Browsing by Author "Huang, H"

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    A phase field model for mass transport with semi-permeable interfaces
    (Journal of Computational Physics, 2022-09-01) Qin, Y; Huang, H; Zhu, Y; Liu, C; Xu, S
    In this paper, a thermaldynamical consistent model for mass transfer across permeable moving interfaces is proposed by using the energy variation method. We consider a restricted diffusion problem where the flux across the interface depends on its conductance and the difference of the concentration on each side. The diffusive interface phase-field framework used here has several advantages over the sharp interface method. First of all, explicit tracking of the interface is no longer necessary. Secondly, interfacial conditions can be incorporated with a variable diffusion coefficient. Finally, topological changes of interfaces can be handed easily. A detailed asymptotic analysis confirms the diffusive interface model converges to the existing sharp interface model as the interface thickness goes to zero. An energy stable numerical scheme is developed to solve this highly nonlinear coupled system.Numerical simulations first illustrate the consistency of theoretical results on the sharp interface limit. Then a convergence study and energy decay test are conducted to ensure the efficiency and stability of the numerical scheme. To illustrate the effectiveness of our phase-field approach, several examples are provided, including a study of a two-phase mass transfer problem where droplets with deformable interfaces are suspended in a moving fluid.
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    Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
    (Remote Sensing of Environment, 2022-03-01) Duncanson, L; Kellner, JR; Armston, J; Dubayah, R; Minor, DM; Hancock, S; Healey, SP; Patterson, PL; Saarela, S; Marselis, S; Silva, CE; Bruening, J; Goetz, SJ; Tang, H; Hofton, M; Blair, B; Luthcke, S; Fatoyinbo, L; Abernethy, K; Alonso, A; Andersen, HE; Aplin, P; Baker, TR; Barbier, N; Bastin, JF; Biber, P; Boeckx, P; Bogaert, J; Boschetti, L; Boucher, PB; Boyd, DS; Burslem, DFRP; Calvo-Rodriguez, S; Chave, J; Chazdon, RL; Clark, DB; Clark, DA; Cohen, WB; Coomes, DA; Corona, P; Cushman, KC; Cutler, MEJ; Dalling, JW; Dalponte, M; Dash, J; de-Miguel, S; Deng, S; Ellis, PW; Erasmus, B; Fekety, PA; Fernandez-Landa, A; Ferraz, A; Fischer, R; Fisher, AG; García-Abril, A; Gobakken, T; Hacker, JM; Heurich, M; Hill, RA; Hopkinson, C; Huang, H; Hubbell, SP; Hudak, AT; Huth, A; Imbach, B; Jeffery, KJ; Katoh, M; Kearsley, E; Kenfack, D; Kljun, N; Knapp, N; Král, K; Krůček, M; Labrière, N; Lewis, SL; Longo, M; Lucas, RM; Main, R; Manzanera, JA; Martínez, RV; Mathieu, R; Memiaghe, H; Meyer, V; Mendoza, AM; Monerris, A; Montesano, P; Morsdorf, F; Næsset, E; Naidoo, L; Nilus, R; O'Brien, M; Orwig, DA; Papathanassiou, K; Parker, G; Philipson, C; Phillips, OL; Pisek, J; Poulsen, JR; Pretzsch, H; Rüdiger, C
    NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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    An Energy Stable $C^0$ Finite Element Scheme for A Phase-Field Model of Vesicle Motion and Deformation
    (SIAM Journal on Scientific Computing, 2022-01) Shen, L; Xu, Z; Lin, P; Huang, H; Xu, S
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    An energy stable C0 finite element scheme for a quasi-incompressible phase-field model of moving contact line with variable density
    (Journal of Computational Physics, 2020-03-15) Shen, L; Huang, H; Lin, P; Song, Z; Xu, S
    In this paper, we focus on modeling and simulation of two-phase flow problems with moving contact lines and variable density. A thermodynamically consistent phase-field model with general Navier boundary condition is developed based on the concept of quasi-incompressibility and the energy variational method. A mass conserving C0 finite element scheme is proposed to solve the PDE system. Energy stability is achieved at the fully discrete level. Various numerical results confirm that the proposed scheme for both P1 element and P2 element are energy stable.
