arXiv subjects

Zhengping Zhu

Publications and source records attributed to Zhengping Zhu.

8 recordsLinked to original sources

arXiv2026

Resolving dense photodissociation regions: the structure of photochemical fronts in three-dimensional gas distributions

For decades, the Orion Bar has been the prototypical photodissociation region. Viewed nearly edge-on, it offers a unique window into the stratified chemical structure of the atomic-to-molecular transition of the interstellar medium. Understanding its photochemistry is essential to interpreting key observations originating from dense photo-dissociation regions. ALMA and JWST observations reveal that H2 photodissociation front overlaps with the C+ recombination front and exhibits a complex spatial morphology. Despite considerable theoretical effort, existing modeling approaches based on simplified geometrical assumptions have difficulties reproducing the spatial emission structure. Our aim is to investigate the response of photochemistry in realistic three-dimensional density distributions, using the Orion Bar as a representative application. We present the first fully three-dimensional high-resolution model of an Orion Bar analogue that resolves the relevant photochemical fronts using the upgraded steady-state 3D-PDR photo-dissociation region code, which allows for the treatment of plane-irradiatation and for the solution of the non-LTE H2 rovibrational levels. We find that the H2 dissociation front is characterized by a complex surface that overlaps with the C+ recombination front. Our 3D model can reproduce the complex morphology of H2 emission seen in observations, in particular the arc- and filament-like features, and provides a physical explanation of its chemistry. The overlapping H2 dissociation and C+ recombination fronts and the spatial emission morphology can be explained due to the three-dimensional gas distribution, resulting in shadowing and shielding by dense substructures. Our results mark a turning point for astrochemistry, where three-dimensional steady-state models can deliver fundamentally new insights into the chemistry of the interstellar medium. (Abridged)

astro-ph.GA↗

arXiv2026

CLIPGen: A Chiplet Link IP Modeling and Generation Framework for 2.5D Architecture Exploration

Advanced 2.5D Systems-in-Package (SiPs) compose a growing portion of high-performance systems. While the packaging and interconnect choices play a large role in the overall system design, system architects still lack a suitable framework for early design space exploration which takes these choices into account. Current interconnect models fall mostly into the categories of 1) detailed models which are generally inflexible and require deep packaging expertise, or 2) high-level models which don't provide enough information to make accurate architectural design decisions. In this work, we present an automated chiplet IP generation framework which provides power, performance, and area estimates for various 2.5D packaging and communication configurations. The IP generator produces standard collaterals required for high-level simulation/estimation, RTL simulation, and place-and-route-level implementation (Verilog, Liberty, LEF, and datasheet). Using our framework, architects can co-optimize the package and chiplet architecture through rapid power, performance, and area estimates of various packaging strategies. As a case study, we examine generated UCIe interfaces across several packaging options.

cs.AR↗

arXiv2026

RAYTHEIA: A high-performance ray-tracing algorithm for three-dimensional direction-dependent equations in astronomical simulations

We present RAYTHEIA, a high-performance reverse ray-tracing algorithm designed to efficiently solve three-dimensional direction-dependent equations in astronomical simulations. The algorithm uses a dual-grid framework in which the native simulation mesh -- serving as the source grid for ray emission -- and an adaptive mesh refinement (AMR) Cartesian contribution grid are constructed for efficient ray-walking and contribution accumulation. The core of the algorithm integrates a leaf-only linear-octree data structure to reduce memory overhead, the digital differential analyzer (DDA) traversal method to efficiently determine the ray-walking path, Morton Code indexing to fast leaf cell lookup during traversal, and the slab method to analytically compute the path length. Furthermore, RAYTHEIA employs a hybrid (MPI/OpenMP) distributed parallel framework with a chunk-to-chunk communication strategy, achieving exceptional, near-ideal linear speed-up ratio and delivering high-end performance. We integrate RAYTHEIA with the 3D-PDR code to solve the complex chemistry and radiation transfer in photodissociation regions (PDRs). This allowed the modelling of three-dimensional PDR chemistry in a turbulent, star-forming cloud at an unprecedented resolution of $512^3$ grid cells. The algorithm demonstrates accuracy and convergence even at low angular resolutions. We further showcase the capabilities of RAYTHEIA by producing high-resolution synthetic emission maps of key diagnostic lines of a star-forming region capturing physical effects such as [O I] $63μ$m self-absorption, measuring the [C I]-bright but CO-dark molecular gas, and deriving a CO-to-H$_2$ conversion factor in agreement with observations.

astro-ph.IM↗

arXiv2025

Causal analysis of inner and outer motions in near-wall turbulence

In this work, we study the causality of near-wall inner and outer turbulent motions. The inner motions are defined as the self-sustained near-wall cycle, and the outer motions as those living in the logarithmic layer exhibiting footprints on the near-wall region. Causal inference with three typical methods is performed, i.e. transfer entropy, information flow, and SURD (synergistic--unique--redundant decomposition of causality). The causal inference methods are first applied to several canonical problems to illustrate their abilities and differences, including a linear problem, a non-linear problem, and a low-dimensional model of near-wall turbulence. It is demonstrated that all three methods can produce consistent causal findings. Furthermore, we study the causalities between the inner and outer turbulent motions in a channel flow using the three methods with an improved inner-outer decomposition method. It is revealed that both the inner and outer motions are self-sustained and independent of each other, supporting the self-sustaining mechanism of turbulent motions at all scales. We also find that there are top-down and bottom-up influences in the outer motions and their near-wall footprints, challenging the traditional sole top-down view. More interestingly, pressure is identified to play an active role in the inner-outer causalities and may act as a bridge in linking the inner and outer turbulent motions.

