Reynolds number effects on scalar dissipation rate transport and its modeling in turbulent premixed combustion

The effects of turbulent Reynolds number, Ret, on the transport of scalar dissipation rate of reaction progress variable in the context of Reynolds averaged Navier-Stokes simulations have been analyzed using three-dimensional simplified chemistry-based direct numerical simulation (DNS) data of freely propagating turbulent premixed flames with different values of Ret. Scaling arguments have been used to explain the effects of Ret on the turbulent transport, scalar-turbulence interaction, and the combined reaction and molecular dissipation terms. Suitable modifications to the models for these terms have been proposed to account for Ret effects, and the model parameters include explicit Ret dependence. These expressions approach expected asymptotic limits for large values of Ret. However, turbulent Reynolds number Ret does not seem to have any major effects on the modeling of the term arising from density variation. Copyright © Taylor and Francis Group, LLC.

Tailored seeding geometries for the multi-fidelity design of compression systems

Design optimisation of compressor systems is a computationally expensive problem due to the large number of variables, complicated design space and expense of the analysis tools. One approach to reduce the expense of the process and make it achievable in industrial timescales is to employ multi-fidelity techniques, which utilise more rapid tools in conjunction with the highest fidelity analyses. The complexity of the compressor design landscape is such that the starting point for these optimisations can influence the achievable results; these starting points are often existing (optimised) compressor designs, which form a limited set in terms of both quantity and diversity of the design. To facilitate the multi-fidelity optimisation procedure, a compressor synthesis code was developed which allowed the performance attributes (e.g. stage loadings, inlet conditions) to be stipulated, enabling the generation of a variety of compressors covering a range of both design topology and quality to act as seeding geometries for the optimisation procedures. Analysis of the performance of the multi-fidelity optimisation system when restricting its exploration space to topologically different areas of the design space indicated little advantage over allowing the system to search the design space itself. However, comparing results from optimisations started from seed designs with different aerodynamic qualites indicated an improved performance could be achieved by starting an optimisation from a higher quality point, and thus that the choice of starting point did affect the final outcome of the optimisations. Both investigations indicated that the performance gains through the optimisation were largely defined by the early exploration of the design space where the multi-fidelity speedup could be exploited, thus extending this region is likely to have the greatest effect on performance of the optimisation system. © 2013 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

An investigation into numerical analysis alternatives for predicting reingestion in turbine disc rim cavities

Reliable means of predicting ingestion in cavities adjacent to the main gas path are increasingly being sought by engineers involved in the design of gas turbines. In this paper, analysis is to be presented that results from an extended research programme, MAGPI, sponsored by the EU and several leading gas turbine manufactures and universities. Extensive use is made of CFD modelling techniques to understand the aerodynamic behaviour of a turbine stator well cavity, focusing on the interaction of cooling air supply with the main annulus gas. The objective of the study has been to benchmark a number of CFD codes and numerical techniques covering RANS and URANS calculations with different turbulence models in order to assess the suitability of the standard settings used in the industry for calculating the mechanics of the flow travelling between cavities in a turbine through the main gas path. The modelling methods employed have been compared making use of experimental data gathered from a dedicated two-stage turbine rig, running at engine representative conditions. Extensive measurements are available for a range of flow conditions and alternative cooling arrangements. The limitations of the numerical methods in calculating the interaction of the cooling flow egress and the main stream gas, and subsequent ingestion into downstream cavities in the engine (i.e. re-ingestion), have been exposed. This has been done without losing sight of the validation of the CFD for its use for predicting heat transfer, which was the main objective of the partners of the MAGPI Work- Package 1 consortium. Copyright © 2012 by ASME.

Experimental studies of an accelerating, pitching, flat plate at low Reynolds number

The understanding of low Reynolds number aerodynamics is becoming increasingly prevalent with the recent surge in interest in advanced Micro-Air Vehicle (MAV) technology. Research in this area has been primarily stimulated by a military need for smaller, more versatile, autonomous, surveillance aircraft. The mechanism for providing the high lift coefficient required forMAV applications is thought to be largely influenced by the formation of a Leading Edge Vortex (LEV). This paper analyses experimentally, the influence of the LEV effect for a flat plate wing (AR = 4) under fast and slow pitch-up motions at Re =10,000 using a combination of dye flow visualisation and PIV measurements. It is found that a fast pitch over 1c shows a flow topology dominant LEV, while for a slow pitch case over 6c, the flow is largely separated. The development of the suction surface flow and the LEV was strongly correlated with the kinematics of the leading edge, suggesting that the effective local angle of incidence at the Leading Edge (LE) is of considerable significance in unsteady pitching motions. © 2013 by P.R.R.J Stevens.

