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DOE OSTI2024

Evaluating integration and performance of containerized climate applications on a Hewlett Packard Enterprise Cray system

Containers have taken over large swaths of cloud computing as the most convenient way of packaging and deploying applications. The features that containers offer for packaging and deploying applications translate to high performance computing (HPC) as well. At The National Oceanic and Atmospheric Administration, containers provide an easy way to build and distribute complex HPC applications, allowing faster collaboration, portability, and experiment computer environment reproducibility amongst the scientific community. The challenge arises when applications rely on message passing interface (MPI). This necessitates investigation into how to properly run these applications with their own unique requirements and produce performance on par with native runs. We investigate the MPI performance for benchmarks and containerized climate models for various containers covering selection of compiler and MPI library combinations from the Cray provided programming environments on the Cray XC supercomputer GAEA. Performance from the benchmarks and the climate models shows that for the most part containerized applications perform on par with the natively built applications when the system optimized Cray MPICH libraries are bound into the container, and the hybrid model containers have poor performance in comparison. We also describe several challenges and our solutions in running these containers, particularly challenges with heterogeneous jobs for the containerized model runs.

Abraham, Subil↗

DOE OSTI2022

Flood hazard model calibration using multiresolution model output

Riverine floods pose a considerable risk to many communities. Improving flood hazard projections has the potential to inform the design and implementation of flood risk management strategies. Current flood hazard projections are uncertain, especially due to uncertain model parameters. Calibration methods use observations to quantify model parameter uncertainty. With limited computational resources, researchers typically calibrate models using either relatively few expensive model runs at high spatial resolutions or many cheaper runs at lower spatial resolutions. This leads to an open question: is it possible to effectively combine information from the high and low resolution model runs? We propose a Bayesian emulation–calibration approach that assimilates model outputs and observations at multiple resolutions. As a case study for a riverine community in Pennsylvania, we demonstrate our approach using the LISFLOOD-FP flood hazard model. Here, the multiresolution approach results in improved parameter inference over the single resolution approach in multiple scenarios. Results vary based on the parameter values and the number of available models runs. Our method is general and can be used to calibrate other high dimensional computer models to improve projections.

multiresolution↗

DOE OSTI2021

Methods for a blind analysis of isobar data collected by the STAR collaboration

In 2018, the STAR collaboration collected data from $_{44}^{96}{\mathrm{Ru}}+_{44}^{96}{\mathrm{Ru}}$ and $_{40}^{96}{\mathrm{Zr}}+_{40}^{96}{\mathrm{Zr}}$ at $\sqrt{s_\text {NN}}=200$ GeV to search for the presence of the chiral magnetic effect in collisions of nuclei. The isobar collision species alternated frequently between $_{44}^{96}{\mathrm{Ru}}+_{44}^{96}{\mathrm{Ru}}$ and $_{40}^{96}{\mathrm{Zr}}+_{40}^{96}{\mathrm{Zr}}$ . In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data, STAR developed a three-step blind analysis procedure. Analysts are initially provided a “reference sample” of data, comprised of a mix of events from the two species, the order of which respects time-dependent changes in run conditions. After tuning analysis codes and performing time-dependent quality assurance on the reference sample, analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual $\approx 30$-min data-taking runs. For this sample, species-specific information is disguised, but individual output files contain data from a single isobar species. Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage. Following these modifications, the “frozen” code is passed over the fully un-blind data, completing the blind analysis. As a check of the feasibility of the blind analysis procedure, analysts completed a “mock data challenge,” analyzing data from Au + Au collisions at $\sqrt{s_\text {NN}}=27$ GeV, collected in 2018. The Au + Au data were prepared in the same manner intended for the isobar blind data. Finally, the details of the blind analysis procedure and results from the mock data challenge are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Designing a repository in domal salt: The influence of design variants in different modelling environments

