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At least 55 records · Page 3

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a k-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity fracture mechanics model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective moduli at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are ≈ O ( 10 6 ) times faster compared to high-fidelity fracture simulations by HOSS. These aspects make the proposed surrogate model attractive for upscaling damage from micro-scale models to continuum models. We would like to emphasize that the surrogate models are not a replacement of physical understanding of fracture propagation. The proposed method in this paper is limited to tensile loading conditions at low-strain rates. This loading condition corresponds to a dominant fracture perpendicular to tensile direction. The proposed method is not applicable for in-plane shear, out-of-plane shear, and higher strain rate loading conditions.

36 MATERIALS SCIENCE↗

The International Virus Bioinformatics Meeting 2020

The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8–9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

59 BASIC BIOLOGICAL SCIENCES↗

Evolution of HEP Processing Frameworks

HEP data-processing software must support the disparate physics needs of many experiments. For both collider and neutrino environments, HEP experiments typically use data-processing frameworks to manage the computational complexities of their large-scale data processing needs. Data-processing frameworks are being faced with new challenges this decade. The computing landscape has changed from the past three decades of homogeneous single-core x86 batch jobs running on grid sites. Frameworks must now work on a heterogeneous mixture of different platforms: multi-core machines, different CPU architectures, and computational accelerators; and different computing sites: grid, cloud, and high-performance computing. We describe these challenges in more detail and how frameworks may confront them. Given their historic success, frameworks will continue to be critical software systems that enable HEP experiments to meet their computing needs. Frameworks have weathered computing revolutions in the past; they will do so again with support from the HEP community

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Sustainable Data Evolution Technology for Power Grid Optimization

The SDET Tool is used to create open-access power grid data sets and facilitate updates of these data sets by the community. Pacific Northwest National Laboratory (PNNL) and its power industry and software vendor partners are developing an innovative sustainable data evolution technology (SDET) to create open-access power grid datasets and facilitate updates to these datasets by the power grid community. The objective is to make this a sustained effort within and beyond the ARPA-E GRID DATA program so that the datasets can evolve over time and meet the current and future needs for power grid optimization and potentially other applications in power grid operation and planning.

Source record↗

Sustainable Data Evolution Technology for Power Grid Optimization

The SDET Tool is used to create open-access power grid data sets and facilitate updates of these data sets by the community. Pacific Northwest National Laboratory (PNNL) and its power industry and software vendor partners are developing an innovative sustainable data evolution technology (SDET) to create open-access power grid datasets and facilitate updates to these datasets by the power grid community. The objective is to make this a sustained effort within and beyond the ARPA-E GRID DATA program so that the datasets can evolve over time and meet the current and future needs for power grid optimization and potentially other applications in power grid operation and planning.

Source record↗

dCache - Keeping up With the Evolution of Science

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements of various scientific communities. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems, including event driven workflow management. With this presentation, we will show some of the recent developments that optimize data management and access to maximise the gain from stored data.

Mkrtchyan, Tigran↗

The Beam-Beam Effect and its Consequences for the Jefferson Lab EIC

In this work we address the effect of beam jitter on emittance growth as caused by the beam-beam effect on the Jefferson Lab Electron Ion Collider (JLEIC). This proposed collider would collide up to 100 GeV proton beams with up to 10 GeV electron beams. Due to the asymmetric rigidities of the beams and their non-linear lensing action on each other during a collision, collective effects can limit beam storage times. Using simulations we determined that one of JLEIC’s synchronization concepts would require a new set of software tools to accurately understand phase space evolution.

Nissen, Edward↗

Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical “doses” of the HealtheRx shared their information with others (“social doses”). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population (“agents”) using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.

59 BASIC BIOLOGICAL SCIENCES↗

Evolution of Oxide–Sulfide Complex Inclusions and Its Correlation with Steel Cleanliness During Electroslag Rapid Remelting (ESRR) of Tool Steel

