Superconductivity for Large Scale Wind Turbines

A conceptual design has been completed for a 10MW superconducting direct drive wind turbine generator employing low temperature superconductors for the field winding. Key technology building blocks from the GE Wind and GE Healthcare businesses have been transferred across to the design of this concept machine. Wherever possible, conventional technology and production techniques have been used in order to support the case for commercialization of such a machine. Appendices A and B provide further details of the layout of the machine and the complete specification table for the concept design. Phase 1 of the program has allowed us to understand the trade-offs between the various sub-systems of such a generator and its integration with a wind turbine. A Failure Modes and Effects Analysis (FMEA) and a Technology Readiness Level (TRL) analysis have been completed resulting in the identification of high risk components within the design. The design has been analyzed from a commercial and economic point of view and Cost of Energy (COE) calculations have been carried out with the potential to reduce COE by up to 18% when compared with a permanent magnet direct drive 5MW baseline machine, resulting in a potential COE of 0.075 $/kWh. Finally, a top-level commercialization plan has been proposed to enable this technology to be transitioned to full volume production. The main body of this report will present the design processes employed and the main findings and conclusions

Design and Simulated Performance of Calorimetry Systems for the ECCE Detector at the Electron Ion Collider

We describe the design and performance the calorimeter systems used in the ECCE detector design to achieve the overall performance specifications cost-effectively with careful consideration of appropriate technical and schedule risks. The calorimeter systems consist of three electromagnetic calorimeters, covering the combined pseudorapdity range from -3.7 to 3.8 and two hadronic calorimeters. Key calorimeter performances which include energy and position resolutions, reconstruction efficiency, and particle identification will be presented.Comment: 19 pages, 22 figures, 5 table

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.Comment: 16 pages, 18 figures, 2 appendices, 3 table

ECCE Sensitivity Studies for Single Hadron Transverse Single Spin Asymmetry Measurements

We performed feasibility studies for various single transverse spin measurements that are related to the Sivers effect, transversity and the tensor charge, and the Collins fragmentation function. The processes studied include semi-inclusive deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The data were obtained in {\sc pythia}6 and {\sc geant}4 simulated e+p collisions at 18 GeV on 275 GeV, 18 on 100, 10 on 100, and 5 on 41 that use the ECCE detector configuration. Typical DIS kinematics were selected, most notably $Q^2 > 1$ GeV$^2$, and cover the $x$ range from $10^{-4}$ to $1$. The single spin asymmetries were extracted as a function of $x$ and $Q^2$, as well as the semi-inclusive variables $z$, and $P_T$. They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. The initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and $e^+e^-$ annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb$^{-1}$ and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. The impact on the knowledge of the Sivers functions, transversity and tensor charges, and the Collins function has then been evaluated in the same phenomenological extractions as in the Yellow Report. The impact is found to be comparable to that obtained with the parameterized Yellow Report detector and shows that the ECCE detector configuration can fulfill the physics goals on these quantities.Comment: 22 pages, 22 figures, to be submitted to joint ECCE proposal NIM-A volum

Open Heavy Flavor Studies for the ECCE Detector at the Electron Ion Collider

The ECCE detector has been recommended as the selected reference detector for the future Electron-Ion Collider (EIC). A series of simulation studies have been carried out to validate the physics feasibility of the ECCE detector. In this paper, detailed studies of heavy flavor hadron and jet reconstruction and physics projections with the ECCE detector performance and different magnet options will be presented. The ECCE detector has enabled precise EIC heavy flavor hadron and jet measurements with a broad kinematic coverage. These proposed heavy flavor measurements will help systematically study the hadronization process in vacuum and nuclear medium especially in the underexplored kinematic region.Comment: Open heavy flavor studies with the EIC reference detector design by the ECCE consortium. 11 pages, 11 figures, to be submitted to the Nuclear Instruments and Methods

ECCE unpolarized TMD measurements

We performed feasibility studies for various measurements that are related to unpolarized TMD distribution and fragmentation functions. The processes studied include semi-inclusive Deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The single hadron cross sections and multiplicities were extracted as a function of the DIS variables $x$ and $Q^2$, as well as the semi-inclusive variables $z$, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton and $P_T$, which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. The expected statistical precision of such measurements is extrapolated to accumulated luminosities of 10 fb$^{-1}$ and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields.Comment: 12 pages, 9 figures, to be submitted in joint ECCE proposal NIM-A volum

The CLAS12 Spectrometer at Jefferson Laboratory

The CEBAF Large Acceptance Spectrometer for operation at 12 GeV beam energy (CLAS12) in Hall B at Jefferson Laboratory is used to study electro-induced nuclear and hadronic reactions. This spectrometer provides efficient detection of charged and neutral particles over a large fraction of the full solid angle. CLAS12 has been part of the energy-doubling project of Jefferson Lab's Continuous Electron Beam Accelerator Facility, funded by the United States Department of Energy. An international collaboration of 48 institutions contributed to the design and construction of detector hardware, developed the software packages for the simulation of complex event patterns, and commissioned the detector systems. CLAS12 is based on a dual-magnet system with a superconducting torus magnet that provides a largely azimuthal field distribution that covers the forward polar angle range up to 35∘, and a solenoid magnet and detector covering the polar angles from 35° to 125° with full azimuthal coverage. Trajectory reconstruction in the forward direction using drift chambers and in the central direction using a vertex tracker results in momentum resolutions of <1% and <3%, respectively. Cherenkov counters, time-of-flight scintillators, and electromagnetic calorimeters provide good particle identification. Fast triggering and high data-acquisition rates allow operation at a luminosity of 1035 cm−2s−1. These capabilities are being used in a broad program to study the structure and interactions of nucleons, nuclei, and mesons, using polarized and unpolarized electron beams and targets for beam energies up to 11 GeV. This paper gives a general description of the design, construction, and performance of CLAS12

Design of the ECCE Detector for the Electron Ion Collider

Preprint submitted to Nuclear Instruments and Methods A. The file archived on this institutional repository has not been certified by peer review.32 pages, 29 figures, 9 tablesThe EIC Comprehensive Chromodynamics Experiment (ECCE) detector has been designed to address the full scope of the proposed Electron Ion Collider (EIC) physics program as presented by the National Academy of Science and provide a deeper understanding of the quark-gluon structure of matter. To accomplish this, the ECCE detector offers nearly acceptance and energy coverage along with excellent tracking and particle identification. The ECCE detector was designed to be built within the budget envelope set out by the EIC project while simultaneously managing cost and schedule risks. This detector concept has been selected to be the basis for the EIC project detector.Office of Science in the Department of Energy, the National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR; This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05- 00OR22725. The work of AANL group are supported by the Science Committee of RA, in the frames of the research project # 21AG-1C028. And we gratefully acknowledge that support of Brookhaven National Lab and the Thomas Jefferson National Accelerator Facility which are operated under contracts DESC0012704 and DE-AC05-06OR23177 respectivel

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

arXiv preprint [v2] Fri, 20 May 2022 03:23:44 UTC (2,296 KB) made available under a Creative Commons (CC BY) Attribution Licence, now in press, published by Elsevier: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, available online 17 November 2022 at: https://doi.org/10.1016/j.nima.2022.167748The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.Office of Nuclear Physics in the Office of Science in the Department of Energy; National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere