High performance scientific computing in applications with direct finite element simulation

xiii, 133 p.La predicción del flujo separado, incluida la pérdida de un avión completo mediantela dinámica de fluidos computacional (CFD) se considera uno de los grandes desaf¿¿os que seresolverán en 2030, según NASA. Las ecuaciones no lineales de Navier-Stokes proporcionan laformulación matemática para flujo de fluidos en espacios tridimensionales. Sin embargo, todaviafaltan soluciones clásicas, existencia y singularidad. Ya que el cálculo de la fuerza bruta esintratable para realizar simulación predictiva para un avión completo, uno puede usar la simulaciónnumérica directa (DNS); sin embargo, prohibitivamente caro ya que necesita resolver laturbulencia a escala de magnitud Re power (9/4). Considerando otros métodos como el estad¿¿sticopromedio Reynolds¿s Average Navier Stokes (RANS), spatial average Large Eddy Simulation(LES), y Hybrid Detached Eddy Simulation (DES), que requieren menos cantidad de grados delibertad. Todos estos métodos deben ajustarse a los problemas de referencia y, además, cerca las paredes, la malla tieneque ser muy fina para resolver las capas l¿¿mite (lo cual significa que el costo computacional es muycostoso). Por encima de todo, los resultados son sensibles a, por ejemplo, parámetros expl¿¿citos enel método, la malla, etc.Como una solución al desaf¿¿o, aqu¿¿ presentamos la adaptación Metodolog¿¿a de solución directa deFEM (DFS) con resolución numérica disparo, como una familia predictiva, libre de parámetros demétodos para flujo turbulento. Resolvimos el modelo de avión JAXA Standard Model (JSM) ennúmero realista de Reynolds, presentado como parte del High Lift Taller de predicción 3.Predijimos un aumento de Cl dentro de un error de 5 % vs experimento, arrastre Cd dentro de 10 %error y detenga 1 ¿ dentro del ángulo de ataque.El taller identificó un probable experimento error depedido 10 % para los resultados de arrastre. La simulación es 10 veces más rápido y más barato encomparación con CFD tradicional o existente enfoques. La eficiencia proviene principalmente dell¿¿mite de deslizamiento condición que permite mallas gruesas cerca de las paredes, orientada aobjetivos control de error adaptativo que refina la malla solo donde es necesario y grandes pasos detiempo utilizando un método de iteración de punto fijo tipo Schur, sin comprometer la precisión delos resultados de la simulación.También presentamos una generalización de DFS a densidad variable y validado contra el problemade referencia MARIN bien establecido. los Los resultados muestran un buen acuerdo con losresultados experimentales en forma de sensores de presión. Más tarde, usamos esta metodolog¿¿apara resolver dos aplicaciones en problemas de flujo multifásico. Uno tiene que ver con un flashtanque de almacenamiento de agua de lluvia (consorcio de agua de Bilbao), y el segundo es sobre eldiseño de una boquilla para impresión 3D. En el agua de lluvia tanque de almacenamiento,predijimos que la altura del agua en el tanque tiene un influencia significativa sobre cómo secomporta el flujo aguas abajo de la puerta del tanque (válvula). Para la impresión 3D,desarrollamos un diseño eficiente con El flujo de chorro enfocado para evitar la oxidación y elcalentamiento en la punta del boquilla durante un proceso de fusión.Finalmente, presentamos aqu¿¿ el paralelismo en múltiples GPU y el incrustado sistema dearquitectura Kalray. Casi todas las supercomputadoras de hoy tienen arquitecturas heterogéneas,1 See the UNESCO Internacional Standard nomenclature for fields of Science and Technologyacomo CPU+GPU u otros aceleradores, y, por lo tanto, es esencial desarrollar marcoscomputacionales para aprovecha de ellos. Como lo hemos visto antes, se comienza a desarrollar eseCFD más tarde en la década de 1060 cuando podemos tener poder computacional, por lo tanto, Esesencial utilizar y probar estos aceleradores para los cálculos de CFD. Las GPU tienen unaarquitectura diferente en comparación con las CPU tradicionales. Técnicamente, la GPU tienemuchos núcleos en comparación con las CPU que hacen de la GPU una buena opción para elcómputo paralelo.Para múltiples GPU, desarrollamos un cálculo de plantilla, aplicado a simulación depliegues geológicos. Exploramos la computación de halo y utilizamos Secuencias CUDA paraoptimizar el tiempo de computación y comunicación. La ganancia de rendimiento resultante fue de23 % para cuatro GPU con arquitectura Fermi, y la mejora correspondiente obtenida en cuatro LasGPU Kepler fueron de 47 %.This research was carried out at the Basque Center for Applied Mathematics (BCAM) within the CFD Computational Technology (CFDCT) and also at the School of Electrical Engineering and Computer Science(Royal Institue of Technology, Stockholm, Sweden). Which is suported by Fundacion Obra Social “la Caixa“, Severo Ochoa Excellence research centre 2014-2018 SEV-2013-0323, Severo Ochoa Excellence research centre 2018-2022 SEV-2017-0718, BERC program 2014-2017, BERC program 2018-2021, MSO4SC European project, Elkartek. This work has been performed using the computing infrastructure from SNIC (Swedish National Infrastructure for Computing)

High Performance Scientific Computing in Applications with Direct Finite Element Simulation

To predict separated flow including stall of a full aircraft with Computational Fluid Dynamics (CFD) is considered one of the problems of the grand challenges to be solved by 2030, according to NASA [1]. The nonlinear Navier- Stokes equations provide the mathematical formulation for fluid flow in 3- dimensional spaces. However, classical solutions, existence, and uniqueness are still missing. Since brute-force computation is intractable, to perform predictive simulation for a full aircraft, one can use Direct Numerical Simulation (DNS); however, it is prohibitively expensive as it needs to resolve the turbulent scales of order Re4 . Considering other methods such as statistical average Reynolds’s Average Navier Stokes (RANS), spatial average Large Eddy Simulation (LES), and hybrid Detached Eddy Simulation (DES), which require less number of degrees of freedom. All of these methods have to be tuned to benchmark problems, and moreover, near the walls, the mesh has to be very fine to resolve boundary layers (which means the computational cost is very expensive). Above all, the results are sensitive to, e.g. explicit parameters in the method, the mesh, etc. As a resolution to the challenge, here we present the adaptive time- resolved Direct FEM Solution (DFS) methodology with numerical tripping, as a predictive, parameter-free family of methods for turbulent flow. We solved the JAXA Standard Model (JSM) aircraft model at realistic Reynolds number, presented as part of the High Lift Prediction Workshop 3. We predicted lift Cl within 5% error vs. experiment, drag Cd within 10% error and stall 1◦ within the angle of attack. The workshop identified a likely experimental error of order 10% for the drag results. The simulation is 10 times faster and cheaper when compared to traditional or existing CFD approaches. The efficiency mainly comes from the slip boundary condition that allows coarse meshes near walls, goal-oriented adaptive error control that refines the mesh only where needed and large time steps using a Schur-type fixed-point iteration method, without compromising the accuracy of the simulation results. As a follow-up, we were invited to the Fifth High Order CFD Workshop, where the approach was validated for a tandem sphere problem (low Reynolds number turbulent flow) wherein a second sphere is placed a certain distance downstream from a first sphere. The results capture the expected slipstream phenomenon, with appx. 2% error. A comparison with the higher-order frameworks Nek500 and PyFR was done. The PyFR framework has demonstrated high effectiveness for GPUs with an unstructured mesh, which is a hard problem in this field. This is achieved by an explicit time-stepping approach. Our study showed that our large time step approach enabled appx. 3 orders of magnitude larger time steps than the explicit time steps in PyFR, which made our method more effective for solving the whole problem. We also presented a generalization of DFS to variable density and validated against the well-established MARIN benchmark problem. The results show good agreement with experimental results in the form of pressure sensors. Later, we used this methodology to solve two applications in multiphase flow problems. One has to do with a flash rainwater storage tank (Bilbao water consortium), and the second is about designing a nozzle for 3D printing. In the flash rainwater storage tank, we predicted that the water height in the tank has a significant influence on how the flow behaves downstream of the tank door (valve). For the 3D printing, we developed an efficient design with the focused jet flow to prevent oxidation and heating at the tip of the nozzle during a melting process. Finally, we presented here the parallelism on multiple GPUs and the embedded system Kalray architecture. Almost all supercomputers today have heterogeneous architectures, such as CPU+GPU or other accelerators, and it is, therefore, essential to develop computational frameworks to take advantage of them. For multiple GPUs, we developed a stencil computation, applied to geological folds simulation. We explored halo computation and used CUDA streams to optimize computation and communication time. The resulting performance gain was 23% for four GPUs with Fermi architecture, and the corresponding improvement obtained on four Kepler GPUs were 47%. The Kalray architecture is designed to have low energy consumption. Here we tested the Jacobi method with different communication strategies. Additionally, visualization is a crucial area when we do scientific simulations. We developed an automated visualization framework, where we could see that task parallelization is more than 10 times faster than data parallelization. We have also used our DFS in the cloud computing setting to validate the simulation against the local cluster simulation. Finally, we recommend the easy pre-processing tool to support DFS simulation.La Caixa 201

