This paper evaluates the applicability of the hexahedral block structured grids for marine propeller performance predictions. Hydrodynamic characteristics for Potsdam Propeller Test Case (PPTC), namely thrust and torque coefficients, were determined using numerical simulations in two commercial solvers: Ansys Fluent and STAR-CCM+. Results were attained for hexahedral and tetrahedral hybrid grids equivalent in terms of cell count and quality, and compared to the experimental results. Furthermore, accuracy of Realizable k- $ϵ$ and SST k- $\omega$ turbulent models when analyzing marine propeller performance was investigated. Finally, performance characteristics were assessed in cavitating flow conditions for a single advance ratio using Zwart–Gerber–Belamri and Schnerr and Sauer models. The resulting cavitation pattern was compared to cavity extents and shape noted during measurements. The results suggest that hexa and hybrid grids, in certain range of advance ratios, do provide similar results; however, for low and high ratios, structured grids in conjunction with Realizable k- $ϵ$ model can achieve more accurate results.

In the present paper, a Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement. A great number of leak scenarios were simulated with Monte Carlo determined leak parameters (leak location and emitter coefficient). In order to account for demand variations that occur on a daily basis and to obtain a larger dataset, scenarios were simulated with random base demand increments or reductions for each network node. Classifier accuracy was assessed for different sensor layouts and numbers of sensors. Multiple prediction models were constructed for differently sized leakage and demand range variations in order to investigate model accuracy under various conditions. Results indicate that the prediction model provides the greatest accuracy for the largest leaks, with the smallest variation in base demand (62% accuracy for greater- and 82% for smaller-sized networks, for the largest considered leak size and a base demand variation of $\pm 2.5\%$). However, even for small leaks and the greatest base demand variations, the prediction model provided considerable accuracy, especially when localizing the sources of leaks when the true leak node and neighbor nodes were considered (for a smaller-sized network and a base demand of variation $\pm 20\%$ the model accuracy increased from 44% to 89% when top five nodes with greatest probability were considered, and for a greater-sized network with a base demand variation of $\pm 10\%$ the accuracy increased from 36% to 77%).

In this paper, a novel machine learning based algorithm for water supply pollution source identification is presented built specifically for high performance parallel systems. The algorithm utilizes the combination of Artificial Neural Networks for classification of the pollution source with Random Forests for regression analysis to determine significant variables of a contamination event such as start time, end time and contaminant chemical concentration. The algorithm is based on performing Monte Carlo water quality and hydraulic simulations in parallel, recording data with sensors placed within a water supply network and selecting a most probable pollution source based on a tournament style selection between suspect nodes in a network with mentioned machine learning methods. The novel algorithmic framework is tested on a small (92 nodes) and medium sized (865 nodes) water supply sensor network benchmarks with a set contamination event start time, end time and chemical concentration. Out of the 30 runs, the true source node was the finalist of the algorithm’s tournament style selection for 30/30 runs for the small network, and 29/30 runs for the medium sized network. For all the 30 runs on the small sensor network, the true contamination event scenario start time, end time and chemical concentration was set as 14:20, 20:20 and 813.7 mg/L, respectively. The root mean square errors for all 30 algorithm runs for the three variables were 48 min, 4.38 min and 18.06 mg/L. For the 29 successful medium sized network runs the start time was 06:50, end time 07:40 and chemical concentration of 837 mg/L and the root mean square errors were 6.06 min, 12.36 min and 299.84 mg/L. The algorithmic framework successfully narrows down the potential sources of contamination leading to a pollution source identification, start and ending time of the event and the contaminant chemical concentration.

Research background. The composition of honey is influenced by the botanical source and geographical area of the nectar from which it is derived. Unifloral honeys reach higher market value than multifloral honeys due to their specific aromas, which result from volatile and phenolic compounds. Experimental approach. The aim of our study is to characterize the phenolic composition of a rare unifloral variety of honey – mint (Mentha spp.) honey. For this purpose, we performed standard physicochemical analyses, pollen analysis, determined total phenolic and flavonoid content, analysed antioxidant activity and performed qualitative and quantitative analyses of phenolic compounds for five mint honeys. Results and conclusions. Our results indicate that mint honey samples have high phenolic content, expressed in gallic acid equivalents, from (76.7±0.6) to (90.1±1.1) mg/100 g, and flavonoid content, expressed as quercetin equivalents, from (6.7±0.6) to (12.5±0.8) mg/100 g. These honey samples also exhibit strong antioxidant activity, expressed as Trolox equivalents, from (33.6±2.8) to (51.3±1.2) mg/100 g and from (14.4±0.8) to (55.1±2.4) mg/100 g when analysed with DPPH and ABTS assays, respectively. Quantitative LC-MS/MS analysis revealed that the most abundant phenols in all samples were chrysin, apigenin and p-coumaric acid. Qualitative LC-MS/MS analysis identified the presence of kaempferide, diosmetin, acacetin and several caffeic acid derivatives. Novelty and scientific contribution. Our study indicates that mint honey contains unique phenolic profiles, which likely contribute to its distinctive aroma and strong antioxidant activity. A detailed description of the rare honey varieties gives beekeepers greater visibility and easier access to the demanding natural product market.

Conventional cavitation assessment methodology in industrial and scientific applications generally depends on cavitation models utilizing homogeneous mixture assumption. These models have been extensively assessed, modified and expanded to account for deficiencies of their predecessors. Unfortunately, none of the proposed models can be classified as the universal solution for all engineering applications, with usage mainly directed by experience or general availability of the models. In this study we propose a workflow through which the empirical constants governing the phase change of the Kunz mixture cavitation model can be calibrated for a given application or a series of problems, with machine learning as a tool for parameter estimation. The proposed approach was validated on a three-dimensional propeller test case with results in excellent agreement for the case in question. Results for thrust and torque were within 2% with cavity extents differing by up to 20%. This is a significant improvement when compared to previously proposed parameters. Despite the lack of generalization due to the limited nature of the dataset on which the model was trained, the proposed parameters entail acceptable results for similar cases as well. The overall methodology is applicable to other problems as well and should lead to more accurate cavitation predictions.