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Yu Zou

Publications and source records attributed to Yu Zou.

36 records · Page 2Linked to original sources
arXiv2024

A survey of energies from pure metals to multi-principal element alloys

In materials science, a wide range of properties of materials are governed by various types of energies, including thermal, physicochemical, structural, and mechanical energies. In 2005, Dr. Frans Spaepen used crystalline face-centered-cubic (fcc) copper as an example to discuss a variety of phenomena that are associated with energies. Inspired by his pioneering work, we broaden our analysis to include a selection of representative pure metals with fcc, hexagonal close-packed (hcp), and body-centered cubic (bcc) structures. Additionally, we extend our comparison to energies between pure metals and equiatomic binary, ternary, and multi-principal element alloys (sometimes also known as high-entropy alloys). Through an extensive collection of data and calculations, we compile energy tables that provide a comprehensive view of how structure and alloying influence the energy profiles of these metals and alloys. We highlight the significant impact of constituent elements on the energies of alloys compared to pure metals and reveal a notable disparity in mechanical energies among materials in fcc-, hcp- and bcc-structured metals and alloys. Furthermore, we discuss the energy relationships, the implications for structural transformations and potential applications, providing insights into the broader context of these energy variations.

cond-mat.mtrl-sci↗
arXiv2024

Optimal upper bounds for anti-canonical volumes of singular toric Fano varieties

Fix two positive integers $d\geq3$ and $q$. We give an upper bound for anti-canonical volumes of $d$-dimensional $\frac{1}{q}$-lc toric Fano varieties, which corresponds to an upper bound for the dual normalized volumes of the associated $d$-dimensional $\frac{1}{q}$-lc Fano polytopes. And we also construct examples to show that these upper bounds are optimal. Besides, we provide an optimal upper bound for volumes of $d$-dimensional lattice simplices $S$ such that $\frac{1}{q}S$ has exactly one interior lattice point.

math.AG↗
arXiv2024

Accurate Inverse Process Optimization Framework in Laser Directed Energy Deposition

In additive manufacturing (AM), particularly for laser-based metal AM, process optimization is crucial to the quality of products and the efficiency of production. The identification of optimal process parameters out of a vast parameter space, however, is a daunting task. Despite advances in simulations, the process optimization for specific materials and geometries is developed through a time-consuming trial-and-error approach, which often lacks the versatility to address multiple optimization objectives. Machine learning (ML) provides a powerful tool to accelerate the optimization process, but most current studies focus on simple single-track prints, which hardly translate to manufacturing 3D components for engineering applications. In this study, we develop an Accurate Inverse process optimization framework in laser Directed Energy Deposition (AIDED), based on machine learning models and a genetic algorithm, to aid process optimization in laser DED processes. Using the AIDED, we demonstrate the following: (i) Accurately predict single-track (R2 score 0.995), multi-track (R2 score 0.969), and multi-layer (1.07% and 10.75% error in width and height, respectively) cross-sectional melt pool geometries directly from process parameters; (ii) Determine appropriate hatch spacing and layer thickness for fabricating fully dense (density > 99.9%) multi-track and multi-layer prints; (iii) Inversely identify optimal process parameters directly from customizable application objectives within 1-3 hours. We also validate the effectiveness of the AIDED experimentally by achieving two exemplary targets: fast print speed and fine print resolution. Furthermore, we show the high transferability of the framework from stainless steel to pure nickel. With AIDED, we pave a new way for ''aiding'' the process optimization in the laser-based AM processes that is applicable to a wide range of materials.

cond-mat.mtrl-sci↗
arXiv2024

Extended free-energy functionals for achiral and chiral ferroelectric nematic liquid crystals