High-Performance Computational and Information Technologies for Numerical Models and Data Processing

This chapter discusses high-performance computational and information technologies for numerical models and data processing. In the first part of the chapter, the numerical model of the oil displacement problem was considered by injection of chemical reagents to increase oil recovery of reservoir. Moreover the fragmented algorithm was developed for solving this problem and the algorithm for high-performance visualization of calculated data. Analysis and comparison of parallel algorithms based on the fragmented approach and using MPI technologies are also presented. The algorithm for solving given problem on mobile platforms and analysis of computational results is given too. In the second part of the chapter, the problem of unstructured and semi-structured data processing was considered. It was decided to address the task of n-gram extraction which requires a lot of computing with large amount of textual data. In order to deal with such complexity, there was a need to adopt and implement parallelization patterns. The second part of the chapter also describes parallel implementation of the document clustering algorithm that used a heuristic genetic algorithm. Finally, a novel UPC implementation of MapReduce framework for semi-structured data processing was introduced which allows to express data parallel applications using simple sequential code

Monitoring the waste to energy plant using the latest AI methods and tools

Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring. In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient. Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme

Applied Visualization in the Neurosciences and the Enhancement of Visualization through Computer Graphics

The complexity and size of measured and simulated data in many fields of science is increasing constantly. The technical evolution allows for capturing smaller features and more complex structures in the data. To make this data accessible by the scientists, efficient and specialized visualization techniques are required. Maximum efficiency and value for the user can only be achieved by adapting visualization to the specific application area and the specific requirements of the scientific field. Part I: In the first part of my work, I address the visualization in the neurosciences. The neuroscience tries to understand the human brain; beginning at its smallest parts, up to its global infrastructure. To achieve this ambitious goal, the neuroscience uses a combination of three-dimensional data from a myriad of sources, like MRI, CT, or functional MRI. To handle this diversity of different data types and sources, the neuroscience need specialized and well evaluated visualization techniques. As a start, I will introduce an extensive software called \"OpenWalnut\". It forms the common base for developing and using visualization techniques with our neuroscientific collaborators. Using OpenWalnut, standard and novel visualization approaches are available to the neuroscientific researchers too. Afterwards, I am introducing a very specialized method to illustrate the causal relation of brain areas, which was, prior to that, only representable via abstract graph models. I will finalize the first part of my work with an evaluation of several standard visualization techniques in the context of simulated electrical fields in the brain. The goal of this evaluation was clarify the advantages and disadvantages of the used visualization techniques to the neuroscientific community. We exemplified these, using clinically relevant scenarios. Part II: Besides the data preprocessing, which plays a tremendous role in visualization, the final graphical representation of the data is essential to understand structure and features in the data. The graphical representation of data can be seen as the interface between the data and the human mind. The second part of my work is focused on the improvement of structural and spatial perception of visualization -- the improvement of the interface. Unfortunately, visual improvements using computer graphics methods of the computer game industry is often seen sceptically. In the second part, I will show that such methods can be applied to existing visualization techniques to improve spatiality and to emphasize structural details in the data. I will use a computer graphics paradigm called \"screen space rendering\". Its advantage, amongst others, is its seamless applicability to nearly every visualization technique. I will start with two methods that improve the perception of mesh-like structures on arbitrary surfaces. Those mesh structures represent second-order tensors and are generated by a method named \"TensorMesh\". Afterwards I show a novel approach to optimally shade line and point data renderings. With this technique it is possible for the first time to emphasize local details and global, spatial relations in dense line and point data.In vielen Bereichen der Wissenschaft nimmt die Größe und Komplexität von gemessenen und simulierten Daten zu. Die technische Entwicklung erlaubt das Erfassen immer kleinerer Strukturen und komplexerer Sachverhalte. Um solche Daten dem Menschen zugänglich zu machen, benötigt man effiziente und spezialisierte Visualisierungswerkzeuge. Nur die Anpassung der Visualisierung auf ein Anwendungsgebiet und dessen Anforderungen erlaubt maximale Effizienz und Nutzen für den Anwender. Teil I: Im ersten Teil meiner Arbeit befasse ich mich mit der Visualisierung im Bereich der Neurowissenschaften. Ihr Ziel ist es, das menschliche Gehirn zu begreifen; von seinen kleinsten Teilen bis hin zu seiner Gesamtstruktur. Um dieses ehrgeizige Ziel zu erreichen nutzt die Neurowissenschaft vor allem kombinierte, dreidimensionale Daten aus vielzähligen Quellen, wie MRT, CT oder funktionalem MRT. Um mit dieser Vielfalt umgehen zu können, benötigt man in der Neurowissenschaft vor allem spezialisierte und evaluierte Visualisierungsmethoden. Zunächst stelle ich ein umfangreiches Softwareprojekt namens \"OpenWalnut\" vor. Es bildet die gemeinsame Basis für die Entwicklung und Nutzung von Visualisierungstechniken mit unseren neurowissenschaftlichen Kollaborationspartnern. Auf dieser Basis sind klassische und neu entwickelte Visualisierungen auch für Neurowissenschaftler zugänglich. Anschließend stelle ich ein spezialisiertes Visualisierungsverfahren vor, welches es ermöglicht, den kausalen Zusammenhang zwischen Gehirnarealen zu illustrieren. Das war vorher nur durch abstrakte Graphenmodelle möglich. Den ersten Teil der Arbeit schließe ich mit einer Evaluation verschiedener Standardmethoden unter dem Blickwinkel simulierter elektrischer Felder im Gehirn ab. Das Ziel dieser Evaluation war es, der neurowissenschaftlichen Gemeinde die Vor- und Nachteile bestimmter Techniken zu verdeutlichen und anhand klinisch relevanter Fälle zu erläutern. Teil II: Neben der eigentlichen Datenvorverarbeitung, welche in der Visualisierung eine enorme Rolle spielt, ist die grafische Darstellung essenziell für das Verständnis der Strukturen und Bestandteile in den Daten. Die grafische Repräsentation von Daten bildet die Schnittstelle zum Gehirn des Menschen. Der zweite Teile meiner Arbeit befasst sich mit der Verbesserung der strukturellen und räumlichen Wahrnehmung in Visualisierungsverfahren -- mit der Verbesserung der Schnittstelle. Leider werden viele visuelle Verbesserungen durch Computergrafikmethoden der Spieleindustrie mit Argwohn beäugt. Im zweiten Teil meiner Arbeit werde ich zeigen, dass solche Methoden in der Visualisierung angewendet werden können um den räumlichen Eindruck zu verbessern und Strukturen in den Daten hervorzuheben. Dazu nutze ich ein in der Computergrafik bekanntes Paradigma: das \"Screen Space Rendering\". Dieses Paradigma hat den Vorteil, dass es auf nahezu jede existierende Visualiserungsmethode als Nachbearbeitunsgschritt angewendet werden kann. Zunächst führe ich zwei Methoden ein, die die Wahrnehmung von gitterartigen Strukturen auf beliebigen Oberflächen verbessern. Diese Gitter repräsentieren die Struktur von Tensoren zweiter Ordnung und wurden durch eine Methode namens \"TensorMesh\" erzeugt. Anschließend zeige ich eine neuartige Technik für die optimale Schattierung von Linien und Punktdaten. Mit dieser Technik ist es erstmals möglich sowohl lokale Details als auch globale räumliche Zusammenhänge in dichten Linien- und Punktdaten zu erfassen

