arXiv subjects

Chao Sun

Publications and source records attributed to Chao Sun.

At least 91 records · Page 5Linked to original sources

arXiv2023

Role of Ambient Pressure on Colliding Jets

In this letter, the merging-vs-bouncing response of obliquely-oriented colliding jets under elevated and reduced gaseous environment pressures was experimentally examined. Experiments with water and n-tetradecane confirmed that the collision outcome transitions from merging to bouncing, and then to merging again, when the impact velocity was increased. This behavior which was previously reported for atmospheric pressure, has now also been observed at elevated and reduced pressures. New results also show that there exists a critical pressure (0.9 bar for tetradecane and 5 bar for water) below which increasing pressure promotes bouncing (expands the bouncing regime), while beyond this, merging is promoted (reduces the bouncing regime) instead. This leads to a non-monotonic influence of pressure on the non-coalescence outcomes of collisional jets, which was not previously reported. The study provides evidence of new behaviors in colliding jets at reduced and elevated pressures, which differs from well-studied droplet-droplet collisions.

physics.flu-dyn↗

arXiv2023

How roughness and thermal properties of a solid substrate determine the Leidenfrost temperature: Experiments and a model

In this Letter, we systematically investigate the Leidenfrost temperature for hot solid substrates with various thermal diffusivities and surface roughnesses. Based on the experimental results, we build a phenomenological model that considers the thermal diffusivity of a solid substrate and derive a relationship between the surface roughness and the resulting vapor film thickness. The generality of this model is supported by experimental data for different liquids and solid substrates. Our model thus allows for a theoretical prediction of the Leidenfrost temperature and develops a comprehensive understanding of the Leidenfrost effect.

physics.flu-dyn↗

arXiv2023

On evaporation dynamics of an acoustically levitated multicomponent droplet: evaporation-triggered phase transition and freezing

Hypothesis: Multi-component droplet evaporation has received significant attention in recent years due to the broad range of applications such as material science, environmental monitoring, and pharmaceuticals. The selective evaporation induced by the different physicochemical properties of components is expected to influence the concentration distributions and the separation of mixtures, thereby leading to rich interfacial phenomena and phase interactions. Experiments: A ternary mixture system containing hexadecane, ethanol, and diethyl ether is investigated in this study. The diethyl ether exhibits both surfactant-like and co-solvent properties. Systematic experiments were performed using acoustic levitation technique to achieve a contact-less evaporation condition. The evaporation dynamics and temperature information are acquired in the experiments, using high-speed photography and infrared thermography technologies. Findings: Three distinct stages, namely, "Ouzo state", "Janus state", and "Encapsulating state", are identified for the evaporating ternary droplet in acoustic levitation. A self-sustaining periodic freezing & melting evaporation mode is reported. A theoretical model is developed to characterize the multi-stage evaporating behaviors. We demonstrate the capability to tune the evaporating behaviors by varying the initial droplet composition. This work provides a deeper understanding of the interfacial dynamics and phase transitions involved in multi-component droplets and proposes novel strategies for the design and control of droplet-based systems

physics.flu-dyn↗

arXiv2023

Numerical study on turbulence modulation of finite-size particles in plane-Couette flow

Turbulent plane-Couette flow suspended with finite-size spheroidal particles is studied using fully particle-resolved direct numerical simulations. The effects of particle aspect ratio on turbulent arguments and particle statistics are explored, leading to the same conclusions as the previous experimental findings \citep{wang2022finite}. By performing stress analysis, we find that the presence of particles introduces extra stresses to the system and accounts for the global drag increases. The particle-laden flow cases exhibit spectra that are consistent with the scaling of $k^{-5/3}$ and $k^{-3}$ in the large and small scales, respectively. While the $k^{-3}$ scaling observed in the particle-laden flow is reminiscent of bubbly flow, an examination of the particle Reynolds number suggests that the mechanism responsible may not be attributable to the pseudo-turbulence induced by particles as in the case of bubbles. In the view of particle statistics, we observe that spherical and non-spherical particles preferentially cluster in the near-wall and the bulk region, respectively, and that the orientations of non-spherical particles are affected by their aspect ratios, especially in the near-wall region. The present numerical results, combined with previous experimental findings in \cite{wang2022finite}, provide in-depth information on both the fluid and the particle phase, contributing to a better understanding of particle suspension in shear flows.

