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DOE OSTI2021

Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

The numerical integration of the exchange–correlation (XC) potential is one of the primary computational bottlenecks in Gaussian basis set Kohn–Sham density functional theory (KS-DFT). To achieve optimal performance and accuracy, care must be taken in this numerical integration to preserve local sparsity as to allow for near linear weak scaling with system size. This leads to an integration scheme with several performance critical kernels which must be hand optimized for each architecture of interest. As the set of available accelerator hardware goes more diverse, a key challenge for developers of KS-DFT software is to maintain performance portability across a wide range of computational architectures. In this article, we examine a modular software design pattern which decouples the implementation details of performance critical kernels from the expression of high-level algorithmic workflows in a device-agnostic language such as C++; thus allowing for developers to target existing and emerging accelerator hardware within a single code base. We consider the efficacy of such a design pattern in the numerical integration of the XC potential by demonstrating its ability to achieve performance portability across a set of accelerator architectures which are representative of those on current and future U.S. Department of Energy Leadership Computing Facilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Conceptual Design of a Thermal–to-14-MeV Neutron Conversion Device for Use in the Advanced Test Reactor

Here, this paper details the conceptual design of a thermal-to-14-MeV neutron converter consisting of a mixture of lithium and deuterium in a blanket material. Such a device operates a two-step reaction, first generating tritons via thermal neutron absorption in the tritium breeding material, and in the second step, high-energy neutrons are produced either via deuterium-tritium fusion reaction or with tritium reacting with lithium. A thermal-to-14-MeV neutron converter significantly hardens the neutron spectrum by virtually removing thermal neutrons and adding a high-energy 14 MeV component to the neutron spectrum. While similar concepts have been previously proposed and tested in other reactors, the unique characteristics of the Advanced Test Reactor (ATR), namely its important thermal flux (up to $10^{15}$ n.cm$^{-2}$.s$^{-1}$) makes it markedly attractive for obtaining a very large fast neutron flux, usable for irradiation studies under neutron flux conditions prototypical of fusion reactors. The paper provides a description of a new computational scheme developed for handling the coupled neutron-triton transport mechanism using the Geant4 toolkit. Resulting neutron spectra and high-energy neutron yields are summarized for different irradiation positions and potential neutron breeder materials. Maximum predicted thermal-to-14-MeV neutron yields are on the order of $2.10^{-4}$, which is consistent with previous studies found in the literature. Thus, when placed inside the ATR, such a neutron converter will be providing the largest high-energy neutron source available for activation and irradiation studies of materials foreseen for use in fusion reactors. Future steps will involve qualifying the computational scheme using the ATR critical facility using activation foil measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Massively Parallel Bayesian Model Calibration and Uncertainty Quantification with Applications to Nuclear Fuels and Materials

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Proceedings of the 1998 International Computational Accelerator Physics Conference

The 1998 International Computational Accelerator Physics Conference, ICAP’98, was held September 14-18, 1998, at the Monterey Convention Center in Monterey, California. The meeting continued the tradition of four earlier Computational Accelerator Physics conferences, held in La Jolla, California (1988), Los Alamos, New Mexico (1990), Pleasanton, California (1993), and Williamsburg, Virginia (1996). ICAP’98 was organized by the Stanford Linear Accelerator Center (SLAC), Los Alamos National Laboratory (LANL), and the National Energy Research Scientific Computing Center (NERSC), and received support from the U.S. Department of Energy. The aim of this conference series is to discuss recent advances in Computational Accelerator Physics and their applications to solving important problems related to present and future accelerators. The ICAP’98 program included plenary sessions on modeling challenges in next-generation accelerators, state-of-the art methods in computational accelerator physics, and a view of what high-end computing would be like in the 21st century. There were also focused sessions on accelerator design and control, modeling high intensity beam phenomena, electromagnetic modeling, advanced algorithms, software environments and libraries, visualization and graphic interfaces, modeling advanced accelerator concepts, modeling the beam-beam interaction, computing wakefields, and combined particle/field simulations. In addition to these oral sessions there was a poster session and a vendor display. Prior to the start of the technical program, a half-day tutorial on High Performance Computing was hosted by NERSC. The ICAP series recognizes that computing and, in particular, advanced computing, is playing an increasingly important role in the successful design and development of major accelerator facilities, and in research and development leading to advances in accelerator technology. We expect that this new emphasis on high-end computing will continue in future ICAP conferences.

