Effective Spectral Emissivity of Gas Turbine Blades for Optical Pyrometry

Turbine blade temperature measurements are important for monitoring the turbine engine performance to protect the hot components from damage due to excess temperatures. However, the reflected radiation from the blades and the surrounding environment complicate the blade temperature measurements by optical pyrometers. This study characterizes the effect of the reflected radiation on the effective spectral emissivity of a three-dimensional turbine blade in a confined turbine space for optical pyrometry temperature measurements. The effective spectral emissivity distribution on a threedimensional blade was numerically determined for various wavelengths (0.8-15.0 lm) and actual blade surface emissivities for a specified turbine blade model. When the actual spectral emissivity of the blade surface is assumed to be 0.5, the effective spectral emissivity varies from 0.5 to 0.538 at the longer wavelength of 10.0 lm and further increases from 0.5 to 1.396 at the shorter wavelength of 0.9 lm. The results show that the effective emissivity distributions at shorter wavelengths differ greatly from those at longer wavelengths. There are also obvious differences between the effective spectral emissivity and the actual surface emissivity at shorter wavelengths. The effect of the effective emissivity on the temperature measurement accuracy, when using the optical pyrometry, was also investigated for various wavelengths (0.8-15.0 lm). The results show that the radiation reflected from the blades has less effect on the temperature measurements than on the effective emissivity, especially at the shorter wavelengths of 0.8-3.0 lm. However, the temperature measurements still need to be corrected using the effective spectral emissivity to improve the temperature calculation accuracy. This analysis provides guidelines for choosing the optimum measurement wavelengths for optical pyrometry in turbine engines

Deep learning-based holographic polarization microscopy

Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an existing holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including e.g., monosodium urate (MSU) and triamcinolone acetonide (TCA) crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view, this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.Comment: 20 pages, 8 figure

A method to measure heat flux in convection using Gardon gauge

This work was supported by the National Natural Science Foundation of China (No. 51576110), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 51321002) and the Program for New Century Excellent Talents in University (NCET-13-0315)

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Correlations between High-Temperature Oxidation Kinetics and Thermal Radiation Characteristics of Micro-Structured Nickel Surfaces Oxidized at 1173 K

Micro-structured surface functional materials were widely used in electronics, batteries, solar cells, and many other products. However, oxidation at high temperatures greatly affects the material service life and performance. This study focuses on the oxide layer characteristics after high-temperature oxidation and the thermal emissivity of metal materials with micro-structured surfaces. Micro-structured surfaces with various groove morphologies were prepared on 99.9% purity nickel samples. The high-temperature oxidation characteristics of the nickel samples with the microstructure surfaces and the total hemispherical emissivities were measured after various oxidation times in high-temperature (1173 K) air to characterize the correlations between the micro-structure surface oxidization and the emissivity at elevated temperatures. The initial surface roughness greatly affects the surface roughness after oxidation with the oxidation increasing the surface roughness on smooth or less rough surfaces but making the surface smoother for very rough surfaces. The oxidation results show that rougher initial surfaces have larger oxide grain sizes with longer oxidation times leading to smaller grain sizes. The measured total hemispherical emissivity increased with the temperature (500–1400 K) and the oxide layer thickness. The experiments further illustrates that, for the same oxide layer thickness, the measured emissivities become larger for oxides with larger grain sizes caused by the rougher original surfaces. This analysis provides an understanding of the oxidation kinetics of microstructured surfaces and how the oxidized microstructure surfaces affect the thermal radiation properties

Bending and Elastic Vibration of a Novel Functionally Graded Polymer Nanocomposite Beam Reinforced by Graphene Nanoplatelets

A novel functionally graded (FG) polymer-based nanocomposite reinforced by graphene nanoplatelets is proposed based on a new distribution law, which is constructed by the error function and contains a gradient index. The variation of the gradient index can result in a continuous variation of the weight fraction of graphene nanoplatelets (GPLs), which forms a sandwich structure with graded mechanical properties. The modified Halpin–Tsai micromechanics model is used to evaluate the effective Young’s modulus of the novel functionally graded graphene nanoplatelets reinforced composites (FG-GPLRCs). The bending and elastic vibration behaviors of the novel nanocomposite beams are investigated. An improved third order shear deformation theory (TSDT), which is proven to have a higher accuracy, is implemented to derive the governing equations related to the bending and vibrations. The Chebyshev–Ritz method is applied to describe various boundary conditions of the beams. The bending displacement, stress state, and vibration frequency of the proposed FG polymer-based nanocomposite beams under uniformly distributed loads are provided in detail. The numerical results show that the proposed distributions of GPL nanofillers can lead to a more effective pattern of improving the mechanical properties of GPL-reinforced composites than the common ones

Provenance of the early to mid-Paleozoic sediments in the northern Alxa area: Implications for tectonic evolution of the southwestern Central Asian Orogenic Belt

The continental fragments in Northwest China are key to revealing the tectonic and crustal evolution of the Central Asian Orogenic Belt (CAOB). However, their tectonic correlation, affinity and implications have not been well defined. The early to mid-Paleozoic sediments in the northern Alxa area can help to understand this question. These sediments were deposited in a deep to shallow marine environment during a regression. The southeast paleocurrent attributes their provenance to the northwest. Detrital zircons from the collected sandstones record peak ages of approximately 1726 Ma, 1462 Ma, 915 Ma and 438 Ma. The zircon εHf(t) values are negative to positive at 1726 Ma, 915 Ma and 438 Ma, but only positive at 1462 Ma. The detrital zircon U–Pb ages and Hf isotopes suggest the provenance to be the blocks in Central Tianshan and Southern Beishan or their analogs, rather than the Tarim Craton. The source blocks show no tectonic affinity to the Tarim Craton but might be accreted to it in the Neoproterozoic Rodinia. The provenance analyses show tectonic correlation among the northern Alxa, Tianshan and Beishan orogenic belts. The Late Devonian molasse deposits, geochemical shifting to continental margins and suddenly increased early Paleozoic zircons indicate an arc-continent collision. The discovery of more indicators for continental fragments advocates a multiterrane model and dominant crustal reworking/contamination for the tectonocrustal evolution of the CAOB at least during the early to mid-Paleozoic.This work was funded by the National Natural Science Foundation of China (41372225), National Key Basic Research Program of China (2013CB429801) and China Scholarship Council (201606010074)