The evolution of CaO-Al{sub 2}O{sub 3}-MgO-(SiO{sub 2}) inclusions associated with CaS during electroslag rapid remelting (ESRR) of the tool steel with ultra low oxygen (8 ppm) under protective atmosphere was investigated. Thermodynamic calculation was performed to evaluate the oxygen level of liquid steel and the inclusion evolution during ESRR with the help of FactSage software (CON2 database). The results demonstrated that the reoxidation of liquid steel took place during industrial ESRR, resulting in the oxygen pickup of approximately double content. The oxygen level in the steel was determined by [Al]-[O] equilibrium during ESRR, whereas FeO in the slag transferred oxygen into the liquid steel, as indicated by the significant oxygen pickup. The inclusions in the steel electrode are oxide–sulfide type of CaO-Al{sub 2}O{sub 3}-MgO partially wrapped with CaS. The original oxide inclusions that had not been removed in ESRR process were transformed to liquid CaO-Al{sub 2}O{sub 3}-SiO{sub 2}-MgO inclusions through reacting with the dissolved oxygen supplied from the reoxidation of liquid steel and concerned elements in liquid steel during ESRR. These CaO-Al{sub 2}O{sub 3}-SiO{sub 2}-MgO inclusions were invariably associated with patch-type or shell-type CaS, which hold different formation mechanisms. The inclusions ranging from 2 to 4 µm take up a dominant proportion (a little more than 50 pct of the total inclusions) in the remelted ingots, and those larger than 8 µm account for about 3 pct. Melting rates of ESRR not only exerted a negligible effect on the steel cleanliness and the removal efficiency of oxide inclusions, but also on the inclusion size distribution.

36 MATERIALS SCIENCE↗

Application of Portable Parallelization Strategies for GPUs on track reconstruction kernels

Utilizing the computational power of GPUs is one of the key ingredients to meet the computing challenges presented to the next generation of High-Energy Physics (HEP) experiments. Unlike CPUs, developing software for GPUs often involves using architecturespecific programming languages promoted by the GPU vendors and hence limits the platform that the code can run on. Various portability solutions have been developed to achieve portable, performant software across different GPU vendors. Given the rapid evolution of these portability solutions, an early adoption of them in simple HEP testbed applications will help us understand the strengths and weaknesses of respective approaches.We apply several portability solutions, including Alpaka, Kokkos, SYCL and std::execution::par, on kernels for track propagation extracted from the mkFit project. We report on the development experience of the same application with different portability solutions, as well as their performance on GPUs, measured as the throughput of the kernels, from different manufacturers such as NVIDIA, AMD and Intel.

Kwok, Martin [Fermilab] (ORCID:0000000286936146)↗

FORECASTOR – II. Simulating galaxy surveys with the Cosmological Advanced Survey Telescope for Optical and UV Research

The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here, we introduce the CASTOR image simulator, a python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the CASTOR Wide, Deep, and Ultra-Deep surveys using simulated lightcones from the Santa Cruz semi-analytic model. We make predictions for the performance of these surveys by comparing galaxies that are extracted from each image using Source Extractor to the input catalogue. We find that the Wide, Deep, and Ultra-Deep surveys will be 75 per cent complete for point sources down to $\sim 27$, 29, and 30 mag, respectively, in the UV, u, and g filters, with the UV-split and u-split filters reaching a shallower depth. With a large area of $\sim 2200$ deg$^2$, the Wide survey will detect hundreds of millions of galaxies out to $z\sim 4$, mostly with $M_\ast \gtrsim 10^{9}\,{\rm M}_{\odot }$. The Ultra-Deep survey will probe to $z\sim 5$, detecting galaxies with $M_\ast \gtrsim 10^{7}{\rm M}_{\odot }$. These galaxy samples will enable precision measurements of the distribution of star formation in the cosmic web, connecting the growth of stellar mass to the assembly of dark matter haloes over two thirds of the history of the Universe, and other core goals of CASTOR’s legacy surveys. These image simulations and the tools developed to generate them will be a vital planning tool to estimate CASTOR’s performance and iterate the telescope and survey designs prior to launch.

79 ASTRONOMY AND ASTROPHYSICS↗

Toward Process-Based Quality through a Fundamental Understanding of Weld Microstructural Evolution

Here, in this overview paper, the possibility of qualifying welded components by integrating software and hardware tools that are capable of describing physical processes that occur during welding is explored. The existing and emerging tools to define geometrical boundary conditions, process parameters, heat and mass transfer, solidification, solid-state transformation, plastic deformation, distortion, residual stress, and performance of welded components are reviewed. Existing challenges relevant to the fundamental understanding of complex alloying, processing environment, and transients in energy deposition are discussed with reference to microstructure evolution. Finally, approaches to address these challenges were articulated with published case studies relevant to welding and additive manufacturing.

36 MATERIALS SCIENCE↗

PyBioNetFit

Systems biology models written in the BioNetGen language often contain parameters that must be fit based on experimental data. PyBioNetFit is a software tool designed to fit parameters of BioNetGen models. The user is expected to provide a BioNetGen model that contains unknown parameters, along with one or more text files containing experimental data. PyBioNetFit will perform a series of calls to the BioNetGen simulator using different parameter sets, to identify the parameter set that best fits the data. The parameter search is dictated by the user's choice of one of several metaheuristic optimization algorithms, including differential evolution, particle swarm optimization, scatter search, and simulated annealing. The software supports parallelized computing on multi-core computers and on computing clusters. We expect this software to be a useful tool for researchers designing BioNetGen models in collaboration with experimentalists.

