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Two-Phase Flow Dynamics
Published in Neil E. Todreas, Mujid S. Kazimi, Nuclear Systems Volume I, 2021
Neil E. Todreas, Mujid S. Kazimi
In general, two approaches for the dynamic analysis have been followed: a frequency domain analysis of the stability of small perturbations in inlet conditions and an investigation of oscillations in the time-dependent behavior of a computer solution of the transport equations. The frequency domain equations are often linearized and thus are considered approximate but adequate for instability onset predictions. However, the approach has the advantage of providing the ratio of the magnitude of sequential oscillations in a system, thus providing a measure for the level of stability margin when the ratio is less than one. On the other hand, a time domain analysis introduces the potential for numerical solution instability as well as the physical one. Thus, both approaches have been used without a clear favorite emerging.
Moments and Wavelets in Signal Estimation
Published in Donald B. Owen, Subir Ghosh, William R. Schucany, William B. Smith, Statistics of Quality, 2020
Edward J. Wegman, Hung T. Le, Wendy L. Poston, Jeffrey L. Solka
The application we have in mind in this paper is to the general class of nonparametric function estimation. Nonparametric density estimation, nonparametric regression, nonparametric failure rate estimation, spectral density estimation, and transfer function estimation are all examples of the generalized function estimation problems we have in mind. Of course, these specific problems have direct applicability to issues of quality assurance and reliability. In this chapter we make the connections between the classic moment-based methods and the modern wavelet methods. Wavelet methods are particularly adept at dealing with rapid changes—for example, transients, edge effects, change points—and so are often superior to traditional nonparametric smoothing techniques. We highlight this by discussing transient signal estimation. A transient signal is a signal of finite duration, typically with a relatively sudden onset. However, the techniques we discuss here are not limited to this application.
Lateral-Directional Flight Dynamics and Control
Published in Nandan K. Sinha, N. Ananthkrishnan, Advanced Flight Dynamics with Elements of Flight Control, 2017
Nandan K. Sinha, N. Ananthkrishnan
The static instability criterion usually correlates with the onset of spiral mode instability; that is the spiral eigenvalue being at the origin on the complex plane. However, our interest presently is more in the onset of Dutch roll instability. Similar to the case of static instability, there is an exact criterion for the oscillatory or “dynamic” instability given by the Routh discriminant; however, that criterion does not yield a simple expression that can be usefully employed. So, before we can derive a condition for Dutch roll instability onset, we need to further approximate the dynamical model in Equation 6.29.
Automated flow synthesis of algorithmically designed ferroelectric nematogens
Published in Liquid Crystals, 2023
Here, we outline the use of a continuous flow reactor to telescope multiple chemical transformations into a single process, which vastly increases the number of materials we can synthesise in a given time. By telescoping multiple optimised reactions into a single continuous flow process, the bottleneck is no longer synthesis but characterisation, chemical analysis and so on. We also deploy an algorithmic method for designing new chemical matter from an initial starting molecule. This method reduces human bias, and produces output structures which range from the obvious to the bizarre, from trivial to unphysical. The output of this algorithmic method is scored using a neural network which is trained to predict the scaled onset temperature of the NF phase. We then synthesise several of the high-scoring candidate structures using our continuous flow system, providing a mini-library of RM734-like materials.
Sprint start performance: the potential influence of triceps surae electromechanical delay
Published in Sports Biomechanics, 2022
Evan D. Crotty, Kevin Hayes, Andrew J. Harrison
The data analysis focused on the quantification of five characteristics: Signal processing time: the time interval from the application of the electronic signal to the onset of EMG activity.Force development time: the time interval from the onset of EMG activity to force onset.Elastic charge time: the time interval between force onset to the activation of the heel switch due to heel movement.Electromechanical delay (EMD): the time interval from the onset of EMG activity to the activation of the heel switch due to heel movement.Heel-lift response time: the time interval between the auditory signal to the activation of the heel switch due to heel movement.
Investigating dilemma zone boundaries for mixed traffic conditions using support vector machines
Published in Transportation Letters, 2022
Bharat Kumar Pathivada, Perumal Vedagiri
This study investigated the position and distribution of dilemma zone for mixed traffic conditions. Although, previous studies have attempted to quantify the dilemma boundaries, due to the significantly diverse behavior of Indian drivers implementing the results directly from the previous studies might not suffice. To compute the dilemma boundaries, driver actions in response to the signal change were video tapped at five signalized intersection approaches. This study employed a machine learning algorithm (Support Vector Machine) to classify the driver responses at the yellow onset. SVM algorithm generates an optimum separating hyperplane (OSH) which separates the two data classes (stop/cross) of driver responses. Dilemma zone, which is an area ahead of the intersection, where the driver has an option to stop/go at the trigger of yellow signal is defined based on the observed 85th and 15th percentile speeds. The boundaries of the dilemma zone in this study were defined in terms of distance to the stop line (DSL). The upper boundary (UB) and lower boundary (LB) of the dilemma zone were then defined based on the 85th percentile and 15th percentile speeds observed at the intersection approaches. The driving population between the 15th and 85th percentile speeds are used to define the dilemma zone since the lower and upper 15th percentile drivers are either too fast or too slow for the existing conditions.