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DOE OSTI2025

Genetic Transfer in Action: Uncovering DNA Flow in an Extremophilic Microbial Community

ABSTRACT Horizontal genetic transfer (HGT) is a significant driver of genomic novelty in all domains of life. HGT has been investigated in many studies however, the focus has been on conspicuous protein‐coding DNA transfers that often prove to be adaptive in recipient organisms and are therefore fixed longer‐term in lineages. These results comprise a subclass of HGTs and do not represent exhaustive (coding and non‐coding) DNA transfer and its impact on ecology. Uncovering exhaustive HGT can provide key insights into the connectivity of genomes in communities and how these transfers may occur. In this study, we use the term frequency‐inverse document frequency (TF‐IDF) technique, that has been used successfully to mine DNA transfers within real and simulated high‐quality prokaryote genomes, to search for exhaustive HGTs within an extremophilic microbial community. We establish a pipeline for validating transfers identified using this approach. We find that most DNA transfers are within‐domain and involve non‐coding DNA. A relatively high proportion of the predicted protein‐coding HGTs appear to encode transposase activity, restriction‐modification system components, and biofilm formation functions. Our study demonstrates the utility of the TF‐IDF approach for HGT detection and provides insights into the mechanisms of recent DNA transfer.

Microbiology↗

DOE OSTI2023

Containerization of Phase-2 Tracker Data Acquisition and Control Framework

The CMS Experiment has started an extensive upgrade program in the context of the High-Luminosity phase of the LHC (Phase-2). In order to cope with the highly demanding High-Luminosity conditions, CMS will need a completely new inner and outer tracking detectors. On top of R&D development, a Data AcQuisition (DAQ) software is being developed along with different applications to control, monitor and validate the newly produced modules of the future tracker. As more and more developers are getting involved to maintain all those codes, an environment where software and applications can work independently of the host machine operating system is crucial. The Phase-2 Tracker group decided to make use of Docker as containerization solution for its framework. This poster describes the containerization of DAQ software/applications utilized to test and validate the Tracker modules. This includes continuous integration and continuous deployment (so-called CI/CD) and running GUI applications inside containers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Coupling Microstructural Evolution Simulations to Material Property Degradation Predictions for Plasma-Facing Materials

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500 C to 1500 C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 D/m^2-s. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Connect microstructure evolution to property degradation with validated simulation

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500$^o$C to 1500$^o$C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 $\frac{D}{m^2s}$. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2021

Challenges and opportunities in verification and validation of low temperature plasma simulations and experiments

This paper describes the verification and validation (V&V) framework developed for the stochastic Particle-in-Cell, Direct Simulation Monte Carlo code Aleph. An ideal framework for V&V from the viewpoint of the authors is described where a physics problem is defined, and relevant physics models and parameters to the defined problem are assessed and captured in a Phenomena Identification and Ranking Table (PIRT). Furthermore, numerous V&V examples guided by the PIRT for a simple gas discharge are shown to demonstrate the V&V process applied to a real-world simulation tool with the overall goal to demonstrably increase the confidence in the results for the simulation tool and its predictive capability. Although many examples are provided here to demonstrate elements of the framework, the primary goal of this work is to introduce this framework and not to provide a fully complete implementation, which would be a much longer document. Comparisons and contrasts are made to more usual approaches to V&V, and techniques new to the low-temperature plasma community are introduced. Specific challenges relating to the sufficiency of available data (e.g., cross sections), the limits of ad hoc validation approaches, the additional difficulty of utilizing a stochastic simulation tool, and the extreme cost of formal validation are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Robust Independent Validation of Experiment and Theory: Rivet version 3

First released in 2010, the Rivet library forms an important repository for analysis code, facilitating comparisons between measurements of the final state in particle collisions and theoretical calculations of those final states. We give an overview of Rivet's current design and implementation, its uptake for analysis preservation and physics results, and summarise recent developments including propagation of MC systematic-uncertainty weights, heavy-ion and ep physics, and systems for detector emulation. In addition, we provide a short user guide that supplements and updates the Rivet user manual.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Validation of New MCNP6.3 Features for Critical and Subcritical Benchmark Simulations [Slides]

A separate MCNP6.3 V&V document reports on all the default calculations for all test suites was introduced. Here, we look at new optional features of the MCNP6.3 code. Then, Fission-matrix-based options with Convergence and population testing and Automated acceleration of the fission source and Doppler Broadening Resonance Correction and HDF5 Particle Track Output (PTRAC) and Validation benchmarks: criticality, Rossi-α, and subcritical multiplication. All the new MCNP6.3 options give statistically equivalent results and should be considered for default usage in criticality and subcritical multiplication calculation work. Discussed are two separate papers at the upcoming 2023 International Conference on Nuclear Criticality Safety (ICNC 2023) that give more details on the results presented here.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Coupling particle scale model and SuperDEM–CFD for multiscale simulation of biomass pyrolysis in a packed bed pyrolyzer

