Development of a turning mid turbine frame with embedded design-part II: Unsteady measurements

© 2014 by ASME. This paper, the second of two parts, presents a new setup for the two-stage two-spool facility located at the Institute for Thermal Turbomachinery and Machine Dynamics (ITTM) of Graz University of Technology. The rig was designed to reproduce the flow behavior of a transonic turbine followed by a counter-rotating low pressure stage such as those in high bypass aero-engines. The meridional flow path of the machine is characterized by a diffusing S-shaped duct between the two rotors. The role of wide chord vanes placed into the mid turbine frame is to lead the flow towards the low pressure (LP) rotor with appropriate swirl. Experimental and numerical investigations performed on this setup showed that the wide chord struts induce large wakes and extended secondary flows at the LP inlet flow. Moreover, large deterministic fluctuations of pressure, which may cause noise and blade vibrations, were observed downstream of the LP rotor. In order to minimize secondary vortices and to damp the unsteady interactions, the mid turbine frame was redesigned to locate two zero-lift splitters into each vane passage. While in the first part of the paper the design process of the splitters and the time-averaged flow field were presented, in this second part the measurements performed by means of a fast response probe will support the explanation of the time-resolved field. The discussion will focus on the comparison between the baseline case (without splitters) and the embedded design.

Development of a turning mid turbine frame with embedded design-part I: Design and steady measurements

© 2014 by ASME. The paper presents a new setup for the two-stage two-spool facility located at the Institute for Thermal Turbomachinery and Machine Dynamics (ITTM) of Graz University of Technology. The rig was designed in order to simulate the flow behavior of a transonic turbine followed by a counter-rotating low pressure (LP) stage like the spools of a modern high bypass aeroengine. The meridional flow path of the machine is characterized by a diffusing S-shaped duct between the two rotors. The role of turning struts placed into the mid turbine frame is to lead the flow towards the LP rotor with appropriate swirl. Experimental and numerical investigations performed on the setup over the last years, which were used as baseline for this paper, showed that wide chord vanes induce large wakes and extended secondary flows at the LP rotor inlet flow. Moreover, unsteady interactions between the two turbines were observed downstream of the LP rotor. In order to increase the uniformity and to decrease the unsteady content of the flow at the inlet of the LP rotor, the mid turbine frame was redesigned with two zero-lifting splitters embedded into the strut passage. In this first part of the paper the design process of the splitters and its critical points are presented, while the time-averaged field is discussed by means of five-hole probe measurements and oil flow visualizations. The comparison between the baseline case and the embedded design configuration shows that the new design is able to reduce the flow gradients downstream of the turning struts, providing a more suitable inlet condition for the low pressure rotor. The improvement in the flow field uniformity is also observed downstream of the turbine and it is, consequently, reflected in an enhancement of the LP turbine performance. In the second part of this paper the influence of the embedded design on the time-resolved field is investigated.

Electrical control of dynamic spin splitting induced by exchange interaction as revealed by time-resolved Kerr rotation in a degenerate spin-polarized electron gas

Manipulation of the spin degree of freedom has been demonstrated in a spin-polarized electron plasma in a heterostructure by using exchange-interaction-induced dynamic spin splitting rather than the Rashba and Dresselhaus types, as revealed by time-resolved Kerr rotation. The measured spin splitting increases from 0.256 meV to 0.559 meV as the bias varies from -0.3 V to -0.6 V. Both the sign switch of the Kerr signal and the phase reversal of Larmor precessions have been observed with biases, which all fit into the framework of exchange-interaction-induced spin splitting. The electrical control of it may provide a new effective scheme for manipulating spin-selected transport in spin FET-like devices. Copyright (C) EPLA, 2008.

Spin dynamics in the second subband of a quasi-two-dimensional system studied in a single-barrier heterostructure by time-resolved Kerr rotation

Spin dynamics in the first and second subbands have been examined simultaneously by time resolved Kerr rotation in a single-barrier heterostructure of a 500 nm thick GaAs absorption layer. By scanning the wavelengths of the probe and pump beams towards the short wavelength in the zero magnetic field, the spin coherent time T-2(1)* in the 1st subband E-1 decreases in accordance with the D'yakonov-Perel' (DP) spin decoherence mechanism. Meanwhile, the spin coherence time T-2(2)* in the 2nd subband E-2 remains very low at wavelengths longer than 810 nm, and then is dramatically enhanced afterwards. At 803 nm, T-2(2)* (450 ps) becomes ten times longer than T-2(1)* (50 ps). A new feature has been discovered at the wavelength of 811nm under the bias of -0.3V (807nm under the bias of -0.6V) that the spin coherence times (T-2(1)* and T-2(2)*) and the effective g* factors (vertical bar g*(E-1)vertical bar and vertical bar g*(E-2)vertical bar) all display a sudden change, presumably due to the "resonant" spin exchange coupling between two spin opposite bands. Copyright (C) EPLA, 2008.

