arXiv subjects

Samuel Clark

Publications and source records attributed to Samuel Clark.

5 recordsLinked to original sources

arXiv2026

Revealing mesoscale bubble and particle dynamics in ultrasound-driven multiphase fluids by ultrafast synchrotron X-ray radiography and hybrid modelling

Multiphase fluid flows comprising of mesoscale solid particles, liquid droplets, or gas bubbles are common in both natural and man-made systems, but quantifying the energy transfer is challenging due to complex bubble-particle interactions. In this study, we used ultrafast synchrotron X-ray imaging to study the mesoscale dynamic interactions among ultrasonic cavitation bubbles and hydrophobic particles or clusters. Critical dynamic information and data were extracted from the vast amount of X-ray images and then fed into the hybrid analytical-numerical model for calculating the energy transfer from the oscillating bubble and the imploding bubble to the nearby hydrophobic particles. Using the Ni spherical microparticles as an example, at bubble oscillation approximately 16% (80-320 nJ) of the local energy was transferred to the particle. At bubble implosion, the transferred energy increased approximately 26% (0.135-1.09 uJ). Local energy transfer occurred on timescales of 1 us to 1 ms and length scales of 1 um to 1 mm. Within each ultrasound cycle, kinetic and potential energy underwent complex exchanges, with local energy exhibiting a stepwise decay at the end of each cycle. The transferred energy was mainly consumed for enabling highly efficient particle dispersion. This research provides quantitative insights into optimizing hydrophobic nanomaterial dispersion and has broader implications for interfacial energy transfer processes such as making suspensions, composite materials and exfoliated 2D materials.

physics.flu-dyn↗

arXiv2023

Picosecond X-ray Imaging of Shockwaves with Non-Rankine-Hugoniot Behavior

The first-known observation of plasma-induced cavitation bubbles and expanding shockwaves in liquid during plasma initiation timescales reveals deviation from expected Rankine-Hugoniot shock behavior due to coupled shock-cavitation dynamics, imaged using megahertz-framerate picosecond X-ray imaging. The imaging target features an inexpensive benchtop-scale pulsed plasma device used to generate well-timed spark discharges in ambient liquid heptane at an unprecedented repetition rate ($>$3/min) compared with more commonly used dynamic targets. These shockwaves are relatively weak (Mach number $\leq$ 1.4) compared with X-ray-imaged shockwaves in prior literature, advancing the resolution and sensitivity limits of this high-speed imaging diagnostic. Phase contrast imaging (PCI) has facilitated enhanced quantitative analysis of the expanding shocks in this work, via comparison to thermodynamic models and a Fresnel-Kirchhoff diffraction model.

physics.plasm-ph↗

arXiv2021

Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors

Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring their own challenges -- many require a trusted third party or else add too much noise to produce useful models. Recent advances in \emph{secure aggregation} using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale. We present a new federated learning protocol that leverages a novel differentially private, malicious secure aggregation protocol based on techniques from Learning With Errors. Our protocol outperforms current state-of-the art techniques, and empirical results show that it scales to a large number of parties, with optimal accuracy for any differentially private federated learning scheme.

cs.CR↗

arXiv2020

Space-Time Smoothing of Survey Outcomes using the R Package SUMMER

The increasing availability of complex survey data, and the continued need for estimates of demographic and health indicators at a fine spatial and temporal scale, which leads to issues of data sparsity, has led to the need for spatio-temporal smoothing methods that acknowledge the manner in which the data were collected. The open source R package SUMMER implements a variety of methods for spatial or spatio-temporal smoothing of survey data. The emphasis is on small-area estimation. We focus primarily on indicators in a low and middle-income countries context. Our methods are particularly useful for data from Demographic Health Surveys and Multiple Indicator Cluster Surveys. We build upon functions within the survey package, and use INLA for fast Bayesian computation. This paper includes a brief overview of these methods and illustrates the workflow of accessing and processing surveys, estimating subnational child mortality rates, and visualizing results with both simulated data and DHS surveys.

stat.AP↗

arXiv2016

Space-time smoothing of complex survey data: Small area estimation for child mortality

Many people living in low- and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data, including many household sample surveys, are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to nonrandom sampling and nonresponse. The application that motivated this work is an estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991-2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA).

stat.AP↗