Preliminary Design and Performance Estimation of Radial Inflow Turbines: An Automated Approach

A comprehensive one-dimensional meanline design approach for radial inflow turbines is described in the present work. An original code was developed in Python that takes a novel approach to the automatic selection of feasible machines based on pre-defined performance or geometry characteristics for a given application. It comprises a brute-force search algorithm that traverses the entire search space based on key non-dimensional parameters and rotational speed. In this study, an in-depth analysis and subsequent implementation of relevant loss models as well as selection criteria for radial inflow turbines is addressed. Comparison with previously published designs, as well as other available codes, showed good agreement. Sample (real and theoretical) test cases were trialed and results showed good agreement when compared to other available codes. The presented approach was found to be valid and the model was found to be a useful tool with regards to the preliminary design and performance estimation of radial inflow turbines, enabling its integration with other thermodynamic cycle analysis and three-dimensional blade design codes.

Performance estimation of streaming applications for hierarchical MPSoCs

Parallel programming and effective partitioning of applications for embedded many-core architectures requires optimization algorithms. However, these algorithms have to quickly evaluate thousands of different partitions. We present a fast performance estimator embedded in a parallelizing compiler for streaming applications. The estimator combines a single execution-based simulation and an analytic approach. Experimental results demonstrate that the estimator has a mean error of 2.6% and computes its estimation 2848 times faster compared to a cycle accurate simulator.

Performance estimation for concretes made with recycled aggregates of construction and demolition waste of some Brazilian cities

The aim of this paper is to verify the influence of composition variability of recycled aggregates (RA) of construction and demolition wastes (CDW) on the performance of concretes. Performance was evaluated building mathematical models for compressive strength, modulus of elasticity and drying shrinkage. To obtain such models, an experimental program comprising 50 concrete mixtures was carried out. Specimens were casted, tested and results for compressive strength, modulus of elasticity and drying shrinkage were statistically analyzed. Models inputs are CDW composition observed at seven Brazilian cities. Results confirm that using RA from CDW for concrete building is quite feasible, independently of its composition, once compressive strength and modulus of elasticity still reached considerable values. We concluded the variability presented by recycled aggregates of CDW does not compromise their use for concrete building. However, this information must be used with caution, and experimental tests should always be performed to certify concrete properties.

Children's performance estimation in mathematics and science tests over a school year: A pilot study

The metacognitve ability to accurately estimate ones performance in a test, is assumed to be of central importance for initializing task-oriented effort. In addition activating adequate problem-solving strategies, and engaging in efficient error detection and correction. Although school children's' ability to estimate their own performance has been widely investigated, this was mostly done under highly-controlled, experimental set-ups including only one single test occasion. Method: The aim of this study was to investigate this metacognitive ability in the context of real achievement tests in mathematics. Developed and applied by a teacher of a 5th grade class over the course of a school year these tests allowed the exploration of the variability of performance estimation accuracy as a function of test difficulty. Results: Mean performance estimations were generally close to actual performance with somewhat less variability compared to test performance. When grouping the children into three achievement levels, results revealed higher accuracy of performance estimations in the high achievers compared to the low and average achievers. In order to explore the generalization of these findings, analyses were also conducted for the same children's tests in their science classes revealing a very similar pattern of results compared to the domain of mathematics. Discussion and Conclusion: By and large, the present study, in a natural environment, confirmed previous laboratory findings but also offered additional insights into the generalisation and the test dependency of students' performances estimations.

High spectral efficiency transmission emulation for non-linear transmission performance estimation for high order modulation formats

We demonstrate a simple method to experimentally evaluate nonlinear transmission performance of high order modulation formats using a low number of channels and channel-like ASE. We verify it's behaviour is consistent with the AWGN model of transmission.

Theoretical estimation of electromigration in metallic carbon nanotubes considering self-heating effect

In this paper, we estimate the solution of the electromigration diffusion equation (EMDE) in isotopically pure and impure metallic single-walled carbon nanotubes (CNTs) (SWCNTs) by considering self-heating. The EMDE for SWCNT has been solved not only by invoking the dependence of the electromigration flux on the usual applied static electric field across its two ends but also by considering a temperature-dependent thermal conductivity (κ) which results in a variable temperature distribution (T) along its length due to self-heating. By changing its length and isotopic impurity, we demonstrate that there occurs a significant deviation in the SWCNT electromigration performance. However, if κ is assumed to be temperature independent, the solution may lead to serious errors in performance estimation. We further exhibit a tradeoff between length and impurity effect on the performance toward electromigration. It is suggested that, to reduce the vacancy concentration in longer interconnects of few micrometers, one should opt for an isotopically impure SWCNT at the cost of lower κ, whereas for comparatively short interconnects, pure SWCNT should be used. This tradeoff presented here can be treated as a way for obtaining a fairly well estimation of the vacancy concentration and mean time to failure in the bundles of CNT-based interconnects. © 2012 IEEE.

