Laplace transform dynamics

Without resorting to the impact approximation, perturbation theory is able to describe in the lowest order in both the dynamics of free rotation and its distortion produced by collisions. An additional advantage of the integral version of the theory is the simplicity of the relation following from Eq. (2.24) for the Laplace transforms of orientational and angular momentum correlation functions [107] ... 

This chapter has presented time-domain solutions of unsteady material and energy balances. The more usual undergraduate treatment of dynamic systems is given in a course on control and relies heavily on Laplace transform techniques. One suitable reference is... 

Complex systems can often be represented by linear time-dependent differential equations. These can conveniently be converted to algebraic form using Laplace transformation and have found use in the analysis of dynamic systems (e.g., Coughanowr and Koppel, 1965, Stephanopolous, 1984 and Luyben, 1990). 

In classical control theory, we make extensive use of Laplace transform to analyze the dynamics of a system. The key point (and at this moment the trick) is that we will try to predict the time response without doing the inverse transformation. Later, we will see that the answer lies in the roots of the characteristic equation. This is the basis of classical control analyses. Hence, in going through Laplace transform again, it is not so much that we need a remedial course. Your old differential equation textbook would do fine. The key task here is to pitch this mathematical technique in light that may help us to apply it to control problems. 

Since Laplace transform can only be applied to a linear differential equation, we must "fix" a nonlinear equation. The goal of control is to keep a process running at a specified condition (the steady state). For the most part, if we do a good job, the system should only be slightly perturbed from the steady state such that the dynamics of returning to the steady state is a first order decay, i.e., a linear process. This is the cornerstone of classical control theory. 

We now derive the time-domain solutions of first and second order differential equations. It is not that we want to do the inverse transform, but comparing the time-domain solution with its Laplace transform helps our learning process. What we hope to establish is a better feel between pole positions and dynamic characteristics. We also want to see how different parameters affect the time-domain solution. The results are useful in control analysis and in measuring model parameters. At the end of the chapter, dead time, reduced order model, and the effect of zeros will be discussed. 

Instead of spacing out in the Laplace-domain, we can (as we are taught) guess how the process behaves from the pole positions of the transfer function. But wouldn t it be nice if we could actually trace the time profile without having to do the reverse Laplace transform ourselves Especially the response with respect to step and impulse inputs Plots of time domain dynamic calculations are extremely instructive and a useful learning tool.1... 

As you will see, several different approaches are used in this book to analyze the dynamics of systems. Direct solution of the differential equations to give functions of time is a time domain teehnique. Use of Laplace transforms to characterize the dynamics of systems is a Laplace domain technique. Frequency response methods provide another approaeh to the problem. 

In the next three chapters we will develop methods of analysis of dynamic systems, both openloop and closedloop, that use Laplace transforms. This form... 

The use of Laplace transfonnations yields some very useful simplifications in notation and computation. Laplace-transforming the linear ordinary differential equations describing our processes in terms of the independent variable t converts them into algebraic equations in the Laplace transform variable s. This provides a very convenient representation of system dynamics. 

LAPUCE-DOUAIM DYNAMICS AND OONTKOL Therefore the Laplace transformation of a ramp function is... 

Our primary use of Laplace transformations in process control involves representing the dynamics of the process in terms of "transfer functions." These are output-input relationships and are obtained by Laplace-transfonning algebraic... 

LA place-domain dynamics and control Laplace transforming gives... 

We could go through the Laplace domain by approximating and then inverting. However, there is a direct conversion V. V. Solodovnilcov, Introduction to Statistical Dynamics of Autoinatic Control, Dover, 1960). Suppose we want to find the impulse response of a stable system (defined as g,), given the system s frequency response. Since the Laplace transformation of the impulse input is unity,... 

In general, it is not possible to characterize all processes exactly, so several approximations have to be made. Normally one assumes that the dynamic characteristic can be reproduced by a process of the first order plus dead time. The Laplace transformation for this assumption (transfer to the s plane) is approximated ... 

Fig. 2.49 Dynamic structure factor at two temperatures for a nearly symmetric PEP-PDMS diblock (V = 1110) determined using dynamic light scattering in the VV geometry at a fixed wavevector q = 2.5 X 10s cm-1 (Anastasiadis et al. 1993a). The inverse Laplace transform of the correlation function for the 90 °C data is shown in the inset. Three dynamic modes (cluster, heterogeneity and internal) are evident with increasing relaxation times.

The use of Cole-Cole plots is not very developed in practice, despite the fact that they open the way for the modeling of the viscoelastic behavior in dynamic as well as in static loading cases (through Laplace transform). By contrast, these plots could be interesting from the fundamental point of view if certain parameters would reveal a clear dependence with the crosslink density. The effects of crosslinking are difficult to detect on the usual viscoelastic properties, except for the variation of the rubbery modulus E0. 

We see that a calculation of Ar involves a Laplace transform of the time-dependent friction kernel. This may typically be determined in a molecular dynamics (MD) simulation, where the autocorrelation function of the random force (R(O)R(t)) may be determined, which then allows us to determine (f) using the fluctuation-dissipation theorem in Eq. (11.58). Note that Eq. (11.85) is an implicit equation for Ar that in general must be solved by iteration. In the absence of friction we see from Eq. (11.85)... 

When calculating the Laplace transform, one finds an enhancement of the dynamic modulus due to the macromolecular coils in the viscoelastic liquid... 

The analysis of the dynamics and dielectric relaxation is made by means of the collective dipole time-correlation function (t) = (M(/).M(0)> /( M(0) 2), from which one can obtain the far-infrared spectrum by a Fourier-Laplace transformation and the main dielectric relaxation time by fitting < >(/) by exponential or multi-exponentials in the long-time rotational-diffusion regime. Results for (t) and the corresponding frequency-dependent absorption coefficient, A" = ilf < >(/) cos (cot)dt are shown in Figure 16-6 for several simulated states. The main spectra capture essentially the microwave region whereas the insert shows the far-infrared spectral region. 

The theory is not limited in its application to the transient properties of amorphous polymers it can be used to make molecular interpretation and prediction of the dynamic viscoelastic properties of crosslinked polymers [24] as well. According to the Fourier-Laplace transformation, the complex tensile modulus can be separated into the real and imaginary parts... 

A wave may be viewed as a unit of the response of the system to applied input or disturbances. These responses could be in terms of physical deflections, pressure, velocity, vorticity, temperature etc., those physical properties relevant to the dynamics, showing up in general, as function of space and time. Any arbitrary function of space and time can be written in terms of Fourier-Laplace transform as given by,... 

All fluid dynamical systems are continuous system with infinite degrees of freedom and the governing equations depend continuously upon both space and time. While for any system, the time-dependent signal cannot move back in time, the space dependent signal can propagate in all directions with respect to the location of the source. This therefore requires that we develop a theory based on bilateral Laplace transform - a topic described in great details in Papoulis (1962) and Van der Pol Bremmer (1959). 

Thus, application of delta function is equivalent to exciting all the circular frequencies with equal emphasis. This is the basis of finding the natural frequency of any oscillator via impulse response. When the oscillator is subjected to an impulse, all frequencies are equally excited and the system dynamics picks out the natural frequency of vibration, leaving others to decay in due course of time. It is noted that this result also applies to Laplace transform and we are going to use it often by replacing time by space and circular frequency by wave numbers. 

Block Diagram Analysis One shortcoming of this feedforward design procedure is that it is based on the steady-state characteristics of the process and, as such, neglects process (Ramies (i.e., how fast the controlled variable responds to changes in the load and manipulated variables). Thus, it is often necessary to include dynamic compensation in the feedforward controller. The most direct method of designing the FF dynamic compensator is to use a block dir rram of a general process, as shown in Fig. 8-34, where G, represents the disturbance transmitter, (iis the feedforward controller, Cj relates the disturbance to the controlled variable, G is the valve, Gp is the process, G is the output transmitter, and G is the feedback controller. All blocks correspond to transfer fimetions (via Laplace transforms). 

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