arXiv subjects

Aytekin Çıbık

Publications and source records attributed to Aytekin Çıbık.

7 recordsLinked to original sources

arXiv2026

Numerical analysis of data assimilation for slightly compressible flow

Continuous data assimilation improves flow predictions by continually nudging a model toward available observational data. For slightly compressible flow, a recent model addresses the limitations of velocity-only nudging by assimilating both velocity and pressure data and nudging both quantities into the incompressible Navier-Stokes equations [5]; continuous-in-time error estimates and preliminary experiments show that this joint nudging is effective and substantially reduces the model error relative to velocity-only nudging. Motivated by these results, we carry out the numerical analysis of the model and its finite element discretizations. We establish stability and error estimates for the semi-discrete scheme and for the fully discrete, linearized backward Euler scheme. The analysis shows an infinite predictability horizon: the effect of the initial error decays exponentially in time, and the model error is first order in the observation resolution H and of order $μ_1^{-1/2}$ in the pressure nudging parameter $μ_1$. Balancing these two error terms, we choose $μ_1=\mathcal{O}(H^{-2})$, which yields the optimal convergence rate. Numerical experiments confirm the predicted rates.

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arXiv2026

Data assimilation for slightly compressible flow

Continuous data assimilation (CDA) nudges observational data into governing equations to recover the underlying flow and improve predictions. Existing rigorous CDA analyses focus primarily on incompressible flows, yet no physical flow is perfectly incompressible. Approximating a slightly compressible flow with an incompressible model introduces non-negligible model errors. Data assimilation for compressible flows remains challenging due to strong nonlinearities and the presence of shocks. We design an algorithm that addresses the limitations of velocity-only nudging for slightly compressible flow. This work incorporates both velocity and pressure data from the slightly compressible flow and nudges both quantities into the incompressible Navier--Stokes equations. Our analysis shows that the model error decays exponentially in the initial error, with an asymptotic residual of order $\mathcal{O}(H)$, where H denotes the observation resolution. The analysis also identifies a scaling for the pressure nudging parameter $μ_1 = O(1/H^2)$ that ensures effective assimilation. We validate the theoretical results through a suite of numerical experiments: a convergence study confirming optimal rates, a modified Taylor--Green vortex benchmark demonstrating synchronization of energy, enstrophy, and pressure, and an acoustic wave propagation test that isolates the role of pressure nudging and achieves a $97.9\%$ reduction in pressure error relative to velocity-only assimilation. Together, these results provide a foundation for discrete error estimates and realistic compressible applications.

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arXiv2025

Modular data assimilation for flow prediction

This report develops several modular, 2-step realizations (inspired by Kalman filter algorithms) of nudging-based data assimilation $$Step \ 1 \quad \frac{\widetilde {v}^{n+1}-v^{n}}{k}+v^{n}\cdot \nabla \widetilde {v}^{n+1}-ν\triangle \widetilde {v}^{n+1}+\nabla q^{n+1}=f(x)$$ $$\nabla \cdot \widetilde {v}^{n+1}=0$$ $$Step \ 2 \quad \frac{v^{n+1}-\widetilde {v}^{n+1}}{k}-χI_{H}(u(t^{n+1})-v^{n+1})=0.$$ Several variants of this algorithm are developed. Three main results are developed. The first is that if $I_{H}^{2}=I_{H}$, then Step 2 can be rewritten as the explicit step $$v^{n+1}=\widetilde {v}^{n+1}+\frac{kχ}{1+kχ}[I_{H}u(t^{n+1})-I_{H} \widetilde {v}^{n+1}].$$ This means Step 2 has the greater stability of an implicit update and the lesser complexity of an explicit analysis step. The second is that the basic result of nudging (that for $H$ small enough and $χ$ large enough predictability horizons are infinite) holds for one variant of the modular algorithm. The third is that, for any $H>0$ and any $χ>0$, one step of the modular algorithm decreases the next step's error and increases (an estimate of) predictability horizons. A method synthesizing assimilation with eddy viscosity models of turbulence is also presented. Numerical tests are given, confirming the effectiveness of the modular assimilation algorithm. The conclusion is that the modular, 2-step method overcomes many algorithmic inadequacies of standard nudging methods and retains a robust mathematical foundation.

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arXiv2024

Adaptive Parameter Selection in Nudging Based Data Assimilation

Data assimilation combines (imperfect) knowledge of a flow's physical laws with (noisy, time-lagged, and otherwise imperfect) observations to produce a more accurate prediction of flow statistics. Assimilation by nudging (from 1964), while non-optimal, is easy to implement and its analysis is clear and well-established. Nudging's uniform in time accuracy has even been established under conditions on the nudging parameter $χ$ and the density of observational locations, $H$, Larios, Rebholz, and Zerfas [1]. One remaining issue is that nudging requires the user to select a key parameter. The conditions required for this parameter, derived through á priori (worst case) analysis are severe (Section 2.1 herein) and far beyond those found to be effective in computational experience. One resolution, developed herein, is self-adaptive parameter selection. This report develops, analyzes, tests, and compares two methods of self-adaptation of nudging parameters. One combines analysis and response to local flow behavior. The other is based only on response to flow behavior. The comparison finds both are easily implemented and yield effective values of the nudging parameter much smaller than those of á priori analysis.

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arXiv2022

On the Analysis of the Second Order Time Filtered Backward Euler Method for the EMAC formulation of Navier-Stokes Equations

This paper considers the backward Euler based linear time filtering method for the EMAC formulation of the incompressible Navier-Stokes equations. The time filtering is added as a modular step to the standard backward Euler code leading to a 2-step, unconditionally stable, second order linear method. Despite its success in conserving important physical quantities when the divergence constraint is only weakly enforced, the EMAC formulation is unable to improve solutions of backward Euler discretized NSE. The combination of the time filtering with the backward Euler discretized EMAC formulation of NSE greatly increases numerical accuracy of solutions and still conserves energy, momentum and angular momentum as EMAC does. Several numerical experiments are provided that both verify the theoretical fidings and demonstrate superiority of the proposed method over the unfiltered case.

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arXiv2018

Long time stability of a linearly extrapolated blended BDF scheme for multiphysics flows

This paper investigates the long time stability behavior of multiphysics flow problems, namely the Navier-Stokes equations, natural convection and double-diffusive convection equations with an extrapolated blended BDF time-stepping scheme. This scheme combines the two-step BDF and three-step BDF time stepping schemes. We prove unconditional long time stability theorems for each of flow systems. Various numerical tests are given for large time step sizes in long time intervals in order to support theoretical results.

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arXiv2018

A Family of Second Order Time Stepping Methods for the Darcy-Brinkman Equations

This study presents an efficient, accurate, effective and unconditionally stable time stepping scheme for the Darcy-Brinkman equations in double-diffusive convection. The stabilization within the proposed method uses the idea of stabilizing the curvature for velocity, temperature and concentration equations. Accuracy in time is proven and the convergence results for the fully discrete solutions of problem variables are given. Several numerical examples including a convergence study are provided that support the derived theoretical results and demonstrate the efficiency and the accuracy of the method.

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