Optical fibre limits:an approach using ASE channel estimation

In this talk we investigate the usage of spectrally shaped amplified spontaneous emission (ASE) in order to emulate highly dispersed wavelength division multiplexed (WDM) signals in an optical transmission system. Such a technique offers various simplifications to large scale WDM experiments. Not only does it offer a reduction in transmitter complexity, removing the need for multiple source lasers, it potentially reduces the test and measurement complexity by requiring only the centre channel of a WDM system to be measured in order to estimate WDM worst case performance. The use of ASE as a test and measurement tool is well established in optical communication systems and several measurement techniques will be discussed [1, 2]. One of the most prevalent uses of ASE is in the measurement of receiver sensitivity where ASE is introduced in order to degrade the optical signal to noise ratio (OSNR) and measure the resulting bit error rate (BER) at the receiver. From an analytical point of view noise has been used to emulate system performance, the Gaussian Noise model is used as an estimate of highly dispersed signals and has had consider- able interest [3]. The work to be presented here extends the use of ASE by using it as a metric to emulate highly dispersed WDM signals and in the process reduce WDM transmitter complexity and receiver measurement time in a lab environment. Results thus far have indicated [2] that such a transmitter configuration is consistent with an AWGN model for transmission, with modulation format complexity and nonlinearities playing a key role in estimating the performance of systems utilising the ASE channel emulation technique. We conclude this work by investigating techniques capable of characterising the nonlinear and damage limits of optical fibres and the resultant information capacity limits. REFERENCES McCarthy, M. E., N. Mac Suibhne, S. T. Le, P. Harper, and A. D. Ellis, “High spectral efficiency transmission emulation for non-linear transmission performance estimation for high order modulation formats," 2014 European Conference on IEEE Optical Communication (ECOC), 2014. 2. Ellis, A., N. Mac Suibhne, F. Gunning, and S. Sygletos, “Expressions for the nonlinear trans- mission performance of multi-mode optical fiber," Opt. Express, Vol. 21, 22834{22846, 2013. Vacondio, F., O. Rival, C. Simonneau, E. Grellier, A. Bononi, L. Lorcy, J. Antona, and S. Bigo, “On nonlinear distortions of highly dispersive optical coherent systems," Opt. Express, Vol. 20, 1022-1032, 2012.

Optimisation methods for coupled thermodynamic and 1D design of radial-inflow turbines

Optimisation is a fundamental step in the turbine design process, especially in the development of non-classical designs of radial-inflow turbines working with high-density fluids in low-temperature Organic Rankine Cycles (ORCs). The present work discusses the simultaneous optimisation of the thermodynamic cycle and the one-dimensional design of radial-inflow turbines. In particular, the work describes the integration between a 1D meanline preliminary design code adapted to real gases and the performance estimation approach for radial-inflow turbines in an established ORC cycle analysis procedure. The optimisation approach is split in two distinct loops; the inner operates on the 1D design based on the parameters received from the outer loop, which optimises the thermodynamic cycle. The method uses parameters including brine flow rate, temperature and working fluid, shifting assumptions such as head and flow coefficients into the optimisation routine. The discussed design and optimisation method is then validated against published benchmark cases. Finally, using the same conditions, the coupled optimisation procedure is extended to the preliminary design of a radial-inflow turbine with R143a as working fluid in realistic geothermal conditions and compared against results from commercially-available software RITAL from Concepts-NREC.

Blade Loading and Its Application in the Mean-Line Design of Low Pressure Turbines

