Adaptive Mesh Fluid Simulations on GPU

We describe an implementation of compressible inviscid fluid solvers with block-structured adaptive mesh refinement on Graphics Processing Units using NVIDIA's CUDA. We show that a class of high resolution shock capturing schemes can be mapped naturally on this architecture. Using the method of lines approach with the second order total variation diminishing Runge-Kutta time integration scheme, piecewise linear reconstruction, and a Harten-Lax-van Leer Riemann solver, we achieve an overall speedup of approximately 10 times faster execution on one graphics card as compared to a single core on the host computer. We attain this speedup in uniform grid runs as well as in problems with deep AMR hierarchies. Our framework can readily be applied to more general systems of conservation laws and extended to higher order shock capturing schemes. This is shown directly by an implementation of a magneto-hydrodynamic solver and comparing its performance to the pure hydrodynamic case. Finally, we also combined our CUDA parallel scheme with MPI to make the code run on GPU clusters. Close to ideal speedup is observed on up to four GPUs.Comment: Submitted to New Astronom

Real-time hybrid cutting with dynamic fluid visualization for virtual surgery

It is widely accepted that a reform in medical teaching must be made to meet today's high volume training requirements. Virtual simulation offers a potential method of providing such trainings and some current medical training simulations integrate haptic and visual feedback to enhance procedure learning. The purpose of this project is to explore the capability of Virtual Reality (VR) technology to develop a training simulator for surgical cutting and bleeding in a general surgery

A Deep Learning based Fast Signed Distance Map Generation

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency

Design and development of a VR system for exploration of medical data using haptic rendering and high quality visualization

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Computational fluid dynamics for propulsion technology: Geometric grid visualization in CFD-based propulsion technology research

The coordination is examined of necessary resources, facilities, and special personnel to provide technical integration activities in the area of computational fluid dynamics applied to propulsion technology. Involved is the coordination of CFD activities between government, industry, and universities. Current geometry modeling, grid generation, and graphical methods are established to use in the analysis of CFD design methodologies

LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields

Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the behavior of a system using a simplified kinematic representation. Typically, ROM is trained on input simulations created with a specific spatial discretization, and then serves to accelerate simulations with the same discretization. This discretization-dependence is restrictive. Becoming independent of a specific discretization would provide flexibility to mix and match mesh resolutions, connectivity, and type (tetrahedral, hexahedral) in training data; to accelerate simulations with novel discretizations unseen during training; and to accelerate adaptive simulations that temporally or parametrically change the discretization. We present a flexible, discretization-independent approach to reduced-order modeling. Like traditional ROM, we represent the configuration as a linear combination of displacement fields. Unlike traditional ROM, our displacement fields are continuous maps from every point on the reference domain to a corresponding displacement vector; these maps are represented as implicit neural fields. With linear continuous ROM (LiCROM), our training set can include multiple geometries undergoing multiple loading conditions, independent of their discretization. This opens the door to novel applications of reduced order modeling. We can now accelerate simulations that modify the geometry at runtime, for instance via cutting, hole punching, and even swapping the entire mesh. We can also accelerate simulations of geometries unseen during training. We demonstrate one-shot generalization, training on a single geometry and subsequently simulating various unseen geometries

Efficient algorithms for the realistic simulation of fluids

Nowadays there is great demand for realistic simulations in the computer graphics field. Physically-based animations are commonly used, and one of the more complex problems in this field is fluid simulation, more so if real-time applications are the goal. Videogames, in particular, resort to different techniques that, in order to represent fluids, just simulate the consequence and not the cause, using procedural or parametric methods and often discriminating the physical solution. This need motivates the present thesis, the interactive simulation of free-surface flows, usually liquids, which are the feature of interest in most common applications. Due to the complexity of fluid simulation, in order to achieve real-time framerates, we have resorted to use the high parallelism provided by actual consumer-level GPUs. The simulation algorithm, the Lattice Boltzmann Method, has been chosen accordingly due to its efficiency and the direct mapping to the hardware architecture because of its local operations. We have created two free-surface simulations in the GPU: one fully in 3D and another restricted only to the upper surface of a big bulk of fluid, limiting the simulation domain to 2D. We have extended the latter to track dry regions and is also coupled with obstacles in a geometry-independent fashion. As it is restricted to 2D, the simulation loses some features due to the impossibility of simulating vertical separation of the fluid. To account for this we have coupled the surface simulation to a generic particle system with breaking wave conditions; the simulations are totally independent and only the coupling binds the LBM with the chosen particle system. Furthermore, the visualization of both systems is also done in a realistic way within the interactive framerates; raycasting techniques are used to provide the expected light-related effects as refractions, reflections and caustics. Other techniques that improve the overall detail are also applied as low-level detail ripples and surface foam

Realistic simulation and animation of clouds using SkewT-LogP diagrams

Nuvens e clima são tópicos importantes em computação gráfica, nomeadamente na simulação e animação de fenómenos naturais. Tal deve-se ao facto de a simulação de fenómenos naturais−onde as nuvens estão incluídas−encontrar aplicações em filmes, jogos e simuladores de voo. Contudo, as técnicas existentes em computação gráfica apenas permitem representações de nuvens simplificadas, tornadas possíveis através de dinâmicas fictícias que imitam a realidade. O problema que este trabalho pretende abordar prende-se com a simulação de nuvens adequadas para utilização em ambientes virtuais, isto é, nuvens com dinâmica baseada em física que variam ao longo do tempo. Em meteorologia é comum usar técnicas de simulação de nuvens baseadas em leis da física, contudoossistemasatmosféricosdeprediçãonuméricasãocomputacionalmente pesados e normalmente possuem maior precisão numérica do que o necessário em computação gráfica. Neste campo, torna-se necessário direcionar e ajustar as características físicas ou contornar a realidade de modo a atingir os objetivos artísticos, sendo um fator fundamental que faz com que a computação gráfica se distinga das ciências físicas. Contudo, simulações puramente baseadas em física geram soluções de acordo com regras predefinidas e tornam-se notoriamente difíceis de controlar. De modo a enfrentar esses desafios desenvolvemos um novo método de simulação de nuvens baseado em física que possui a característica de ser computacionalmente leve e simula as propriedades dinâmicas relacionadas com a formação de nuvens. Este novo modelo evita resolver as equações físicas, ao apresentar uma solução explícita para essas equações através de diagramas termodinâmicos SkewT/LogP. O sistema incorpora dados reais de forma a simular os parâmetros necessários para a formação de nuvens. É especialmente adequado para a simulação de nuvens cumulus que se formam devido ao um processo convectivo. Esta abordagem permite não só reduzir os custos computacionais de métodos baseados em física, mas também fornece a possibilidade de controlar a forma e dinâmica de nuvens através do controlo dos níveis atmosféricos existentes no diagrama SkewT/LogP. Nestatese,abordámostambémumoutrodesafio,queestárelacionadocomasimulação de nuvens orográficas. Do nosso conhecimento, esta é a primeira tentativa de simular a formação deste tipo de nuvens. A novidade deste método reside no fato de este tipo de nuvens serem não convectivas, oque se traduz nocálculodeoutrosníveis atmosféricos. Além disso, atendendo a que este tipo de nuvens se forma sobre montanhas, é também apresentadoumalgoritmoparadeterminarainfluênciadamontanhasobreomovimento da nuvem. Em resumo, esta dissertação apresenta um conjunto de algoritmos para a modelação e simulação de nuvens cumulus e orográficas, recorrendo a diagramas termodinâmicos SkewT/LogP pela primeira vez no campo da computação gráfica.Clouds and weather are important topics in computer graphics, in particular in the simulation and animation of natural phenomena. This is so because simulation of natural phenomena−where clouds are included−find applications in movies, games and flight simulators. However, existing techniques in computer graphics only offer the simplified cloud representations, possibly with fake dynamics that mimic the reality. The problem that this work addresses is how to find realistic simulation of cloud formation and evolution, that are suitable for virtual environments, i.e., clouds with physically-based dynamics over time. It happens that techniques for cloud simulation are available within the area of meteorology, but numerical weather prediction systems based on physics laws are computationally expensive and provide more numerical accuracy than the required accuracy in computer graphics. In computer graphics, we often need to direct and adjust physical features, or even to bend the reality, to meet artistic goals, which is a key factor that makes computer graphics distinct from physical sciences. However, pure physically-based simulations evolve their solutions according to pre-set physics rules that are notoriously difficult to control. In order to face these challenges we have developed a new lightweight physically-based cloudsimulationschemethatsimulatesthedynamicpropertiesofcloudformation. This new model avoids solving the physically-based equations typically used to simulate the formation of clouds by explicitly solving these equations using SkewT/LogP thermodynamic diagrams. The system incorporates a weather model that uses real data to simulate parameters related to cloud formation. This is specially suitable to the simulation of cumulus clouds, which result from a convective process. This approach not only reduces the computational costs of previous physically-based methods, but also provides a technique to control the shape and dynamics of clouds by handling the cloud levels in SkewT/LogP diagrams. In this thesis, we have also tackled a new challenge, which is related to the simulation oforographic clouds. From ourknowledge, this isthefirstattempttosimulatethis type of cloud formation. The novelty in this method relates to the fact that these clouds are non-convective, so that different atmospheric levels have to be determined. Moreover, since orographic clouds form over mountains, we have also to determine the mountain influence in the cloud motion. In summary, this thesis presents a set of algorithms for the modelling and simulation of cumulus and orographic clouds, taking advantage of the SkewT/LogP diagrams for the first time in the field of computer graphics