On Modeling the Response of Synovial Fluid: Unsteady Flow of a Shear-Thinning, Chemically-Reacting Fluid Mixture

We study the flow of a shear-thinning, chemically-reacting fluid that could be used to model the flow of the synovial fluid. The actual geometry where the flow of the synovial fluid takes place is very complicated, and therefore the governing equations are not amenable to simple mathematical analysis. In order to understand the response of the model, we choose to study the flow in a simple geometry. While the flow domain is not a geometry relevant to the flow of the synovial fluid in the human body it yet provides a flow which can be used to assess the efficacy of different models that have been proposed to describe synovial fluids. We study the flow in the annular region between two cylinders, one of which is undergoing unsteady oscillations about their common axis, in order to understand the quintessential behavioral characteristics of the synovial fluid. We use the three models suggested by Hron et al. [ J. Hron, J. M\'{a}lek, P. Pust\v{e}jovsk\'{a}, K. R. Rajagopal, On concentration dependent shear-thinning behavior in modeling of synovial fluid flow, Adv. in Tribol. (In Press).] to study the problem, by appealing to a semi-inverse method. The assumed structure for the velocity field automatically satisfies the constraint of incompressibility, and the balance of linear momentum is solved together with a convection-diffusion equation. The results are compared to those associated with the Newtonian model. We also study the case in which an external pressure gradient is applied along the axis of the cylindrical annulus.Comment: 25 pages, 11 figures, accepted in Computers & Applications with Mathematic

Lagrangian Neural Style Transfer for Fluids

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.Comment: ACM Transaction on Graphics (SIGGRAPH 2020), additional materials: http://www.byungsoo.me/project/lnst/index.htm

Hybrid smoothed particle hydrodynamics

We present a new algorithm for enforcing incompressibility for Smoothed Particle Hydrodynamics (SPH) by preserving uniform density across the domain. We propose a hybrid method that uses a Poisson solve on a coarse grid to enforce a divergence free velocity field, followed by a local density correction of the particles. This avoids typical grid artifacts and maintains the Lagrangian nature of SPH by directly transferring pressures onto particles. Our method can be easily integrated with existing SPH techniques such as the incompressible PCISPH method as well as weakly compressible SPH by adding an additional force term. We show that this hybrid method accelerates convergence towards uniform density and permits a significantly larger time step compared to earlier approaches while producing similar results. We demonstrate our approach in a variety of scenarios with significant pressure gradients such as splashing liquids

Incompressibility Enforcement for Multiple-fluid SPH Using Deformation Gradient

To maintain incompressibility in SPH fluid simulations is important for visual plausibility. However, it remains an outstanding challenge to enforce incompressibility in such recent multiple-fluid simulators as the mixture-model SPH framework. To tackle this problem, we propose a novel incompressible SPH solver, where the compressibility of fluid is directly measured by the deformation gradient. By disconnecting the incompressibility of fluid from the conditions of constant density and divergence-free velocity, the new incompressible SPH solver is applicable to both single- and multiple-fluid simulations. The proposed algorithm can be readily integrated into existing incompressible SPH frameworks developed for single-fluid, and is fully parallelizable on GPU. Applied to multiple-fluid simulations, the new incompressible SPH scheme significantly improves the visual effects of the mixture-model simulation, and it also allows exploitation for artistic controlling

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification

Doctor of Philosophy

dissertationPhysical simulation has become an essential tool in computer animation. As the use of visual effects increases, the need for simulating real-world materials increases. In this dissertation, we consider three problems in physics-based animation: large-scale splashing liquids, elastoplastic material simulation, and dimensionality reduction techniques for fluid simulation. Fluid simulation has been one of the greatest successes of physics-based animation, generating hundreds of research papers and a great many special effects over the last fifteen years. However, the animation of large-scale, splashing liquids remains challenging. We show that a novel combination of unilateral incompressibility, mass-full FLIP, and blurred boundaries is extremely well-suited to the animation of large-scale, violent, splashing liquids. Materials that incorporate both plastic and elastic deformations, also referred to as elastioplastic materials, are frequently encountered in everyday life. Methods for animating such common real-world materials are useful for effects practitioners and have been successfully employed in films. We describe a point-based method for animating elastoplastic materials. Our primary contribution is a simple method for computing the deformation gradient for each particle in the simulation. Given the deformation gradient, we can apply arbitrary constitutive models and compute the resulting elastic forces. Our method has two primary advantages: we do not store or compare to an initial rest configuration and we work directly with the deformation gradient. The first advantage avoids poor numerical conditioning and the second naturally leads to a multiplicative model of deformation appropriate for finite deformations. One of the most significant drawbacks of physics-based animation is that ever-higher fidelity leads to an explosion in the number of degrees of freedom