physics.flu-dyn↗

arXiv2023

On the thermal effect of porous material in porous media Rayleigh-Bénard convection

We perform a two-dimensional numerical study on the thermal effect of porous media on global heat transport and flow structure in Rayleigh-Bénard (RB) convection, focusing on the role of thermal conductivity $λ$ of porous media, which ranges from $0.1$ to $50$ relative to the fluid. The simulation is carried out in a square RB cell with the Rayleigh number $Ra$ ranging from $10^7$ to $10^9$ and the Prandtl number $Pr$ fixed at $4.3$. The porosity of the system is fixed at $ϕ=0.812$, with the porous media modeled by a set of randomly displayed circular obstacles. For a fixed $Ra$, the increase of conductivity shows a small effect on the total heat transfer, slightly depressing the Nusselt number. The limited influence comes from the small number of obstacles contacting with thermal plumes in the system as well as the counteraction of the increased plume area and the depressed plume strength. The study shows that the global heat transfer is insensitive to the conduction effect of separated porous media in the bulk region, which may have implications for industrial designs.

physics.flu-dyn↗

arXiv2023

Sea Water Freezing Modes in a Natural Convection System

Sea ice is crucial in many natural processes and human activities. Understanding the dynamical couplings between the inception, growth and equilibrium of sea ice and the rich fluid mechanical processes occurring at its interface and interior is of relevance in many domains ranging from geophysics to marine engineering. Here we experimentally investigate the complete freezing process of water with dissolved salt in a standard natural convection system, i.e., the prototypical Rayleigh-Bénard cell. Due to the presence of a mushy phase, the studied system is considerably more complex than the freezing of freshwater in the same conditions (Wang et al. 2021c). We measure the ice thickness and porosity at the dynamical equilibrium state for different initial salinities of the solution and temperature gaps across the cell. These observables are non-trivially related to the controlling parameters of the system as they depend on the heat transport mode across the cell. We identify in the experiments 5 out of the 6 possible modes of heat transport. We highlight the occurrence of brine convection through the mushy ice and of penetrative convection in stably stratified liquid underlying the ice. A one-dimensional multi-layer heat flux model built on the known scaling relations of global heat transport in natural convection systems in liquids and porous media is proposed. It allows, given the measured porosity of the ice, to predict the corresponding ice thickness, in a unified framework.

physics.flu-dyn↗

arXiv2023

Ultra-high-speed high-resolution laser lithography for lithium niobate integrated photonics

Photolithography assisted chemo-mechanical etching (PLACE), a technique specifically developed for fabricating highquality large-scale photonic integrated circuits (PICs) on thin-film lithium niobate (TFLN), has enabled fabrication of a series of building blocks of PICs ranging from high-quality (high-Q) microresonators and low-loss waveguides to electrooptically (EO) tunable lasers and waveguide amplifiers. Aiming at high-throughput manufacturing of the PIC devices and systems, we have developed an ultra-high-speed high-resolution laser lithography fabrication system employing a high repetition rate femtosecond laser and a high-speed polygon laser scanner, by which a lithography fabrication efficiency of 4.8 cm2/h has been achieved at a spatial resolution of 200 nm. We demonstrate wafer-scale fabrication of TFLN-based photonic structures, optical phase masks as well as color printing