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    Analysis of main risk factors causing stroke in Shanxi Province based on machine learning models
    (Informatics in Medicine Unlocked, 2021-01-01) Liu, J; Sun, Y; Ma, J; Tu, J; Deng, Y; He, P; Li, R; Hu, F; Huang, H; Zhou, X; Xu, S
    Background: In China, stroke has been the first leading cause of death in recent years. It is a major cause of long-term physical and cognitive impairment, which bring great pressure on the National Public Health System. On the other hand, China is a big country, evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China. Methods: A data set with 2000 hospitalized stroke patients in 2018 and 27583 residents during the year 2017 to 2020 is analyzed in this study. With the cleaned data, three models on stroke risk levels are built by using machine learning methods. The importance of “8+2” factors from China National Stroke Prevention Project (CSPP) is evaluated via decision tree and random forest models. The importance of more detailed features and their SHAP values are evaluated and ranked via random forest model. Furthermore, a logistic regression model is applied to evaluate the probability of getting stroke for different risk levels. Results: Among all “8+2” risk factors of getting stroke, the decision tree model reveals that top three factors are Hypertension (0.4995), Physical Inactivity (0.08486) and Diabetes Mellitus (0.07889), and the random forest model shows that top three factors are Hypertension (0.3966), Hyperlipidemia (0.1229) and Physical Inactivity (0.1146). In addition to “8+2” factors the importance of features for lifestyle information, demographic information and medical measurement are evaluated via random forest model. It shows that top five features are Systolic Blood Pressure (SBP) (0.3670), Diastolic Blood Pressure (DBP) (0.1541), Physical Inactivity (0.0904), Body Mass Index (BMI) (0.0721) and Fasting Blood Glucose (FBG)(0.0531). SHAP values show that DBP, Physical Inactivity, SBP, BMI, Smoking, FBG, and Triglyceride(TG) are positively correlated to the risk of getting stroke. High-density Lipoprotein (HDL) is negatively correlated to the risk of getting stroke. Combining with the data of 2000 hospitalized stroke patients, the logistic regression model shows that the average probabilities of getting stroke are 7.20%±0.55% for the low-risk level patients, 19.02%±0.94% for the medium-risk level patients and 83.89%±0.97% for the high-risk level patients. Conclusion: Based on the census data from Shanxi Province, we investigate stroke risk factors and their ranking. It shows that Hypertension, Physical Inactivity, and Overweight are ranked as the top three high stroke risk factors in Shanxi. The probability of getting a stroke is also estimated through our interpretable machine learning methods.
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    Droplet dynamics: A phase-field model of mobile charges, polarization, and its leaky dielectric approximation
    (Physics of Fluids, 2023-08-01) Qin, Y; Huang, H; Song, Z; Xu, S
    This paper presents a Poisson–Nernst–Planck–Navier–Stokes–Cahn–Hillard (PNP–NS–CH) model for an electrically charged droplet suspended in a viscous fluid under an external electric field. Our model incorporates spatial variations in electric permittivity and diffusion constants, as well as interfacial capacitance. Based on a time scale analysis, we derive two approximations of the original model: a dynamic model for the net charge (assuming unchanged conductance) and a leaky-dielectric model (assuming unchanged conductance and net charge). For the leaky-dielectric model, we perform a detailed asymptotic analysis to demonstrate the convergence of the diffusive-interface leaky-dielectric model to the sharp interface model as the interface thickness approaches zero. Numerical computations are conducted to validate the asymptotic analysis and demonstrate the model's effectiveness in handling topology changes, such as electro-coalescence. Our numerical results from these two approximation models reveal that the polarization force, induced by the spatial variation in electric permittivity perpendicular to the external electric field, consistently dominates the Lorentz force arising from the net charge. The equilibrium shape of droplets is determined by the interplay between these two forces along the direction of the electric field. Moreover, in the presence of interfacial capacitance, a local variation in effective permittivity results in the accumulation of counter-ions near the interface, leading to a reduction in droplet deformation. Our numerical solutions also confirm that the leaky-dielectric model is a reasonable approximation of the original PNP–NS–CH model when the electric relaxation time is sufficiently short. Both the Lorentz force and droplet deformation decrease significantly when the diffusion of net charge increases.
