Towards Exascale Computation for Turbomachinery Flows

A state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA, which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components.Comment: SC23, November, 2023, Denver, CO., US

A Review on the Optimal Fingerprinting Approach in Climate Change Studies

We provide a review on the "optimal fingerprinting" approach as summarized in Allen and Tett (1999) from a point view of statistical inference in light of the recent criticism of McKitrick (2021). Our review finds that the "optimal fingerprinting" approach would survive much of McKitrick (2021)'s criticism under two conditions: (i) the null simulation of the climate model is independent of the physical observations and (ii) the null simulation provides consistent estimation of the residual covariance matrix of the physical observations, both depend on the conduction and the quality of the climate models. If the latter condition fails, the estimator would be still unbiased and consistent under routine conditions, but losing the "optimal" aspect of the approach. The residual consistency test suggested by Allen and Tett (1999) is valid for checking the agreement between the residual covariances of the null simulation and the physical observations. We further outline the connection between the "optimal fingerprinting" approach and the Feasible Generalized Least Square

Estimate Forest Aboveground Biomass of Mountain by ICESat-2/ATLAS Data Interacting Cokriging

Compared with the previous full-waveform data, the new generation of ICESat-2/ATLAS (Advanced Terrain Laser Altimeter System) has a larger footprint overlap density and a smaller footprint area. This study used ATLAS data to estimate forest aboveground biomass (AGB) in a high-altitude, ecologically fragile area. The paper used ATLAS data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Then, we combined biomass data from 54 ground samples to obtain the estimated AGB of 74,873 footprints using a hyperparametric optimized random forest (RF) model. The total AGB was estimated by combining the best variance function model in geostatistics with the slope that is the covariates. The results showed that among the 50 index parameters and three topographic variables extracted based on ATLAS, six variables showed a significant correlation with AGB. They were, in order, number of canopy photons, Landsat percentage canopy, canopy photon rate, slope, number of photons, and apparent surface reflectance. The optimized random forest model was used to estimate the AGB within the footprints. The model accuracy was the coefficient of determination (R2) = 0.93, the root mean square error (RMSE) = 10.13 t/hm2, and the population estimation accuracy was 83.3%. The optimized model has a good estimation effect and can be used for footprint AGB estimation. The spatial structure analysis of the variance function of footprint AGB showed that the spherical model had the largest fitting accuracy (R2 = 0.65, the residual sum of squares (RSS) = 2.65 × 10−4), the nugget (C0) was 0.21, and the spatial structure ratio was 94.0%. It showed that the AGB of footprints had strong spatial correlation and could be interpolated by kriging. Finally, the slope in the topographic variables was selected as the co-interpolation variable, and cokriging spatial interpolation was performed. Furthermore, a continuous map of AGB spatial distribution was obtained, and the total AGB was 6.07 × 107 t. The spatial distribution of AGB showed the same trend as the distribution of forest stock. The absolute accuracy of the estimation was 82.6%, using the statistical value of the forest resource planning and design survey as a reference. The ATLAS data can improve the accuracy of AGB estimation in mountain forests

Estimation of Forest Canopy Cover by Combining ICESat-2/ATLAS Data and Geostatistical Method/Co-Kriging

Accurately estimating forest canopy cover (FCC) is challenging by using traditional remote sensing images at the regional level due to the spectral saturation phenomenon. In this study, to improve the estimation accuracy, a new method of FCC wall-to-wall mapping was suggested based on ice, cloud, and land elevation satellite/advanced topographic laser altimeter system (ATLAS) data. Specifically, one dataset of FCC's observations was combined with preprocessed ATLAS data and topographic factors to build a random forest regression (RFR) model. Moreover, the Co-Kriging method was used to generate spatially explicit values that are required by the RFR from the point data of ATLAS parameters, and then the wall-to-wall mapping of the FCC was conducted. The results showed that the RFR model had an accuracy of relative root-mean-square error (rRMSE) = 0.09 with a coefficient of determination (R2) = 0.91. The best-fit semivariogram models between primary variables and covariates were asr and TR (Model: Gaussian model, R2 = 0.94, the residual sum of squares (RSS) = 1.73 × 10−6), landsat_perc and NDVI (Model: spherical model, R2 = 0.46, RSS = 1.58 × 10−4), and photon_rate_can and slope (Model: exponential model, R2 = 0.77, RSS = 6.45 × 10−4), respectively. FCC validation result showed that the FCC's wall-to-wall mapping was in great agreement with the dataset-2 (R2 = 0.79; rRMSE = 0.11)

COVID-19 related outcomes among individuals with neurodegenerative diseases : a cohort analysis in the UK biobank

Publisher Copyright: © 2022, The Author(s). This work is supported by the National Natural Science Foundation of China (No. 81971262 to HS), West China Hospital COVID-19 Epidemic Science and Technology Project (No. HX-2019-nCoV-014 to HS), Sichuan University Emergency Grant (No. 2020scunCoVyingji10002 to HS), National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (No. Z20201013 to HS), Project Grant form Science & Technology Department of Sichuan Providence (2020YFS0575 to HS), and NordForsk grant (105668 to UV and FF). The funding agencies did not have any role in the design of study, data collection, data analysis, interpretation or writing the manuscript.Background: An increased susceptibility to COVID-19 has been suggested for individuals with neurodegenerative diseases, but data are scarce from longitudinal studies. Methods: In this community-based cohort study, we included 96,275 participants of the UK Biobank who had available SARS-CoV-2 test results in Public Health England. Of these, 2617 had a clinical diagnosis of neurodegenerative diseases in the UK Biobank inpatient hospital data before the outbreak of COVID-19 (defined as January 31st, 2020), while the remaining participants constituted the reference group. We then followed both groups from January 31st, 2020 to June 14th, 2021 for ascertainment of COVID-19 outcomes, including any COVID-19, inpatient care for COVID-19, and COVID-19 related death. Logistic regression was applied to estimate the association between neurogenerative disease and risks of COVID-19 outcomes, adjusted for multiple confounders and somatic comorbidities. Results: We observed an elevated risk of COVID-19 outcomes among individuals with a neurodegenerative disease compared with the reference group, corresponding to a fully adjusted odds ratio of 2.47 (95%CI 2.25–2.71) for any COVID-19, 2.18 (95%CI 1.94–2.45) for inpatient COVID-19, and 3.67 (95%CI 3.11–4.34) for COVID-19 related death. Among individuals with a positive test result for SARS-CoV-2, individuals with neurodegenerative diseases had also a higher risk of COVID-19 related death than others (fully adjusted odds ratio 2.08; 95%CI 1.71–2.53). Conclusion: Among UK Biobank participants who received at least one test for SARS-CoV-2, a pre-existing diagnosis of neurodegenerative disease was associated with a subsequently increased risk of COVID-19, especially COVID-19 related death.Peer reviewe

Generation and [2,3]-Sigmatropic Rearrangement of Ammonium Ylides from Cyclopropyl Ketones for Chiral Indolizidines with Bridgehead Quaternary Stereocenters

A sequence of nucleophilic ring opening of cyclopropyl ketones, N-quaternization, deprotonation, and [2,3]-sigmatropic rearrangement of ammonium ylides has been developed. This method enables efficient synthesis of bicyclic indolizidines bearing bridgehead aza-quaternary stereocenters from easily available chiral cyclopropyl ketones. The reactions proceeded with an excellent level of chirality transfer and tolerated various functional groups, providing a diverse array of allenyl- or allyl-substituted indolizidines with high enantiomeric purities (up to >99% ee)