physics.optics↗

arXiv2023

DISCO: Distributed Inference with Sparse Communications

Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficulty to deploy a large DNN model to a single resource-limited device with small memory capacity. Distributed computing is a common approach to reduce single-node memory consumption and to accelerate the inference of DNN models. In this paper, we explore the "within-layer model parallelism", which distributes the inference of each layer into multiple nodes. In this way, the memory requirement can be distributed to many nodes, making it possible to use several edge devices to infer a large DNN model. Due to the dependency within each layer, data communications between nodes during this parallel inference can be a bottleneck when the communication bandwidth is limited. We propose a framework to train DNN models for Distributed Inference with Sparse Communications (DISCO). We convert the problem of selecting which subset of data to transmit between nodes into a model optimization problem, and derive models with both computation and communication reduction when each layer is inferred on multiple nodes. We show the benefit of the DISCO framework on a variety of CV tasks such as image classification, object detection, semantic segmentation, and image super resolution. The corresponding models include important DNN building blocks such as convolutions and transformers. For example, each layer of a ResNet-50 model can be distributively inferred across two nodes with five times less data communications, almost half overall computations and half memory requirement for a single node, and achieve comparable accuracy to the original ResNet-50 model. This also results in 4.7 times overall inference speedup.

cs.CV↗

arXiv2023

A Multilevel Scheduling Framework for Distributed Time-domain Large-area Sky Survey Telescope Array

Telescope arrays are receiving increasing attention due to their promise of higher resource utilization, greater sky survey area, and higher frequency of full space-time monitoring than single telescopes. Compared with the ordinary coordinated operation of several telescopes, the new astronomical observation mode has an order of magnitude difference in the number of telescopes. It requires efficient coordinated observation by large-domain telescopes distributed at different sites. Coherent modeling of various abstract environmental constraints is essential for responding to multiple complex science goals. Also, due to competing science priorities and field visibility, how the telescope arrays are scheduled for observations can significantly affect observation efficiency. This paper proposes a multilevel scheduling model oriented toward the problem of telescope-array scheduling for time-domain surveys. A flexible framework is developed with basic functionality encapsulated in software components implemented on hierarchical architectures. An optimization metric is proposed to self-consistently weight contributions from time-varying observation conditions to maintain uniform coverage and efficient time utilization from a global perspective. The performance of the scheduler is evaluated through simulated instances. The experimental results show that our scheduling framework performs correctly and provides acceptable solutions considering the percentage of time allocation efficiency and sky coverage uniformity in a feasible amount of time. Using a generic version of the telescope-array scheduling framework, we also demonstrate its scalability and its potential to be applied to other astronomical applications.

astro-ph.IM↗

arXiv2023

The emergence of bubble-induced scaling in thermal spectra in turbulence

We report on the modification of the spectrum of a passive scalar inside a turbulent flow by the injection of large bubbles. While the spectral modification through bubbles is well known and well analyzed for the velocity fluctuations, little is known on how bubbles change the fluctuations of an approximately passive scalar, in our case temperature. Here we uncover the thermal spectral scaling behavior of a turbulent multiphase thermal mixing layer. The development of a $-3$ spectral scaling is triggered. By injecting large bubbles ($\text{Re}_{\text{bub}} = \mathcal{O}(10^2)$) with gas volume fractions $α$ up to 5\%. For these bubbly flows, the $-5/3$ scaling is still observed at intermediate frequencies for low $α$ but becomes less pronounced when $α$ further increases and it is followed by a steeper $-3$ slope for larger frequencies. This $-3$ scaling range extends with increasing gas volume fraction. The $-3$ scaling exponent coincides with the typical energy spectral scaling for the velocity fluctuations in high Reynolds number bubbly flows. We identify the frequency scale of the transition from the $-5/3$ scaling to the $-3$ scaling and show how it depends on the gas volume fraction.

physics.flu-dyn↗

arXiv2023

HLC2: a highly efficient cross-matching framework for large astronomical catalogues on heterogeneous computing environments

Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues,is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical cataloguesfrom survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers.

