Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MgU”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Materials Data on MgU by Materials Project

MgU crystallizes in the orthorhombic Pmma space group. The structure is three-dimensional. Mg is bonded to four equivalent Mg and eight equivalent U atoms to form distorted MgMg4U8 cuboctahedra that share corners with eight equivalent UMg8U4 cuboctahedra, corners with ten equivalent MgMg4U8 cuboctahedra, edges with six equivalent MgMg4U8 cuboctahedra, edges with twelve equivalent UMg8U4 cuboctahedra, faces with eight equivalent UMg8U4 cuboctahedra, and faces with twelve equivalent MgMg4U8 cuboctahedra. There are two shorter (2.80 Å) and two longer (2.81 Å) Mg–Mg bond lengths. There are a spread of Mg–U bond distances ranging from 3.24–3.56 Å. U is bonded to eight equivalent Mg and four equivalent U atoms to form distorted UMg8U4 cuboctahedra that share corners with eight equivalent MgMg4U8 cuboctahedra, corners with ten equivalent UMg8U4 cuboctahedra, edges with six equivalent UMg8U4 cuboctahedra, edges with twelve equivalent MgMg4U8 cuboctahedra, faces with eight equivalent MgMg4U8 cuboctahedra, and faces with twelve equivalent UMg8U4 cuboctahedra. There are two shorter (2.65 Å) and two longer (2.80 Å) U–U bond lengths.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on MgU(PbO3)2 by Materials Project

Pb2MgUO6 crystallizes in the monoclinic P2_1/c space group. The structure is three-dimensional. Mg2+ is bonded to six O2- atoms to form MgO6 octahedra that share corners with six equivalent UO6 octahedra. The corner-sharing octahedra tilt angles range from 18–24°. There are a spread of Mg–O bond distances ranging from 2.12–2.17 Å. U6+ is bonded to six O2- atoms to form UO6 octahedra that share corners with six equivalent MgO6 octahedra. The corner-sharing octahedra tilt angles range from 18–24°. There are four shorter (2.08 Å) and two longer (2.14 Å) U–O bond lengths. Pb2+ is bonded in a 6-coordinate geometry to six O2- atoms. There are a spread of Pb–O bond distances ranging from 2.52–2.76 Å. There are three inequivalent O2- sites. In the first O2- site, O2- is bonded in a 4-coordinate geometry to one Mg2+, one U6+, and two equivalent Pb2+ atoms. In the second O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one Mg2+, one U6+, and two equivalent Pb2+ atoms. In the third O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one Mg2+, one U6+, and two equivalent Pb2+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Challenges in correlating oxygen stable isotope ratios of hydrates on uranium ore concentrates to process waters

Exchange of oxygen stable isotopes (δ 18 O values) between precipitation waters and uranium oxides is governed by thermodynamics or kinetics. It has been assumed that meteoric waters can be related to precipitation waters in uranium ore concentrates and their calcination and reduced uranium oxide products. With this assumption, the δ 18 O values of uranium materials could provide forensic signatures that identify the production history and geolocation of nuclear materials. To further exploit the potential of δ 18 O values in nuclear material analysis, this study examines the oxygen stable isotope exchange in two UOCs, magnesium diuranate (MDU) and sodium diuranate (SDU). MDU and SDU were synthesized from solutions of uranyl nitrate hexahydrate using precipitation waters with unique oxygen isotope compositions. The structures of the MDU and SDU were analyzed using powder X-ray diffraction (p-XRD) and thermal mass loss curves, while the δ 18 O values of waters generated during thermal decomposition were analyzed using a thermogravimetric analyzer coupled to an isotope ratio infrared spectrometer (TGA-IRIS). By p-XRD, the MDU was uniform and amorphous across all syntheses with residual crystalline material incorporated as a minor component. Combined with the TGA results, all of the MDU is likely amorphous MgU 2 O 7 ·3H 2 O with MgO impurities present throughout. In contrast, the SDU synthesis resulted in multiple phases with many samples exhibiting crystalline phases including a combination of Na(UO 2 ) 4 O 2 (OH) 5 ·5H 2 O and Na 2 (UO2) 6 O 4 (OH) 6 ·8H 2 O with a Na 2 U 2 O 7 minor phase. A small fraction of the SDU samples were amorphous with no crystalline XRD peaks observed. Mass loss curves of the SDU samples revealed that the amorphous samples contained inclusions of similar crystalline phases compared to the crystalline materials. The uniformity of the MDU samples enabled highly reproducible measurements of δ 18 O values of the water vapor yielded for two dehydration events at 170 °C and 500 °C. In contrast, the multiphase composition of the SDU samples resulted in poor reproducibility in δ 18 O values. In conclusion, neither system revealed any correlation between the δ 18 O values of precipitation water, and the waters released during dehydration of the UOCs.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Hybrid Heavy Duty Diesel Powertrain for Off-Road Applications (Final Technical/Scientific Report)

In this program, a heavy-duty hybrid powersystem, consisting of a 30% downsized diesel engine and a front-end accessory drive (FEAD) incorporating a high-speed flywheel (HSFW) energy storage system, a mechanical-drive turbocharger (SuperTurbo), and motor-generator units (MGU) was developed and demonstrated. The high efficiency core 13L engine coupled with the hybrid elements was physically validated across various off-road machine and transient work cycles with the ultimate goal of demonstrating 17% efficiency improvement with equivalent transient response as the 18L diesel engine this concept powersystem would replace. The project culminated in a physical demonstration of the high-efficiency powersystem in a high-capability test cell with the engine, all the hybrid devices, controls, and required performance and emissions measurements. Based on the combination of physical validation and rigorous system simulation, the following program conclusions may be drawn: (1) The developed hybrid H2D2 powersystem demonstrated a range of efficiency improvement of 10.5 - 25.6%, with a midpoint of 17.9%; (2) Transient load response on; (3) The HSFW system was validated to achieve peak assisting of 110kW at 12,000 Nm/sec ramp rates; (4) The powersystem was validated to be capable of meeting U.S. EPA Tier 4 Final off-road emissions through transient NRTC testing; (5) A Total Cost of Ownership (TCO) analysis and found that the core 13L engine would pay back immediately (adoption of start/stop would pay back in less than one year, and the full hybrid system payback was three years.)

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