Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network.

In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations.

Assessment of head loss coefficients for water turbine intake trash-racks by numerical modeling.

In this work, numerical simulations of fluid flow around trash-rack for different bar cross sections are conducted to investigate cross section influence on head losses. Comparison with experimental data is conducted to validate the usage of numerical simulations which enable investigation of great number of trash-rack configurations. In previous experimental studies researchers mostly focused on trash-rack parameters (bar spacing, bar length, inclinations etc.) where bar cross section was mainly rectangular or streamlined shape. Therefore, 2D simulations for different cross sections are carried out for a range of trash-rack configurations in order to provide better insight how it affects energy losses. It is shown that head loss reduction due to change in cross section is greatly dependent on trash-rack configuration, therefore optimization of simplified real water turbine trash-rack is also conducted to produce the cross section that generates smallest head losses for given configuration.

Comparison of apical irrigant solution extrusion among conventional and laser-activated endodontic irrigation.

The aim of this study was to determine the amount of extruded endodontic irrigant among needle-syringe irrigation (NSI) and laser-activated irrigation (LAI) regimens. Twenty extracted maxillary central incisors were prepared utilizing GT professional rotary files (size 40, taper 0.06). Irrigation was performed with two 27 G irrigation needles (notched open ended (ON) and single side vented (SV)) each at two different irrigant volumetric flow rates (VFR)-0.05 ml/s (3 ml/min) and 0.10 ml/s (6 ml/min). LAI was performed with Er:YAG (erbium-doped yttrium aluminum garnet) using different fiber types (X-Pulse-14/400 cylindrical tip, Preciso- 14/300 flat cylindrical tip, PIPS- 14/400 quartz tapered tip). The Er:YAG laser with a wavelength of 2940 nm (Lightwalker AT, Fotona, Ljubljana, Slovenia) was used according to the following protocol: 10 mJ per pulse, 15 Hz, pulse duration 50 µs. Irrigation time was 60 s for all protocols. Precision syringe pump (PSP) maintained constant irrigant volumetric flow rate. Apically extruded irrigant was collected and net weighed for each protocol (N = 10). Data were analyzed by t tests and Kruskal-Wallis. All LAI regimens had statistically significant lower irrigant extrusion compared with NSI except for the SV 27 G needle used with 0.05 ml/s VFR when compared with the Preciso fiber tip (p = 0,230). The largest amount of extruded irrigant was with the ON 27 G needle at the 0.10 ml/s VFR, while the smallest was after LAI with PIPS fiber tip. The lower quantity of apically extruded irrigant during LAI (X-Pulse and PIPS) points out a safer endodontic irrigation method compared with conventional irrigations.

Irrigation of human prepared root canal--ex vivo based computational fluid dynamics analysis.

AIM: To analyze the influence of the needle type, insertion depth, and irrigant flow rate on irrigant flow pattern, flow velocity, and apical pressure by ex-vivo based endodontic irrigation computational fluid dynamics (CFD) analysis. METHODS: Human upper canine root canal was prepared using rotary files. Contrast fluid was introduced in the root canal and scanned by computed tomography (CT) providing a three-dimensional object that was exported to the computer-assisted design (CAD) software. Two probe points were established in the apical portion of the root canal model for flow velocity and pressure measurement. Three different CAD models of 27G irrigation needles (closed-end side-vented, notched open-end, and bevel open-end) were created and placed at 25, 50, 75, and 95% of the working length (WL). Flow rates of 0.05, 0.1, 0.2, 0.3, and 0.4 mL/s were simulated. A total of 60 irrigation simulations were performed by CFD fluid flow solver. RESULTS: Closed-end side-vented needle required insertion depth closer to WL, regarding efficient irrigant replacement, compared to open-end irrigation needle types, which besides increased velocity produced increased irrigant apical pressure. For all irrigation needle types and needle insertion depths, the increase of flow rate was followed by an increased irrigant apical pressure. CONCLUSIONS: The human root canal shape obtained by CT is applicable in the CFD analysis of endodontic irrigation. All the analyzed values -irrigant flow pattern, velocity, and pressure - were influenced by irrigation needle type, as well as needle insertion depth and irrigant flow rate.