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Polymeric based closed cell foams, such as shape memory polymer foams, contain bubbles. Making these bubbles continuous is called reticulation. Disclosed are embodiments of a device and method to controllably reticulate polymer-based closed cell foams by puncturing the membranes of these polymer-based closed cell foams.
Polymeric based closed cell foams, such as shape memory polymer foams, contain bubbles. Making these bubbles continuous is called reticulation. Disclosed are embodiments of a device and method to controllably reticulate polymer-based closed cell foams by puncturing the membranes of these polymer-based closed cell foams.
Polymeric based closed cell foams, such as shape memory polymer foams, contain bubbles. Making these bubbles continuous is called reticulation. Disclosed are embodiments of a device and method to controllably reticulate polymer-based closed cell foams by puncturing the membranes of these polymer-based closed cell foams.
We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.
Abstract Topological magnetic textures are promising candidates as binary data units for the next‐generation memory device. The precise generation and convenient control of nontrivial spin topology at zero field near room temperature endows the critical advantages in skyrmionic devices but is not simultaneously integrated into one material. Here, in the Kagome plane of quantum TbMn 6 Sn 6 , the expedient generation of the skyrmion bubbles in versatile forms of lattice, chain, and isolated one by converging the electron beam, where the electron intensity gradient contributes to the dynamic generation from local anisotropy variation near spin reorientation transition (SRT) is reported. Encouragingly, by utilizing the dynamic shift of the SRT domain interface, the straight movement is actualized with the skyrmion bubble slave to the SRT domain interface forming an elastic composite object, avoiding the usual deflection from the skyrmion Hall effect. The critical contribution of the SRT domain interface via conveniently electron‐assisted heating is further theoretically validated in micromagnetic simulation, highlighting the compatible application possibility in advanced devices.
Antiferromagnetically (AFM) coupled skyrmions offer potential advantages for spintronic devices, including reduced dipolar fields that may enable smaller skyrmion sizes and a reduction of the skyrmion Hall effect. However, the topological stability of AFM-coupled skyrmions subjected to dramatic spin deformation through low-temperature cycling has not been investigated. We report the discovery of a topological spin memory effect for AFM-coupled skyrmion pairs in [Co/Gd/Pt](10) multilayered films. Photoemission electron microscopy imaging shows that bubble skyrmions in the multilayer that are stable at room temperature evolve into complex in-plane spin textures as the temperature is lowered and reform completely when the sample is warmed back up. Simulations demonstrate that Dzyaloshinskii-Moriya interactions play a key role in this spin memory effect, and furthermore reveal that the topological charge is preserved throughout the dramatic spin texture rearrangement and recovery. These results highlight a key aspect of topological protection-the preservation of the topological properties under continuous deformation-and also provide a promising avenue for information encryption and recovery.
Various flow regimes exist in a boiling water reactor (BWR) as the steam quality increases in the uprising coolant flow, from bubbly flow, slug/churn flow, to annular flow. The annular flow is characterized by the presence of a fast-moving gas core and the surrounding liquid film flowing on the conduit wall. Additionally, entrained droplets can be observed in the gas core with ingested bubbles in the liquid film. The dynamics occurring on the wavy interface between the liquid film and gas core plays a crucial role in affecting the heat transfer rate and pressure drop within the BWR core. However, a fundamental understanding of annular flow is still lacking, partly due to the difficulty in obtaining detailed local data in annular flow experiments. In the current study, a novel simulation framework is developed for the annular flow by coupling a computational fluid dynamics flow solver with state-of-the-art meshing software. The gas-liquid interface is tracked with the level set method. Based on the computed flow solutions, the computational mesh is dynamically adapted in memory to meet the local mesh resolution requirement. This iterative simulation-adaptation framework can ensure the fine mesh resolution across the interface, which not only helps mitigate the mass conservation degradation known to level set methods but also improves the representation of dramatic interface topological changes such as wave breaking and droplet entrainment. The present investigation will shed light onto the complex interfacial processes involved in annular flow and generate much needed simulation data for annular flow modeling.
