Real-Time Simulation and Prognosis of Smoke Propagation in Compartments Using a GPU

The evaluation of life safety in buildings in case of fire is often based on smoke spread calculations. However, recent simulation models – in general, based on computational fluid dynamics – often require long execution times or high-performance computers to achieve simulation results in or faster than real-time. Therefore, the objective of this study is the development of a concept for the real-time and prognosis simulation of smoke propagation in compartments using a graphics processing unit (GPU). The developed concept is summarized in an expandable open source software basis, called JuROr (Jülich's Real-time simulation within ORPHEUS). JuROr simulates buoyancy-driven, turbulent smoke spread based on a reduced modeling approach using finite differences and a Large Eddy Simulation turbulence model to solve the incompressible Navier-Stokes and energy equations. This reduced model is fully adapted to match the target hardware of highly parallel computer architectures. Thereby, the code is written in the object-oriented programming language C++ and the pragma-based programming model OpenACC. This model ensures to maintain a single source code, which can be executed in serial and parallel on various architectures. Further, the study provides a proof of JuROr's concept to balance sufficient accuracy and practicality. First, the code was successfully verified using unit and (semi-) analytical tests. Then, the underlying model was validated by comparing the numerical results to the experimental results of scenarios relevant for fire protection. Thereby, verification and validation showed acceptable accuracy for JuROr's application. Lastly, the performance criteria of JuROr – being real-time and prognosis capable with comparable performance across various architectures – was successfully evaluated. Here, JuROr also showed high speedup results on a GPU and faster time-to-solution compared to the established Fire Dynamics Simulator. These results show JuROr's practicality.Die Bewertung der Personensicherheit bei Feuer in Gebäuden basiert häufig auf Berechnungen zur Rauchausbreitung. Bisherige Simulationsmodelle – im Allgemeinen basierend auf numerischer Strömungsdynamik – erfordern jedoch lange Ausführungszeiten oder Hochleistungsrechner, um Simulationsergebnisse in und schneller als Echtzeit liefern zu können. Daher ist das Ziel dieser Arbeit die Entwicklung eines Konzeptes für die Echtzeit- und Prognosesimulation der Rauchausbreitung in Gebäuden mit Hilfe eines Grafikprozessors (GPU). Zusammengefasst ist das entwickelte Konzept in einer erweiterbaren Open-Source-Software, genannt JuROr (Jülich's Real-time Simulation in ORPHEUS). JuROr simuliert die Ausbreitung von auftriebsgetriebenem, turbulentem Rauch basierend auf einem reduzierten Modellierungsansatz mit finiten Differenzen und einem Large Eddy Simulation Turbulenzmodell, um inkompressible Navier- Stokes und Energiegleichungen zu lösen. Das reduzierte Modell ist voll- ständig angepasst an hochparallele Computerarchitekturen. Dabei ist der Code implementiert mit C++ und OpenACC. Dies hat den Vorteil mit nur einem Quellcode verschiedenste serielle und parallele Ausführungen des Programms für unterschiedliche Architekturen erstellen zu können. Die Studie liefert weiterhin einen Konzeptnachweis dafür, ausreichende Genauigkeit und Praktikabilität im Gleichgewicht zu halten. Zunächst wurde der Code erfolgreich mit Modul- und (semi-) analytischen Tests verifiziert. Dann wurde das zugrundeliegende Modell durch einen Vergleich der numerischen mit den experimentellen Ergebnissen für den Brandschutz relevanter Szenarien validiert. Die Verifizierung und Validierung zeigten dabei ausreichende Genauigkeit für JuROr. Zuletzt, wurden die Kriterien von JuROr – echtzeit- und prognosefähig zu sein mit vergleichbarer Leistung auf unterschiedlichsten Architekturen – erfolgreich geprüft. Zudem zeigte JuROr hohe Beschleunigungseffekte auf einer GPU und schnellere Lösungszeiten im Vergleich zum etablierten Fire Dynamics Simulator. Diese Ergebnisse zeigen JuROr's Praktikabilität