Polar nematic liquid crystals are new classes of condensed-matter states where the inversion symmetry common to the traditional apolar nematics is broken. Establishing theoretical descriptions for the novel phase states is an urgent task. Here, we develop a Landau-type mean-field theory for both the achiral and chiral ferroelectric nematics. In the polar nematic states, the inversion symmetry breaking adds two new contributions: an additional odd elastic term (corresponding to the flexoelectricity in symmetry) to the standard Oseen-Frank free energy and an additional Landau term relating to the gradient of local polarisation. As a general necessity, the coupling between the scalar order parameter and polarisation order is further considered. In the chiral and polar nematic state, we reveal that the competition between the twist elasticity and polarity dictates effective compressive energy arising from the quasi-layer structure. The polarisation gradient is an essential term for describing the ferroelectric nature of the systems. The approaches provide theoretical foundations for testing and predicting polar structures in emerging polar liquid crystals.

cond-mat.soft↗
arXiv2023

Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework

Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, further limiting their applications in solving the inverse material design problem. Here, we establish a deep learning and genetic algorithm framework that integrates forward prediction and inverse exploration. This framework provides an end-to-end solution to achieve application-specific mechanical properties by microstructure optimization. In this study, we select the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by tailoring its microstructure and achieving various yield strength and elastic modulus across a large design space, while minimizing the stress concentration factor. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to other materials and properties. Paired with additive manufacturing's potential in controlling local microstructural features, our method has far-reaching potential for accelerating the development of application-specific, high-performing materials.

cond-mat.mtrl-sci↗
arXiv2023

HMT: A Hardware-Centric Hybrid Bonsai Merkle Tree Algorithm for High-Performance Authentication

Merkle tree is a widely used tree structure for authentication of data/metadata in a secure computing system. Recent state-of-the art secure systems use a smaller-sized MT, namely Bonsai Merkle Tree (BMT) to protect the metadata such as encryption counters. Common BMT algorithms were designed for traditional Von Neumann architectures with a software-centric implementation in mind, hence they use a lot of recursions and are often sequential in nature. However, the modern heterogeneous computing platforms employing Field-Programmable Gate Array (FPGA) devices require concurrency-focused algorithms to fully utilize the versatility and parallel nature of such systems. Our goal for this work is to introduce HMT, a hardware-friendly BMT algorithm that enables the verification and update processes to function independently and provides the benefits of relaxed update while being comparable to eager update in terms of update complexity. The methodology of HMT contributes both novel algorithm revisions and innovative hardware techniques to implementing BMT. We introduce a hybrid BMT algorithm that is hardware-targeted, parallel and relaxes the update depending on BMT cache hit but makes the update conditions more flexible compared to lazy update to save additional write-backs. Deploying this new algorithm, we have designed a new BMT controller with a dataflow architecture, speculative buffers and parallel write-back engines that allows for multiple concurrent relaxed authentication. Our empirical performance measurements have demonstrated that HMT can achieve up to 7x improvement in bandwidth and 4.5x reduction in latency over baseline in subsystem level tests. In a real secure-memory system on a Xilinx U200 accelerator FPGA, HMT exhibits up to 14\% faster execution in standard benchmarks compared to state-of-the art BMT solution on FPGA.

cs.CR↗
arXiv2023

Harnessing dislocation motion using an electric field

Dislocations, line defects in crystalline materials, play an essential role in the mechanical[1,2], electrical[3], optical[4], thermal[5], and phase transition[6] properties of these materials. Dislocation motion, an important mechanism underlying crystal plasticity, is critical for the hardening, processing, and application of a wide range of structural and functional materials[1,7,8]. For decades, the movement of dislocations has been widely observed in crystalline solids under mechanical loading[9-11]. However, the goal of manipulating dislocation motion via a non-mechanical field alone remains elusive. Here, we present real-time observations of dislocation motion controlled solely by an external electric field in single-crystalline zinc sulfide (ZnS). We find that 30° partial dislocations can move back and forth depending on the direction of the electric field, while 90° partial dislocations are motionless. We reveal the nonstoichiometric nature of dislocation cores using atomistic imaging and determine their charge characteristics by density functional theory calculations. The glide barriers of charged 30° partial dislocations, which are lower than those of 90° partial dislocations, further decrease under an electric field, explaining the experimental observations. This study provides direct evidence of dislocation dynamics under a non-mechanical stimulus and opens up the possibility of modulating dislocation-related properties.