To understand the long-term environmental impact of disposing radioactive waste of in a deep geological repository and to optimise its design, performance assessments are used. In this study, four teams (COVRA, GRS, Quintessa, and DOE) modified the previously developed generic repository of DECOVALEX task F2 to identify commonalities and differences between the teams for specific changes in repository design. The teams tested six design modifications: (1) Replacing concrete abutments with run-of-mine salt; (2) Replacing the salt seal with a concrete abutment and using run-of-mine salt instead for the two concrete abutments in each drift seal; (3) Halving the size of the infrastructure area; (4) Using run-of-mine salt instead of gravel for backfilling the infrastructure area; (5) Disposal of spent nuclear fuel without the POLLUX-10 containers (6); Lower initial saturation of the spent nuclear fuel and vitrified waste disposal drifts. Despite different modelling strategies used, models agreed that a smaller infrastructure area has a limited effect on radionuclide transport. Responses to the absence of the two concrete abutments in each seal, the use of single large concrete abutments (200 m each), or the use of run-of-mine salt in the infrastructure area differ between teams due to differing modelling assumptions. Based on these results, the estimated efficacy of containment depends strongly on the model assumptions of each team. More specifically, it appears to depend on the compaction model used and therefore on the backfill material used in different areas of the repository. However, the drift seal appears to be a critical design element in all models, effectively limiting radionuclide transport by hydrologically disconnecting sections of the repository. Additional beneficial design choices include the use of dry salt in disposal drifts to limit radionuclide transport and reducing the infrastructure area costs and minimizing host rock damage.

DECOVALEX↗

DOE OSTI2023

Model to inform the expansion of hydrogen distribution infrastructure

A growing hydrogen economy requires new hydrogen distribution infrastructure to link geographically distributed hubs of supply and demand. The Hydrogen Optimization with Deployment of Infrastructure (HOwDI) Model helps meet this requirement. The model is a spatially resolved optimization framework that determines location-specific hydrogen production and distribution infrastructure to cost-optimally meet a specified location-based demand. While these results are useful in understanding hydrogen infrastructure development, there is uncertainty in some costs that the model uses for inputs. Thus, the project team took the modeling effort a step further and developed a Monte Carlo methodology to help manage uncertainties. Seven scenarios were run using existing infrastructure and new demand in Texas exploring different policy and tax approaches. The inclusion of tax credits increased the percentage of runs that could deliver hydrogen at <$\$4$ /kg from 31% to 77% and decreased the average dispensed cost from $\$4.35$ /kg to $\$3.55$ /kg. However, even with tax credits there are still some runs where unabated SMR is deployed to meet new demand as the low-carbon production options are not competitive. Every scenario, except for the zero-carbon scenario (without tax credits), resulted in at least 20% of the runs meeting the $\$4$ /kg dispensed fuel cost target. This indicates that multiple pathways exist to deliver $\$4$ /kg hydrogen.

08 HYDROGEN↗

DOE OSTI2026

Hydrogen production from full-strength corn stover fermentation effluent in single-chamber replaceable-cathode microbial electrolysis cells

Lignocellulosic residual biomass generated by the agricultural sector is an abundant feedstock for biohydrogen production via dark fermentation. However, this process is intrinsically inefficient, converting only ~30% of the reductant energy into H2 and leaving substantial amounts of reduced byproducts. These byproducts, mostly found in the fermentation effluents, can be further valorized in microbial electrolysis cells (MECs) to enhance the overall H2 recovery. However, current MEC configurations are typically dual- or single-chamber systems, yet both suffer from key inefficiencies. Dual-chamber systems rely on proton exchange membranes that are costly and prone to rapid biofouling, whereas single-chamber, membraneless systems are limited by reduced productivity due to H2 recycling and methanogenic consumption In this study, three single-chamber, 50-mL replaceable-cathode microbial electrolysis cells (RC-MECs) were 3D-printed and equipped with a physical separator to isolate anode and cathode compartments and limit H2 migration. Full-strength milled corn stover (MCS) fermentation effluent (COD of ~23.8 g-COD/L) was treated in fed-batch mode over two operational periods spanning 21 (Run 1) and 80 (Run 2) days. The RC-MECs exhibited comparable performance in both runs: after biofilm maturation, current densities exceeded 100 A/m²_cathode, COD removal reached up to 43%. Notably, extended RC-MECs operation led to a substantial methanogenic activity with the CH4 fraction in the cathode gas increasing to as high as 80% of the total biogas. Additions of a methanogenesis inhibitor 2-bromoethanesulfonate (2-BES) produced transient increases in hydrogen yields (11.51 and 5.12 L-H2/L_reactor/day in Runs 1 and 2, respectively); however, sustained 2-BES addition in subsequent cycles reduced total biogas production, decreased COD removal, and led to volatile fatty acid accumulation. Overall, single-chamber MECs can treat high-strength dark fermentation effluents while improving H2 recovery, but methanogenesis remains a key bottleneck, and complete long-term inhibition may be operationally unsustainable.

