Several finite element methods for simulating incompressible flows rely on the streamline upwind Petrov-Galerkin stabilization (SUPG) term, which is weighted by [Formula: see text]. The conventional formulation of [Formula: see text] includes a constant that depends on the time step size, producing an overall method that becomes exceedingly less accurate as the time step size approaches zero. In practice, such method inconsistency introduces significant error in the solution, especially in cardiovascular simulations, where small time step sizes may be required to resolve multiple scales of the blood flow. To overcome this issue, we propose a consistent method that is based on a new definition of [Formula: see text]. This method, which can be easily implemented on top of an existing streamline upwind Petrov-Galerkin and pressure stabilizing Petrov-Galerkin method, involves the replacement of the time step size in [Formula: see text] with a physical time scale. This time scale is calculated in a simple operation once every time step for the entire computational domain from the ratio of the L-norm of the acceleration and the velocity. The proposed method is compared against the conventional method using four cases: a steady pipe flow, a blood flow through vascular anatomy, an external flow over a square obstacle, and a fluid-structure interaction case involving an oscillatory flexible beam. These numerical experiments, which are performed using linear interpolation functions, show that the proposed formulation eliminates the inconsistency issue associated with the conventional formulation in all cases. While the proposed method is slightly more costly than the conventional method, it significantly reduces the error, particularly at small time step sizes. For the pipe flow where an exact solution is available, we show the conventional method can over-predict the pressure drop by a factor of three. This large error is almost completely eliminated by the proposed formulation, dropping to approximately 1% for all time step sizes and Reynolds numbers considered.
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http://dx.doi.org/10.1038/s41598-023-46316-4 | DOI Listing |
PLoS One
January 2025
School of Mathematics and Finance, Hunan University of Humanities, Science and Technology, Loudi, China.
In (Dai et al. 2023), the authors proposed a fast algorithm for surface reconstruction that converges rapidly from point cloud data by alternating Anderson extrapolation with implicit progressive iterative approximation (I-PIA). This algorithm employs a fixed step size during iterations to enhance convergence.
View Article and Find Full Text PDFPLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
View Article and Find Full Text PDFAnal Chem
January 2025
Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstraße 5, 45141 Essen, Germany.
Compound-specific stable isotope analysis (CSIA) using liquid chromatography-isotope ratio mass spectrometry (LC-IRMS) is a powerful tool for determining the isotopic composition of carbon in analytes from complex mixtures. However, LC-IRMS methods are constrained to fully aqueous eluents. Previous efforts to overcome this limitation were unsuccessful, as the use of organic eluents in LC-IRMS was deemed impossible.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
Introduction: We aimed to compare gait between individuals with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and cognitively unimpaired (CU) individuals and to evaluate the association between gait and regional amyloid beta (Aβ) burden in AD and DLB.
Methods: We included 420 participants (70 AD, 70 DLB, 280 CU) in the Mayo Clinic Study of Aging (MCSA). Gait was assessed using a pressure-sensor walkway.
Alzheimers Dement
January 2025
Department of Public Health Sciences, University of California, Davis, Davis, California, USA.
Introduction: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.
Methods: We define a syndrome as a unique biomarker progression pattern and develop a lag measure to cluster pre-dementia individuals, reflecting their pathology's multi-dimensionality. The technique uses the time-ordering of events to group individuals based on their position along the disease process and the relative positions of their markers.
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