Real-time Building Airflow Simulation Aided by GPU and FFD

Two recent methods for the fast simulation of the building airflow are studied: the fast fluid dynamics (FFD) algorithm and the use of graphic processing unit (GPU) for scientific computing in building engineering. A GOOGLE SketchUp plug-in for the FFD program was also developed as a model-creating tool to enhance the accessibility of the operation and to extend the range of users. The new methods are verified to be much faster than conventional computational fluid dynamics (CFD) models and they can achieve real-time simulations. This thesis focuses on the applications of the FFD program to illustrate its functions and abilities. The application fields include but not limited to fast building airflow analysis, architectural design and urban planning associated with airflows. Although the results are not as accurate as the conventional CFD, it is designed for the needs of fast simulations and analysis with less requirement of accuracy. With further improvements in the future, the developed FFD program in this study can become an important tool to bring the engineering analysis of building simulation into the early stage of the architectural designs

Algorithms for massively parallel generic hp-adaptive finite element methods

Efficient algorithms for the numerical solution of partial differential equations are required to solve problems on an economically viable timescale. In general, this is achieved by adapting the resolution of the discretization to the investigated problem, as well as exploiting hardware specifications. For the latter category, parallelization plays a major role for modern multi-core and multi-node architectures, especially in the context of high-performance computing. Using finite element methods, solutions are approximated by discretizing the function space of the problem with piecewise polynomials. With hp-adaptive methods, the polynomial degrees of these basis functions may vary on locally refined meshes. We present algorithms and data structures required for generic hp-adaptive finite element software applicable for both continuous and discontinuous Galerkin methods on distributed memory systems. Both function space and mesh may be adapted dynamically during the solution process. We cover details concerning the unique enumeration of degrees of freedom with continuous Galerkin methods, the communication of variable size data, and load balancing. Furthermore, we present strategies to determine the type of adaptation based on error estimation and prediction as well as smoothness estimation via the decay rate of coefficients of Fourier and Legendre series expansions. Both refinement and coarsening are considered. A reference implementation in the open-source library deal.II is provided and applied to the Laplace problem on a domain with a reentrant corner which invokes a singularity. With this example, we demonstrate the benefits of the hp-adaptive methods in terms of error convergence and show that our algorithm scales up to 49,152 MPI processes.Für die numerische Lösung partieller Differentialgleichungen sind effiziente Algorithmen erforderlich, um Probleme auf einer wirtschaftlich tragbaren Zeitskala zu lösen. Im Allgemeinen ist dies durch die Anpassung der Diskretisierungsauflösung an das untersuchte Problem sowie durch die Ausnutzung der Hardwarespezifikationen möglich. Für die letztere Kategorie spielt die Parallelisierung eine große Rolle für moderne Mehrkern- und Mehrknotenarchitekturen, insbesondere im Kontext des Hochleistungsrechnens. Mit Hilfe von Finite-Elemente-Methoden werden Lösungen durch Diskretisierung des assoziierten Funktionsraums mit stückweisen Polynomen approximiert. Bei hp-adaptiven Verfahren können die Polynomgrade dieser Basisfunktionen auf lokal verfeinerten Gittern variieren. In dieser Dissertation werden Algorithmen und Datenstrukturen vorgestellt, die für generische hp-adaptive Finite-Elemente-Software benötigt werden und sowohl für kontinuierliche als auch diskontinuierliche Galerkin-Verfahren auf Systemen mit verteiltem Speicher anwendbar sind. Sowohl der Funktionsraum als auch das Gitter können während des Lösungsprozesses dynamisch angepasst werden. Im Besonderen erläutert werden die eindeutige Nummerierung von Freiheitsgraden mit kontinuierlichen Galerkin-Verfahren, die Kommunikation von Daten variabler Größe und die Lastenverteilung. Außerdem werden Strategien zur Bestimmung des Adaptierungstyps auf der Grundlage von Fehlerschätzungen und -prognosen sowie Glattheitsschätzungen vorgestellt, die über die Zerfallsrate von Koeffizienten aus Reihenentwicklungen nach Fourier und Legendre bestimmt werden. Dabei werden sowohl Verfeinerung als auch Vergröberung berücksichtigt. Eine Referenzimplementierung erfolgt in der Open-Source-Bibliothek deal.II und wird auf das Laplace-Problem auf einem Gebiet mit einer einschneidenden Ecke angewandt, die eine Singularität aufweist. Anhand dieses Beispiels werden die Vorteile der hp-adaptiven Methoden hinsichtlich der Fehlerkonvergenz und die Skalierbarkeit der präsentierten Algorithmen auf bis zu 49.152 MPI-Prozessen demonstriert

Aeronautical engineering: A continuing bibliography with indexes (supplement 260)