cond-mat.mtrl-sci↗
arXiv2022

Spontaneous electric-polarization topology in confined ferroelectric nematics

Topological spin and polar textures have fascinated people in different areas of physics and technologies. However, the observations are limited in magnetic and solid-state ferroelectric systems. Ferroelectric nematic is the first liquid-state ferroelectric that would carry many possibilities of spatially distributed polarization fields. Contrary to traditional magnetic or crystalline systems, anisotropic liquid crystal interactions can compete with the polarization counterparts, thereby setting a challenge in understating their interplays and the resultant topologies. Here, we discover chiral polarization meron-like structures during the emergence and growth of quasi-2D ferroelectric nematic domains, which are visualized by fluorescence confocal polarizing microscopy and second harmonic generation microscopies. Such micrometre-scale polarization textures are the modified electric variants of the magnetic merons. Unlike the conventional liquid crystal textures driven solely by the elasticity, the polarization field puts additional topological constraints, e.g., head-to-tail asymmetry, to the systems and results in a variety of previously unidentified polar topological patterns. The chirality can emerge spontaneously in polar textures and can be additionally biased by introducing chiral dopants. An extended mean-field modelling for the ferroelectric nematics reveals that the polarization strength of systems plays a dedicated role in determining polarization topology, providing a guide for exploring diverse polar textures in strongly-polarized liquid crystals.

cond-mat.soft↗
arXiv2022

The influence of beam focus during laser powder bed fusion of a high reflectivity aluminium alloy -- AlSi$_{10}$Mg

LPBF of Al alloys is associated with numerous challenges when compared to other commonly used alloys due to their higher reflectivity and thermal conductivity. In this work, processing diagrams, temperature prediction models, XCT, and metallography are used for establishing criteria in process parameter optimization of high reflectivity Al alloys based on AlSi$_{10}$Mg response in using 57 different process parameter combinations - 21 using a focused Gaussian laser beam and 36 using divergent beams. For LPBF systems with focused beam diameters <100 μm, divergent beams obtained by defocusing to a position above the LPBF build plate primarily lead to conduction mode melt pools, while a focused beam leads to transition and keyhole mode melt pools. Conduction mode melting helps in avoiding keyhole mode defects, resulting in parts with densities >99.98%. Additionally, an analytical model-guided selection of laser power and velocity settings for a focused beam help in stabilizing melt pool and spatter dynamics in the transition melting mode thereby enabling a potential to obtain density values close to conduction mode densities (~99.98%). A dimensionless keyhole number (Ke) was derived in this work to identify distinct regions of conduction (Ke of 0-12), transition (Ke of 12-20), and keyhole (Ke > 20) mode melting during LPBF of AlSi$_{10}$. A melt pool aspect ratio (ratio of melt pool depth to width) of ~0.4 is observed to be the threshold between conduction and transition/keyhole mode melt pools for AlSi$_{10}$, different from the conventionally assumed 0.5. Lastly, inferred laser absorptivity values (from experimental melt pools) of transition/keyhole mode melt pools are observed to be >40% higher when compared to conduction mode melt pools. This work demonstrates a dimensionless-process map method to obtain near fully dense parts that can be generalized for LPBF of high reflectivity alloys.

cond-mat.mtrl-sci↗
arXiv2019

GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.

astro-ph.IM↗
arXiv2017

Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation

Among the many additive manufacturing (AM) processes for metallic materials, selective laser melting (SLM) is arguably the most versatile in terms of its potential to realize complex geometries along with tailored microstructure. However, the complexity of the SLM process, and the need for predictive relation of powder and process parameters to the part properties, demands further development of computational and experimental methods. This review addresses the fundamental physical phenomena of SLM, with a special emphasis on the associated thermal behavior. Simulation and experimental methods are discussed according to three primary categories. First, macroscopic approaches aim to answer questions at the component level and consider for example the determination of residual stresses or dimensional distortion effects prevalent in SLM. Second, mesoscopic approaches focus on the detection of defects such as excessive surface roughness, residual porosity or inclusions that occur at the mesoscopic length scale of individual powder particles. Third, microscopic approaches investigate the metallurgical microstructure evolution resulting from the high temperature gradients and extreme heating and cooling rates induced by the SLM process. Consideration of physical phenomena on all of these three length scales is mandatory to establish the understanding needed to realize high part quality in many applications, and to fully exploit the potential of SLM and related metal AM processes.

