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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is forecast to collect a large sample of Type Ia supernovae (SNe Ia) expected to be instrumental in unveiling the nature of dark energy. The feat, however, requires accurately measuring the two components of the Hubble diagram, distance modulus and redshift. Distance is estimated from SN Ia parameters extracted from light-curve fits, where the average quality of light curves is primarily driven by survey parameters. An optimal observing strategy is thus critical for measuring cosmological parameters with high accuracy. We present in this paper a three-stage analysis to assess the impact of the deep drilling (DD) strategy parameters on three critical aspects of the survey: redshift completeness, the number of well-measured SNe Ia, and cosmological measurements. We demonstrate that the current DD survey plans (internal LSST simulations) are characterized by a low completeness (z ~ 0.55–0.65), and irregular and low cadences (several days), which dramatically decrease the size of the well-measured SN Ia sample. We propose a method providing the number of visits required to reach higher redshifts. We use the results to design a set of optimized DD surveys for SN Ia cosmology taking full advantage of spectroscopic resources for host galaxy redshift measurements. The most accurate cosmological measurements are achieved with deep rolling surveys characterized by a high cadence (1 day), a rolling strategy (at least two seasons of observation per field), and ultradeep (z ≳ 0.8) and deep (z ≳ 0.6) fields. A deterministic scheduler including a gap recovery mechanism is critical to achieving a high-quality DD survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

An Ab Initio Multiple Cloning Method for Non-Adiabatic Excited-State Molecular Dynamics in NWChem

The recently developed ab-initio multiple cloning (AIMC) approach based on the multicongurational Ehrenfest (MCE) method provides a powerful and accurate way of describing the excited-state dynamics of molecular systems. The AIMC method is a controlled approximation to non-adiabatic dynamics with a particular strength in the proper description of decoherence effects due to branching of vibrational wavepackets at a level crossing. In this work, we report a new implementation of the AIMC algorithm in the open source NWChem computational chemistry program. The framework combines linear-response time-dependent density functional theory with Ehrenfest mean-eld theory to determine the equations of motion for classical trajectories. The multi-dimensional wave function is decomposed into a superposition of Gaussian coherent states guided by Ehrenfest trajectories (i.e., MCE approach), which can clone with fully quantum mechanical amplitudes and phases. By using an efficient time-derivative based non-adiabatic coupling approach within the AIMC method, all observable are calculated on-the-y in the non-adiabatic molecular dynamics process. As a representative example, we apply our implementation to study the ultrafast photoinduced electronic and vibrational energy transfer in a pyridine molecule. The effects of the cloning procedure on electronic and vibrational coherence, relaxation and unidirectional energy transfer are discussed. This new AIMC implementation provides a high-level non-adiabatic molecular dynamics framework for simulating photoexcited dynamics in complex molecular systems and experimentally relevant ultrafast spectroscopic probes, such as nonlinear coherent optical and X-ray signals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Stochastic pre-event preparation for enhancing resilience of distribution systems

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. Here, in this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Searches for New Long-Lived Particles and Upgrade to the ATLAS Inner Detector (Final Technical Report)

The search for new fundamental particles is one of the defining goals of the Large Hadron Collider (LHC). The discovery of the Higgs Boson by the ATLAS and CMS collaborations provided the capstone of the Standard Model of particle physics, but outstanding questions remain. Why does the Higgs boson have a mass of 125 GeV when its natural mass would be many orders of magnitude larger? Is there a universal symmetry which unites all three forces described by the Standard Model? Can that symmetry be extended to include gravity? Is dark matter, evidenced by astronomical observations, made of a particle that interacts via Standard Model forces with the rest of matter? Together, these motivations provide compelling arguments that new physical processes await discovery. This project addressed some outstanding questions about the fundamental particles and their interactions with the ATLAS experiment at the Large Hadron Collider. In particular, the project improved the discovery potential for new, long- lived particles produced via electroweak processes in proton-proton collisions and set world-leading limits on their existence for certain values of their potential mass and lifetime. To achieve this, the project developed new data analysis methods, developed new triggers to select events with new long-lived particles during data-taking of the ATLAS experiment, and analyzed the largest proton–proton collision dataset ever produced. The project also supported significant development of the data acquisition software for the upgrade to the ATLAS inner detector, the Inner TracKer (ITk). The upgrade of the ATLAS inner detector is essential to the success of the entire Phase II physics program on ATLAS. Personnel supported by the project provided support for integration, assembly, and testing of the inner two layers of the ITk pixel system during its prototype and pre-production phase. Four PhD students and two post-doctoral scholars were supported by the grant and received invaluable scientific training as part of the research endeavor. The students and postdocs gained essential professional skills in the areas of advanced data analysis techniques, statistical analysis of data and simulation, programming in C++ and Python, hardware and instrumentation development, and presentation and collaboration skills. Additionally, approximately ten undergraduate students supported through other funding sources participated in research activities synergistic with the goals of this project, receiving essential mentorship from the personnel supported by this project.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancements in NEAMS Tool Capabilities for Multiphysics Simulation of Fast Reactor Core Bowing and Identification of Validation Test Data

