wind turbine aimed to stabilize the oscillations and reduce the tensions and the fatigue loads. The paper describes the decision process to define the optimization problem, as well as the issues and solutions applied to deal with a huge computational cost.

Monte Carlo-Based and Sampling-Based Methods and Their Range of Applicability

María Jesús Samper — Thu, 13 Feb 2020 14:48:35 +0100

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.

Summary of UMRIDA Best Practices

María Jesús Samper — Thu, 13 Feb 2020 14:43:24 +0100

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.

General Introduction to Monte Carlo and Multi-level Monte Carlo Methods

María Jesús Samper — Thu, 13 Feb 2020 14:39:52 +0100

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).

Description of the Test Cases

María Jesús Samper — Thu, 13 Feb 2020 14:11:50 +0100

The high-level objective of MARS project was to understand the formation and behaviour of turbulent structures which affects the Reynolds stress and skin friction. The aim was, once understood, to apply flow control techniques in order to control these structures and reduce the overall drag derived from the Reynolds stress and mainly from the skin friction. Active flow control devices were the main interest; DBD plasma, Synthetic jets, Micro Blowing and Suction, Moving Surfaces were included on the list. To test all these devices, two test cases were defined, and a database and file repository were established in the project webserver. The present chapter is aimed to describe the test cases, including the set-up of the flow control devices, as well as to describe the file repository were all the data was stored.

Optimization of the Experimental Set-up for a Turbulent Separated Shear Flow Control by Plasma Actuator Using Genetic Algorithms

María Jesús Samper — Thu, 13 Feb 2020 14:00:27 +0100

Since 1947, when Schubauer and Skramstad 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.

Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach

María Jesús Samper — Thu, 13 Feb 2020 10:18:17 +0100

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.

Applying multi-objective robust design optimization procedure to the route planning of a commercial aircraft

María Jesús Samper — Tue, 03 Dec 2019 10:48:33 +0100

Aircraft emission targets worldwide and their climatic effects have put pressure in government agencies, aircraft manufacturers and airlines to reduce water vapour, carbon dioxide (CO2CO2) and oxides of nitrogen (NOxNOx) resulting from aircraft emissions. The difficulty of reducing emissions including water vapor, carbon dioxide (CO2CO2) and oxides of nitrogen (NOxNOx) 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.

A semi-analytical model for droplet dynamics on the GDL surface of a PEFC electrode

María Jesús Samper — Thu, 07 Mar 2019 13:12:28 +0100

Water management is one of the key factors in Proton Exchange Fuel Cell (PEFC) performance. The water produced within the fuel cell is evacuated through the gas channels, but at high current densities water can block the channel, thus limiting the current density generated in the fuel cell. A semi-analytical model of a water droplet emerging from a gas diffusion layer pore in a PEFC channel is developed. The transient model contains a detailed adhesion and drag force estimation model. Results show that the predicted values for both drag and surface tension force are higher than the results found in literature. The results for the detachment force are consistent with the experimental data available. Higher air velocity values lead to more deformation of the droplet and oscillation with lower frequency but higher amplitude. Similar effects have been identified when the liquid mass flow is increased, leading to faster detachment of the droplet.

Contribution to the definition of non deterministic robust optimization in aeronautics accounting with variable uncertainties

María Jesús Samper — Fri, 20 Oct 2017 11:06:15 +0200

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields. Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and
Lewis (2001). Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its
vicinity, it is clearly the most robust regarding input variability. Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes.

This research presents a methodology to integrate evolutionary algorithms and stochastic analysis, in order to deal with uncertainty and to obtain robust solutions.

Robust design optimization in aeronautics using stochastic analysis and evolutionary algorithms

María Jesús Samper — Thu, 25 Apr 2019 12:24:18 +0200

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.