This bibliography lists 405 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Efficient Motion Planning for Deformable Objects with High Degrees of Freedom

Many robotics and graphics applications need to be able to plan motions by interacting with complex environmental objects, including solids, sands, plants, and fluids. A key aspect of these deformable objects is that they have high-DOF, which implies that they can move or change shapes in many independent ways subject to physics-based constraints. In these applications, users also impose high-level goals on the movements of high-DOF objects, and planning algorithms need to model their motions and determine the optimal control actions to satisfy the high-level goals. In this thesis, we propose several planning algorithms for high-DOF objects. Our algorithms can improve the scalability considerably and can plan motions for different types of objects, including elastically deformable objects, free-surface flows, and Eulerian fluids. We show that the salient deformations of elastically deformable objects lie in a low-dimensional nonlinear space, i.e., the RS space. By embedding the configuration space in the RS subspace, our optimization-based motion planning algorithm can achieve over two orders of magnitude speedup over prior optimization-based formulations. For free surface flows such as liquids, we utilize features of the planning problems and machine learning techniques to identify low-dimensional latent spaces to accelerate the motion planning computation. For Eulerian fluids without free surfaces, we present a scalable planning algorithm based on novel numerical techniques. We show that the numerical discretization scheme exhibits strong regularity, which allows us to accelerate optimization-based motion planning algorithms using a hierarchical data structure and we can achieve 3-10 times speedup over gradient-based optimization techniques. Finally, for high-DOF objects with many frictional contacts with the environment, we present a contact dynamic model that can handle contacts without expensive combinatorial optimization. We illustrate the benefits of our high-DOF planning algorithms for three applications. First, we can plan contact-rich motion trajectories for general elastically deformable robots. Second, we can achieve real-time performance in terms of planning the motion of a robot arm to transfer the liquids between containers. Finally, our method enables a more intuitive user interface. We allow animation editors to modify animations using an offline motion planner to generate controlled fluid animations.Doctor of Philosoph

Laplacian Projection Based Global Physical Prior Smoke Reconstruction

We present a novel framework for reconstructing fluid dynamics in real-life scenarios. Our approach leverages sparse view images and incorporates physical priors across long series of frames, resulting in reconstructed fluids with enhanced physical consistency. Unlike previous methods, we utilize a differentiable fluid simulator (DFS) and a differentiable renderer (DR) to exploit global physical priors, reducing reconstruction errors without the need for manual regularization coefficients. We introduce divergence-free Laplacian eigenfunctions (div-free LE) as velocity bases, improving computational efficiency and memory usage. By employing gradient-related strategies, we achieve better convergence and superior results. Extensive experiments demonstrate the effectiveness of our method, showcasing improved reconstruction quality and computational efficiency compared to existing approaches. We validate our approach using both synthetic and real data, highlighting its practical potential

Interactive Simulation of Fluid Flow

The simulation of fluid flow on rectangular grids using a discretized version of the Navier Stokes Equations for incompressible fluid flow can be simultaneously described as an aesthetically pleasing and computationally intensive embarrassingly parallel problem. Ideally, the aesthetics of the fluid simulation should, given some set of parameters, feel natural despite the synthetic nature of the underlying grids. This natural feel, paramount to the success of the system, should fool a person into believing that they are interacting with a real fluid. The number of calculations and data accesses increases with the number of cells present in the rectangular grid upon which the fluid is simulated. An increased number of calculations are required for augmented accuracy, different external forces, and additional dimensions. Since it is a trivial task to increase the complexity of the simulation, interactivity becomes a challenge of balancing accuracy, stability, and detail against speed of execution. A simple solution is to throw more processing power through increased instruction execution speeds or additional cores. Throwing additional cores at the problem strains the memory bus making it the point that slows down the simulation. Therefore for a given algorithm, respecting data locality and processor peculiarities can be used to minimize execution times. This document introduces a means of caching corrected velocity fields, a task scheduler that attempts to maximize the usage of the cache on multi-core processors, and a na\"ive compression algorithm based on run-length encoding

Ein Gas-Kinetic Scheme Ansatz zur Modellierung und Simulation von Feuer auf massiv paralleler Hardware

