Nonstationary Axisymmetric Elastic Processes in an Incompressible Medium with Preliminary Finite Irreversible Deformations

2020 ◽  
Vol 23 (5) ◽  
pp. 390-401
Author(s):  
Yu. N. Kulchin ◽  
V. E. Ragozina ◽  
O. V. Dudko
Author(s):  
J. Silcox

In this introductory paper, my primary concern will be in identifying and outlining the various types of inelastic processes resulting from the interaction of electrons with matter. Elastic processes are understood reasonably well at the present experimental level and can be regarded as giving information on spatial arrangements. We need not consider them here. Inelastic processes do contain information of considerable value which reflect the electronic and chemical structure of the sample. In combination with the spatial resolution of the electron microscope, a unique probe of materials is finally emerging (Hillier 1943, Watanabe 1955, Castaing and Henri 1962, Crewe 1966, Wittry, Ferrier and Cosslett 1969, Isaacson and Johnson 1975, Egerton, Rossouw and Whelan 1976, Kokubo and Iwatsuki 1976, Colliex, Cosslett, Leapman and Trebbia 1977). We first review some scattering terminology by way of background and to identify some of the more interesting and significant features of energy loss electrons and then go on to discuss examples of studies of the type of phenomena encountered. Finally we will comment on some of the experimental factors encountered.


1972 ◽  
Vol 5 (11) ◽  
pp. 2864-2868
Author(s):  
Paul Langacker

NeuroSci ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 372-382
Author(s):  
Alfredo Pereira

I discuss some concepts advanced for the understanding of the complex dynamics of brain functions, and relate them to approaches in affective, cognitive and action neurosciences. These functions involve neuro-glial interactions in a dynamic system that receives sensory signals from the outside of the central nervous system, processes information in frequency, amplitude and phase-modulated electrochemical waves, and control muscles and glands to generate behavioral patterns. The astrocyte network is in charge of controlling global electrochemical homeostasis, and Hodgkin–Huxley dynamics drive the bioelectric homeostasis of single neurons. In elastic processes, perturbations cause instability, but the system returns to the basal equilibrium. In allostatic processes, perturbations elicit a response from the system, reacting to the deviation and driving the system to stable states far from the homeostatic equilibrium. When the system does not return to a fixed point or region of the state space, the process is called homeorhetic, and may present two types of evolution: (a) In flexible processes, there are previously existing “attractor” stable states that may be achieved after the perturbation, depending on context; (b) In plastic processes, the homeostatic set point(s) is(are) changed; the system is in a process of adaptation, in which the allostatic forces do not drive it back to the previous set point, but project to the new one. In the temporal phase from the deviant state to the recovery of stability, the system generates sensations that indicate if the recovery is successful (pleasure-like sensations) or if there is a failure (pain-like sensations).


The solution for a Volterra dislocation, with edge and screw components, is given for an incompressible medium with a power law hardening or softening stress-strain law. The form of the stress, strain and displacement fields is identified with angular variations satisfying nonlinear integral equations. Results are presented for various values of the hardening (or softening) parameter.


Author(s):  
Sergey Timushev ◽  
Alexandr Gamarnik ◽  
Anton Tsipenko

The noise of domestic machines including lawnmowers be comes an urgent issue. As the technology matures, designers need better tools to predict performance and efficiency of these machines across a wide range of operating conditions and find optimal ways to reduce noise. Computational fluid dynamics is an increasingly powerful tool which enables designer to better understand all features of unsteady flow in these machines and to find optimal designs providing higher energetic characteristics, better cutting quality and lower pressure pulsation, vibration and noise. Cutting quality linked with evacuation of grass is a key lawnmower characteristic. Due to this fact application of two-phase (air-grass) lawnmower flow model is inevitable in a prediction procedure. The modeling procedure comprises determination of lawnmower average aerodynamic characteristics and CFD-CAA analysis by acoustic-vortex method to predict sound power data. This method is based on splitting the equations of compressible fluid dynamics into two modes — vortex and acoustic Computational approach applied for the vortex mode flow is a “moving body”-technique: The problem is solved in the absolute frame of coordinates and computational grid changes during the blade passing. Computations can be made in 4 stages: 1) Computation of the incompressible medium with getting average values of energetic parameters; 2) Computation of the incompressible medium for definition the source function of inhomogeneous acoustic-vortex wave equation; 3) Solution of the acoustic-vortex wave equation; 4) Computation of 2-phase flow. In the 3rd stage the pressure pulsation field can be represented like a sum of acoustic and vortex oscillation. Wave equation is solved relatively to pressure oscillation using an explicit numerical procedure. Zero pulsatory pressure is an initial condition for solution of the wave equation. The local complex specific acoustic impedance is used to define boundary conditions for the acoustical part of the pressure field. Thus the numerical procedure gives pressure pulsations field and sound power data on blade passing frequencies (BPF). For the 4th stage computations effective grass particle parameters are determined with accounting the stubble effect on flow parameters and particularities of grass particle interaction with rigid surfaces. Results of a lawnmower air-grass flow (grass particle trajectories and concentration) and corresponding BPF sound power data prediction are presented as an example of modeling procedure application.


2015 ◽  
Vol 64 (10) ◽  
pp. 1303-1308 ◽  
Author(s):  
Souad Mbarek ◽  
Patrick Baroni ◽  
Laurence Noirez

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