In the past two decades, the Argo Program has collected, processed, and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1,000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002 • C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.
In this study, a Bayesian optimization (BO) based computational framework is developed to investigate the design of transcatheter aortic valve (TAV) leaflets and to optimize leaflet geometry such that its peak stress under the blood pressure of 120 mmHg is reduced. A generic TAV model is parametrized by mathematical equations describing its 2D shape and its 3D stent-leaflet assembly line. Material properties previously obtained for bovine pericardium (BP) and porcine pericardium (PP) via a combination of flexural and biaxial tensile testing were incorporated into the finite element (FE) model of TAV. A BO approach was employed to investigate about 1000 leaflet designs for each material under the nominal circular deployment and physiological loading conditions. The optimal parameter values of the TAV model were obtained, corresponding to leaflet shapes that can reduce the peak stress by 16.7% in BP and 18.0% in PP, compared with that from the initial generic TAV model. Furthermore, it was observed that while peak stresses tend to concentrate near the stent-leaflet attachment edge, optimized geometries benefit from more uniform stress distributions in the leaflet circumferential direction. Our analysis also showed that increasing leaflet contact area redistributes peak stresses to the belly region contributing to peak stress reduction. The results from this study may inspire new TAV designs that can have better durability.
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