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    Homogenization theory of ion transportation in multicellular tissue
    (Discrete and Continuous Dynamical Systems - B, 2023) Xiao, C; Huang, H; Xu, S; Yu, T; Yue, X
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    Learning interacting particle systems: diffusion parameter estimation for aggregation equations
    (2018-02-14) Huang, H; Liu, JG; Lu, J
    In this article, we study the parameter estimation of interacting particle systems subject to the Newtonian aggregation. Specifically, we construct an estimator $\widehat{\nu}$ with partial observed data to approximate the diffusion parameter $\nu$, and the estimation error is achieved. Furthermore, we extend this result to general aggregation equations with a bounded Lipschitz interaction field.
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    On the mean-field limit for the Vlasov-Poisson-Fokker-Planck system
    Huang, H; Liu, JG; Pickl, P
    We devise and study a random particle blob method for approximating the Vlasov-Poisson-Fokkker-Planck (VPFP) equations by a $N$-particle system subject to the Brownian motion in $\mathbb{R}^3$ space. More precisely, we show that maximal distance between the exact microscopic and the mean-field trajectories is bounded by $N^{-\frac{1}{3}+\varepsilon}$ ($\frac{1}{63}\leq\varepsilon<\frac{1}{36}$) for a system with blob size $N^{-\delta}$ ($\frac{1}{3}\leq\delta<\frac{19}{54}-\frac{2\varepsilon}{3}$) up to a probability $1-N^{-\alpha}$ for any $\alpha>0$, which improves the cut-off in [10]. Our result thus leads to a derivation of VPFP equations from the microscopic $N$-particle system. In particular we prove the convergence rate between the empirical measure associated to the particle system and the solution of the VPFP equations. The technical novelty of this paper is that our estimates crucially rely on the randomness coming from the initial data and from the Brownian motion.
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    Optic nerve microcirculation: Fluid flow and electrodiffusion
    (Physics of Fluids, 2021-04-01) Zhu, Y; Xu, S; Eisenberg, RS; Huang, H
    Complex fluids flow in complex ways in complex structures. Transport of water and various organic and inorganic molecules in the central nervous system (CNS) are important in a wide range of biological and medical processes [C. Nicholson and S. Hrabětová, "Brain extracellular space: The final frontier of neuroscience,"Biophys. J. 113(10), 2133 (2017)]. However, the exact driving mechanisms are often not known. In this paper, we investigate flows induced by action potentials in an optic nerve as a prototype of the CNS. Different from traditional fluid dynamics problems, flows in biological tissues such as the CNS are coupled with ion transport. It is driven by osmosis created by the concentration gradient of ionic solutions, which in turn influence the transport of ions. Our mathematical model is based on the known structural and biophysical properties of the experimental system used by the Harvard group [R. K. Orkand, J. G. Nicholls, and S. W. Kuffler, "Effect of nerve impulses on the membrane potential of glial cells in the central nervous system of amphibia,"J. Neurophysiol. 29(4), 788 (1966)]. Asymptotic analysis and numerical computation show the significant role of water in convective ion transport. The full model (including water) and the electrodiffusion model (excluding water) are compared in detail to reveal an interesting interplay between water and ion transport. In the full model, convection due to water flow dominates inside the glial domain. This water flow in the glia contributes significantly to the spatial buffering of potassium in the extracellular space. Convection in the extracellular domain does not contribute significantly to spatial buffering. Electrodiffusion is the dominant mechanism for flows confined to the extracellular domain.