physics.app-ph↗
arXiv2006

Heterogeneous Cell Population Dynamics: Equation-Free Uncertainty Quantification Computations

We propose a computational approach to modeling the collective dynamics of populations of coupled heterogeneous biological oscillators. In contrast to Monte Carlo simulation, this approach utilizes generalized Polynomial Chaos (gPC) to represent random properties of the population, thus reducing the dynamics of ensembles of oscillators to dynamics of their (typically significantly fewer) representative gPC coefficients. Equation-Free (EF) methods are employed to efficiently evolve these gPC coefficients in time and compute their coarse-grained stationary state and/or limit cycle solutions, circumventing the derivation of explicit, closed-form evolution equations. Ensemble realizations of the oscillators and their statistics can be readily reconstructed from these gPC coefficients. We apply this methodology to the synchronization of yeast glycolytic oscillators coupled by the membrane exchange of an intracellular metabolite. The heterogeneity consists of a single random parameter, which accounts for glucose influx into a cell, with a Gaussian distribution over the population. Coarse projective integration is used to accelerate the evolution of the population statistics in time. Coarse fixed-point algorithms in conjunction with a Poincaré return map are used to compute oscillatory solutions for the cell population and to quantify their stability.

q-bio.QM↗
arXiv2006

Uncertainty Quantification for Atomistic Reaction Models: An Equation-Free Stochastic Simulation Algorithm Example

We describe a computational framework linking Uncertainty Quantification (UQ) methods for continuum problems depending on random parameters with Equation-Free (EF) methods for performing continuum deterministic numerics by acting directly on atomistic/stochastic simulators. Our illustrative example is a heterogeneous catalytic reaction mechanism with an uncertain atomistic kinetic parameter; the "inner" dynamic simulator of choice is a Gillespie Stochastic Simulation Algorithm (SSA). We demonstrate UQ computations at the coarse-grained level in a nonintrusive way, through the design of brief, appropriately initialized computational experiments with the SSA code. The system is thus observed at three levels: (a) a fine scale for each stochastic simulation at each value of the uncertain parameter; (b) an intermediate coarse-grained state for the expected behavior of the SSA at each value of the uncertain parameter; and (c) the desired fully coarse-grained level: distributions of the coarse-grained behavior over the range of uncertain parameter values. The latter are computed in the form of generalized Polynomial Chaos (gPC) coefficients in terms of the random parameter. Coarse projective integration and coarse fixed point computation are employed to accelerate the computational evolution of these desired observables, to converge on random stable/unstable steady states, and to perform parametric studies with respect to other (nonrandom) system parameters.

math.DS↗
arXiv2005

Equation-Free Particle-Based Computations: Coarse Projective Integration and Coarse Dynamic Renormalization in 2D

Equation-free approaches have been proposed in recent years for the computational study of multiscale phenomena in engineering problems where evolution equations for the coarse-grained, system-level behavior are not explicitly available. In this paper we study the dynamics of a diffusive particle system in a laminar shear flow, described by a two-dimensional Brownian motion; in particular, we perform coarse projective integration and demonstrate the particle-based computation of coarse self-similar and asymptotically self-similar solutions for this problem. We use marginal and conditional Inverse Cumulative Distribution Functions (ICDFs) as the macroscopic observables of the evolving particle distribution.

math.DS↗
arXiv2005

Equation-Free Dynamic Renormalization: Self-Similarity in Multidimensional Particle System Dynamics

We present an equation-free dynamic renormalization approach to the computational study of coarse-grained, self-similar dynamic behavior in multidimensional particle systems. The approach is aimed at problems for which evolution equations for coarse-scale observables (e.g. particle density) are not explicitly available. Our illustrative example involves Brownian particles in a 2D Couette flow; marginal and conditional Inverse Cumulative Distribution Functions (ICDFs) constitute the macroscopic observables of the evolving particle distributions.

math.DS↗