scholarly journals Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 913 ◽  
Author(s):  
A. Navarro ◽  
J. A. Delgado-Aguiñaga ◽  
J. D. Sánchez-Torres ◽  
O. Begovich ◽  
G. Besançon
Keyword(s):  
Heuristic Method ◽  
Pressure Head ◽  
Search Space ◽  
Observation Error ◽  
State Variables ◽  
Test Bed ◽  
Interior Node ◽  
Water Pipelines ◽  
Exponentially Stable ◽  
Integral Observation

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.

scholarly journals Two Simultaneous Leak Diagnosis in Pipelines Based on Input–Output Numerical Differentiation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8035
Author(s):  
Adrián Navarro-Díaz ◽  
Jorge-Alejandro Delgado-Aguiñaga ◽  
Ofelia Begovich ◽  
Gildas Besançon
Keyword(s):  
State Vector ◽  
Pressure Head ◽  
Numerical Differentiation ◽  
State Variables ◽  
Test Bed ◽  
Water Shortages ◽  
Third Order ◽  
Large Cities ◽  
Noise Rejection ◽  
Derivatives Of

This paper addresses the two simultaneous leak diagnosis problem in pipelines based on a state vector reconstruction as a strategy to improve water shortages in large cities by only considering the availability of the flow rate and pressure head measurements at both ends of the pipeline. The proposed algorithm considers the parameters of both leaks as new state variables with constant dynamics, which results in an extended state representation. By applying a suitable persistent input, an invertible mapping in x can be obtained as a function of the input and output, including their time derivatives of the third-order. The state vector can then be reconstructed by means of an algebraic-like observer through the computation of time derivatives using a Numerical Differentiation with Annihilatorsconsidering its inherent noise rejection properties. Experimental results showed that leak parameters were reconstructed with accuracy using a test bed plant built at Cinvestav Guadalajara.

scholarly journals Hydraulic and transport parameter assessment using column infiltration experiments

2016 ◽  
Author(s):  
A. Younes ◽  
T. A. Mara ◽  
M. Fahs ◽  
O. Grunenberger ◽  
Ph. Ackerer
Keyword(s):  
Pressure Head ◽  
Solute Concentration ◽  
Richards Equation ◽  
Transport Parameter ◽  
State Variables ◽  
Transport Parameters ◽  
Uncertainty Range ◽  
Infiltration Experiment ◽  
Statistical Calibration

Abstract. In the present work, we study the quality of the statistical calibration of hydraulic and transport soil properties using an infiltration experiment in which, over a given period, tracer-contaminated water is injected into a laboratory column filled with a homogeneous soil. The numerical model is based on the Richards' equation for solving water flow and the advection-dispersion equation for solving solute transport. Several state variables (e.g., water content, solute concentration, pressure head) are measured during the experiment. Statistical calibration of the computer model is then carried out for different data sets and injection scenarios with the DREAM(ZS) Markov Chain Monte Carlo sampler. The results show that the injection period has a significant effect on the quality of the estimation, in particular, the posterior uncertainty range. The hydraulic and transport parameters of the investigated soil can be estimated from the infiltration experiment using the concentration and cumulative outflow, which are measured non-intrusively. A significant improvement of the identifiability of the parameters is observed when the pressure data from measurements taken inside the column are also considered in the inversion.

scholarly journals A Computationally Improved Heuristic Algorithm for Transmission Switching Using Line Flow Thresholds for Load Shed Reduction

2021 ◽  
Author(s):  
Tanveer Hussain ◽  
S M Shafiul Alam ◽  
Timothy M. Hansen ◽  
Siddharth Suryanarayanan
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Heuristic Method ◽  
Search Space ◽  
Load Shedding ◽  
Mesh Topology ◽  
Transmission Switching ◽  
Computational Performance ◽  
Topology Changes ◽  
Line Flow

A computationally improved algorithm to find the best transmission switching (TS) candidate for load shed reduction after (<i>N</i>-2) contingencies is presented. TS is a planned line outage and research from the past shows that changing transmission system's mesh topology changes the power flows and removes post contingency violations (PCVs). One of the major challenges is to find the best TS candidate in a suitable time. Here, the best TS candidate is determined by using a novel heuristic method by decreasing the search space based on proximity to load shedding bus (LSB). The proposed method is capable of finding the best TS candidate faster than the well-known existing algorithm in the literature and guarantees removal of PCVs. Moreover, proposed algorithm is compatible with both AC and DC optimal power flow (OPF) formulations. Finally, the proposed method is implemented by modifying the topology of the transmission system after (<i>N</i>-2) contingencies in the IEEE 39-bus, IEEE 118-bus, and Polish 2383-bus test systems. Two metrics are used to compare results from the proposed method with those from state-of-the-art to show the speedup and accuracy achieved. Parallel computing is used to increase the computational performance of the proposed algorithm.