Magnetic thin films hosting topological spin textures, such as magnetic skyrmions, hold high potential for breakthroughs in the field of spintronics, due to good scalability and energy efficiency. Novel computational architectures such as memory-in-logic devices rely on material platforms able to host those topological spin textures. Furthermore, recently proposed designs of novel quantum information technologies are based on heterostructures where topological spin textures are in direct proximity to a superconducting layer. Here, we demonstrate the stabilization of out-of-plane magnetic bubbles in highly ordered [Ni/Co]n multilayers on a Nb(110) single crystal. This is achieved without the need for the removal of the well-known Nb(110)-oxide surface reconstruction, due to the introduction of an ultra-thin Cu interlayer in between the Nb substrate and the magnetic multilayer. The Cu interlayer generates a well-ordered hexagonal surface, which is key for the epitaxial growth of the [Ni/Co]n multilayers hosting the desired out-of-plane anisotropy. The magnetic ground state of the prepared material stacks is directly imaged via spin-polarized low-energy electron microscopy, revealing the presence of magnetic bubble domains with lateral sizes as small as 450 nm.
van der Waals (vdW) magnetic materials, such as Cr 2 Ge 2 Te 6 (CGT), show promise for memory and logic applications. This is due to their broadly tunable magnetic properties and the presence of topological magnetic features such as skyrmionic bubbles. A systematic study of thickness and oxidation effects on magnetic domain structures is important for designing devices and vdW heterostructures for practical applications. Here, we investigate thickness effects on magnetic properties, magnetic domains, and bubbles in oxidation-controlled CGT crystals. We find that CGT exposed to ambient conditions for 5 days forms an oxide layer approximately 5 nm thick. This oxidation leads to a significant increase in the oxidation state of the Cr ions, indicating a change in local magnetic properties. This is supported by real-space magnetic texture imaging through Lorentz transmission electron microscopy. By comparing the thickness-dependent saturation field of oxidized and pristine crystals, we find that oxidation leads to a nonmagnetic surface layer that is thicker than the oxide layer alone. We also find that the stripe domain width and skyrmionic bubble size are strongly affected by the crystal thickness in pristine crystals. Finally, these findings underscore the impact of thickness and surface oxidation on the properties of CGT, such as saturation field and domain/skyrmionic bubble size, and suggest a pathway for manipulating magnetic properties through a controlled oxidation process.
Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace existing Deep Neural Networks (DNNs). Despite the remarkable triumphs, the prolonged turnaround time of Transformer models is a widely recognized roadblock. The variety of sequence lengths imposes additional computing overhead where inputs need to be zero-padded to the maximum sentence length in the batch to accommodate the parallel computing platforms. This paper targets the field-programmable gate array (FPGA) and proposes a coherent sequence length adaptive algorithm–hardware co-design for Transformer acceleration. Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. The proposed sparse attention operator brings the complexity of attention-based models down to linear complexity and alleviates the off-chip memory traffic. The proposed length-aware resource hardware scheduling algorithm dynamically allocates the hardware resources to fill up the pipeline slots and eliminates bubbles for NLP tasks. Experiments show that our design has very small accuracy loss and has 80.2 × and 2.6 × speedup compared to CPU and GPU implementation, and 4 × higher energy efficiency than state-of-the-art GPU accelerator optimized via CUBLAS GEMM.
As we enter the exascale computing regime, powerful supercomputers continue to produce much higher amounts of data than what can be stored for offline data processing. To utilize such high compute capabilities on these machines, much of the data processing needs to happen in situ, when the full high-resolution data is available at the supercomputer memory. In this article, we discuss our MFiX-Exa simulation, which models multiphase flow by tracking a very large number of particles through the simulation domain. In one of the use cases, the carbon particles interact with air to produce carbon dioxide bubbles from the reactor. These bubbles are of primary interest to the domain experts for these simulations. For this particle-based simulation, we propose a streaming technique that can be deployed in situ to efficiently identify the bubbles, track them over time, and use them to down-sample the data with minimal loss in these features.
Here, we consider the random-field Heisenberg model, a paradigmatic model for many-body localization (MBL) and add a Markovian dephasing bath coupled to the Anderson orbitals of the model's noninteracting limit. We map this system to a classical facilitated hopping model that is computationally tractable for large system sizes and investigate its dynamics. The classical model exhibits a robust crossover between an ergodic (thermal) phase and a frozen (localized) phase. The frozen phase is destabilized by thermal subregions (bubbles), which thermalize surrounding sites by providing a fluctuating interaction energy and so enable off-resonance particle transport. Investigating steady-state transport, we observe that the interplay between thermal and frozen bubbles leads to a clear transition between diffusive and subdiffusive regimes. This phenomenology both describes the MBL system coupled to a bath and provides a classical analog for the MBL transition in the corresponding quantum model, in that the classical model displays long local memory times. It also highlights the importance of the details of the bath coupling in studies of MBL systems coupled to thermal environments.
