Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior node of the pipeline, it has been shown that observability is indeed satisfied in this case, which allows designing an observer for the unmeasurable state variables, i.e., the pressure head at leak position. Relying on the fact that the origin of the observation error is exponentially stable if all parameters (including the leak coefficients) are known and uniformly ultimately bounded otherwise, the authors propose a bank of observers as follows: taking into account that the physical pipeline parameters are well-known, and there is only uncertainty about leak coefficients (position and magnitude), a pair of such coefficients is taken from a search space and is assigned to an observer. Then, a Genetic Algorithm (GA) is exploited to minimize the integration of the square observation error. The minimum integral observation error will be reached in the observer where the estimated leak parameters match the real ones. Finally, some results are presented by using real-noisy databases coming from a test bed plant built at Cinvestav-Guadalajara, aiming to show the potentiality of this method.

Adaptive Uzawa algorithm for the Stokes equation

Based on the Uzawa algorithm, we consider an adaptive finite element method for the Stokes system. We prove linear convergence with optimal algebraic rates for the residual estimator (which is equivalent to the total error), if the arising linear systems are solved iteratively,e.g., by PCG. Our analysis avoids the use of discrete efficiency of the estimator. Unlike prior work, our adaptive Uzawa algorithm can thus avoid to discretize the given data and does not rely on an interior node property for the refinement.

Low-Reynolds Number Turbulence Models: An Approach for Reducing Mesh Sensitivity

This study presents a new near-wall treatment for low-Reynolds number (LRN) turbulence models that maintains accuracy in ‘coarse’ mesh predictions. The method is based on a thorough examination of approximations made when integrating the discretized equations in the near-wall region. A number of modifications are proposed that counteract errors introduced when an LRN-model is used on meshes for which the first interior node is located at y+≈5. Here the methodology is applied to the k−ω turbulence model by Bredberg et al., although similar corrections are relevant for all LRN models. The modified model gives asymptotically, in the sense of mesh refinement, identical results to the baseline model. For coarser meshes y+⩽10, the present method improves numerical stability with less mesh-dependency than the non-modified model. Results are included for fully developed channel flow, a backward-facing step flow and heat transfer in a periodic rib-roughened channel.