Under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program, an integrated multiphysics approach is being developed to model the core bowing phenomena important to liquid metal-cooled fast reactors. Core bowing is an important passive safety mechanism in liquid metal-cooled fast reactors and involves Multiphysics effects including radiation transport, fluid flow, heat transfer, and mechanical response to temperature and flux gradients. This report summarizes recent progress on developing a multiphysics, MOOSE-based workflow to predict core bowing and associated reactivity feedback. Last year, thermal fluids and mechanics were coupled on a multi-assembly benchmark problem based on ABR-1000 design. This year, the reactor physics code Griffin was assessed for readiness of core bowing calculations. Preliminary integration of Griffin’s ring-heterogeneous model with thermal fluids and thermal mechanics solvers was performed. Specifically, thermal-mechanics and reactor physics were coupled for single- and multi-assembly problems, and reactor physics and subchannel methods were coupled for a single assembly model. Finally, the workflow of all three physics was preliminarily demonstrated on a single assembly model. Caveats and future development needed have been identified. To supplement the multiphysics demonstration, verification and assessment efforts of thermos-mechanical capabilities for modeling thermo-mechanical core bowing behavior were continued by analyzing IAEA Verification Problem 5 which includes radiation swelling and creep. Additionally, a small core reactor physics benchmark defined by Japan Atomic Energy Agency (JAEA) was performed to assess neutronics models for estimating reactivity feedback. Finally, Fast Flux Test Facility (FFTF) validation test data for core bowing phenomena has been identified and summarized, with a recommended path forward for validation once this capability is mature.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The DECADE cosmic shear project III: validation of analysis pipeline using spatially inhomogeneous data

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within 1σ to 2σ. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Integration of Data Analytics with Plant System Health Program

This report summarizes the R&D activities of the Plant Health Management (PHM) project during fiscal year 2020 (FY20). This project focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate system sources of risk. This project development lives in cooperation with the Risk Informed Asset Management (RIAM) project. The RIAM project, in fact, uses the reliability models and data generated by the PHM project to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture). This year’s activities for the PHM project focused mainly on the development of two classes of models. The first one includes a series of component reliability models which include aging, testing and maintenance. The second class includes a series of system reliability models which focus on the secondary side of exiting U.S. reactors. These models are based on fault tree logic structures such that they can be used by existing plant PRA software. We also started to focus on the management of health data and we tackled this issue in two directions. The first one focuses on the integration of monitor data with simulation models to assess component health. This approach is moving from a classical data based to a model+data based approach with the goal of improving higher component health information. The second direction focuses on linking equipment reliability data (e.g., maintenance/failure reports, component monitoring data) directly to system reliability models using a safety margin based language rather than a probability based language. The main advantage of a safety margin based language is that it can provide to system engineers more tangible information on system/component health and how it propagates to the system level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Atmospheric Aerosol Short Course for Early Career Scientists

The first Department of Energy (DOE) Environmental and Molecular Science Laboratory (EMSL)/Atmospheric Radiation Measurement (ARM) aerosol summer school was held July 15-19, 2019 at the EMSL user facility located at Pacific Northwest National Laboratory. The school set out to provide graduate students and early-career scientists an overview of atmospheric aerosol science and an introduction to the measurement techniques available through the EMSL and ARM user facilities to address current aerosol science issues. Instructors for the course were drawn from scientists from the EMSL (Laskin et al., 2016; China et al., 2018) and ARM facilities (McComiskey and Ferrare, 2016) as well as from the DOE Atmospheric System Research (ASR) program. Student support for the summer school was also jointly provided by EMSL, ARM, and ASR. The school spanned five days with each day covering a particular theme. 25 students from 21 institutions attended morning lectures on aerosol composition, properties, theory, measurements, and modeling. During afternoons, the students explored instruments that collect and analyze aerosol samples at EMSL and PNNL’s Atmospheric Measurements Laboratory. They also tested relevant computer modeling simulations and learned about NWChem, EMSL’s open-source computational chemistry software suite.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗
DOE OSTI2020

Modeling thin layers of analytes on substrates for spectral analysis: use of solid/liquid n and k values to model reflectance spectra

