A body force-based method for prediction of multiple-pure-tone noise: Validation

Emissions, fuel burn, and noise are the main drivers for innovative aircraft design. Embedded propulsion systems, such as for example used in hybrid-wing body aircraft, can offer fuel burn and noise reduction benefits but the impact of inlet flow distortion on the generation and propagation of turbomachinery noise has yet to be assessed. A novel approach is used to quantify the effects of non-uniform flow on the creation and propagation of multiple pure tone (MPT) noise. The ultimate goal is to conduct a parametric study of S-duct inlets to quantify the effects of inlet design parameters on the acoustic signature. The key challenge is that the effects of distortion transfer, noise source generation and propagation through the non-uniform flow field are inherently coupled such that a simultaneous computation of the aerodynamics and acoustics is required to capture the mechanisms at play. The technical approach is based on a body force description of the fan blade row that is able to capture the distortion transfer and the blade-to-blade flow variations that cause the MPT noise while reducing computational cost. A single, 3-D full-wheel CFD simulation, in which the Euler equations are solved to second-order spatial and temporal accuracy, simultaneously computes the MPT noise generation and its propagation in distorted inlet flow. A new method of producing the blade-to-blade variations in the body force field for MPT noise generation has been developed and validated. The numerical dissipation inherent to the solver is quantified and used to correct for non-physical attenuation in the far-field noise spectra. Source generation, acoustic propagation and acoustic energy transfer between modes is examined in detail. The new method is validated on NASA's Source Diagnostic Test fan and inlet, showing good agreement with experimental data for aerodynamic performance, acoustic source generation, and far-field noise spectra. The next steps involve the assessment of MPT noise in serpentine inlet ducts and the development of a reduced order formulation suitable for incorporation into NASA's ANOPP framework. © 2010 by Jeff Defoe, Alex Narkaj & Zoltan Spakovszky.

A novel MPT noise prediction methodology for highly-integrated propulsion systems with inlet flow distortion

Embedded propulsion systems, such as for example used in advanced hybrid-wing body aircraft, can potentially offer major fuel burn and noise reduction benefits but introduce challenges in the aerodynamic and acoustic integration of the high-bypass ratio fan system. A novel approach is proposed to quantify the effects of non-uniform flow on the generation and propagation of multiple pure tone noise (MPTs). The new method is validated on a conventional inlet geometry first. The ultimate goal is to conduct a parametric study of S-duct inlets in order to quantify the effects of inlet design parameters on the acoustic signature. The key challenge is that the mechanism underlying the distortion transfer, noise source generation and propagation through the non-uniform flow field are inherently coupled such that a simultaneous computation of the aerodynamics and acoustics is required. The technical approach is based on a body force description of the fan blade row that is able to capture the distortion transfer and the MPT noise generation mechanisms while greatly reducing computational cost. A single, 3-D full-wheel unsteady CFD simulation, in which the Euler equations are solved to second-order spatial and temporal accuracy, simultaneously computes the MPT noise generation and its propagation in distorted mean flow. Several numerical tools were developed to enable the implementation of this new approach. Parametric studies were conducted to determine appropriate grid and time step sizes for the propagation of acoustic waves. The Ffowcs-Williams and Hawkings integral method is used to propagate the noise to far field receivers. Non-reflecting boundary conditions are implemented through the use of acoustic buffer zones. The body force modeling approach is validated and proof-of-concept studies demonstrate the generation of disturbances at both blade-passing and shaft-order frequencies using the perturbed body force method. The full methodology is currently being validated using NASA's Source Diagnostic Test (SDT) fan and inlet geometry. Copyright © 2009 by Jeff Defoe, Alex Narkaj & Zoltan Spakovszky.

Prediction of FRP debonding using the global-energy-balance approach

A major research program was carried out to analyze the mechanism of FRP debonding from concrete beams using global-energy-balance approach (GEBA). The key findings are that the fracture process zone is small so there is no R-curve to consider, failure is dominated by Mode I behavior, and the theory agrees well with tests. The analyses developed in the study provide an essential tool that will enable fracture mechanics to be used to determine the load at which FRP plates will debond from concrete beams. This obviates the need for finite element (FE) analyses in situations where reliable details of the interface geometry and crack-tip stress fields are not attainable for an accurate analysis. This paper presents an overview of the GEBA analyses that is described in detail elsewhere, and explains the slightly unconventional assumptions made in the analyses, such as the revised moment-curvature model, the location of an effective centroid, the separate consideration of the FRP and the RC beam for the purposes of the analysis, the use of Mode I fracture energies and the absence of an R-curve in the fracture mechanics analysis.

Model-based influences on humans' choices and striatal prediction errors.

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

Neural activity associated with the passive prediction of ambiguity and risk for aversive events.

In economic decision making, outcomes are described in terms of risk (uncertain outcomes with certain probabilities) and ambiguity (uncertain outcomes with uncertain probabilities). Humans are more averse to ambiguity than to risk, with a distinct neural system suggested as mediating this effect. However, there has been no clear disambiguation of activity related to decisions themselves from perceptual processing of ambiguity. In a functional magnetic resonance imaging (fMRI) experiment, we contrasted ambiguity, defined as a lack of information about outcome probabilities, to risk, where outcome probabilities are known, or ignorance, where outcomes are completely unknown and unknowable. We modified previously learned pavlovian CS+ stimuli such that they became an ambiguous cue and contrasted evoked brain activity both with an unmodified predictive CS+ (risky cue), and a cue that conveyed no information about outcome probabilities (ignorance cue). Compared with risk, ambiguous cues elicited activity in posterior inferior frontal gyrus and posterior parietal cortex during outcome anticipation. Furthermore, a similar set of regions was activated when ambiguous cues were compared with ignorance cues. Thus, regions previously shown to be engaged by decisions about ambiguous rewarding outcomes are also engaged by ambiguous outcome prediction in the context of aversive outcomes. Moreover, activation in these regions was seen even when no actual decision is made. Our findings suggest that these regions subserve a general function of contextual analysis when search for hidden information during outcome anticipation is both necessary and meaningful.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Change prediction using interface data

Managing change can be challenging due to the high levels of interdependency in concurrent engineering processes. A key activity in engineering change management is propagation analysis, which can be supported using the change prediction method. In common with most other change prediction approaches, the change prediction method has three important limitations: L1: it depends on highly subjective input data; L2: it is capable of modelling 'generalised cases' only and cannot be; customised to assess specific changes; and L3: the input data are static, and thus, guidance does not reflect changes in the design. This article contributes to resolving these limitations by incorporating interface information into the change prediction method. The enhanced method is illustrated using an example based on a flight simulator. © The Author(s) 2013.

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

Vibration from railways: Can we achieve better than +/-10dB prediction accuracy?

This paper examines the sources of uncertainly in models used to predict vibration from underground railways. It will become clear from this presentation that by varying parameters by a small amount, consistent with uncertainties in measured data, the predicted vibration levels vary significantly, often by more than 10dB. This error cannot be forecast. Small changes made to soil parameters (Compressive and Shear Wave velocities and density), to slab bending stiffness and mass and to the measurement position give rise to changes in vibration levels of more than lOdB. So if 10dB prediction error results from small uncertainties in soil parameters and measurement position it cannot be sensible to rely on prediction models for accuracy better than 10dB. The presentation will demonstrate in real time the use of the new - and freely-available - PiP software for calculating vibration from railway tunnels in real time.