astro-ph.IM↗

arXiv2022

LeaFTL: A Learning-Based Flash Translation Layer for Solid-State Drives

In modern solid-state drives (SSDs), the indexing of flash pages is a critical component in their storage controllers. It not only affects the data access performance, but also determines the efficiency of the precious in-device DRAM resource. A variety of address mapping schemes and optimization techniques have been proposed. However, most of them were developed with human-driven heuristics. They cannot automatically capture diverse data access patterns at runtime in SSD controllers, which leaves a large room for improvement. In this paper, we present a learning-based flash translation layer (FTL), named LeaFTL, which learns the address mapping to tolerate dynamic data access patterns via linear regression at runtime. By grouping a large set of mapping entries into a learned segment, it significantly reduces the memory footprint of the address mapping table, which further benefits the data caching in SSD controllers. LeaFTL also employs various optimization techniques, including out-of-band metadata verification to tolerate mispredictions, optimized flash allocation, and dynamic compaction of learned index segments. We implement LeaFTL with an SSD simulator and evaluate it with various storage workloads. LeaFTL saves the memory consumption of the mapping table by 2.9x on average and improves the storage performance by 1.4x on average, in comparison with state-of-the-art FTL schemes.

cs.OS↗

arXiv2022

Understanding Performance Problems in Deep Learning Systems

Deep learning (DL) has been widely applied to many domains. Unique challenges in engineering DL systems are posed by the programming paradigm shift from traditional systems to DL systems, and performance is one of the challenges. Performance problems (PPs) in DL systems can cause severe consequences such as excessive resource consumption and financial loss. While bugs in DL systems have been extensively investigated, PPs in DL systems have hardly been explored. To bridge this gap, we present the first comprehensive study to i) characterize symptoms, root causes, and introducing and exposing stages of PPs in DL systems developed in TensorFLow and Keras, with 224 PPs collected from 210 StackOverflow posts, and to ii) assess the capability of existing performance analysis approaches in tackling PPs, with a constructed benchmark of 58 PPs in DL systems. Our findings shed light on the implications on developing high-performance DL systems, and detecting and localizing PPs in DL systems. To demonstrate the usefulness of our findings, we develop a static checker Deep-Perf to detect three types of PPs. It has detected 488 new PPs in 130 GitHub projects. 105 and 27 PPs have been confirmed and fixed.

cs.SE↗

arXiv2022

Turbulence drag modulation by dispersed droplets in Taylor-Couette flow: the effects of the dispersed phase viscosity

The dispersed phase in turbulence can vary from almost inviscid fluid to highly viscous fluid. By changing the viscosity of the dispersed droplet phase, we experimentally investigate how the deformability of dispersed droplets affects the global transport quantity of the turbulent emulsion. Different kinds of silicone oil are employed to result in the viscosity ratio, $ζ$, ranging from $0.53$ to $8.02$. The droplet volume fraction, $ϕ$, is varied from 0\% to 10\% with a spacing of 2\%. The global transport quantity, quantified by the normalized friction coefficient $c_{f,ϕ}/c_{f,ϕ=0}$, shows a weak dependence on the turbulent intensity due to the vanishing finite-size effect of the droplets. The interesting fact is that, with increasing $ζ$, the $c_{f,ϕ}/c_{f,ϕ=0}$ first increases and then saturates to a plateau value which is similar to that of the rigid particle suspension. By performing image analysis, this drag modification is interpreted from the aspect of droplet deformability, which is responsible for the breakup and coalescence effect of the droplets. The statistics of the droplet size distribution show that, with increasing $ζ$, the stabilizing effect induced by interfacial tension comes to be substantial and the pure inertial breakup process becomes dominant. The measurement of the droplet distribution along the radial direction of the system shows a bulk-clustering effect, which can be attributed to the non-negligible coalescence effect of the droplet. It is found that the droplet coalescence effect could be suppressed as the $ζ$ increases, thereby affecting the contribution of interfacial tension to the total stress, and accounting for the observed emulsion rheology.

physics.flu-dyn↗

arXiv2022

How does gravity influence freezing dynamics of drops on a solid surface

Water droplet freezing is a common phenomenon in our daily life. In both natural scenarios and industrial production, different surface inclinations bring distinctive deformation and freezing dynamics to frozen droplets. We explore the freezing of pendent and sessile droplets at different Bond number regimes. The effect of gravity on the droplet freezing process is analyzed by considering droplet morphology, freezing front dynamics, and freezing time. It is found that gravity can significantly influence droplet freezing processes via shaping the initial droplet, resulting in the flattening or elongation of pendent and sessile droplets, respectively. We show that the droplet initial geometry is the most important parameter and it completely controls the droplet freezing. Despite the significant difference in the initial droplet shape several remarkable similarities have been found for pendent and sessile droplets at small and large Bond numbers. The final height of a frozen droplet is found to be linearly proportional to its initial height. The time evolution of the ice-liquid-air contact line is found to reproduce the power-law $t^{0.5}$, but noticeably faster than the Stefan 1-D icing front propagation. As a consequence, the time to freeze a droplet is faster than predicted by the Stefan model and it is found to be dependent on the initial droplet height and base radius through a simple power-law.

physics.flu-dyn↗

arXiv2022

Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes

The manual annotation for large-scale point clouds costs a lot of time and is usually unavailable in harsh real-world scenarios. Inspired by the great success of the pre-training and fine-tuning paradigm in both vision and language tasks, we argue that pre-training is one potential solution for obtaining a scalable model to 3D point cloud downstream tasks as well. In this paper, we, therefore, explore a new self-supervised learning method, called Mixing and Disentangling (MD), for 3D point cloud representation learning. As the name implies, we mix two input shapes and demand the model learning to separate the inputs from the mixed shape. We leverage this reconstruction task as the pretext optimization objective for self-supervised learning. There are two primary advantages: 1) Compared to prevailing image datasets, eg, ImageNet, point cloud datasets are de facto small. The mixing process can provide a much larger online training sample pool. 2) On the other hand, the disentangling process motivates the model to mine the geometric prior knowledge, eg, key points. To verify the effectiveness of the proposed pretext task, we build one baseline network, which is composed of one encoder and one decoder. During pre-training, we mix two original shapes and obtain the geometry-aware embedding from the encoder, then an instance-adaptive decoder is applied to recover the original shapes from the embedding. Albeit simple, the pre-trained encoder can capture the key points of an unseen point cloud and surpasses the encoder trained from scratch on downstream tasks. The proposed method has improved the empirical performance on both ModelNet-40 and ShapeNet-Part datasets in terms of point cloud classification and segmentation tasks. We further conduct ablation studies to explore the effect of each component and verify the generalization of our proposed strategy by harnessing different backbones.

cs.CV↗

arXiv2022

The Decay constants of $B_c(nS)$ and $B^*_c(nS)$

The decay constants of the low lying S-wave $B_c$ mesons, i.e. $B_c(nS)$ and $B^*_c(nS)$ with $n\leq 3$, are calculated in the nonrelativistic quark model. The running coupling of the strong interaction is taken into account, and the uncertainties due to varying parameters and losing Lorentz covariance are considered carefully. As a byproduct, the decay constants of the low lying S-wave charmonium and bottomium states are given in the appendixes.

hep-ph↗

arXiv2022

Recent developments of turbulent emulsions in Taylor-Couette flow

Emulsions are common in many natural and industrial settings. Recently, much attention has been put on understanding the dynamics of turbulent emulsions. This paper reviews some recent studies of emulsions in turbulent Taylor-Couette flow, mainly focusing on the statistics of the dispersed phase and the global momentum transport of the system. We first study the size distribution and the breakup mechanism of the dispersed droplets for turbulent emulsions with a low volume-fraction (dilute) of the dispersed phase. For systems with a high volume-fraction of the dispersed phase (dense), we address the detailed response of the global transport (effective viscosity) of the turbulent emulsion and its connection to the droplet statistics. Finally, we will discuss catastrophic phase inversions, which can happen when the volume fraction of the dispersed phase exceeds a critical value during dynamic emulsification. We end the manuscript with a summary and an outlook including some open questions for future research. This article is part of the theme issue `Taylor-Couette and Related Flows on the Centennial of Taylor's Seminal Philosophical Transactions Paper'.

physics.flu-dyn↗