A crucial step in any computational fluid dynamics (CFD) analysis is the discretization of the domain because it influences truncation errors, numerical stability, and the convergence of the model. Therefore, the appropriate selection of numerical mesh parameters crucially contributes to the reliability of the obtained results. Therefore, an innovative approach to reducing the mesh-induced error in CFD analysis of an impinging jet using fuzzy logic is proposed within the paper. The flow parameters were obtained using the Reynolds-averaged Navier–Stokes calculations, based on the mesh parameters obtained using the grid convergence index and fuzzy logic, were compared to each other and to experimental research results. The fuzzy logic approach to define mesh parameters turned out to be a very promising method as it allowed us to obtain results that are qualitatively and quantitatively comparable to commonly used but far more time-consuming methods.
Background: Engagement of mass during a strike in martial arts and its relation to generated force is one of the factor, deciding on the success of an athlete. The aim of this study was to calculate the quantitative portion of effective mass of a athlete who execute striking techniques, by registering a force of strike and time of its contact with a sensor (target). Material and methods: Black belt taekwon-do (International Taekwon-do Federation) master (age 32 years, body mass of 60 kg, height of 160 cm) performed three types of techniques for three times; roundhouse kick, front kick, side kick and straight punch. His target was a shield mounted on force plate MC 12-2K with amplifier GEN5. Acceleration data was obtained by mounting wireless IMU sensor manufactured by Noraxon attached to a lateral side of a foot. Results: The highest force was registered for side kick (2406.9 ± 299.8 N), and the lowest for front kick (2008.6 ± 284.8 N). The shortest time of contact with a target had roundhouse kick (0.026 ± 0.010 s), while the longest front kick (0.119 ± 0.052 s). The highest effective mass was achieved by front kick (44%). The highest effect of force coefficient was obtained by roundhouse kick. Other techniques with much lower values seems to be push-like movements. Conclusions: During strike, a crucial factor for its effectiveness lies in its destructive power. It does not only depend on generated force and engaged mass, but also on contact duration. Proposed quantitative indicators could be beneficial during preparation of an athlete to sport competition. Correctly calculated effective mass allows to measure force in a training environment.
Using the classical Kedem–Katchalsky’ membrane transport theory, a mathematical model was developed and the original concentration volume flux (Jv), solute flux (Js) characteristics, and S-entropy production by Jv, ( ( ψ S ) J v ) and by Js ( ( ψ S ) J s ) in a double-membrane system were simulated. In this system, M1 and Mr membranes separated the l, m, and r compartments containing homogeneous solutions of one non-electrolytic substance. The compartment m consists of the infinitesimal layer of solution and its volume fulfills the condition Vm → 0. The volume of compartments l and r fulfills the condition Vl = Vr → ∞. At the initial moment, the concentrations of the solution in the cell satisfy the condition Cl < Cm < Cr. Based on this model, for fixed values of transport parameters of membranes (i.e., the reflection (σl, σr), hydraulic permeability (Lpl, Lpr), and solute permeability (ωl, ωr) coefficients), the original dependencies Cm = f(Cl − Cr), Jv = f(Cl − Cr), Js = f(Cl − Cr), ( Ψ S ) J v = f(Cl − Cr), ( Ψ S ) J s = f(Cl − Cr), Rv = f(Cl − Cr), and Rs = f(Cl − Cr) were calculated. Each of the obtained features was specially arranged as a pair of parabola, hyperbola, or other complex curves.
Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.