physics.flu-dyn↗

arXiv2024

An open-source, adaptive solver for particle-resolved simulations with both subcycling and non-subcycling methods

We present the IAMReX, an adaptive and parallel solver for particle-resolved simulations on the multi-level grid. The fluid equations are solved using a finite-volume scheme on the block-structured semi-staggered grids with both subcycling and non-subcycling methods. The particle-fluid interaction is resolved using the multidirect forcing immersed boundary method. The associated Lagrangian markers used to resolve fluid-particle interface only exist on the finest-level grid, which greatly reduces memory usage. The volume integrals are numerically calculated to capture the free motion of particles accurately, and the repulsive potential model is also included to account for the particle-particle collision. We demonstrate the versatility, accuracy, and efficiency of the present multi-level framework by simulating fluid-particle interaction problems with various types of kinematic constraints. The cluster of monodisperse particles case is presented at the end to show the capability of the current solver in handing with multiple particles. The source code and testing cases used in this work can be accessed at https://github.com/ruohai0925/IAMR/tree/development. Input scripts and raw postprocessing data are also available for reproducing all results.

physics.flu-dyn↗

arXiv2023

A multiple-time-step integration algorithm for particle-resolved simulation with physical collision time

In this paper, we present a multiple-time-step integration algorithm (MTSA) for particle collisions in particle-resolved simulations. Since the time step required for resolving a collision process is much smaller than that for a fluid flow, the computational cost of the traditional soft-sphere model by reducing the time step is quite high in particle-resolved simulations. In one state-of-the-art methodology, collision time is stretched to several times the flow solver time step for the fluid to adapt to the sudden change in particle motion. However, the stretched collision time is not physical, the hydrodynamic force may be severely underestimated during a stretched collision, and the simulation of sediment transport may be sensitive to the stretched collision time. The proposed MTSA adopts different time steps to resolve fluid flow, fluid-particle interaction, and particle collision. We assessed the MTSA for particle-wall collisions as well as particle-particle collisions, determined the optimal iteration number in the algorithm, and obtained excellent agreements with experimental measurements and reference simulations. The computational cost of the MTSA can be reduced to about one order of magnitude less than that using the traditional soft-sphere model with almost the same accuracy. The MTSA was then implemented in a particle-resolved simulation of sediment transport with thousands of particles. {By comparing the results obtained using the MTSA and a version of the stretching collision time algorithm similar to Costa et al.(2015), we found that stretching the collision time reduced particle stiffness, weakened particle entrainment, and affected some turbulence and particle statistics.

physics.flu-dyn↗

arXiv2022

Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference

By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or continue the next layer has to go through every pre-placed exiting layer until it exits. In addition, it is also hard to adjust the configurations of the computing platforms alongside the inference proceeds. By incorporating a low-cost prediction engine, we propose a Predictive Exit framework for computation- and energy-efficient deep learning applications. Predictive Exit can forecast where the network will exit (i.e., establish the number of remaining layers to finish the inference), which effectively reduces the network computation cost by exiting on time without running every pre-placed exiting layer. Moreover, according to the number of remaining layers, proper computing configurations (i.e., frequency and voltage) are selected to execute the network to further save energy. Extensive experimental results demonstrate that Predictive Exit achieves up to 96.2% computation reduction and 72.9% energy-saving compared with classic deep learning networks; and 12.8% computation reduction and 37.6% energy-saving compared with the early exit under state-of-the-art exiting strategies, given the same inference accuracy and latency.

cs.LG↗

arXiv2022

Particle resolved simulation of sediment transport by a hybrid parallel approach

Sediment transport over an erodible sediment bed is studied by particle resolved simulations with a hybrid parallel approach. To overcome the challenges of load imbalance in the traditional domain decomposition method when encountering highly uneven distributions of particles in sediment transport, the parallel approach of Darmana et al.(2006) originally developed for point particle simulations is modified and implemented into particle resolved simulations. A novel memory optimization technique is proposed to reduce the memory requirement of the hybrid approach for spherical particles with equal size. The present hybrid parallel approach shows good scalability and high parallel efficiency in a challenging sediment transport test case with more than a million spherical particles. Our code is validated by several benchmark cases, and the results show good agreement with experimental and computational data in the literature. Furthermore, a turbulent flow over an erodible sediment bed is simulated. An extraction method is proposed to distinguish the saltating and rolling particles and extract impact and rebound information of the particle-mobile bed interaction. The probability distribution functions (PDF) of several saltation parameters such as velocity, angle, and spanwise angular velocity of impact and rebound events are presented. Splash functions are established for the particle-mobile bed interaction in the turbulent flow, which was rarely investigated in the experiments and is helpful to model the complex particle-bed interactions in turbulent flow.

physics.flu-dyn↗