Performance prediction of graphene nanoribbon and carbon nanotube transistors

Carbon nanotubes (CNTs) and graphene nanoribbons (GNRs) field-effect transistor (FET) can be the basis for a quasi-one- dimensional (Q1D) transistor technology. Recent experiments show that the on-off ratio for GNR devices can be improved to level exploration of transistor action is justified. Here we use the tight-binding energy dipersion approximation, to assess the performance of semiconducting CNT and GNR is qualitatively in terms of drain current drive strength, bandgap and density of states for a specified device. By reducing the maximum conductance 4e2/h by half, we observed that our model has a particularly good fit with 50 nm channel single walled carbon nanotube (SWCNT) experimental data. Given the same bandgap, CNTs outperform GNRs due to valley degeneracy. Nevertheless, the variation of the device contacts will decide which transistor will exhibit better conductivity and thus higher ON currents. © 2011 American Institute of Physics.

Statistics anxiety, trait anxiety, learning behavior, and academic performance

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N=147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical selfconcept, learning strategies, and procrastination. Additionally, their performance in the examination was recorded. The structural equation model showed that statistics anxiety held a crucial role as the strongest direct predictor of performance. Students with higher statistics anxiety achieved less in the examination and showed higher procrastination scores. Statistics anxiety was related indirectly to spending less effort and time on learning. Trait anxiety was related positively to statistics anxiety and, counterintuitively, to academic performance. This result can be explained by the heterogeneity of the measure of trait anxiety. The part of trait anxiety that is unrelated to the specific part of statistics anxiety correlated positively with performance.

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.

The role of adaptive thermal comfort in the prediction of the thermal performance of a modern mixed-mode office building in the UK under climate change

Climate change is becoming a serious issue for the construction industry, since the time scales at which climate change takes place can be expected to show a true impact on the thermal performance of buildings and HVAC systems. In predicting this future building performance by means of building simulation, the underlying assumptions regarding thermal comfort conditions and the related heating, ventilating and air conditioning (HVAC) control set points become important. This article studies the thermal performance of a reference office building with mixedmode ventilation in the UK, using static and adaptive thermal approaches, for a series of time horizons (2020, 2050 and 2080). Results demonstrate the importance of the implementation of adaptive thermal comfort models, and underpin the case for its use in climate change impact studies. Adaptive thermal comfort can also be used by building designers to make buildings more resilient towards change. © 2010 International Building Performance Simulation Association (IBPSA).