EFFICIENT PARTICLE-BASED VISCOUS FLUID SIMULATION WITH VIDEO-GUIDED REAL-TO-VIRTUAL PARAMETER TRANSFER

Viscous fluids, such as honey and molten chocolate, are common materials frequently seen in our daily life. These viscous fluids exhibit characteristic behaviors. Capturing and understanding such dynamics have been required for various applications. Although recent research made advances in simulating the viscous fluid dynamics, still many challenges are left to be addressed. In this dissertation, I present novel techniques to more efficiently and accurately simulate viscous fluid dynamics and propose a parameter identification framework to facilitate the tedious parameter tuning steps for viscous materials. In fluid simulation, enforcing the incompressibility robustly and efficiently is essential. One known challenge is how to set appropriate boundary conditions for free surfaces and solid boundaries. I propose a new boundary handling approach for an incompressible particle-based solver based on the connectivity analysis for simulation particles. Another challenge is that previously proposed techniques do not scale well. To address this, I propose a new multilevel particle-based solver which constructs the hierarchy of simulation particles. These techniques improve the robustness and efficiency achieving the nearly linear scaling unlike previous approaches. To simulate characteristic behaviors of viscous fluids, such as coiling and buckling phenomena and adhesion to other materials, it is necessary to develop a specialized solver. I propose a stable and efficient particle-based solver for simulating highly viscous fluids by using implicit integration with the full form of viscosity. To simulate more accurate interactions with solid objects, I propose a new two-way fluid-solid coupling method for viscous fluids via the unified minimization. These approaches also improve the robustness and efficiency while generating rotational and sticky behaviors of viscous fluids. One important challenge for the physically-based simulation is that it is not obvious how to choose appropriate material parameters to generate our desirable behaviors of simulated materials. I propose a parameter identification framework that helps to tune material parameters for viscous fluids with example video data captured from real world fluid phenomena. This framework identifies viscosity parameters for the real viscous fluids while estimating the hidden variables for the fluids, and enables the parameter transfer from the real world to virtual environment.Doctor of Philosoph

Energy based dissolution simulation using smoothed particle hydrodynamic sampling

Fluid simulation plays an important role in Computer Graphics and has wide applications in film and games. The desire for an improved physically-based fluid simulation solver has grown hand in hand with the advances made in Computer Graphics. Interesting fluid behaviours emerge when solid objects are added to a simulation: when fluid and solid make contact, they do not only have a physical interaction (e.g., buoyancy), but also a chemical reaction (e.g., dissolution) under the right conditions. Dissolution is one of the most common natural phenomena which is an important visual effect in fluid simulation. However this phe- nomenon is difficult to simulate due to the complexity of the behaviour and there are only few techniques available. A novel unified particle-based method for approximating chemical dis- solution is introduced in this thesis which is fast, predictable and visually plausible. The dissolution algorithm is derived using chemical Collision Theory and integrated into a Smoothed Particle Hydrodynamics (SPH) framework. The Collision Theory of chemistry is used as an analogy to the dissolution process modelling. Dissolution occurs when solute submerges into solvent. Physical laws govern the local excitation of so- lute particles based on the relative motion with solvent particles. When the local excitation energy exceeds a user specified threshold (activation energy), the particle will be dislodged from the solid. Unlike previous methods, this dissolution model ensures that the dissolution result is in- dependent of solute sampling resolution. A mathematical relationship is also established between the activation energy, the interfacial surface area, and the total dissolution time — allowing for intuitive artistic con- trol over the global dissolution rate. Applications of this method are demonstrated using a number of practical examples, including antacid pills dissolving in water and hydraulic erosion of non-homogeneous ter- rains. Both solutes and solvents are represented by particles, and the dis- tribution of the solute particles greatly affects the plausibility of the dissolution simulation. An even but stochastic distribution of particles on both the surface and within the volume of the solute is essential for a good visual simulation of the dynamic process of dissolution. A new iterative particle-based sampling method derived from SPH is introduced in this thesis which can generate a range of blue noise pat- terns and is computationally efficient, controllable and has a variety of applications. This approach resolves many of the limitations of classic blue noise methods, such as the lack of controllability or varying the dis- tribution properties of the generated samples. Fast sampling is achieved in general dimensions for curves, surfaces and volumes. By varying a sin- gle parameter, the proposed method can generate a range of controllable blue noise samples with different distribution properties which are suit- able for various applications such as adaptive sampling and multi-class sampling. The SPH sampling approach is used for solute particle distribution which guarantees a predictable and smooth dissolution process thanks to the evenly distributed density and also gives the user control of the volume change during the phase transition. The proposed SPH sampling method achieves better visual effects compared with simple grid sampling and other blue noise sampling methods. Our energy based dissolution simulation with SPH sampled solute and solvent ensures that the dissolution behaviour is physically and chemi- cally plausible, while supporting features such as object separation and sharp feature rounding. The simulation is parallelized per particle on a GPU to enhance the performance