In order to minimize the number of iterations to a turbine design, reasonable choices of the key parameters must be made at the preliminary design stage. The choice of blade loading is of particular concern in the low pressure (LP) turbine of civil aero engines, where the use of high-lift blades is widespread. This paper considers how blade loading should be measured, compares the performance of various loss correlations, and explores the impact of blade lift on performance and lapse rates. To these ends, an analytical design study is presented for a repeating-stage, axial-flow LP turbine. It is demonstrated that the long-established Zweifel lift coefficient (Zweifel, 1945, "The Spacing of Turbomachine Blading, Especially with Large Angular Deflection" Brown Boveri Rev., 32(1), pp. 436-444) is flawed because it does not account for the blade camber. As a result the Zweifel coefficient is only meaningful for a fixed set of flow angles and cannot be used as an absolute measure of blade loading. A lift coefficient based on circulation is instead proposed that accounts for the blade curvature and is independent of the flow angles. Various existing profile and secondary loss correlations are examined for their suitability to preliminary design. A largely qualitative comparison demonstrates that the loss correlations based on Ainley and Mathieson (Ainley and Mathieson, 1957, "A Method of Performance Estimation for Axial-Flow Turbines," ARC Reports and Memoranda No. 2974; Dunham and Came, 1970, "Improvements to the Ainley-Mathieson Method of Turbine Performance Prediction," Trans. ASME: J. Eng. Gas Turbines Power, July, pp. 252-256; Kacker and Okapuu, 1982, "A Mean Line Performance Method for Axial Flow Turbine Efficiency," J. Eng. Power, 104, pp. 111-119). are not realistic, while the profile loss model of Coull and Hodson (Coull and Hodson, 2011, "Predicting the Profile Loss of High-Lift Low Pressure Turbines," J. Turbomach., 134(2), pp. 021002) and the secondary loss model of (Traupel, W, 1977, Thermische Turbomaschinen, Springer-Verlag, Berlin) are arguably the most reasonable. A quantitative comparison with multistage rig data indicates that, together, these methods over-predict lapse rates by around 30%, highlighting the need for improved loss models and a better understanding of the multistage environment. By examining the influence of blade lift across the Smith efficiency chart, the analysis demonstrates that designs with higher flow turning will tend to be less sensitive to increases in blade loading. © 2013 American Society of Mechanical Engineers.

Experimental and Numerical Benchmarking of an Improved Impeller Meanline Modelling Method for Automotive Turbocharger Centrifugal Compressors

After the development of a new single-zone meanline modelling technique, benchmarking of the technique and the modelling methods used during its development are presented. The new meanline model had been developed using the results of three automotive turbocharger centrifugal compressors, and single passage CFD models based on their geometry.

The target of the current study was to test the new meanline modelling method on two new centrifugal compressor stages, again from the automotive turbocharger variety. Furthermore the single passage CFD modelling method used in the previous study would be again employed here and also benchmarked.

The benchmarking was twofold; firstly test the overall performance prediction accuracy of the single-zone meanline model. Secondly, test the detailed performance estimation of the CFD model using detailed interstage static pressure tappings.

The final component of this study exposed the weaknesses in the current modelling methods used (explicitly during this study). The non-axisymmetric flow field at the leading and trailing edges for the two compressors was measured and is presented here for the complete compressor map, highlighting the distortion relative to the tongue.

The effects of estimating good vs. poor knowledge of results during acquisition of a spatial motor task

Recent studies have shown that providing learners Knowledge of Results (KR) after “good trials” rather than “poor trials” is superior for learning. The present study examined whether requiring participants to estimate their three best or three worst trials in a series of six trial blocks before receiving KR would prove superior to learning compared to not estimating their performance. Participants were required to push and release a slide along a confined pathway using their non-dominant hand to a target distance (133cm). The retention and transfer data suggest those participants who received KR after good trials demonstrated superior learning and performance estimations compared to those receiving KR after poor trials. The results of the present experiment offer an important theoretical extension in our understanding of the role of KR content and performance estimation on motor skill learning.

Efeitos genéticos aditivos diretos e maternos e heterozigóticos sobre os desempenhos pré e pós-desmama em uma população multirracial Aberdeen Angus × Nelore

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

What are the metacognitive costs of young children's overconfidence?

Children typically hold very optimistic views of their own skills but so far, only a few studies have investigated possible correlates of the ability to predict performance accurately. Therefore, this study examined the role of individual differences in performance estimation accuracy as a global metacognitive index for different monitoring and control skills (item-level judgments of learning [JOLs] and confidence judgments [CJs]), metacognitive control processes (allocation of study time and control of answers), and executive functions (cognitive flexibility, inhibition, working memory) in 6-year-olds (N=93). The three groups of under estimators, realists and over estimators differed significantly in their monitoring and control abilities: the under estimators outperformed the over estimators by showing a higher discrimination in CJs between correct and incorrect recognition. Also, the under estimators scored higher on the adequate control of incorrectly recognized items. Regarding the interplay of monitoring and control processes, under estimators spent more time studying items with low JOLs, and relied more systematically on their monitoring when controlling their recognition compared to over estimators. At the same time, the three groups did not differ significantly from each other in their executive functions. Overall, results indicate that differences in performance estimation accuracy are systematically related to other global and item-level metacognitive monitoring and control abilities in children as young as six years of age, while no meaningful association between performance estimation accuracy and executive functions was found.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours