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WTc crystallizes in the orthorhombic Cmmm space group. The structure is two-dimensional and consists of two WTc sheets oriented in the (0, 1, 0) direction. W3+ is bonded in a 4-coordinate geometry to four equivalent Tc3- atoms. All W–Tc bond lengths are 2.70 Å. Tc3- is bonded in a 4-coordinate geometry to four equivalent W3+ atoms.
As part of a Phase II Front End Engineering Design (FEED) study for the DOE Fossil Fuel Large Scale Pilots program, investigations of novel start-up and emergency shutdown methods for a stoker-fed, coal-fired, 10MWe sCO₂ power plant proposal were performed. These investigations were performed using 1D transient system models of the plant created within the GT-SUITE system modeling platform. The system model components were created using vendor quotation data (heat exchangers), performance maps (turbomachines), or FMU models (fired heater). Previously, Echogen Power Systems (Echogen) has used a sCO₂ power cycle start-up method that fills the system with liquid CO₂ to facilitate easy use of an electrically-driven start pump to transition the system from initial CO₂ fill to turbo-compressor initialization. The novel start-up method presented attempts this transition with a minimal filling of the system with liquid CO₂, to reduce the total CO₂ inventory required. This method leaves the start pump vulnerable to a two phase inlet condition as system pressure is below the CO₂ saturation pressure for the condenser cooling water temperature (Tcw). System model cases at Tcw of 18°C, 26°C, and 32°C, corresponding to cold, design, and hot ambient days, were analyzed to determine if the SP inlet condition could be kept as a subcooled liquid and estimate the CO₂ inventory reduction amount./p> Stoker-fed, coal-fired heaters continue to emit heat for some time even after emergency shutdown from events such as a power failure. This heat emission would lead to a failure of the fired heater, as the metal overheats, if the CO₂ flow is shut off. The emergency shutdown method presented utilizes CO₂ vented by a controllable vent valve (CRV) to provide CO₂ cooling flow to the fired heater. To determine the effectiveness of this method at keeping the fired heater peak tubing metal temperature below the ASME material temperature limit, for pressures below 5 MPa, of 816 °C, system model cases with CRV diameters ranging from 4” to 10”, and an alternative CO₂ vent routing with CRV size of 6”, were investigated./p>
Driven by the1.4 GW generator, the 60T Controlled Waveform (60 TCW) magnet was the most powerful controlled waveform system in the world and had always been one of most important magnets to the National High Magnetic Field (NHMFL) and high-field research community because of its following unique features: (1) quasi-static field up to 60 T with 100 ms flat-top and total pulse-length of about 2000 ms, (2) variable magnetic field waveforms such as staircase and triangle with flat-top (3) relative large bore (32 mm) and (4) very fast cooling time (20 minutes) between pulses [Boebinger, 2001], [Crooker et al. 2001]. The magnet is composed of nine concentric coils, with each coil consisting of several conductor winding layers reinforced by a high-strength metallic shell. The magnet underwent a catastrophic failure in 2000 and all the coils had to be rebuilt. In late 2014 the second magnet version failed near the mid-plane of coil 7. The simulations afterward that incident indicated that the overall strength of the coil would be increased by replacing a section of the reinforcing shell with Zylon fiber-epoxy composite. This reduces the stress and thus significantly lowers the level of plastic deformation in the windings. The role of the metal and Zylon fiber reinforcing layers in bearing the axial and radial Lorentz forces has been studied to optimize the magnet design. Here, the results of the optimization will be discussed as well as challenges that have been presented in rebuilding the individual coils of the magnet.
Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.