Parametric study of turbine NGV blade lean and vortex design

The effects of blade lean and vortex design on the aerodynamics of a turbine entry nozzle guide vane (NGV) are considered using computational fluid dynamics. The aim of the work is to address some of the uncertainties which have arisen from previous studies where conflicting results have been reported for the effect on the NGV. The configuration was initially based on the energy efficient engine turbine which also served as the validation case for the computational method. A total of 17 NGV configurations were evaluated to study the effects of lean and vortex design on row efficiency and secondary kinetic energy. The distribution of mass flow ratio is introduced as an additional factor in the assessment of blade lean effects. The results show that in the turbine entry NGV, the secondary flow strength is not a dominant factor that determines NGV losses and therefore the changes of loading distribution due to blade lean and the associated loss mechanisms should be regarded as a key factor. Radial mass flow redistribution under different NGV lean and twist is demonstrated as an addition key factor influencing row efficiency

On the Survival of Overconfident Traders in a Competitive Securities Market

Recent research has proposed several ways in which overconfident traders can persist in competition with rational traders. This paper offers an additional reason: overconfident traders do better than purely rational traders at exploiting mispricing caused by liquidity or noise traders. We examine both the static profitability of overconfident versus rational trading strategies, and the dynamic evolution of a population of overconfident, rational and noise traders. Replication of overconfident and rational types is assumed to be increasing in the recent profitability of their strategies. The main result is that the long-run steady-state equilibrium always involves overconfident traders as a substantial positive fraction of the population.Survivorship, Natural Selection, Overconfident Traders, Noise traders

Fast Covariance Estimation for High-dimensional Functional Data

For smoothing covariance functions, we propose two fast algorithms that scale linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension $J \times J$ with $J>500$; the recently introduced sandwich smoother is an exception, but it is not adapted to smooth covariance matrices of large dimensions such as $J \ge 10,000$. Covariance matrices of order $J=10,000$, and even $J=100,000$, are becoming increasingly common, e.g., in 2- and 3-dimensional medical imaging and high-density wearable sensor data. We introduce two new algorithms that can handle very large covariance matrices: 1) FACE: a fast implementation of the sandwich smoother and 2) SVDS: a two-step procedure that first applies singular value decomposition to the data matrix and then smoothes the eigenvectors. Compared to existing techniques, these new algorithms are at least an order of magnitude faster in high dimensions and drastically reduce memory requirements. The new algorithms provide instantaneous (few seconds) smoothing for matrices of dimension $J=10,000$ and very fast ($<$ 10 minutes) smoothing for $J=100,000$. Although SVDS is simpler than FACE, we provide ready to use, scalable R software for FACE. When incorporated into R package {\it refund}, FACE improves the speed of penalized functional regression by an order of magnitude, even for data of normal size ($J <500$). We recommend that FACE be used in practice for the analysis of noisy and high-dimensional functional data.Comment: 35 pages, 4 figure

Dynamic finite-strain modelling of the human left ventricle in health and disease using an immersed boundary-finite element method

Detailed models of the biomechanics of the heart are important both for developing improved interventions for patients with heart disease and also for patient risk stratification and treatment planning. For instance, stress distributions in the heart affect cardiac remodelling, but such distributions are not presently accessible in patients. Biomechanical models of the heart offer detailed three-dimensional deformation, stress and strain fields that can supplement conventional clinical data. In this work, we introduce dynamic computational models of the human left ventricle (LV) that are derived from clinical imaging data obtained from a healthy subject and from a patient with a myocardial infarction (MI). Both models incorporate a detailed invariant-based orthotropic description of the passive elasticity of the ventricular myocardium along with a detailed biophysical model of active tension generation in the ventricular muscle. These constitutive models are employed within a dynamic simulation framework that accounts for the inertia of the ventricular muscle and the blood that is based on an immersed boundary (IB) method with a finite element description of the structural mechanics. The geometry of the models is based on data obtained non-invasively by cardiac magnetic resonance (CMR). CMR imaging data are also used to estimate the parameters of the passive and active constitutive models, which are determined so that the simulated end-diastolic and end-systolic volumes agree with the corresponding volumes determined from the CMR imaging studies. Using these models, we simulate LV dynamics from end-diastole to end-systole. The results of our simulations are shown to be in good agreement with subject-specific CMR-derived strain measurements and also with earlier clinical studies on human LV strain distributions

Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Biomechanical computational models have potential prognostic utility in patients after an acute ST-segment–elevation myocardial infarction (STEMI). In a proof-of-concept study, we defined two groups (1) an acute STEMI group (n = 6, 83% male, age 54 ± 12 years) complicated by left ventricular (LV) systolic dysfunction; (2) an age- and sex- matched hyper-control group (n = 6, 83% male, age 46 ± 14 years), no prior history of cardiovascular disease and normal systolic blood pressure (SBP &#60; 130 mmHg). Cardiac MRI was performed in the patients (2 days &#38; 6 months post-STEMI) and the volunteers, and biomechanical heart models were synthesized for each subject. The candidate parameters included normalized active tension (ATnorm) and active tension at the resting sarcomere length (Treq, reflecting required contractility). Myocardial contractility was inversely determined from personalized heart models by matching CMR-imaged LV dynamics. Compared with controls, patients with recent STEMI exhibited increased LV wall active tension when normalized by SBP. We observed a linear relationship between Treq 2 days post-MI and global longitudinal strain 6 months later (r = 0.86; p = 0.03). Treq may be associated with changes in LV function in the longer term in STEMI patients complicated by LV dysfunction. Further studies seem warranted

Employment-Contingent Health Insurance, Illness, and Labor Supply of Women: Evidence from Married Women with Breast Cancer

We examine the effects of employment-contingent health insurance on married women's labor supply following a health shock. First, we develop a theoretical model that examines the effects of employment-contingent health insurance on the labor supply response to a health shock, to clarify under what conditions employment-contingent health insurance is likely to dampen the labor supply response. Second, we empirically evaluate this relationship using primary data. The results from our analysis find that -- as the model suggests is likely -- health shocks decrease labor supply to a greater extent among women insured by their spouse's policy than among women with health insurance through their own employer. Employment-contingent health insurance appears to create incentives to remain working and to work at a greater intensity when faced with a serious illness.