Hydrogen Production↗

DOE OSTI2024

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Kubernetes for the Deep Underground Neutrino Experiment Data Acquisition

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment based in the USA which is expected to start taking data in 2029. DUNE aims to precisely measure neutrino oscillation parameters by detecting neutrinos from the LBNF beamline (Fermilab) at the Far Detector, 1,300 kilometres away, in South Dakota at the Sanford Underground Research Facility. The Far Detector will consist of four cryogenic Liquid Argon Time Projection Chamber detectors of 17 kT, each producing more than 1 TB/sec of data. The main requirements for the data acquisition system are the ability to run continuously for extended periods of time, with a 99% up-time requirement, and the functionality to record both beam neutrinos and low energy neutrinos from the explosion of a neighbouring supernova, should one occur during the lifetime of the experiment. The key challenges are the high data rates that the detectors generate and the deep underground environment, which places constraints on power and space. To overcome these challenges, DUNE plans to use a highly optimised C++ software suite and a server farm of about 110 nodes continuously running about two hundred multicore processes located close to the detector, 1.5 kilometres underground. Thirty nodes will be at the surface and will run around two hundred processes simultaneously. DUNE is studying the use of the Kubernetes framework to manage containerised workloads and take advantage of its resource definitions and high up-time services to run the DAQ system. Progress in deploying these systems at the CERN neutrino platform on the prototype DUNE experiments is reported.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

MiniDAQ-3: Providing concurrent independent subdetector data-taking on CMS production DAQ resources

The data acquisition (DAQ) of the Compact Muon Solenoid (CMS) experiment at CERN, collects data for events accepted by the Level-1 Trigger from the different detector systems and assembles them in an event builder prior to making them available for further selection in the High Level Trigger, and finally storing the selected events for offline analysis. In addition to the central DAQ providing global acquisition functionality, several separate, so-called “MiniDAQ” setups allow operating independent data acquisition runs using an arbitrary subset of the CMS subdetectors. During Run 2 of the LHC, MiniDAQ setups were running their event builder and High Level Trigger applications on dedicated resources, separate from those used for the central DAQ. This cleanly separated MiniDAQ setups from the central DAQ system, but also meant limited throughput and a fixed number of possible MiniDAQ setups. In Run 3, MiniDAQ-3 setups share production resources with the new central DAQ system, allowing each setup to operate at the maximum Level-1 rate thanks to the reuse of the resources and network bandwidth. Configuration management tools had to be significantly extended to support the synchronization of the DAQ configurations needed for the various setups. We report on the new configuration management features and on the first year of operational experience with the new MiniDAQ-3 system.