Graphical Computing Solution for Industrial Plant Engineering

When preparing an engineering operation on an industrial plant, reliable and updated models of the plant must be available for correct decisions and planning. However, especially in the case of offshore oil and gas installations, it can hazardous and expensive to send an engineering party to assess and update the model of the plant. To reduce the cost and risk of modelling the plant, there are methods for quickly generating a 3D representation, such as LiDAR and stereoscopic reconstruction. However, these methods generate large files with no inherit cohesion. To address this, we propose to find a solution to efficiently transform point clouds from stereoscopic reconstruction into small mesh files that can be streamed or shared across teams. With that in mind, different techniques for treating point clouds and generating meshes were tested independently to measure their performance and effectiveness on an artifact-rich data set, such as the ones this work is aimed for. Afterwards, the techniques were combined into pipelines and compared with each other in terms of efficiency, file size output, and quality. With all results in place, the best solution from the ones tested was identified and validated with large real-world data sets.Master's Thesis in InformaticsINF39

A Data-Virtualization System for Large Model Visualization

Interactive scientific visualizations are widely used for the visual exploration and examination of physical data resulting from measurements or simulations. Driven by technical advancements of data acquisition and simulation technologies, especially in the geo-scientific domain, large amounts of highly detailed subsurface data are generated. The oil and gas industry is particularly pushing such developments as hydrocarbon reservoirs are increasingly difficult to discover and exploit. Suitable visualization techniques are vital for the discovery of the reservoirs as well as their development and production. However, the ever-growing scale and complexity of geo-scientific data sets result in an expanding disparity between the size of the data and the capabilities of current computer systems with regard to limited memory and computing resources. In this thesis we present a unified out-of-core data-virtualization system supporting geo-scientific data sets consisting of multiple large seismic volumes and height-field surfaces, wherein each data set may exceed the size of the graphics memory or possibly even the main memory. Current data sets fall within the range of hundreds of gigabytes up to terabytes in size. Through the mutual utilization of memory and bandwidth resources by multiple data sets, our data-management system is able to share and balance limited system resources among different data sets. We employ multi-resolution methods based on hierarchical octree and quadtree data structures to generate level-of-detail working sets of the data stored in main memory and graphics memory for rendering. The working set generation in our system is based on a common feedback mechanism with inherent support for translucent geometric and volumetric data sets. This feedback mechanism collects information about required levels of detail during the rendering process and is capable of directly resolving data visibility without the application of any costly occlusion culling approaches. A central goal of the proposed out-of-core data management system is an effective virtualization of large data sets. Through an abstraction of the level-of-detail working sets, our system allows developers to work with extremely large data sets independent of their complex internal data representations and physical memory layouts. Based on this out-of-core data virtualization infrastructure, we present distinct rendering approaches for specific visualization problems of large geo-scientific data sets. We demonstrate the application of our data virtualization system and show how multi-resolution data can be treated exactly the same way as regular data sets during the rendering process. An efficient volume ray casting system is presented for the rendering of multiple arbitrarily overlapping multi-resolution volume data sets. Binary space-partitioning volume decomposition of the bounding boxes of the cube-shaped volumes is used to identify the overlapping and non-overlapping volume regions in order to optimize the rendering process. We further propose a ray casting-based rendering system for the visualization of geological subsurface models consisting of multiple very detailed height fields. The rendering of an entire stack of height-field surfaces is accomplished in a single rendering pass using a two-level acceleration structure, which combines a minimum-maximum quadtree for empty-space skipping and sorted lists of depth intervals to restrict ray intersection searches to relevant height fields and depth ranges. Ultimately, we present a unified rendering system for the visualization of entire geological models consisting of highly detailed stacked horizon surfaces and massive volume data. We demonstrate a single-pass ray casting approach facilitating correct visual interaction between distinct translucent model components, while increasing the rendering efficiency by reducing processing overhead of potentially invisible parts of the model. The combination of image-order rendering approaches and the level-of-detail feedback mechanism used by our out-of-core data-management system inherently accounts for occlusions of different data types without the application of costly culling techniques. The unified out-of-core data-management and virtualization infrastructure considerably facilitates the implementation of complex visualization systems. We demonstrate its applicability for the visualization of large geo-scientific data sets using output-sensitive rendering techniques. As a result, the magnitude and multitude of data sets that can be interactively visualized is significantly increased compared to existing approaches