Tailored seeding geometries for the multi-fidelity design of compression systems

Design optimisation of compressor systems is a computationally expensive problem due to the large number of variables, complicated design space and expense of the analysis tools. One approach to reduce the expense of the process and make it achievable in industrial timescales is to employ multi-fidelity techniques, which utilise more rapid tools in conjunction with the highest fidelity analyses. The complexity of the compressor design landscape is such that the starting point for these optimisations can influence the achievable results; these starting points are often existing (optimised) compressor designs, which form a limited set in terms of both quantity and diversity of the design. To facilitate the multi-fidelity optimisation procedure, a compressor synthesis code was developed which allowed the performance attributes (e.g. stage loadings, inlet conditions) to be stipulated, enabling the generation of a variety of compressors covering a range of both design topology and quality to act as seeding geometries for the optimisation procedures. Analysis of the performance of the multi-fidelity optimisation system when restricting its exploration space to topologically different areas of the design space indicated little advantage over allowing the system to search the design space itself. However, comparing results from optimisations started from seed designs with different aerodynamic qualites indicated an improved performance could be achieved by starting an optimisation from a higher quality point, and thus that the choice of starting point did affect the final outcome of the optimisations. Both investigations indicated that the performance gains through the optimisation were largely defined by the early exploration of the design space where the multi-fidelity speedup could be exploited, thus extending this region is likely to have the greatest effect on performance of the optimisation system. © 2012 AIAA.

Updated correlation between aircraft smoke number and black carbon concentration

Aircraft emissions of black carbon (BC) contribute to anthropogenic climate forcing and degrade air quality. The smoke number (SN) is the current regulatory measure of aircraft particulate matter emissions and quantifies exhaust plume visibility. Several correlations between SN and the exhaust mass concentration of BC (CBC) have been developed, based on measurements relevant to older aircraft engines. These form the basis of the current standard method used to estimate aircraft BC emissions (First Order Approximation version 3 [FOA3]) for the purposes of environmental impact analyses. In this study, BC with a geometric mean diameter (GMD) of 20, 30, and 60 nm and filter diameters of 19 and 35 mm are used to investigate the effect of particle size and sampling variability on SN measurements. For BC with 20 and 30 nm GMD, corresponding to BC emitted by modern aircraft engines, a smaller SN results from a given CBC than is the case for BC with 60 nm GMD, which is more typical of older engines. An updated correlation between CBC and SNthat accounts for typical size of BC emitted by modern aircraft is proposed. An uncertainty of ±25% accounts for variation in GMD in the range 20-30 nm and for the range of filter diameters. The SN-CBC correlation currently used in FOA3 underestimates by a factor of 2.5-3 for SN <15, implying that current estimates of aircraft BC emissions derived from SN are underestimated by the same factor. Copyright © American Association for Aerosol Research.

On-line policy optimisation of Bayesian spoken dialogue systems via human interaction

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy. © 2013 IEEE.

Diameter and wall number control of carbon nanotubes by chemical vapor deposition

We analyze the relationship between the average wall number (N) and the diameter (d) for carbon nanotubes (CNTs) grown by chemical vapour deposition. It is found that N depends linearly on d for diameters in the range of 2.5-10 nm, while single wall nanotubes predominate for diameters under about 2.1 nm. The linear relationship is found to depend somewhat on the growth conditions. It is also verified that the mean diameter depends on the diameter of the originating catalyst nanoparticle, and thus on the initial catalyst thickness where a thin film catalyst is used. This simplifies the characterisation of CNTs by electron microscopy. We also find a linear relationship between nanotube diameter and initial catalyst film thickness. © 2013 AIP Publishing LLC.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.