An efficient biomass pyrolysis process requires a comprehensive understanding of the chemical and physical phenomena that occur at multi-length and time scales. In this study, a multiscale computational approach was developed and validated for biomass pyrolysis in a packed-bed reactor by integrating pyrolysis kinetics, a particle scale model, and SuperDEM-CFD in open-source code MFiX. A one-dimensional particle-scale model that discretizes the characteristic length of biomass particle into layers was developed to predict the intraparticle phenomena inside a single particle. The 1D model was validated by comparing it with a single biomass particle pyrolysis experiment. Here, a recently developed SuperDEM-CFD model was employed to simulate the non-spherical particle-particle contact and fluid-particle interaction. The coupled model was applied to simulate the pyrolysis of cubic biomass particles in a packed bed and validated by comparing with experimental data. Simulation with and without particle-scale model was compared, and the effect of the gas-solid heat transfer models was also investigated.

09 BIOMASS FUELS↗

DOE OSTI2021

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Accelerated kinetic model for global macro stability studies of high-beta fusion reactors

The field reversed configuration (FRC), such as studied in the C-2W experiment at TAE Technologies, is an attractive candidate for realizing a nuclear fusion reactor. In an FRC, kinetic ion effects play the majority role in macroscopic stability, which allows global stability studies to make use of fluid-kinetic hybrid (also referred to as Ohm's law) models wherein ions are treated kinetically while electrons are treated as a fluid. The development and validation of such a hybrid particle-in-cell algorithm in the Exascale Computing Project code WarpX are reported here. Implementation of this model in the WarpX framework benefits from the numerical efficiency of WarpX as well as its scalability on large HPC systems and portability to different architectures. Performance benchmarks of the new algorithm for large, 3-dimensional, full device simulations from the Perlmutter supercomputer are presented. Results of a series of FRC simulations are discussed in which the impact of two-fluid effects on the tilt-mode growth rate was studied. It was observed that, in agreement with previous Hall-MHD studies, two-fluid effects have a stabilizing impact on the tilt mode.

Physics↗

DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

The DESI N -body simulation project – I. Testing the robustness of simulations for the DESI dark time survey

ABSTRACT Analysis of large galaxy surveys requires confidence in the robustness of numerical simulation methods. The simulations are used to construct mock galaxy catalogues to validate data analysis pipelines and identify potential systematics. We compare three N-body simulation codes, abacus, gadget-2, and swift, to investigate the regimes in which their results agree. We run N-body simulations at three different mass resolutions, 6.25 × 108, 2.11 × 109, and 5.00 × 109 h−1 M⊙, matching phases to reduce the noise within the comparisons. We find systematic errors in the halo clustering between different codes are smaller than the Dark Energy Spectroscopic Instrument (DESI) statistical error for $s\ \gt\ 20\ h^{-1}$ Mpc in the correlation function in redshift space. Through the resolution comparison we find that simulations run with a mass resolution of 2.1 × 109 h−1 M⊙ are sufficiently converged for systematic effects in the halo clustering to be smaller than the DESI statistical error at scales larger than $20\ h^{-1}$ Mpc. These findings show that the simulations are robust for extracting cosmological information from large scales which is the key goal of the DESI survey. Comparing matter power spectra, we find the codes agree to within 1 per cent for k ≤ 10 h Mpc−1. We also run a comparison of three initial condition generation codes and find good agreement. In addition, we include a quasi-N-body code, FastPM, since we plan use it for certain DESI analyses. The impact of the halo definition and galaxy–halo relation will be presented in a follow-up study.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Computational Fluid Dynamics (CFD) Simulations of Taylor Bubbles in Vertical and Inclined Pipes with Upward and Downward Liquid Flow

Summary Two-phase flow is a common occurrence in pipes of oil and gas developments. Current predictive tools are based on the mechanistic two-fluid model, which requires the use of closure relations to predict integral flow parameters such as liquid holdup (or void fraction) and pressure gradient. However, these closure relations carry the highest uncertainties in the model. In particular, significant discrepancies have been found between experimental data and closure relations for the Taylor bubble velocity in slug flow, which has been determined to strongly affect the mechanistic model predictions (Lizarraga-García 2016). In this work, we study the behavior of Taylor bubbles in vertical and inclined pipes with upward and downward flow using a validated 3D computational fluid dynamics (CFD) approach with level set method implemented in a commercial code. A total of 56 cases are simulated, covering a wide range of fluid properties, pipe diameters, and inclination angles: Eo ∈ [10, 700]; Mo ∈ [1×10–6, 5×103]; ReSL ∈ [–40, 10]; θ ∈ [5°, 90°]. For bubbles in vertical upward flows, the simulated distribution parameter, C0, is successfully compared with an existing model. However, the C0 values of downward and inclined slug flows where the bubble becomes asymmetric are shown to be significantly different from their respective vertical upward flow values, and no current model exists for the fluids simulated here. The main contributions of this work are (1) the relatively large 3D numerical database generated for this type of flow, (2) the study of the asymmetric nature of inclined and some vertical downward slug flows, and (3) the analysis of its impact on the distribution parameter, C0.

