General Electric

About: General Electric is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Turbine & Signal. The organization has 76365 authors who have published 110557 publications receiving 1885108 citations. The organization is also known as: General Electric Company & GE.

High-temperature refractory metal-intermetallic composites

Abstract: In this article, toughness, oxidation, and rupture behaviors of present-generation refractory metal-intermetallic composites are compared to the performance requisites necessary to make these materials a competitive choice for the jet engine turbine environment of the future.

Absolute rotational Raman cross sections for N2, O2, and CO2

Abstract: We present absolute intensities for rotational Raman scattering (RRS) from N2, O2, and CO2, excited at 488.0 and 647.1 nm. The absolute scattering intensity for N2 at 488.0 nm is characterized by its differential cross section for backscattering, summed over Stokes and anti-Stokes bands and over scattered-light polarizations, which we find to be 1.64 × 10−29 cm2/sr ±8%. The ratio of the cross section for O2 to that for N2 at 488.0 nm is 2.61 ± 5%, whereas the corresponding ratio for CO2 to N2 is 10.6 ± 10%. Our values for RRS cross sections relative to the N2 vibrational Raman cross section are in reasonable agreement with corresponding ratios reported recently by Fenner et al. On the other hand, our absolute cross sections are approximately twice as large as those obtained from the results of Fenner et al., but agree closely with values calculated from recent measurements of the depolarization of Rayleigh scattering. Detailed observations of relative rotational-Raman-line intensities at temperatures of 22, 75, and 125 °C are consistent with theoretical predictions.

A Megawatt-Scale Power Hardware-in-the-Loop Simulation Setup for Motor Drives

Abstract: We report on the application of a 5-MW variable voltage source (VVS) amplifier converter for utilization in power hardware-in-the-loop (PHIL) experiments with megawatt-scale motor drives. In particular, a commercial 2.5-MW variable speed motor drive (VSD) with active front end was connected to a virtual power system using the VVS for integrating the drive with a simulated power system. An illustrative example is given, whereby a 4-MW gas turbine generator system, including various loads, is simulated and interfaced with the VSD hardware in the lab through the VVS using current feedback to the simulation. Mechanical loading is applied to the motor via an identical 2.5-MW dynamometer connected to the same shaft. This paper first describes the PHIL facility, illustrates the challenges of powering a motor drive from a controlled voltage source converter at the multimegawatt scale, and provides experimental results from dynamic simulations. While certain challenges remain with the accuracy of the interface, it is concluded that PHIL simulations at the megawatt power level are possible and may prove useful for validating models of drive systems in the future.

Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI

Abstract: Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density–weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUVmax was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.