Mitra, Eshan↗

Roadrunner

SAND2026-17073O Roadrunner software provides a comprehensive platform for simulating the mechanical behavior of crystalline materials under various loading conditions, allowing users to investigate the effects of dislocation slip hardening and damage evolution. Developed as a fork of the Multiphysics Object Oriented Simulation Environment (MOOSE) software from Idaho National Laboratory, Roadrunner is optimized for high-performance computing and can simulate large-scale problems, enabling researchers to explore complex scenarios. Its applications include material design and optimization in aerospace and automotive industries, investigation of failure mechanisms in structural materials, and development of predictive models for crystalline materials under various loading conditions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Lim, Hojun [Sandia National Lab. (SNL-CA), Livermo↗

The dynamics of stellar discs in live dark-matter haloes

Recent developments in computer hardware and software enable researchers to simulate the self-gravitating evolution of galaxies at a resolution comparable to the actual number of stars. Here we present the results of a series of such simulations. In this study, we performed N-body simulations of disc galaxies with between 100 and 500 million particles over a wide range of initial conditions. Our calculations include a live bulge, disc, and dark-matter halo, each of which is represented by self-gravitating particles in the N-body code. The simulations are performed using the gravitational N-body tree-code BONSAI running on the Piz Daint supercomputer. We find that the time-scale over which the bar forms increases exponentially with decreasing disc-mass fraction and that the bar formation epoch exceeds a Hubble time when the disc-mass fraction is ~0.35. These results can be explained with the swing-amplification theory. The condition for the formation of m = 2 spirals is consistent with that for the formation of the bar, which is also an m = 2 phenomenon. We further argue that the non-barred grand-design spiral galaxies are transitional, and that they evolve to barred galaxies on a dynamical time-scale. We also confirm that the disc-mass fraction and shear rate are important parameters for the morphology of disc galaxies. The former affects the number of spiral arms and the bar formation epoch, and the latter determines the pitch angle of the spiral arms.

79 ASTRONOMY AND ASTROPHYSICS↗

Dimming the Lights: 2D Simulations of Deflagrations of Hybrid C/O/Ne White Dwarfs Using FLASH

The dimmest and most numerous outlier of the Type Ia supernova population, the Type Iax event, is increasingly being found in the results of observational campaigns. There is currently no single accepted model to describe these events. This 2D study explores the viability of modeling Type Iax events as a hybrid C/O/Ne white dwarf progenitor undergoing a deflagration using the multiphysics software FLASH. This hybrid was created using the stellar evolution code MESA, and its C-depleted core and mixed structure have demonstrated lower yields than traditional C/O progenitors in previous deflagration-to-detonation studies. To generate a sample, 30 “realizations” of this simulation were performed, the only difference being the shape of the initial match head used to start the deflagration. Consistent with earlier work, these realizations produce the familiar hot dense bound remnant surrounded by sparse ejecta. Our results indicate that the majority of the star remains unburned (∼70%) and bound (>90%). Our realizations produce total ejecta yields on the order of 10 −2 –10 −1 M ☉ , ejected 56 Ni yields on the order of 10 −4 –10 −2 M ☉ , and ejecta kinetic energies on the order of 10 48 –10 49 erg. Compared to yields inferred from recent observations of the dimmest Type Iax events—SN 2007qd, SN 2008ha, SN 2010ae, SN 2019gsc, SN 2019muj, SN 2020kyg, and SN 2021fcg—our simulation produces comparable 56 Ni yields but too-small total yields and kinetic energies. Reignition of the remnant is also seen in some realizations.

Hydrodynamical simulations↗

Modelling and measurements of bunch profiles at the LHC

The bunch profiles in the LHC are often observed to be non-Gaussian, both at Flat Bottom (FB) and Flat Top (FT) energies. Especially at FT, an evolution of the tail population in time is observed. In this respect, the Monte-Carlo Software for IBS and Radiation effects (SIRE) is used to track different types of beam distributions. The impact of the distribution shape on the evolution of bunch characteristics is studied. The results are compared with observations from the LHC Run 2 data.

43 PARTICLE ACCELERATORS↗

CHEMNODE: CHEMICAL KINETICS SOLVER APPROACH BASED ON NEURAL ORDINARY DIFFERENTIAL EQUATIONS

This software contains an algorithm to model and predict the time evolution of chemical kinetics in reacting flow simulations using neural ordinary differential equations (NODEs). The approach works by using artificial neural networks to predict the chemical source terms. It achieves this by using forward-mode automatic differentiation and the Levenberg-Marquardt algorithm to adjust the neural network parameters, such that the discrepancies between the actual and predicted species profiles are minimized.

OWOYELE, OPEOLUWA↗