Energy Conversion Alternatives Study (ECAS), General Electric Phase 1. Volume 3: Energy conversion subsystems and components. Part 1: Bottoming cycles and materials of construction

Energy conversion subsystems and components were evaluated in terms of advanced energy conversion systems. Results of the bottoming cycles and materials of construction studies are presented and discussed

Optimal Pricing Policies For Deteriorating items With Preservation Technology And Price Sensitive Demand

This paper considers the problem of determining the price, cycle time and preservation technology cost strategies for deteriorating items. It is assumed that preservation technology investment and demand rate do follow the function of selling price. The objective is to maximize the total profit per unit time with determining the optimal selling price, length of replenishment cycle and preservation technology investment. We will be proving that the optimal cycle length and selling price are unique with respect to given preservation cost. Also, total profit per unit time will be a concave function as it will reach its optimum value for optimum value of selling price, cycle length and preservation technology cost. Numerical examples are also presented to demonstrate the solution process

Gas phase hydrogen permeation in alpha titanium and carbon steels

Commercially pure titanium and heats of Armco ingot iron and steels containing from 0.008-1.23 w/oC were annealed or normalized and machined into hollow cylinders. Coefficients of diffusion for alpha-Ti and alpha-Fe were determined by the lag-time technique. Steady state permeation experiments yield first power pressure dependence for alpha-Ti and Sievert's law square root dependence for Armco iron and carbon steels. As in the case of diffusion, permeation data confirm that alpha-titanium is subject to at least partial phase boundary reaction control while the steels are purely diffusion controlled. The permeation rate in steels also decreases as the carbon content increases. As a consequence of Sievert's law, the computed hydrogen solubility decreases as the carbon content increases. This decreases in explained in terms of hydrogen trapping at carbide interfaces. Oxidizing and nitriding the surfaces of alpha-titanium membranes result in a decrease in the permeation rate for such treatment on the gas inlet surfaces but resulted in a slight increase in the rate for such treatment on the gas outlet surfaces. This is explained in terms of a discontinuous TiH2 layer

Scaling and Formulary cross sections for ion-atom impact ionization

The values of ion-atom ionization cross sections are frequently needed for many applications that utilize the propagation of fast ions through matter. When experimental data and theoretical calculations are not available, approximate formulas are frequently used. This paper briefly summarizes the most important theoretical results and approaches to cross section calculations in order to place the discussion in historical perspective and offer a concise introduction to the topic. Based on experimental data and theoretical predictions, a new fit for ionization cross sections is proposed. The range of validity and accuracy of several frequently used approximations (classical trajectory, the Born approximation, and so forth) are discussed using, as examples, the ionization cross sections of hydrogen and helium atoms by various fully stripped ions.Comment: 46 pages, 8 figure

Design of Subsurface Geodrain for Automated Industrial Unit – Case Study

This paper describes the pre-construction modeling for design and post-construction evaluation of subsurface drainage systems for an industrial plant. Rajshree Polyfil Ltd has a polyester filament manufacturing plant spread over 50 hectare area in Bharuch district of Gujarat State, India. The plant is fully automatic and robotics operated. The cable duct for control system was laid below formation level. The seepage water was observed in the cable trench and nearby vicinity. This seriously affects the functioning of computer controlled production system. Preliminary investigation revealed that the ground water level was around 1.0m depth below formation level, which was more than 15m depth during the construction of unit. Detailed subsurface investigations and field permeability tests are carried out. Subsurface drainage system was designed and its performance was estimated prior to construction of drain with the help of computer modeling using software MODFLOW. The model area was divided in three to five layers having different permeability values obtained from field test. After construction of subsurface geodrain, discharge was measured and water level was also measured at few piezometers installed near the drain. It is found that the performance of the drain is well in accordance with the design

Curvature dependence of the effect of ionic functionalization on the attraction among nanoparticles in dispersion

