407 research outputs found
Adaptive time-integration for goal-oriented and coupled problems
We consider efficient methods for the partitioned time-integration of multiphysics problems, which commonly exhibit a multiscale behavior, requiring independent time-grids. Examples are fluid structure interaction in e.g., the simulation of blood-flow or cooling of rocket engines, or ocean-atmosphere-vegetation interaction. The ideal method for solving these problems allows independent and adaptive time-grids, higher order time-discretizations, is fast and robust, and allows the coupling of existing subsolvers, executed in parallel. We consider Waveform relaxation (WR) methods, which can have all of these properties. WR methods iterate on continuous-in-time interface functions, obtained via suitable interpolation. The difficulty is to find suitable convergence acceleration, which is required for the iteration converge quickly. We develop a fast and highly robust, second order in time, adaptive WR method for unsteady thermal fluid structure interaction (FSI), modelled by heterogeneous coupled linear heat equations. We use a Dirichlet-Neumann coupling at the interface and an analytical optimal relaxation parameter derived for the fully-discrete scheme. While this method is sequential, it is notably faster and more robust than similar parallel methods.We further develop a novel, parallel WR method, using asynchronous communication techniques during time-integration to accelerate convergence. Instead of exchanging interpolated time-dependent functions at the end of each time-window or iteration, we exchange time-point data immediately after each timestep. The analytical description and convergence results of this method generalize existing WR theory.Since WR methods allow coupling of problems in a relative black-box manner, we developed adapters to PDE-subsolvers implemented using DUNE and FEniCS. We demonstrate this coupling in a thermal FSI test case.Lastly, we consider adaptive time-integration for goal-oriented problems, where one is interested in a quantity of interest (QoI), which is a functional of the solution. The state-of-the-art method is the dual-weighted residual (DWR) method, which is extremely costly in both computation and implementation. We develop a goal oriented adaptive method based on local error estimates, which is considerably cheaper in computation. We prove convergence of the error in the QoI for tolerance to zero under a controllability assumption. By analyzing global error propagation with respect to the QoI, we can identify possible issues and make performance predictions. Numerical results verify these results and show our method to be more efficient than the DWR method
A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing
This work introduces an innovative parallel, fully-distributed finite element
framework for growing geometries and its application to metal additive
manufacturing. It is well-known that virtual part design and qualification in
additive manufacturing requires highly-accurate multiscale and multiphysics
analyses. Only high performance computing tools are able to handle such
complexity in time frames compatible with time-to-market. However, efficiency,
without loss of accuracy, has rarely held the centre stage in the numerical
community. Here, in contrast, the framework is designed to adequately exploit
the resources of high-end distributed-memory machines. It is grounded on three
building blocks: (1) Hierarchical adaptive mesh refinement with octree-based
meshes; (2) a parallel strategy to model the growth of the geometry; (3)
state-of-the-art parallel iterative linear solvers. Computational experiments
consider the heat transfer analysis at the part scale of the printing process
by powder-bed technologies. After verification against a 3D benchmark, a
strong-scaling analysis assesses performance and identifies major sources of
parallel overhead. A third numerical example examines the efficiency and
robustness of (2) in a curved 3D shape. Unprecedented parallelism and
scalability were achieved in this work. Hence, this framework contributes to
take on higher complexity and/or accuracy, not only of part-scale simulations
of metal or polymer additive manufacturing, but also in welding, sedimentation,
atherosclerosis, or any other physical problem where the physical domain of
interest grows in time
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Integrated Heart - Coupling multiscale and multiphysics models for the simulation of the cardiac function
Mathematical modelling of the human heart and its function can expand our understanding of various cardiac
diseases, which remain the most common cause of death in the developed world. Like other physiological
systems, the heart can be understood as a complex multiscale system involving interacting phenomena at the
molecular, cellular, tissue, and organ levels. This article addresses the numerical modelling of many aspects
of heart function, including the interaction of the cardiac electrophysiology system with contractile muscle
tissue, the sub-cellular activation-contraction mechanisms, as well as the hemodynamics inside the heart
chambers. Resolution of each of these sub-systems requires separate mathematical analysis and specially
developed numerical algorithms, which we review in detail. By using specific sub-systems as examples, we
also look at systemic stability, and explain for example how physiological concepts such as microscopic force
generation in cardiac muscle cells, translate to coupled systems of differential equations, and how their stability
properties influence the choice of numerical coupling algorithms. Several numerical examples illustrate
three fundamental challenges of developing multiphysics and multiscale numerical models for simulating
heart function, namely: (i) the correct upscaling from single-cell models to the entire cardiac muscle, (ii) the
proper coupling of electrophysiology and tissue mechanics to simulate electromechanical feedback, and (iii)
the stable simulation of ventricular hemodynamics during rapid valve opening and closure
Methods and Implementation of Fluid-Structure Interaction Modeling into an Industry-Accepted Design Tool
Fluid-structure interaction (FSI) modeling is a method by which fluid and solid domains are coupled together to produce a single result that cannot be produced if each physical domain was evaluated individually. The work presented in this dissertation is a demonstration of the methods and implementation of FSI modeling into an industry-appropriate design tool. Through utilizing computationally inexpensive equipment and commercially available software, the studies presented in this work demonstrate the ability for FSI modeling to become a tool used broadly in industry.
To demonstrate this capability, the cases studied purposely include substantial complexity to demonstrate the stability techniques required for modeling the inherent instabilities of FSI models that contain three-dimensional geometries, nonlinear materials, thin-walled geometries, steep gradients, and transient behavior. The work also modeled scenarios that predict system failure and optimal design to extend service lifetime, thereby expanding upon current FSI literature. Four independent studies were performed, evaluating three separate modes of failure in FSI models, to demonstrate that FSI modeling is a viable design tool for widespread industry use.
The first study validates FSI modeling techniques by comparing the results of a thin-walled FSI geometry model under hydrostatic forces with existing experimental data.
The second study explored a parametric study that evaluated the factors influencing an FSI model containing a highly complex thermal-fluid fatigue model. This model involved dynamically changing temperature loads resulting in significant thermal expansion that led to material yielding and dynamic fatigue life.
The third study evaluated a thermal-fluid conjugate heat transfer problem. The model was tuned, validated, and optimized for lifetime, and the validation of the system was performed using experimental data.
The final study modeled the highly complex fluid and solid phenomena involved in a peristaltic pump where the goal was to demonstrate that the lifetime performance of the tubing could be altered by changing the geometry, material properties, and operating temperature. The model in this final study combined all the methods and techniques from the three earlier studies and applied them to a thin-walled tube geometry with nonlinear and temperature-dependent material properties to create large solid deformation and fluid motion
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