Since solids are only sometimes seen en masse in a pure bulk form, and for liquids other than water almost never, a capability to model reflectance spectra from analytes deposited on various substrates would be highly advantageous. If available, the real, n ( ν ) , and imaginary, k ( ν ) , components of the complex refractive index, n n + i k , can be used to simulate infrared spectra, accounting for reflection, refraction, and absorption phenomena as a function of wavelength. We focus on using the Pacific Northwest National Laboratory (PNNL) derived n / k vectors for solid and liquid analytes deposited as thin layers on different types of substrates including conductors, such as aluminum, and inorganic dielectrics, such as glass. The model is an adaptation of the Monte Carlo ray trace modeling program, TracePro, extended through the use of its macrolanguage. The model is tested using thin films of organic liquids including silicone oil and no. 2 diesel fuel, as well as organic solids such as caffeine and acetaminophen on aluminum and glass. The predicted spectra for the solid films were compared to experimental hemispherical reflectance data measured using a Fourier transform spectrometer with an integrating sphere. The thickness of the calculated layer is a parameter for predicting the (transflectance) spectra and is obtained using the areal density measured from gravimetric methods to generate the thin-layer samples. Comparison of the calculated spectra with experimental hemispherical reflectance data shows excellent agreement, indicating promise for the use of measured n / k data to synthesize reference spectral data. In particular, accounting for the inhomogeneity of the deposits greatly improved the match with experimental data. Finally, the theoretical modeling shows that for thicker layers (ca. 20 to 100 μm) of typical organics possessing moderately strong k values, the longwave infrared features are often saturated and better spectral contrast is obtained from the overtone/combination bands in the shortwave infrared.

42 ENGINEERING↗
DOE OSTI2022

Software Development for Data Visualization and Analysis of PN-Diodes & MOSFET Devices

The U.S. Department of Energy has a broad mission to ensure the nation’s security by addressing ongoing environmental challenges, developing novel energy production technologies, and mitigating nuclear security concerns. These efforts benefit from developing more effective semiconductor materials and devices. The research discussed in this report aims to further the development of these new electronics by improving implementations of vertical device structures, which offer theoretically better performance compared to lateral structures. Specifically, it reviews the background and development of a new and easy-to-use program for data processing and analysis from experiments run on vertical gallium nitride power devices. Current vertical structures fall short in critical performance metrics, such as On-Resistance and Breakdown Voltage, due to poor management of the device’s electric field. Junction termination extensions, or JTEs, are a field management technique that increases a device’s resilience to expected failure modes by controlling its surface electric fields. Each JTE design requires significant experimental validation, consisting of hundreds of tested devices, each outputting an enormous set of data points. The new software includes multiple methods of filtering large sets of device testing data to identify and remove flawed devices, automate device analyses, and provide statistical breakdowns for four metrics used to define the effectiveness of the JTE and the current carrying capacity of the device. These results can then be related back to fabrication techniques and device design, and in the future, be combined with processing simulations to further improve our understanding of the JTE mechanisms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories

In this work, we present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction processes relevant for neutrino observatories: neutrino-nucleon deep-inelastic scattering and neutrino-electron annihilation. In this paper, we discuss the event generation algorithm, the weighting algorithm, and the main functions of the publicly available code, with examples. Program summary: Program Titles: LeptonInjector and LeptonWeighter CPC Library link to program files: https://doi.org/10.17632/662gkpjfd9.1 Developer's repository links: https://github.com/icecube/LeptonInjector and https://github.com/icecube/LeptonWeighter Licensing provisions: GNU Lesser General Public License, version 3. Programming Language: C++11 External Routines: • Boost • HDF5 • nuflux (https://github.com/icecube/nuflux) • nuSQuIDS (https://github.com/arguelles/nuSQuIDS) • Photospline (https://github.com/icecube/photospline) • SuiteSparse (https://github.com/DrTimothyAldenDavis/SuiteSparse) Nature of problem: LeptonInjector: Generate neutrino interaction events of all possible topologies and energies throughout and around a detector volume. LeptonWeighter: Reweight Monte Carlo events, generated by a set of LeptonInjector Generators, to any desired physical neutrino flux or cross section. Solution method: LeptonInjector: Projected ranges of generated leptons and the extent of the detector, in terms of column depth, are used to inject events in and around the detector volume. Event kinematics follow distributions provided in cross section files. LeptonWeighter: Event generation probabilities are calculated for each Generator, which are then combined into a generation weight and used to calculate an overall event weight.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

SCALE 6.3.2 User Manual

SCALE is a comprehensive modeling and simulation suite for nuclear safety analysis and design developed and maintained by Oak Ridge National Laboratory under contract with the U.S. Nuclear Regulatory Commission, U.S. Department of Energy, and the National Nuclear Security Administration to perform reactor physics, criticality safety, radiation shielding, and spent fuel characterization for nuclear facilities and transportation/storage package designs. Visit the SCALE website for additional information.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