scholarly journals On the Selection of Localization Radius in Ensemble Filtering for Multiscale Quasigeostrophic Dynamics

2018 ◽  
Vol 146 (2) ◽  
pp. 543-560 ◽  
Author(s):  
Yue Ying ◽  
Fuqing Zhang ◽  
Jeffrey L. Anderson
Keyword(s):  
Correlation Length ◽  
Large Scale ◽  
Observation Error ◽  
State Variables ◽  
Model Resolution ◽  
Ensemble Size ◽  
Sampling Errors ◽  
Localization Radius ◽  
Spectral Variance ◽  
Best Localization

Covariance localization remedies sampling errors due to limited ensemble size in ensemble data assimilation. Previous studies suggest that the optimal localization radius depends on ensemble size, observation density and accuracy, as well as the correlation length scale determined by model dynamics. A comprehensive localization theory for multiscale dynamical systems with varying observation density remains an active area of research. Using a two-layer quasigeostrophic (QG) model, this study systematically evaluates the sensitivity of the best Gaspari–Cohn localization radius to changes in model resolution, ensemble size, and observing networks. Numerical experiment results show that the best localization radius is smaller for smaller-scale components of a QG flow, indicating its scale dependency. The best localization radius is rather insensitive to changes in model resolution, as long as the key dynamical processes are reasonably well represented by the low-resolution model with inflation methods that account for representation errors. As ensemble size decreases, the best localization radius shifts to smaller values. However, for nonlocal correlations between an observation and state variables that peak at a certain distance, decreasing localization radii further within this distance does not reduce analysis errors. Increasing the density of an observing network has two effects that both reduce the best localization radius. First, the reduced observation error spectral variance further constrains prior ensembles at large scales. Less large-scale contribution results in a shorter overall correlation length, which favors a smaller localization radius. Second, a denser network provides more independent pieces of information, thus a smaller localization radius still allows the same number of observations to constrain each state variable.

scholarly journals Uncertainty reduction and parameters estimation of a~distributed hydrological model with ground and remote sensing data

2014 ◽  
Vol 11 (6) ◽  
pp. 6215-6271
Author(s):  
F. Silvestro ◽  
S. Gabellani ◽  
R. Rudari ◽  
F. Delogu ◽  
P. Laiolo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
Parameters Estimation ◽  
State Variables ◽  
Test Bed ◽  
Sensing Data

Abstract. During the last decade the opportunity and usefulness of using remote sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite based products often provide the advantage of observing hydrologic variables in a distributed way while offering a different view that can help to understand and model the hydrological cycle. Moreover, remote sensing data are fundamental in scarce data environments. The use of satellite derived DTM, which are globally available (e.g. from SRTM as used in this work), have become standard practice in hydrologic model implementation, but other types of satellite derived data are still underutilized. In this work, Meteosat Second Generation Land Surface Temperature (LST) estimates and Surface Soil Moisture (SSM) available from EUMETSAT H-SAF are used to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. This work aims at proving that satellite observations dramatically reduce uncertainties in parameters calibration by reducing their equifinality. Two parameter estimation strategies are implemented and tested: a multi-objective approach that includes ground observations and one solely based on remotely sensed data. Two Italian catchments are used as the test bed to verify the model capability in reproducing long-term (multi-year) simulations.

Analysis of a Vertical-Axis Spherical Turbine for Energy Harvesting in Urban Water Supply Systems

2019 ◽  
Author(s):  
Adriana S. Valencia ◽  
Hugo Jativa Cervantes ◽  
Eduardo Castillo ◽  
Oguier A. Garavitto ◽  
Guillermo E. Soriano ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Energy Demand ◽  
Internal Flow ◽  
Pressure Head ◽  
Vertical Axis ◽  
Water Supply Systems ◽  
Urban Water ◽  
Element Analysis ◽  
Electrical Grid ◽  
Water Pipelines

Abstract Fast-growing cities are a challenge for its current energy demand, especially in developing countries. Replacement of micro-turbines instead of dropping pressure valves in urban-water pipelines may assist in supplying energy to the electrical grid. The understanding of turbine design and its operational characteristics can help for efficient energy harvesting in these cities. The aim of this work is to design a cheap and versatile hydrokinetic vertical axis spherical turbine for extracting energy from water pipelines of 800 mm in diameter. The turbine runner is based on a NACA0018 airfoil. Performance prediction is obtained by implementing a double multiple stream tube (DMST) based model. Computational fluid dynamics (CFD) and finite element analysis are used for performance and design improvements. Based on the analysis, the turbine can generate an output power of approximately 1.71 kW with a dropping pressure head of 0.4 m and an internal flow velocity of 2.07 m/s with an efficiency of approximately 42.7%. The proposed method allows determining the available energy of 390 kW in the city of Guayaquil, Ecuador.

scholarly journals A Symmetry-Breaking Node Equivalence for Pruning the Search Space in Backtracking Algorithms

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1300
Author(s):  
Uroš Čibej ◽  
Luka Fürst ◽  
Jurij Mihelič
Keyword(s):  
Symmetry Breaking ◽  
Optimization Problem ◽  
Heuristic Method ◽  
Search Space ◽  
Search Tree ◽  
Backtracking Search ◽  
Complete Problems ◽  
Np Complete ◽  
Complexity Hierarchy ◽  
General Graphs

We introduce a new equivalence on graphs, defined by its symmetry-breaking capability. We first present a framework for various backtracking search algorithms, in which the equivalence is used to prune the search tree. Subsequently, we define the equivalence and an optimization problem with the goal of finding an equivalence partition with the highest pruning potential. We also position the optimization problem into the computational-complexity hierarchy. In particular, we show that the verifier lies between P and NP -complete problems. Striving for a practical usability of the approach, we devise a heuristic method for general graphs and optimal algorithms for trees and cycles.

scholarly journals Scheduling-Based Optimization for Motion Coordination of Autonomous Vehicles at Multilane Intersections

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Mehmet Ali Guney ◽  
Ioannis A. Raptis
Keyword(s):  
Autonomous Vehicles ◽  
Heuristic Method ◽  
Control Policy ◽  
Search Space ◽  
Motion Coordination ◽  
Coordination Problem ◽  
One Dimensional ◽  
Traffic Manager ◽  
And Performance ◽  
Complete Account

This paper considers the motion coordination problem of autonomous vehicles in an intersection of a traffic network. The featured challenge is the design of an intersection traffic manager, in the form of a supervisory control algorithm, that regulates the motion of the autonomous vehicles in the intersection. We cast the multivehicle coordination task as an optimization problem, with a one-dimensional search-space. A model- and optimization-based heuristic method is employed to compute the control policy that results in the collision-free motion of the vehicles at the intersection and, at the same time, minimizes their delay. Our approach depends on a computation framework that makes the need for complex analytical derivations obsolete. A complete account of the computational complexity of the algorithm, parameterized by the configuration parameters of the problem, is provided. Extensive numerical simulations validate the applicability and performance of the proposed autonomous intersection traffic manager.

Sensor Synthesis for Control of Manufacturing Processes

1992 ◽  
Vol 114 (2) ◽  
pp. 158-174 ◽  
Author(s):  
G. Chryssolouris ◽  
M. Domroese ◽  
P. Beaulieu
Keyword(s):  
The State ◽  
Process Models ◽  
State Variables ◽  
Test Bed ◽  
Control Approach ◽  
State Variable ◽  
Sensor Signals ◽  
Multiple Sensor ◽  
Sensor Information ◽  
Basic Approaches

When a human controls a manufacturing process he or she uses multiple senses to monitor the process. Similarly, one can consider a control approach where measurements of process variables are performed by several sensing devices which in turn feed their signals into process models. Each of these models contains mathematical expressions based on the physics of the process which relate the sensor signals to process state variables. The information provided by the process models should be synthesized in order to determine the best estimates for the state variables. In this paper two basic approaches to the synthesis of multiple sensor information are considered and compared. The first approach is to synthesize the state variable estimates determined by the different sensors and corresponding process models through a mechanism based on training such as a neural network. The second approach utilizes statistical criteria to estimate the best synthesized state variable estimate from the state variable estimates provided by the process models. As a “test bed” for studying the effectiveness of the above sensor synthesis approaches turning has been considered. The approaches are evaluated and compared for providing estimates of the state variable tool wear based on multiple sensor information. The robustness of each scheme with respect to noisy and inaccurate sensor information is investigated.

scholarly journals Smartphone Pressure Collection and Bias Correction Using Machine Learning

2018 ◽  
Vol 35 (3) ◽  
pp. 523-540 ◽  
Author(s):  
Conor McNicholas ◽  
Clifford F. Mass
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Bias Correction ◽  
Control Strategies ◽  
Weather Prediction ◽  
Sensor Data ◽  
Observation Error ◽  
Test Bed ◽  
Machine Learning Approach ◽  
Complex Relationships

AbstractOver half a billion smartphones worldwide are now capable of measuring atmospheric pressure, providing a pressure network of unprecedented density and coverage. This paper describes novel approaches for the collection, quality control, and bias correction of such smartphone pressures. An Android app was developed and distributed to several thousand users, serving as a test bed for onboard pressure collection and quality-control strategies. New methods of pressure collection were evaluated, with a focus on reducing and quantifying sources of observation error and uncertainty. Using a machine learning approach, complex relationships between pressure bias and ancillary sensor data were used to predict and correct future pressure biases over a 4-week period from 10 November to 5 December 2016. This approach, in combination with simple quality-control checks, produced an 82% reduction in the average smartphone pressure bias, substantially improving the quality of smartphone pressures and facilitating their use in numerical weather prediction.

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