General Electric (Israel)

This study proposes a novel, highly sensitive neutron detector design utilizing a unique multi-layered configuration. Each layer consists of a LiF: ZnS(Ag) scintillator coupled with a transparent neutron moderator that also functions as a light guide for the Silicon Photomultiplier (SiPM) light sensor. This design offers a cost-effective and readily available alternative for existing neutron detectors. The research focused on optimizing a single layer for maximum sensitivity and proper gamma rejection capabilities. This optimized configuration is then replicated to form the multi-layer detector. A primary challenge with the LiF:ZnS(Ag) scintillator is its inherent opacity, limiting its width and consequently, its detection efficiency. Our proposed multi-layer structure addresses this limitation by employing a thin scintillator in each layer. This strategic design minimizes emitted light attenuation while simultaneously enhancing sensitivity through the cumulative effect of multiple layers. Our experiments demonstrate a significant improvement in detection efficiency compared to the single-layer setup. Additionally, our architecture offers an actual improvement in differentiating between gamma and neutron signals. By analyzing count rates across the detector’s layers, we gain valuable operational insights, such as the ability to predict the source direction. Our finding demonstrates an improved sensitivity achieved by minimal loss of neutrons during the moderation of 329% compared to a single layer, aligned with the potential range of improvement while maintaining extremely high gamma rejection. Supported by the presented findings, this design represents a noticeable advancement over existing solutions, offering scalable customization to user requirements. Notably, it outperforms traditional 3He tube-based detection configurations, positioning it as a compelling and advantageous replacement option.

The Multi-Megawatts Any-Altitude Gas-Insulated and Cooled (MAAGIC) cable and connector system developed and prototyped at GE Research for 10-MW power distribution on electrified aircraft is introduced in this paper. This cabling system, including the thermal management system (TMS), is expected to achieve >3x lower weight than off-the-shelf state-of-the-art (SOA) high-temperature superconducting (HTS) cabling system by utilizing light-weight aviation-grade aluminum, gaseous CO2 as electrical insulation to enable high-voltage power transfer, and supercritical CO2 as advanced heat transfer fluid to significantly boost conductor current density. MAAGIC cable will enable MW-level electric power transfer for future all-electric large aircrafts to achieve zero emissions. This technology can also be used in other power transfer needs requiring high power density with small footprint including applications such as electric ships, submarines, and offshore wind turbine platforms.

The validation of a partial discharge inception model for insulation surface discharges is presented and discussed in this paper, referring to its behavior with pressure. Since the model contains an explicit dependence on pressure, it can be used to develop partial-discharge free designs of any electrical insulation used in aircraft/aerospace. Initial fitting of the model to partial discharge inception voltage measurements (for surface discharges) at pressures lower and higher than standard atmospheric pressure turns out to be very satisfactory, thus opening exciting new perspectives for electrical asset component design of electrical or hybrid aircrafts and, in general, aerospace applications.

Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However, obtaining annotated datasets from real radar images, crucial for training these networks, is challenging, especially in scenarios with long-range detection and adverse weather and lighting conditions where radar performance excels. To address this challenge, we present Rad-SimReal, an innovative physical radar simulation capable of generating synthetic radar images with accompanying annotations for various radar types and environmental conditions, all without the need for real data collection. Remarkably, our findings demonstrate that training object detection models on RadSimReal data and subsequently evaluating them on real-world data produce performance levels comparable to models trained and tested on real data from the same dataset, and even achieves better performance when testing across different real datasets. Rad-SimReal offers advantages over other physical radar simulations that it does not necessitate knowledge of the radar design details, which are often not disclosed by radar suppliers, and has faster run-time. This innovative tool has the potential to advance the development of computer vision algorithms for radar-based autonomous driving applications. Our GitHub: https://yuvalhg.github.io/RadSimReal.

The monitoring unit used in the nuclear magnetic resonance system, as an important unit of the system, faces a high thermal risk during its entire life cycle. This paper ensures the high efficiency and reliability of the thermal design of the product module from the two dimensions of structural design and device derating design. In order to reduce the risk of thermal design of electronic modules and comprehensively verify the effectiveness of thermal design of electronic modules, the design verification is carried out by combining simulation and experiment. In the simulation process, by establishing a thermal simulation model at the circuit board level, the crustal temperature of the core device is numerically calculated, and the index is compared with the thermal design index value and the test value, on the one hand, to verify the correctness of the simulation model. On the other hand, the validity of thermal design is verified. In the testing process, a thermal test platform for product modules is built, and the thermal characteristics test values of the core components of the module under extreme electrical conditions are obtained, and the corresponding conversion methods are used to predict the thermal performance and thermal design margin of the product at different altitudes. The results show that the electronic module can meet the thermal design requirements in terms of structural design and derating design of core components, and can ensure that the product module can work safely and reliably during the entire life cycle of the NMR system.

Advanced additive manufacturing opens ways to improve gas turbine fuel nozzles to enhance engine durability and performance while reducing gas emissions including NOx, CO, and soot. Additively building complex fuel passages enables engineers to better control the fuel-air mixing and its burning, not only in gas-only but also in dual fuel engine configurations. Dual fuel engines have additional requirements for proper liquid jet trajectory and spray quality and droplet evaporation that needs to be met through the careful injector configuration within the liquid fuel cartridges. Additionally, recent liquid fuel cartridges in the combustor inject a set of discrete liquid jets into a non-uniform cross flow, which can lead to complex interactions between liquid jets and incoming air during the jet breakup and evaporation phases. This can be an engineering challenge during engine development because the classical evaporation models need to be calibrated to get the accurate evaporation profile. This paper investigates the evaporation profile of liquid jets in non-uniform cross flow along the combustor axis numerically and experimentally. Three different crossflow temperatures are simulated and the effect of crossflow temperature on the spray evaporation and residual droplet characteristics is documented. Two different evaporation models, diffusion controlled, and convection/diffusion controlled based evaporation models have been utilized to predict the spray trajectory and spray density after the evaporation. Results were compared with experiments conducted at University of Cincinnati Research Institute (UCRI) with reasonable agreement. The fundamental physics and understanding of spray evaporation can help the combustor engineers to configure liquid fuel injectors that provide a suitable fuel/air mixture distribution for flame stabilization as well as complete combustion with minimal soot and CO emission in gas turbine engines.

Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation. Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task. Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were—(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0. Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

The importance of developing a robust remote workforce in academic radiology has come to the forefront owing to several converging factors. COVID-19, as well as the abrupt transformation it precipitated in terms of how radiologists work, has been the biggest impetus for change; concurrent factors such as increasing examination volumes and radiologist burnout have also contributed. How to best advance the most desirable and favorable aspects of remote work while preserving an academic environment that fulfills the tripartite mission is a critical challenge that nearly all academic institutions face today. In this AJR Expert Panel Narrative Review, we discuss current challenges in academic radiology, including effects of the COVID-19 pandemic, from three perspectives-those of the radiologist, the learner, and the health system-and address the following topics: productivity, recruitment, wellness, clinical supervision, mentorship and research, educational engagement, radiologist access, investments in technology, and radiologist value. Throughout, we focus on the opportunities and drawbacks of remote work, to help guide its effective and reliable integration into academic radiology practices.