20,241 research outputs found
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
The present article presents a summarizing view at differential-algebraic
equations (DAEs) and analyzes how new application fields and corresponding
mathematical models lead to innovations both in theory and in numerical
analysis for this problem class. Recent numerical methods for nonsmooth
dynamical systems subject to unilateral contact and friction illustrate the
topicality of this development.Comment: Preprint of Book Chapte
A Numerical Slow Manifold Approach to Model Reduction for Optimal Control of Multiple Time Scale ODE
Time scale separation is a natural property of many control systems that can
be ex- ploited, theoretically and numerically. We present a numerical scheme to
solve optimal control problems with considerable time scale separation that is
based on a model reduction approach that does not need the system to be
explicitly stated in singularly perturbed form. We present examples that
highlight the advantages and disadvantages of the method
Constraint-consistent Runge-Kutta methods for one-dimensional incompressible multiphase flow
New time integration methods are proposed for simulating incompressible
multiphase flow in pipelines described by the one-dimensional two-fluid model.
The methodology is based on 'half-explicit' Runge-Kutta methods, being explicit
for the mass and momentum equations and implicit for the volume constraint.
These half-explicit methods are constraint-consistent, i.e., they satisfy the
hidden constraints of the two-fluid model, namely the volumetric flow
(incompressibility) constraint and the Poisson equation for the pressure. A
novel analysis shows that these hidden constraints are present in the
continuous, semi-discrete, and fully discrete equations.
Next to constraint-consistency, the new methods are conservative: the
original mass and momentum equations are solved, and the proper shock
conditions are satisfied; efficient: the implicit constraint is rewritten into
a pressure Poisson equation, and the time step for the explicit part is
restricted by a CFL condition based on the convective wave speeds; and
accurate: achieving high order temporal accuracy for all solution components
(masses, velocities, and pressure). High-order accuracy is obtained by
constructing a new third order Runge-Kutta method that satisfies the additional
order conditions arising from the presence of the constraint in combination
with time-dependent boundary conditions.
Two test cases (Kelvin-Helmholtz instabilities in a pipeline and liquid
sloshing in a cylindrical tank) show that for time-independent boundary
conditions the half-explicit formulation with a classic fourth-order
Runge-Kutta method accurately integrates the two-fluid model equations in time
while preserving all constraints. A third test case (ramp-up of gas production
in a multiphase pipeline) shows that our new third order method is preferred
for cases featuring time-dependent boundary conditions
A geometric method for model reduction of biochemical networks with polynomial rate functions
Model reduction of biochemical networks relies on the knowledge of slow and
fast variables. We provide a geometric method, based on the Newton polytope, to
identify slow variables of a biochemical network with polynomial rate
functions. The gist of the method is the notion of tropical equilibration that
provides approximate descriptions of slow invariant manifolds. Compared to
extant numerical algorithms such as the intrinsic low dimensional manifold
method, our approach is symbolic and utilizes orders of magnitude instead of
precise values of the model parameters. Application of this method to a large
collection of biochemical network models supports the idea that the number of
dynamical variables in minimal models of cell physiology can be small, in spite
of the large number of molecular regulatory actors
Fixed Boundary Flows
We consider the fixed boundary flow with canonical interpretability as
principal components extended on the non-linear Riemannian manifolds. We aim to
find a flow with fixed starting and ending point for multivariate datasets
lying on an embedded non-linear Riemannian manifold, differing from the
principal flow that starts from the center of the data cloud. Both points are
given in advance, using the intrinsic metric on the manifolds. From the
perspective of geometry, the fixed boundary flow is defined as an optimal curve
that moves in the data cloud. At any point on the flow, it maximizes the inner
product of the vector field, which is calculated locally, and the tangent
vector of the flow. We call the new flow the fixed boundary flow. The rigorous
definition is given by means of an Euler-Lagrange problem, and its solution is
reduced to that of a Differential Algebraic Equation (DAE). A high level
algorithm is created to numerically compute the fixed boundary. We show that
the fixed boundary flow yields a concatenate of three segments, one of which
coincides with the usual principal flow when the manifold is reduced to the
Euclidean space. We illustrate how the fixed boundary flow can be used and
interpreted, and its application in real data
GPU Accelerated Explicit Time Integration Methods for Electro-Quasistatic Fields
Electro-quasistatic field problems involving nonlinear materials are commonly
discretized in space using finite elements. In this paper, it is proposed to
solve the resulting system of ordinary differential equations by an explicit
Runge-Kutta-Chebyshev time-integration scheme. This mitigates the need for
Newton-Raphson iterations, as they are necessary within fully implicit time
integration schemes. However, the electro-quasistatic system of ordinary
differential equations has a Laplace-type mass matrix such that parts of the
explicit time-integration scheme remain implicit. An iterative solver with
constant preconditioner is shown to efficiently solve the resulting multiple
right-hand side problem. This approach allows an efficient parallel
implementation on a system featuring multiple graphic processing units.Comment: 4 pages, 5 figure
A tensor approximation method based on ideal minimal residual formulations for the solution of high-dimensional problems
In this paper, we propose a method for the approximation of the solution of
high-dimensional weakly coercive problems formulated in tensor spaces using
low-rank approximation formats. The method can be seen as a perturbation of a
minimal residual method with residual norm corresponding to the error in a
specified solution norm. We introduce and analyze an iterative algorithm that
is able to provide a controlled approximation of the optimal approximation of
the solution in a given low-rank subset, without any a priori information on
this solution. We also introduce a weak greedy algorithm which uses this
perturbed minimal residual method for the computation of successive greedy
corrections in small tensor subsets. We prove its convergence under some
conditions on the parameters of the algorithm. The residual norm can be
designed such that the resulting low-rank approximations are quasi-optimal with
respect to particular norms of interest, thus yielding to goal-oriented order
reduction strategies for the approximation of high-dimensional problems. The
proposed numerical method is applied to the solution of a stochastic partial
differential equation which is discretized using standard Galerkin methods in
tensor product spaces
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