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Bounds on mean energy in the Kuramoto-Sivashinsky equation computed using semidefinite programming
- Publication Year :
- 2018
- Publisher :
- arXiv, 2018.
-
Abstract
- We present methods for bounding infinite-time averages in dynamical systems governed by nonlinear PDEs. The methods rely on auxiliary functionals, which are similar to Lyapunov functionals but satisfy different inequalities. The inequalities are enforced by requiring certain expressions to be sums of squares of polynomials, and the optimal choice of auxiliary functional is posed as a semidefinite program (SDP) that can be solved computationally. To formulate these SDPs we approximate the PDE by truncated systems of ODEs and proceed in one of two ways. The first approach is to compute bounds for the ODE systems, increasing the truncation order until bounds converge numerically. The second approach incorporates the ODE systems with analytical estimates on their deviation from the PDE, thereby using finite truncations to produce bounds for the full PDE. We apply both methods to the Kuramoto-Sivashinsky equation, where we compute upper bounds on the spatiotemporal average of energy by employing polynomial auxiliary functionals up to degree six. The first approach is used for most computations, but a subset of results are checked using the second approach, and the results agree to high precision. These bounds apply to all odd solutions of period $2\pi L$, where $L$ is varied. Sharp bounds are obtained for $L\le10$, and trends suggest that more expensive computations would yield sharp bounds at larger $L$ also. The bounds are known to be sharp (to within 0.1% numerical error) because they are saturated by the simplest nonzero steady states, which apparently have the largest mean energy among all odd solutions. Prior authors have conjectured that mean energy remains $O(1)$ for $L\gg1$ since no particular solutions with larger energy have been found. Our bounds constitute the first positive evidence for this conjecture, albeit up to finite $L$, and they offer some guidance for analytical proofs.<br />Comment: 32 pages, 6 figures; v2: revisions after review, new proposition added
- Subjects :
- INCOMPRESSIBLE FLOWS
Polynomial
Truncation
LAMINAR FLAMES
Mathematics, Applied
General Physics and Astronomy
Dynamical Systems (math.DS)
01 natural sciences
0102 Applied Mathematics
Mathematics - Dynamical Systems
Mathematical Physics
Mathematics
Semidefinite programming
Conjecture
Degree (graph theory)
Physics
Applied Mathematics
Physics - Fluid Dynamics
POLYNOMIAL OPTIMIZATION
Numerical Analysis (math.NA)
semidefinite programming
HYDRODYNAMIC INSTABILITY
Physics, Mathematical
010101 applied mathematics
VARIATIONAL BOUNDS
Physical Sciences
sum of squares polynomials
math.DS
math.NA
Dynamical systems theory
General Mathematics
FOS: Physical sciences
SQUARES
FOS: Mathematics
Applied mathematics
Mathematics - Numerical Analysis
bounds
0101 mathematics
DISSIPATION
Kuramoto-Sivashinsky equation
Science & Technology
STABILITY
010102 general mathematics
Ode
Fluid Dynamics (physics.flu-dyn)
SUMS
Statistical and Nonlinear Physics
NON-LINEAR ANALYSIS
Nonlinear system
auxiliary functional
physics.flu-dyn
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....36836d3f8de576c3fec5d28befa78642
- Full Text :
- https://doi.org/10.48550/arxiv.1802.08240