Accurate Frequency Domain Modelling of an Electric Furnace

Controllers of industrial furnaces may operate differently in different temperature ranges. The controller has different parameter sets for each of these ranges. The operation of controllers is switched according to the temperature. It is desirable to change the parameters continuously following the temperature. The continuous change of parameters instead of mode switching may decrease the switching transients and lead to more accurate temperature control. A laboratory-scale electric furnace is investigated in this paper. The main focus is on the temperature dependent behavior of the furnace. The objective is to build up a temperature dependent model that is capable to describe the furnace in a wider temperature range

Electronic excitations in quasi-2D crystals: What theoretical quantities are relevant to experiment?

The ab initio theory of electronic excitations in atomically thin [quasi-two-dimensional (Q2D)] crystals presents extra challenges in comparison to both the bulk and purely 2D cases. We argue that the conventionally used energy-loss function $-$Im $1/\epsilon({\bf q},\omega)$ (where $\epsilon$, ${\bf q}$, and $\omega$ are the dielectric function, the momentum, and the energy transfer, respectively) is not, generally speaking, the suitable quantity for the interpretation of the electron-energy loss spectroscopy (EELS) in the Q2D case, and we construct different functions pertinent to the EELS experiments on Q2D crystals. Secondly, we emphasize the importance and develop a convenient procedure of the elimination of the spurious inter-layer interaction inherent to the use of the 3D super-cell method for the calculation of excitations in Q2D crystals. Thirdly, we resolve the existing controversy in the interpretation of the so-called $\pi$ and $\pi+\sigma$ excitations in monolayer graphene by demonstrating that both dispersive collective excitations (plasmons) and non-dispersive single-particle (inter-band) transitions fall in the same energy ranges, where they strongly influence each other.Comment: 19 pages, 6 figure

Transient excitation and data processing techniques employing the fast fourier transform for aeroelastic testing

The development of testing techniques useful in airplane ground resonance testing, wind tunnel aeroelastic model testing, and airplane flight flutter testing is presented. Included is the consideration of impulsive excitation, steady-state sinusoidal excitation, and random and pseudorandom excitation. Reasons for the selection of fast sine sweeps for transient excitation are given. The use of the fast fourier transform dynamic analyzer (HP-5451B) is presented, together with a curve fitting data process in the Laplace domain to experimentally evaluate values of generalized mass, model frequencies, dampings, and mode shapes. The effects of poor signal to noise ratios due to turbulence creating data variance are discussed. Data manipulation techniques used to overcome variance problems are also included. The experience is described that was gained by using these techniques since the early stages of the SST program. Data measured during 747 flight flutter tests, and SST, YC-14, and 727 empennage flutter model tests are included

Nonlinear system-identification of the filling phase of a wet-clutch system

The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation

Fast, non-monte-carlo estimation of transient performance variation due to device mismatch

This paper describes an efficient way of simulating the effects of device random mismatch on circuit transient characteristics, such as variations in delay or in frequency. The proposed method models DC random offsets as equivalent AC pseudo-noises and leverages the fast, linear periodically time-varying (LPTV) noise analysis available from RF circuit simulators. Therefore, the method can be considered as an extension to DC match analysis and offers a large speed-up compared to the traditional Monte-Carlo analysis. Although the assumed linear perturbation model is valid only for small variations, it enables easy ways to estimate correlations among variations and identify the most sensitive design parameters to mismatch, all at no additional simulation cost. Three benchmarks measuring the variations in the input offset voltage of a clocked comparator, the delay of a logic path, and the frequency of an oscillator demonstrate the speed improvement of about 100-1000x compared to a 1000-point Monte-Carlo method