This work presents a simulation approach based on a Gas Kinetic Scheme (GKS) for the simulation of fire that is implemented on massively parallel hardware in terms of Graphics Processing Units (GPU) in the framework of General Purpose computing on Graphics Processing Units (GPGPU). Gas kinetic schemes belong to the class of kinetic methods because their governing equation is the mesoscopic Boltzmann equation, rather than the macroscopic Navier-Stokes equations. Formally, kinetic methods have the advantage of a linear advection term which simplifies discretization. GKS inherently contains the full energy equation which is required for compressible flows. GKS provides a flux formulation derived from kinetic theory and is usually implemented as a finite volume method on cell-centered grids. In this work, we consider an implementation on nested Cartesian grids. To that end, a coupling algorithm for uniform grids with varying resolution was developed and is presented in this work. The limitation to local uniform Cartesian grids allows an efficient implementation on GPUs, which belong to the class of many core processors, i.e. massively parallel hardware. Multi-GPU support is also implemented and efficiency is enhanced by communication hiding. The fluid solver is validated for several two- and three-dimensional test cases including natural convection, turbulent natural convection and turbulent decay. It is subsequently applied to a study of boundary layer stability of natural convection in a cavity with differentially heated walls and large temperature differences. The fluid solver is further augmented by a simple combustion model for non-premixed flames. It is validated by comparison to experimental data for two different fire plumes. The results are further compared to the industry standard for fire simulation, i.e. the Fire Dynamics Simulator (FDS). While the accuracy of GKS appears slightly reduced as compared to FDS, a substantial speedup in terms of time to solution is found. Finally, GKS is applied to the simulation of a compartment fire. This work shows that the GKS has a large potential for efficient high performance fire simulations.Diese Arbeit präsentiert einen Simulationsansatz basierend auf einer gaskinetischen Methode (eng. Gas Kinetic Scheme, GKS) zur Simulation von Bränden, welcher für massiv parallel Hardware im Sinne von Grafikprozessoren (eng. Graphics Processing Units, GPUs) implementiert wurde. GKS gehört zur Klasse der kinetischen Methoden, die nicht die makroskopischen Navier-Stokes Gleichungen, sondern die mesoskopische Boltzmann Gleichung lösen. Formal haben kinetische Methoden den Vorteil, dass der Advektionsterms linear ist. Dies vereinfacht die Diskretisierung. In GKS ist die vollständige Energiegleichung, die zur Lösung kompressibler Strömungen benötigt wird, enthalten. GKS formuliert den Fluss von Erhaltungsgrößen basierend auf der gaskinetischen Theorie und wird meistens im Rahmen der Finiten Volumen Methode umgesetzt. In dieser Arbeit betrachten wir eine Implementierung auf gleichmäßigen Kartesischen Gittern. Dazu wurde ein Kopplungsalgorithmus für die Kombination von Gittern unterschiedlicher Auflösung entwickelt. Die Einschränkung auf lokal gleichmäßige Gitter erlaubt eine effiziente Implementierung auf GPUs, welche zur Klasse der massiv parallelen Hardware gehören. Des Weiteren umfasst die Implementierung eine Unterstützung für Multi-GPU mit versteckter Kommunikation. Der Strömungslöser ist für zwei und dreidimensionale Testfälle validiert. Dabei reichen die Tests von natürlicher Konvektion über turbulente Konvektion bis hin zu turbulentem Zerfall. Anschließend wird der Löser genutzt um die Grenzschichtstabilität in natürlicher Konvektion bei großen Temperaturunterschieden zu untersuchen. Darüber hinaus umfasst der Löser ein einfaches Verbrennungsmodell für Diffusionsflammen. Dieses wird durch Vergleich mit experimentellen Feuern validiert. Außerdem werden die Ergebnisse mit dem gängigen Brandsimulationsprogramm FDS (eng. Fire Dynamics Simulator) verglichen. Die Qualität der Ergebnisse ist dabei vergleichbar, allerdings ist der in dieser Arbeit entwickelte Löser deutlich schneller. Anschließend wird das GKS noch für die Simulation eines Raumbrandes angewendet. Diese Arbeit zeigt, dass GKS ein großes Potential für die Hochleistungssimulation von Feuer hat

General Purpose Programming on Modern Graphics Hardware

I start with a brief introduction to the graphics processing unit (GPU) as well as general-purpose computation on modern graphics hardware (GPGPU). Next, I explore the motivations for GPGPU programming, and the capabilities of modern GPUs (including advantages and disadvantages). Also, I give the background required for further exploring GPU programming, including the terminology used and the resources available. Finally, I include a comprehensive survey of previous and current GPGPU work, and end with a look at the future of GPU programming