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1. A Linear and Angular Momentum Conserving Hybrid Particle/Grid Iteration for Volumetric Elastic Contact

   https://doi.org/10.1145/3606924  

   Razon, Alan Marquez; Chen, Yizhou; Han, Yushan; Gagniere, Steven; Tupek, Michael; Teran, Joseph (August 2023, Proceedings of the ACM on Computer Graphics and Interactive Techniques)

   We present a momentum conserving hybrid particle/grid iteration for resolving volumetric elastic collision. Our hybrid method uses implicit time stepping with a Lagrangian finite element discretization of the volumetric elastic material together with impulse-based collision-correcting momentum updates designed to exactly conserve linear and angular momentum. We use a two-step process for collisions: first we use a novel grid-based approach that leverages the favorable collision resolution properties of Particle-In-Cell (PIC) techniques, then we finalize with a classical collision impulse strategy utilizing continuous collision detection. Our PIC approach uses Affine-Particle-In-Cell momentum transfers as collision preventing impulses together with novel perfectly momentum conserving boundary resampling and downsampling operators that prevent artifacts in portions of the boundary where the grid resolution is of disparate resolution. We combine this with a momentum conserving augury iteration to remove numerical cohesion and model sliding friction. Our collision strategy has the same continuous collision detection as traditional approaches, however our hybrid particle/grid iteration drastically reduces the number of iterations required. Lastly, we develop a novel symmetric positive semi-definite Rayleigh damping model that increases the convexity of the nonlinear systems associated with implicit time stepping. We demonstrate the robustness and efficiency of our approach in a number of collision intensive examples. 

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2. Evaluation of Filtration Efficiency of Various Filter Media in Addressing Wildfire Smoke in Indoor Environments: Importance of Particle Size and Composition

   https://doi.org/10.3390/atmos14121729  

   Shirman, Tanya; Shirman, Elijah; Liu, Sissi (December 2023, Atmosphere)

   Sahar Zahiri (Ed.)

   Sub-micron particles are ubiquitous in the indoor environment, especially during wildfire smoke episodes, and have a higher impact on human health than larger particles. Conventional fibrous air filters installed in heating, ventilation, and air conditioning (HVAC) systems play an important role in controlling indoor air quality by removing various air pollutants, including particulate matter (PM). However, it is evident that the removal efficiency of wildfire smoke PM and its effect on filter performance is significantly under-studied. This study delves into the size-specific removal efficiency of pine needle smoke, a representative of wildfire smoke and emissions. We test an array of filter media with minimum efficiency reporting values (MERV) spanning 11–15. Both size-resolved particle number concentrations and mass concentrations were measured using an Optical Particle Sizer (OPS, TSI, Inc.) and a Scanning Mobility Particle Sizer (SMPS, TSI, Inc.). Furthermore, we characterize the filter media morphology and smoke particles deposited on filter fibers using Scanning Electron Microscopy (SEM) to gain insights into the interaction dynamics of these particles. Our findings add to the comprehension of the relationship between MERV designations and smoke removal efficiency. Such insight can inform standards and guidelines and equip decision-makers with the knowledge needed to initiate measures for mitigating the impact of air pollution, specifically on the indoor environment. 

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3. Multi-scale Fisher’s independence test for multivariate dependence

   https://doi.org/10.1093/biomet/asac013  

   Gorsky, S; Ma, L (February 2022, Biometrika)

   Summary Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational burden. We introduce a scalable, resampling-free approach to testing the independence between two random vectors by breaking down the task into simple univariate tests of independence on a collection of $$2\times 2$$ contingency tables constructed through sequential coarse-to-fine discretization of the sample , transforming the inference task into a multiple testing problem that can be completed with almost linear complexity with respect to the sample size. To address increasing dimensionality, we introduce a coarse-to-fine sequential adaptive procedure that exploits the spatial features of dependency structures. We derive a finite-sample theory that guarantees the inferential validity of our adaptive procedure at any given sample size. We show that our approach can achieve strong control of the level of the testing procedure at any sample size without resampling or asymptotic approximation and establish its large-sample consistency. We demonstrate through an extensive simulation study its substantial computational advantage in comparison to existing approaches while achieving robust statistical power under various dependency scenarios, and illustrate how its divide-and-conquer nature can be exploited to not just test independence, but to learn the nature of the underlying dependency. Finally, we demonstrate the use of our method through analysing a dataset from a flow cytometry experiment. 

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4. Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

   https://doi.org/10.3390/math12162584  

   Liang, Siming; Sun, Hui; Archibald, Richard; Bao, Feng (August 2024, Mathematics)

   This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings. 

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5. An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods

   Khaki, Bahman; Das, Pritam (January 2021, Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   A B S T R A C T This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model’s discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%. 

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