A time-varying controllable fault-tolerant field associative memory model and its simulation

A time-varying controllable fault-tolerant field associative memory model and the realization algorithms are proposed. On the one hand, this model simulates the time-dependent changeability character of the fault-tolerant field of human brain's associative memory. On the other hand, fault-tolerant fields of the memory samples of the model can be controlled, and we can design proper fault-tolerant fields for memory samples at different time according to the essentiality of memory samples. Moreover, the model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. And the fault-tolerant fields of the memory samples are full of the whole real space R-n. The simulation shows that the model has the above characters and the speed of associative memory about the model is faster.

Brittle-ductile transition of polypropylene/ethyllene-propylene-diene monomer blends induced by size, temperature, and time

To study the brittle-ductile transition (BDT) of polypropylene (PP)/ethylene-propylene-diene monomer (EPDM) blends induced by size, temperature, and time, the toughness of the PP/EPDM blends was investigated over wide ranges of EPDM content, temperature, and strain rate. The toughness of the blends was determined from the tensile fracture energy of the side-edge notched samples. The concept of interparticle distance (ID) was introduced into this study to probe the size effect on the BDT of PP/EPDM blends, whereas the effect of time corresponded to that of strain rate. The BDT induced by size, temperature, and time was observed in the fracture energy versus ID, temperature, and strain rate. The critical BDT temperatures for various EPDM contents at different initial strain rates were obtained from these transitions. The critical interparticle distance (IDc) increased nonlinearly with increasing temperature, and when the initial strain rate was lower, the IDc was larger. Moreover, the variation of the reciprocal of the initial strain rate with the reciprocal of temperature followed different straight lines for various EPDM contents. These straight lines were with the same slope.

Optical Flow From 1D Correlation: Application to a Simple Time-To-Crash Detector

In the first part of this paper we show that a new technique exploiting 1D correlation of 2D or even 1D patches between successive frames may be sufficient to compute a satisfactory estimation of the optical flow field. The algorithm is well-suited to VLSI implementations. The sparse measurements provided by the technique can be used to compute qualitative properties of the flow for a number of different visual tsks. In particular, the second part of the paper shows how to combine our 1D correlation technique with a scheme for detecting expansion or rotation ([5]) in a simple algorithm which also suggests interesting biological implications. The algorithm provides a rough estimate of time-to-crash. It was tested on real image sequences. We show its performance and compare the results to previous approaches.

Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1

Rowley, J.& Urquhart, C. (2007). Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1. Journal of the American Society for Information Science and Technology, 58(8), 1162-1174. Sponsorship: JISC

Exhibiting the behaviour of time-delayed systems via an extension to qualitative simulation

I. Miguel and Q. Shen. Exhibiting the behaviour of time-delayed systems via an extension to qualitative simulation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 35(2):298-305, 2005.

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.

Space, Time and Learning in the Hippocampus: How Fine Spatial and Temporal Scales Are Expanded into Population Codes for Behavioral Control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing, and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus, and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

The role of synchronization in digital communications using chaos - Part II: Chaotic modulation and chaotic synchronization.

For pt. I see ibid., vol. 44, p. 927-36 (1997). In a digital communications system, data are transmitted from one location to another by mapping bit sequences to symbols, and symbols to sample functions of analog waveforms. The analog waveform passes through a bandlimited (possibly time-varying) analog channel, where the signal is distorted and noise is added. In a conventional system the analog sample functions sent through the channel are weighted sums of one or more sinusoids; in a chaotic communications system the sample functions are segments of chaotic waveforms. At the receiver, the symbol may be recovered by means of coherent detection, where all possible sample functions are known, or by noncoherent detection, where one or more characteristics of the sample functions are estimated. In a coherent receiver, synchronization is the most commonly used technique for recovering the sample functions from the received waveform. These sample functions are then used as reference signals for a correlator. Synchronization-based coherent receivers have advantages over noncoherent receivers in terms of noise performance, bandwidth efficiency (in narrow-band systems) and/or data rate (in chaotic systems). These advantages are lost if synchronization cannot be maintained, for example, under poor propagation conditions. In these circumstances, communication without synchronization may be preferable. The theory of conventional telecommunications is extended to chaotic communications, chaotic modulation techniques and receiver configurations are surveyed, and chaotic synchronization schemes are described

"It is Quietly Chaotic. It Confuses Time"*: Final Report of Excavations at the Bordley-Randall Site in Annapolis, Maryland, 1993-1995 (18AP50)

*This extract is from Gay P. Crowther's description of the Randall Court pathway (Cowther 1985).