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It is generally accepted that the evolution of the deep-water surface gravity wave spectrum is governed by quartet resonant and quasi-resonant interactions. However, it has also been reported in both experimental and computational studies that non-resonant triad interactions can play a role, e.g. generation of bound waves. In this study, we investigate the effects of triad and quartet interactions on the spectral evolution, by numerically tracking the contributions from quadratic and cubic terms in the dynamical equation. In a finite time interval, we find that the contribution from triad interactions follows the trend of that from quartet resonances (with comparable magnitude) for most wavenumbers, except that it peaks at low wavenumbers with very low initial energy. This result reveals two effects of triad interactions. (1) The non-resonant triad interactions can be connected to form quartet resonant interactions (hence exhibiting the comparable trend), which is a reflection of the normal form transformation applied in wave turbulence theory of surface gravity waves. (2) The triad interactions can fill energy into the low-energy portion of the spectrum (low wavenumber part in this case) on a very fast time scale, with energy distributed in both bound and free modes at the same wavenumber. We further analyse the latter mechanism using a simple model with two initially active modes in the wavenumber domain. Analytical formulae describing the distribution of energy in free and bound modes are provided, along with numerical validations.
This lab presents two devices, both partially digital, that have in common the use of feedback to generate an output related in a useful way to an input signal. The first circuit, an analog-to-digital converter, uses feedback to generate the digital equivalent to an analog input voltage.
Thermal Marangoni effects play important roles in bubble dynamics such as bubbles generated by water electrolysis or boiling. As macroscopic bubbles often originate from nucleated nanobubbles, it is crucial to understand how thermocapillarity operates at the nanoscale. In this study, the motion of transient bulk gas nanobubbles in water driven by a vertical temperature gradient between two solid plates is investigated using molecular dynamics simulations and analytical theory. The simulation results show that due to the thermal Marangoni force, nanobubbles move towards the hot plate at a constant velocity, similar to the behaviour of macroscale gas bubbles. However, unlike macroscale gas bubbles whose thermal conductivity and viscosity are negligible compared to those of water, the thermal conductivity and viscosity of nanoscale gas bubbles are significantly increased due to their large gas density. The thermal resistance and the slip length are also found to matter at the liquid–gas interface, though they decrease with increasing gas densities. The previous thermocapillary theory for macroscale bubbles is extended by considering all these nanoscopic effects. Expressions of the Marangoni force and the drag force are derived. By balancing the Marangoni force and the drag force, the theoretical velocity of the nanobubble migration in a thermal gradient is obtained. When using the measured transport properties of liquid, gas, and their interfaces, the theoretically obtained velocity is consistent with the result of the molecular simulations. We find that the slip length is too small to have considerable effects on nanobubble motions in the current liquid–gas system.
In addition, packaged logic gates are low density, typically containing only a few gates.1 That means any reasonably complex digital systems might need tens or hundreds of DIP packages. Because signals have to travel between packages, systems built with discrete logic are limited in speed as well.
AoE works a similar problem in detail: §2.2.5A. The example below differs in describing a follower for AC signals. That makes a difference, as you will see, but the problems are otherwise very similar.
In the last chapter’s Worked Examples, we looked at several digital comparators constructed out of gates. We certainly could translate those to structural models in Verilog, but that misses the point. The advantage of an HDL is it frees us from truth tables, Boolean equations, and the need to implement the result with logic gates. Instead, we can describe the desired result behaviorally.
Use a logic probe, not DVM or – worse – your eyes This should go without saying, but we’re not sure it yet does. We find it boring to stare at a wire, trying to see if it goes where it should.
Defines the level (high or low) in which a signal is “True,” or – better – “Asserted” (see next term). We avoid the former because many people associate “True” with “High,” and that is an association we must break.
An important feature of the dynamics of double-diffusive fluids is the spontaneous formation of thermohaline staircases, where wide regions of well-mixed fluid are separated by sharp density interfaces. Recent developments have produced a number of one-dimensional reduced models to describe the evolution of such staircases in the salt fingering regime relevant to mid-latitude oceans; however, there has been significantly less work done on layer formation in the diffusive convection regime. We aim to fill this gap by presenting a new model for staircases in diffusive convection based on a regularisation of the $\gamma$-instability (Radko 2003 J. Fluid Mech. vol. 805, 147–170), with a range of parameter values relevant to both polar oceans and astrophysical contexts. We use the results of numerical simulations to inform turbulence-closure parametrisations as a function of the horizontally averaged kinetic energy $e$, and ratio of the haline to thermal gradients $R_0^*$. These parametrisations result in a one-dimensional model that reproduces the critical value of $R_0^*$ for the layering instability, and the spatial scale of layers, for a wide range of parameter values, although there is a mismatch between the range of $R_0^*$ for layer formation in the model and observational values from polar oceans. Staircases form in the one-dimensional model, evolving gradually through layer merger events that closely resemble simulations.
Here we will do a problem much like the one we did more sketchily in . If you are comfortable with the design process, skip to §§3W.1.6 and 3W.1.7, where we meet some new issues.
In the lab exercises, from now and ever after, you will want to be able to read resistor values without pulling out a meter to measure the part’s value (we do sometimes find desperate students resorting to such desperate means). The process will seem laborious, at first; but soon, as you get used to at least the common resistance values, you will be able to read many color codes at a glance.
We want to solve the problem of optimizing circuit performance by selecting from the great variety of available op-amps. We will try to make sense of the fact – not predictable from our first view of op-amps as essentially ideal – that there are not one or two op-amps available but approximately 30,000 listed (on the day of this writing) on one distributor’s website (DigiKey).
The problem – just analysis this time: This is a rare departure from our practice of asking you to design, not to analyze. Inventing a difference amp1 seemed a tall order, and, on the other hand, the difference amplifier’s behavior seems far from obvious. So, here’s a little workout in seeing how the circuit operates.
Serial data input and output are classic applications where interrupt-driven I/O makes sense. Rather than sit in a loop wasting CPU cycles waiting for each byte to be sent or received, an ISR can load a new byte into the output register each time the previous byte has been sent, or store each new byte in a buffer as they are received.