The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

Contra-rotating open rotor operation for improved aerodynamics and noise at takeoff

The contra-rotating open rotor is, once again, being considered as an alternative to the advanced turbofan to address the growing pressure to cut aviation fuel consumption and carbon dioxide emissions. One of the key challenges is meeting community noise targets at takeoff. Previous open rotor designs are subject to poor efficiency at takeoff due to the presence of large regions of separated flow on the blades as a result of the high incidence needed to achieve the required thrust. This is a consequence of the fixed rotor rotational speed constraint typical of variable pitch propellers. Within the study described in this paper, an improved operation is proposed to improve performance and reduce rotorrotor interaction noise at takeoff. Three-dimensional computational fluid dynamics (CFD) calculations have been performed on an open rotor rig at a range of takeoff operating conditions. These have been complemented by analytical tone noise predictions to quantify the noise benefits of the approach. The results presented show that for a given thrust, a combination of reduced rotor pitch and increased rotor rotational speed can be used to reduce the incidence onto the front rotor blades. This is shown to eliminate regions of flow separation, reduce the front rotor tip loss and reduce the downstream stream tube contraction. The wakes from the front rotor are also made wider with lower velocity defect, which is found to lead to reduced interaction tone noise. Unfortunately, the necessary increase in blade speed leads to higher relative Mach numbers, which can increase rotor alone noise. In summary, the combined CFD and aero-acoustic analysis in this paper shows how careful operation of an open rotor at takeoff, with moderate levels of re-pitch and speed increase, can lead to improved front rotor efficiency as well as appreciably lower overall noise across all directivities. Copyright © 2011 by ASME.

A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.

A design strategy in the propulsion system attachment to a submarine hull to minimise radiated noise

Vibration modes of a submerged hull are excited by fluctuating forces generated at the propeller and transmitted to the hull via the propeller-shafting system. The low frequency hull vibrational modes result in significant sound radiation. This work investigates the reduction of the far-field radiated sound pressure by optimising the connection point of the shafting system to the hull. The submarine hull is modelled as a fluid loaded cylindrical hull with truncated conical shells at each end. The propeller-shafting system consists of the propeller, shaft, thrust bearing and foundation, and is modelled in a modular approach using a combination of spring-mass-damper elements and continuous systems (beams, plates, shells). The foundation is attached to the stern side end plate of the hull, which is modelled as a circular plate coupled to an annular plate. By tuning the connection radius of the foundation to the end plate, the maximum radiated noise in a given frequency range can be minimised.

Sensor data scheduling for linear quadratic Gaussian control with full state feedback

Networked control systems (NCSs) have attracted much attention in the past decade due to their many advantages and growing number of applications. Different than classic control systems, resources in NCSs, such as network bandwidth and communication energy, are often limited, which degrade the closed-loop system performance and may even cause the system to become unstable. Seeking a desired trade-off between the closed-loop system performance and the limited resources is thus one heated area of research. In this paper, we analyze the trade-off between the sensor-to-controller communication rate and the closed-loop system performance indexed by the conventional LQG control cost. We present and compare several sensor data schedules, and demonstrate that two event-based sensor data schedules provide better trade-off than an optimal offline schedule. Simulation examples are provided to illustrate the theories developed in the paper. © 2012 AACC American Automatic Control Council).

Shock Propagation and MPT Noise From a Transonic Rotor in Nonuniform Flow

One of the major challenges in high-speed fan stages used in compact, embedded propulsion systems is inlet distortion noise. A body-force-based approach for the prediction of multiple-pure-tone (MPT) noise was previously introduced and validated. In this paper, it is employed with the objective of quantifying the effects of nonuniform flow on the generation and propagation of MPT noise. First-of-their-kind back-to-back coupled aero-acoustic computations were carried out using the new approach for conventional and serpentine inlets. Both inlets delivered flow to the same NASA/GE R4 fan rotor at equal corrected mass flow rates. Although the source strength at the fan is increased by 38 dB in sound power level due to the nonuniform inflow, far-field noise for the serpentine inlet duct is increased on average by only 3.1 dBA overall sound pressure level in the forward arc. This is due to the redistribution of acoustic energy to frequencies below 11 times the shaft frequency and the apparent cut-off of tones at higher frequencies including blade-passing tones. The circumferential extent of the inlet swirl distortion at the fan was found to be two blade pitches, or 1/11th of the circumference, suggesting a relationship between the circumferential extent of the inlet distortion and the apparent cut-off frequency perceived in the far field. A first-principles-based model of the generation of shock waves from a transonic rotor in nonuniform flow showed that the effects of nonuniform flow on acoustic wave propagation, which cannot be captured by the simplified model, are more dominant than those of inlet flow distortion on source noise. It demonstrated that nonlinear, coupled aerodynamic and aero-acoustic computations, such as those presented in this paper, are necessary to assess the propagation through nonuniform mean flow. A parametric study of serpentine inlet designs is underway to quantify these propagation effects. © 2013 American Society of Mechanical Engineers.

Shock propagation and MPT noise from a transonic rotor in non-uniform flow

One of the major challenges in hig4h-speed fan stages used in compact, embedded propulsion systems is inlet distortion noise. A body-force-based approach for the prediction of multiple-pure-tone (MPT) noise was previously introduced and validated. In this paper, it is employed with the objective of quantifying the effects of non-uniform flow on the generation and propagation of MPT noise. First-of-their-kind back-to-back coupled aero-acoustic computations were carried out using the new approach for conventional and serpentine inlets. Both inlets delivered flow to the same NASA/GE R4 fan rotor at equal corrected mass flow rates. Although the source strength at the fan is increased by 45 dB in sound power level due to the non-uniform inflow, farfield noise for the serpentine inlet duct is increased on average by only 3.1 dBA overall sound pressure level in the forward arc. This is due to the redistribution of acoustic energy to frequencies below 11 times the shaft frequency and the apparent cut-off of tones at higher frequencies including blade-passing tones. The circumferential extent of the inlet swirl distortion at the fan was found to be 2 blade pitches, or 1/11th of the circumference, suggesting a relationship between the circumferential extent of the inlet distortion and the apparent cut-off frequency perceived in the far field. A first-principles-based model of the generation of shock waves from a transonic rotor in non-uniform flow showed that the effects of non-uniform flow on acoustic wave propagation, which cannot be captured by the simplified model, are more dominant than those of inlet flow distortion on source noise. It demonstrated that non-linear, coupled aerodynamic and aeroacoustic computations, such as those presented in this paper, are necessary to assess the propagation through non-uniform mean flow. A parametric study of serpentine inlet designs is underway to quantify these propagation effects. Copyright © 2011 by ASME.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.

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.

Review of computational aeroacoustics for application in electronics cooler noise

A review of computational aeroacoustics (CCA) was made for application in electronics cooler noise. Computational aeroacoustics encompasses all numerical methods where the purposes is to predict the noise emissions from a simulated flow. Numerical simulation of the flow inside and around heat sinks and fans can lead to a prediction of the emitted noise while they are still in the design phase. Direct CCA is theoretically the best way to predict flow-based acoustic phenomena numerically. It is typically used only for low-frequency sound prediction. The boundary element method offers low computational cost and does not use a computational grid, but instead use vortex-surface calculations to determine tonal noise. Axial fans are commonly used to increase the airflow and thus the heat transfer over the heat sinks within the computer cases. Very detailed source simulations in the fan and heat sink region coupled with the use of analogy methods could result in excellent simulation results with a reasonable computational effort.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.