First patch release of v2.0.0 Changes since v2.0.0 [Important change] Fix ocean threading bug seen in debug cases on Chrysalis with Intel 20.0.4. Was introduced around time of v2.0.0 tag. Does not change v2.0.0 answers on Chrysalis because those didn't use threading or debugging. [EAM] Add semi-lagrangian tracer transport for theta-l (F90 and C++), add new algorithm for finding tropopause, add DSCREAM to allow v2 and SCREAM settings in same code such as adjust_ps [EAM-MMF] 60L default, allow C++ back end of RRTMGP (EAM too). [EAMxx] add nu-top functionality, fix forcing functor, add ttype9 and dcmip2012 tests 2.1, 2.2, and 3 HOMME: remove obsolete remap algs, option to specify dynamics alg indep of tracer, new sponge layer, add imex tests [ELM] Add topography-based subgrid (topounits), add FATES-ELM Nitro., Phos. and CH4 coupling, add land-use ts for NARRM, add lulc for SSP3 RCP7, Fix nutrient fertilization exp test and carbon isotope flux, Fix xactive lnd dry deposition, add lake water storage option, fix plant hydraulics 2d params, fix carbon budget calc, fix soil nutrient conc. bug, fix mosart dam bug, add test for new ELM, MOSART features, fix bug in O3 dry dep stomatal resistances, fix plant hydraulics restart BFB error, update mkmapdata. [MOSART] fix bug for reading the latitude from an unstructured input file, fix oversat in bubble test. [MPAS-ocean] Add CFC11, CFC12 tracers, add 2D spherical transport tests, fix del4 tracer mixing, add MARBL ocean tracer mixing, modify harmonic analysis options, add GPU port of vmix routines, fix calc of ML-averaged BV freq. [MPAS-seaice] Change extents of initial polar disks for oRRS18to6v3 grid, fix ice BGC with MARBL, update spherical test cases, fix DON coupling, Remove Cf from sea ice constants. [MPAS-landice] add CRYO1850-4xCO2 compset [CIME] add GCP, ANL GCE, Spock, Perlmutter, deprecate config_compilers.xml, fix and clean-up cmake macros, fix slurm bindings, refactor CIME internal testing, cleanup SCORPIO perf data, allow position independent compset naming, [also] update v2 benchmarking suite, extend e3sm_prod with throughput and memory checks
Topological spin/polarization structures in ferroic materials continue to draw great attention as a result of their fascinating physical behaviors and promising applications in the field of high-density nonvolatile memories as well as future energy-efficient nanoelectronic and spintronic devices. Such developments have been made, in part, based on recent advances in theoretical calculations, the synthesis of high-quality thin films, and the characterization of their emergent phenomena and exotic phases. Herein, progress over the last decade in the study of topological structures in ferroic thin films and heterostructures is explored, including the observation of topological structures and control of their structures and emergent physical phenomena through epitaxial strain, layer thickness, electric, magnetic fields, etc. First, the evolution of topological spin structures (e.g., magnetic skyrmions) and associated functionalities (e.g., topological Hall effect) in magnetic thin films and heterostructures is discussed. Then, the exotic polar topologies (e.g., domain walls, closure domains, polar vortices, bubble domains, and polar skyrmions) and their emergent physical properties in ferroelectric oxide films and heterostructures are explored. Lastly, a brief overview and prospectus of how the field may evolve in the coming years is provided.
Magnetic skyrmions are localized chiral magnetic textures in the form of nanoscale vortices or bubbles that are topologically protected from being ‘unwound’. Their topological nature gives rise to rich behaviors including ordered lattice formation, emergent electrodynamics and robust current-driven displacement at remarkably low current densities. However, magnetic skyrmions have so far been restricted to just a few materials and observed only at low temperatures, limiting the experimental accessibility and technological application of these unique topological objects. This project aimed to realize magnetic skyrmions at room temperature in a new class of engineered chiral ferromagnets that exploit broken mirror symmetry at interfaces to generate helical magnetic order. Ultrathin magnetic heterostructures were engineered and patterned into laterally-confined nanostructures in which skyrmions were stabilized, manipulated, and detected for the first time under ambient conditions. Static and dynamic properties were examined using advanced x-ray imaging to provide a fundamental understanding of topological magnetic phases. The interactions between magnetic skyrmions and electron charge and spin currents were studied to identify fundamental new physics and enable device applications. These experimental studies, supported by detailed modeling, will help launch a new subfield of skyrmion-based spintronics in which individual skyrmions can be used to encode, store, and transport information in high-performance, low power memory and logic devices.