Amoiridis, Vassileios↗

DOE OSTI2024

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Photonuclear Production of 67 Cu From Gallium

Copper-67 is a radioisotope of interest for medical imaging and therapy as well as for under- standing stellar and interstellar evolution pertaining to the formation of proton-rich nuclei. Since 67 Cu decays 100% to 67 Zinc, understanding this reaction can shed light on the abundance of this and other p-nuclei elements in the universe. Here, the photonuclear production of 67 Cu from 71 Ga and natural gallium is examined as an alternative to its photoproduction from zinc. Two research and development production runs were performed at Thomas Jefferson National Accelerator Facility using an electron linac. During the first run, an 805-W, 30.9-MeV beam was used to irradiate a 1-mm tungsten radiator to create a bremsstrahlung flux. The resulting gamma photons irradiated 50.9 g of natural gallium encased in a graphite crucible for 24.2 h; 7.02 Bq/W∙s∙kg of 67 Cu activity was produced. During the second run, a 4380-W, 31.5-MeV beam was used for 12.0 h on the same target containing 60 g of natural gallium; 6.41 Bq/W∙s∙kg of 67 Cu activity was produced. Because of the difficulties in spectroscopically differentiating 67 Cu from 67 Ga, prior to each run, an isotopically pure 71 Ga disk was irradiated using a 100-W beam for 1 h, at the same respective energies. Finally, these baseline irradiations allowed for separation of 67 Cu from 67 Ga in the spectroscopic measurements of the natural gallium targets.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2020

Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s} =$ 13 TeV

At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-of-mass energy of 13\TeV. During Run 2 (years 2015–2018) the LHC eventually reached a luminosity of 2.1× 1034 cm-2s-1, almost three times that reached during Run 1 (2009–2013) and a factor of two larger than the LHC design value, leading to events with up to a mean of about 50 simultaneous inelastic proton-proton collisions per bunch crossing (pileup). The CMS Level-1 trigger was upgraded prior to 2016 to improve the selection of physics events in the challenging conditions posed by the second run of the LHC. This paper describes the performance of the CMS Level-1 trigger upgrade during the data taking period of 2016–2018. The upgraded trigger implements pattern recognition and boosted decision tree regression techniques for muon reconstruction, includes pileup subtraction for jets and energy sums, and incorporates pileup-dependent isolation requirements for electrons and tau leptons. In addition, the new trigger calculates high-level quantities such as the invariant mass of pairs of reconstructed particles. The upgrade reduces the trigger rate from background processes and improves the trigger efficiency for a wide variety of physics signals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Supersymmetry with scalar sequestering

Supersymmetric models with a strongly interacting superconformal hidden sector (HS) may drive soft supersymmetry (SUSY) breaking scalar masses, bilinear soft term B μ and Higgs combinations m H u , d 2 + μ 2 to small values at some intermediate scale, leading to unique sparticle mass spectra along with possibly diminished fine-tuning in spite of a large superpotential μ parameter. We set up a computer code to calculate such spectra, which are then susceptible to a variety of constraints: (1) possible charge-or-color breaking (CCB) minima in the scalar potential, (2) unbounded from below (UFB) scalar potential, (3) improper electroweak symmetry breaking, (4) a charged or sneutrino lightest SUSY particle (LSP), (5) generating m h ∼ 125 GeV , (6) consistency with LHC sparticle mass limits, and (7) naturalness. We find this bevy of constraints leaves little or no viable parameter space for the case where hidden sector dynamics dominates minimal supersymmetric standard model (MSSM) running, even for the case of nonuniversal gaugino masses. For the case with moderate HS running with comparable MSSM running, and with universal gaugino masses, then the fine-tuning is ameliorated, but nonetheless remains high. Viable spectra with moderate HS running and with low fine-tuning and large μ can be found for nonuniversal gaugino masses. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2024

Precision measurement of the Ξ b − baryon lifetime

A sample of p p collision data, corresponding to an integrated luminosity of 5.5 fb − 1 and collected by the LHCb experiment during LHC Run 2, is used to measure the ratio of the lifetime of the Ξ b − baryon to that of the Λ b 0 baryon, r τ ≡ τ Ξ b − / τ Λ b 0 . The value r τ = 1.076 ± 0.013 ± 0.006 is obtained, where the first uncertainty is statistical and the second systematic. This value is averaged with the corresponding value from Run 1 to obtain r τ Run 1 , 2 = 1.078 ± 0.012 ± 0.007 . Multiplying by the world-average value of the Λ b 0 lifetime yields τ Ξ b − Run 1 , 2 = 1.578 ± 0.018 ± 0.010 ± 0.011 ps , where the uncertainties are statistical, systematic, and due to the limited knowledge of the Λ b 0 lifetime. This measurement improves the precision of the current world average of the Ξ b − lifetime by about a factor of 2, and is in good agreement with the most recent theoretical predictions. © 2024 CERN, for the LHCb Collaboration 2024 CERN

