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- isothermal Euler equations (2)
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We consider optimization problems with a joint probabilistic constraint under
normally distributed uncertain parameters. The parametric constraints
are replaced by one constraint stating that the probability of being feasible
shall exceed or be equal to a prescribed threshold. In order to apply the
concept to gas network optimization under uncertain boundary flows, which
corresponds to the demand of customers, we derive an analytic gradient formula.
The integral corresponding to the probability can be parameterized by
spherical radial decomposition. For this parameterization gradient formulas
are known under convexity assumptions of the parametric constraints in the
parameter. For the application in gas networks that we have in mind, the
convexity assumption of the parametric constraints is not satisfied. Therefore,
we weaken it to convexity of the region of feasible parameters for a fixed
optimization variable. We proceed to show that the assumptions needed for
the gradient formula are met in the gas network optimization problem on
a tree. For the numerical implementation we propose a multilevel sampling
algorithm that uses a coarse approximation of the chance constraint to generate
a warm start for the expensive approximation with fine sampling. The
numerical results illustrate that this approach significantly reduces the computation
time.
For the management of gas transportation networks, it is essential to know how the stationary states of the system are determined by the boundary data. The isothermal Euler equations are an accurate pde-model for the gas flow through each pipe. A compressibility factor is used to model the nonlinear relationship between density and pressure that occurs in real gas in contrast to ideal gas. The gas flow through the nodes is governed by algebraic node conditions that require the conservation of mass and the continuity of the pressure. We examine networks that are described by arbitrary finite graphs and show that for suitably chosen boundary data, subsonic stationary states exist and are uniquely determined by the boundary data. Our construction of the stationary states is based upon explicit representations of the stationary states on each single pipe that can easily be evaluated numerically. We also use the monotonicity properties of these states as functions of the boundary data.
Finite Time Blow-up of Traveling Wave Solutions for the Flow of Real Gas through Pipeline Networks
(2016)
In the context of gas transportation, analytical solutions are essential
for the understanding of the underlying dynamics described
by a system of partial differential equations. We derive traveling wave
solutions for the 1-d isothermal Euler equations. A non-constant compressibility
factor is used to describe the correlation between density
and pressure. The blow-up of the traveling wave solution in finite time
is proven. We then extend our analysis to networks under appropriate
coupling conditions and derive compatibility conditions to fulfill these
coupling conditions.
We propose a decomposition based method for solving mixed-integer nonlinear optimization problems with “black-box” nonlinearities, where the latter, e.g., may arise due to differential equations or expensive simulation runs. The method alternatingly solves a mixed-integer linear master problem and a separation problem for iteratively refining the mixed-integer linear relaxation of the nonlinear equalities. The latter yield nonconvex feasible sets for the optimization model but we have to restrict ourselves to convex and monotone constraint functions. Under these assumptions, we prove that our algorithm finitely terminates with an approximate feasible global optimal solution of the mixed integer nonlinear problem. Additionally, we show the applicability of our approach for three applications from optimal control with integer variables, from the field of pressurized flows in pipes with elastic walls, and from steady-state gas transport. For the latter we also present promising numerical results of our method applied to real-world instances that particularly show the effectiveness of our method for problems defined on networks.
We study the transient optimization of gas transport networks including both discrete controls due to switching of controllable elements and nonlinear fluid dynamics described by the system of isothermal Euler equations, which are partial differential equations in time and 1-dimensional space. This combination leads to mixed-integer optimization problems subject to nonlinear hyperbolic partial differential equations on a graph. We propose an instantaneous control approach in which suitable Euler discretizations yield systems of ordinary differential equations on a graph. This networked system of ordinary differential equations is shown to be well-posed and affine-linear solutions of these systems are derived analytically. As a consequence, finite-dimensional mixed-integer linear optimization problems are obtained for every time step that can be solved to global optimality using general-purpose solvers. We illustrate our approach in practice by presenting numerical results on a realistic gas transport network.
Joint model of probabilistic/robust (probust) constraints applied to gas network optimization
(2017)
Optimization tasks under uncertain conditions abound in many
real-life applications. Whereas solution approaches for probabilistic constraints
are often developed in case the uncertainties can be assumed to follow a
certain probability distribution, robust approaches are usually used in case
solutions are sought that are feasible for all realizations of uncertainties within
some pre-defined uncertainty set. As many applications contain different types
of uncertainties that require robust as well as probabilistic treatments, we deal with a class of joint probabilistic/robust constraints as its appears in
optimization problems under uncertainty. Focusing on complex uncertain gas
network optimization problems, we show the relevance of this class of problems
for the task of maximizing free booked capacities in an algebraic model for a
stationary gas network. We furthermore present approaches for their solution.
Finally, we study the problem of controlling a transient system that is governed
by the wave equation. The task consists in determining controls such that a
certain robustness measure remains below some given upper bound, with high
probability.