Solubilization of nanoparticles facilitates nanomaterial processing and enables new applications. An effective method to improve dispersibility in water is provided by ionic functionalization.We explore how the necessary extent of functionalization depends on the particle geometry. Using molecular dynamics/umbrella sampling simulations, we determine the effect of the solute curvature on solventaveraged interactions among ionizing graphitic nanoparticles in aqueous dispersion. We tune the hydrophilicity of molecular-brush coated fullerenes, carbon nanotubes, and graphane platelets by gradually replacing a fraction of the methyl end groups of the alkyl coating by the ionizing –COOK or –NH3Cl groups. To assess the change in nanoparticles’ dispersibility in water, we determine the potential-of-mean-force profiles at varied degrees of ionization. When the coating comprises only propyl groups, the attraction between the hydrophobic particles intensifies from spherical to cylindrical to planar geometry. This is explained by the increasing fraction of surface groups that can be brought into contact and the reduced access to water molecules, both following the above sequence. When ionic groups are added, however, the dispersibility increases in the opposite order, with the biggest effect in the planar geometry and the smallest in the spherical geometry. These results highlight the important role of geometry in nanoparticle solubilization by ionic functionalities, with about twice higher threshold surface charge necessary to stabilize a dispersion of spherical than planar particles. At 25%–50% ionization, the potential of mean force reaches a plateau because of the counterion condensation and saturated brush hydration. Moreover, the increase in the fraction of ionic groups can weaken the repulsion through counterion correlations between adjacent nanoparticles. High degrees of ionization and concomitant ionic screening gradually reduce the differences among surface interactions in distinct geometries until an essentially curvature-independent dispersion environment is created. Insights into tuning nanoparticle interactions can guide the synthesis of a broad class of nonpolar nanoparticles, where solubility is achieved by ionic functionalization

Development of Zwitterionic Hydrophilic Liquid Chromatography (ZICⓇHILIC-MS) metabolomics method for Shotgun analysis of human urine

Urine is a product of the body’s metabolism and the majority of the metabolic products exiting via the renal system are rendered polar in order to be water soluble. Resolution of urinary metabolites for metabolomic studies requires the development of HPLC separation techniques that match this feature of biological chemistry. ZIC –HILIC is an ideal candidate to take forward resolution of such metabolites where reverse phase is unable to give adequate separation. Metabolomic data has to be processed by Shotgun multivariate analysis to sift through thousands of analytes and their variables such as ion intensity. In the development of ZIC-HILIC separation with mass spectrometric (IT-ToF) detection, methodological variability have to be minimized so that any Shotgun data analysis does not reveal potential biomarker analytes that are artifacts or are adversely affected of the separation and detection technique. Here, we report the development of a ZIC-HILIC mass spectrometry method that is suitable for SIMCA P+ data analysis of urine. Variables such as resolution, run reproducibility and sample storage temperature were evaluated in tandem with SIMCA P+ data analysis and quality control pre-processing. The developed method couples quality control runs that pre-process and exclude analytes that are insufficiently robust for further candidate biomarker studies. This meant labile analytes that could not be reproduced in 70% of QC runs (which are pools of all samples run that day) were excluded. However, urine samples stored at 4°C for more than 9 months will contain metabolites that will alter and produce small molecule marker artifacts when compared to samples stored at -20°C. In conclusion, the developed method is a robust method of ZIC-HILIC mass spectrometry shotgun analysis suitable for urinary metabolome discovery of robust biomarkers

Sandwich Boosting for Accurate Estimation in Partially Linear Models for Grouped Data

We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear model parameters through weighted least squares, with optimal weights (given by the inverse covariance of the response, conditional on the covariates) typically estimated by maximising a (restricted) likelihood from random effects modelling or by using generalised estimating equations. We introduce a new 'sandwich loss' whose population minimiser coincides with the weights of these approaches when the parametric forms for the conditional covariance are well-specified, but can yield arbitrarily large improvements in linear parameter estimation accuracy when they are not. Under relatively mild conditions, our estimated coefficients are asymptotically Gaussian and enjoy minimal variance among estimators with weights restricted to a given class of functions, when user-chosen regression methods are used to estimate nuisance functions. We further expand the class of functional forms for the weights that may be fitted beyond parametric models by leveraging the flexibility of modern machine learning methods within a new gradient boosting scheme for minimising the sandwich loss. We demonstrate the effectiveness of both the sandwich loss and what we call 'sandwich boosting' in a variety of settings with simulated and real-world data