A three-dimensional numerical simulation of particle motion in a pipe with a rough bed is presented. The simulation based on the Lattice Boltzmann Method (LBM) employs the hybrid diffuse bounce-back approach to model moving boundaries. The bed of the pipe is formed by stationary spherical particles of the same size as the moving particles. Particle movements are induced by gravitational and hydrodynamic forces. To evaluate the hydrodynamic forces, the Momentum Exchange Algorithm is used. The LBM unified computational frame makes it possible to simulate both the particle motion and the fluid flow and to study mutual interactions of the carrier liquid flow and particles and the particle–bed and particle–particle collisions. The trajectories of simulated and experimental particles are compared. The
Particle Tracking method is used to track particle motion. The correctness of the applied approach is assessed.
The aim of this paper is to document Laudan’s rejection of the appeal to intuition in the context of his development of normative naturalism. At one point in the development of his methodological thinking, Laudan appealed to pre-analytic intuitions, which might be employed to identify episodes in the history of science against which theories of scientific methodology are to be tested. However, Laudan came to reject this appeal to intuitions, and rejected this entire approach to the evaluation of a theory of method. This is an important stage in the development of his normative naturalist meta-methodology.
We show that the learning of (uncertain) conditional and/or causal information may be modelled by (Jeffrey) imaging on Stalnaker conditionals. We adapt the method of learning uncertain conditional information proposed in Günther (2017) to a method of learning uncertain causal information. The idea behind the adaptation parallels Lewis (1973c)’s analysis of causal dependence. The combination of the methods provides a unified account of learning conditional and causal information that manages to clearly distinguish between conditional, causal and conjunctive information. Moreover, our framework seems to be the first general solution that generates the correct predictions for Douven (2012)’s benchmark examples and the Judy Benjamin Problem., Ukazujeme, že učení (neurčitých) podmíněných a / nebo kauzálních informací může být modelováno zobrazením (Jeffrey) na Stalnakerových podmínkách. Metodu učení nejistých podmíněných informací navrhovaných v Güntheru (2017) přizpůsobujeme metodě učení nejistých kauzálních informací. Myšlenka adaptačních paralel Lewisova (1973c) analýza kauzální závislosti. Kombinace metod poskytuje jednotný popis učení podmíněných a příčinných informací, které dokáží jasně rozlišit mezi podmíněnými, příčinnými a spojovacími informacemi. Náš rámec se navíc jeví jako první obecné řešení, které vytváří správné předpovědi pro příklady benchmarku Douven (2012) a problém Judy Benjaminové., and Mario Günther