Engineering↗

DOE OSTI2022

Validation of energetic particle transport models for time-dependent integrated simulations of burning plasmas (Final ST Report)

The major goal of this project is to deliver a suite of validated energetic particle (EP) modeling tools for time-dependent integrated tokamak simulations (with the code TRANSP) that can be used for modeling plasma discharges featuring a substantial EP content, such as burning plasmas. During FY20, issues were resolved with an energetic particle transport model called TGLF-EP, with improvements made to the TGLF-EP workflow, generation of input files, and interpretation of runs on the NERSC supercomputing cluster. The recipient also gave input on simulations with the code TRANSP to reproduce the reference JET discharge, and assessed the accuracy and uncertainties of the simulated B field, current evolution, thermal plasma profiles, stored energy, etc. Input was also given on interpretation of fast-ion instability calculations and recommendations for modifications to JET afterglow discharge for the upcoming DT campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

CSRI Summer Proceedings 2021

The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia National Laboratories for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researches to learn about problems in computer and computational science at DOE laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, algebraic preconditioners, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, validation and verification methods, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers, beyond Moore’s Law computing, exascale computing tools and application design, reduced order and multiscale modeling, parallel input/output, and theoretical computer science. The CSRI Summer Program is organized by CSRI and includes a weekly seminar series and the publication of a summer proceedings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

CSRI Summer Proceedings 2021

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces

The objective of this project is to develop, and integrate, high-performance simulation tools capable of predicting plasma-facing component (PFC) operating lifetime and the impact of the evolving surface morphology of tungsten-based PFCs on plasma contamination, including the dynamic recycling of fuel species and tritium retention, in future magnetic fusion devices. Establishing a fundamental physical understanding and developing predictive capabilities of plasma-surface interactions (PSI) requires simultaneously addressing complex and diverse physics occurring over a wide range of length (Angstroms to meters) and time (femtoseconds to years) scales, as well as integrating extensive physical processes across the plasma–surface interface. This requires development of not only detailed physics models and computational strategies at each scale, but also algorithms and methods to couple them effectively in a way that can be robustly validated. Deploying these tools requires the continued development and coupling of leadership-scale computational codes to describe the boundary plasma and the evolving PFC surface, as well as a host of simulations that bridge disparate scales to address complex physical and computational issues at the plasma–surface interface in multi-component materials systems for magnetic fusion energy development beyond ITER.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation

The utility of transfer learning to improve the performance of deep learning in axon segmentation Data Data: All the input and labeled volumes tf-logs: Tensorflow logs, view with command "tensorboard --logdir [name of folder]" Model Weights: model_weights: the argument list under variable combo indicate 1) no oversampling, 2) no rotation, 3) no learn scheduler, and 4) flipping on all three dimensions, and the additional values indicate 5) elastic deformation percentage, 6) rotate deformation percentage, 7) layer setting , 8) learning rate, and 9) training/validation/test data division suffix (leave '' if not using suffix). Results: Output from inference segment_total_results_validation_final: All validation results and calculations segment_total_results: All test results and calculations Authors The modified code was created for a paper by: Marjolein Oostrom, Michael A. Muniak, Rogene Eichler West, Sarah Akers, Paritosh Pande, Moses Obiri, Wei Wang, Kasey Bowyer, Zhuhao Wu, Lisa Bramer, Tianyi Mao, Bobbie Jo Webb-Robertson The work is adapted from Github TrailMap, which was created by Albert Pun and Drew Friedmann Acknowledgments MO, RMEW, SA, MO, LB, BJWR were supported by the Laboratory Directed Research and Development at Pacific Northwest National Laboratory (PNNL), a Department of Energy facility operated by Battelle under contract DE-AC05-76RLO01830. WW, KB, and ZW were supported in part by a NIH/BRAIN Initiative Grant RF1MH128969. MAM and TM were supported by two NIH/BRAIN Initiative Grants R01NS104944, RF1MH120119 and NIH R01NS081071. This research is affiliated with the Pacific northwest bioMedical Innovation Co-laboratory (PMedIC) collaboration between OHSU and PNNL.

Oostrom, Marjolein T↗