Aaij, R. (ORCID:0000000305331952)↗

DOE OSTI2020

Directional locking effects for active matter particles coupled to a periodic substate

Directional locking occurs when a particle moving over a periodic substrate becomes constrained to travel along certain substrate symmetry directions. Such locking effects arise for colloids and superconducting vortices moving over ordered substrates when the direction of the external drive is varied. Here we study the directional locking of run-and-tumble active matter particles interacting with a periodic array of obstacles. In the absence of an external biasing force, we find that the active particle motion locks to various symmetry directions of the substrate when the run time between tumbles is large. The number of possible locking directions depends on the array density and on the relative sizes of the particles and the obstacles. For a square array of large obstacles, the active particle only locks to the x, y, and 45° directions, while for smaller obstacles, the number of locking angles increases. Each locking angle satisfies θ = arctan (p/q) , where p and q are integers, and the angle of motion can be measured using the ratio of the velocities or the velocity distributions in the x and y directions. When a biasing driving force is applied, the directional locking behavior is affected by the ratio of the self-propulsion force to the biasing force. For large biasing, the behavior resembles that found for directional locking in passive systems. For large obstacles under biased driving, a trapping behavior occurs that is nonmonotonic as a function of increasing run length or increasing self-propulsion force, and the trapping diminishes when the run length is sufficiently large.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficiency and effectiveness of correctness testing for stochastic programs. Recently, the sensitivity of deep learning training and inference pipelines to floating-point non-associativity has been found to sometimes be extreme. It can prevent certification for commercial applications, accurate assessment of robustness and sensitivity, and bug detection. New approaches in scientific computing applications have coupled deep learning models with high-performance computing, leading to an aggravation of debugging and testing challenges. Here we perform an investigation of the statistical properties of floating-point non-associativity within modern parallel programming models, and analyze performance and productivity impacts of replacing atomic operations with deterministic alternatives on GPUs. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning, uncovering and quantifying the impacts of input parameters triggering run to run variability and reporting on the reliability and completeness of the documentation. Finally, we evaluate the strategy of exploiting automatic determinism that could be provided by deterministic hardware, using the Groq LPUTM accelerator for inference portions of the deep learning pipeline. We demonstrate the benefits that a hardware-based strategy can provide within reproducibility and correctness efforts.

Shanmugavelu, Sanjif↗

DOE OSTI2020

Multi-Scale Thermal Analysis for Design of SiC-Based Medium Voltage Motor Drive: Preprint

This paper describes a multi-scale thermal analysis approach for the design of an air-cooled 1.7-kV SiC MOSFET-based medium-voltage variable-speed motor drive. The National Renewable Energy Laboratory supported the project objectives by providing thermal management design support to the project partner The Ohio State University (OSU). The scope of the models and limited resources required efficient and flexible thermal models to be developed. Two modeling techniques are described that significantly reduced model run time and enabled more complex models to be run faster while retaining needed accuracy. The first technique uses the effectiveness-NTU method to extract convection boundary conditions from a computational fluid dynamics (CFD) model that can be applied to a fast-running finite element analysis model. The second is a porous media technique that enables system-level CFD simulations that incorporate effects from heat exchangers (e.g., pin fin heat sinks) that run in a fraction of the time required for fully resolved CFD simulations. The multi-scale approach to the thermal analysis enabled fast and accurate simulation for the converter design ranging from the die level up to the full system with 36 submodules. The modeling results were validated against experimental data from system tests performed by OSU.

DIRECT ENERGY CONVERSION↗