Ko, Kwok↗

DOE OSTI2022

Methods for Translating ReEDS Solutions to Production Cost Modeling Tools

Capacity expansion modeling tools are increasingly being utilized to investigate a wide range of potential future scenarios, particularly with high penetrations of variable and energy-constrained resources that may be operated differently than current dispatch paradigms. While capacity expansion models are particularly adept at making investment decisions for future years, they must make compromises in operational aspects to maintain computational tractability. However, it is of high interest to determine if such future systems would be able to maintain reliability during a variety of grid conditions, and to identify any potential operational challenges for these systems at finer temporal resolution than is typically captured in CEMs. As such, there is value in having an automated tool that can convert many CEM investment pathways into inputs for a more detailed production cost model, in this case the PLEXOS commercial software package. This paper describes the methodology used to make that translation for the NREL-developed ReEDS model, making use of two internally developed tools - PIDG and beetle - along with a set of processing scripts. We describe the current assumptions, data sets, and important operational characteristics of these translations along with an example of the extended analyses that may be done through this connection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Argonne Leadership Computing Facility (2020 Operational Assessment Report)

In 2004, the U.S. Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program founded the Leadership Computing Facility (LCF) with a mission to provide the world’s most advanced computational resources to the open science community. The LCF is a huge investment in the nation’s scientific and technological future, inspired by a growing demand for capability computing and its impact on science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging to realize in practice. The learning-from-mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain-like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade-offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade-offs between depth, controllability, and capacity—the number of learnable patterns—in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all-memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Media Relations Strategies in a Post-Information Age - 18452

Many organizations that deal with radioactive materials reluctantly engage in media relations and social media. This paper explores the drivers behind the dramatic coverage of clean-up activities nearest to large media markets. Interviews and media analysis reveal how companies and government actors responded to aggressive media investigations. Insights from professional journalists demonstrate the significance of Freedom of Information Act (FOIA) requests and computer assisted reporting on past and future coverage. Coverage of Hanford and Santa Susana Field Laboratory set the stage for creative engagement strategies developed to support public interest in clean-up efforts. Industrial and engineering firms, particularly small to mid-sized subcontractors, often place less priority on media and public relations. However, recent examples demonstrate that organizations that deal with scientific risk must evolve. Firms that acknowledge changing media trends update their communication strategies in ways that improve public trust and understanding. It's easy to understand why many organizations resist communicating when public concerns arise. Legal issues and contract restrictions may limit communications. The return on investment may seem low. Understanding exposure and hazard requires perspective, context and accuracy. Reporters and citizens also have better access to primary sources. Computer assisted reporting (CAR) and the dramatic rise of requests under the Freedom of Information Act (FOIA) lay bare details of the inner workings of complex situations, often without context. Examples from FOIA requests and challenges filed in the U.S. district courts, are described to help project administrators understand the points of view that can take shape on government projects from filings and disclosures. The range of benefits and risks of ongoing interaction with reporters provides a framework for assessing the right level of effort. For remediation efforts near large media markets, the influence of effective communication is magnified. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗

DOE OSTI2022

A scalable compressible volume of fluid solver using a stratified flow model

Here, in this paper, a Volume of Fluid method which uses a Stratified Flow model for flux calculation is proposed to solve a compressible multiphase flow in a way that has high parallel efficiency. The solver is part of an open-source computing tool to study fuel entrainment and combustion in the application of paraffin-based hybrid rocket motors. This paper focuses on the compressible Volume of Fluid solver for the liquid and gaseous phases in this environment. This solver uses perfect gas and stiffened gas models for equation of state, with the ability to easily add other models as desired. Additionally a comparison of a Riemann solver versus an AUSM+up scheme is conducted, showing improvement with the later in the solution of multiphase flows. This solver successfully reproduces shock tube cases with the expected accuracy, as well as simulating two-dimensional shear and gravity-driven flows. Results also show that the code is able to utilize a Stratified Flow model to evaluate shear flow without the need for interface reconstruction or gradient calculations. Finally, it is shown that the solver has near-ideal scaling under strong scaling tests as well as good performance in static scaling, giving improved performance over current options and implying future advancement of high-performance computing efficiency for multiphase flow solvers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

The Improved Simple Corner Balance Method and Efforts to Enhance its Computational Performance

The diamond difference (DD) spatial discretization has long been a work horse of the neutron transport community. For nearly as long as DD has been used in neutron transport, it has been observed that DD produces undamped, non-physical angular fluxes in spatially under resolved problems and can fail in the thick diffusion limit. A long list of alternative spatial discretizations, all with better physics properties: accuracy, robustness, and/or preservation of the thick diffusion limit, have been proposed as alternatives to DD. Unfortunately, these alternative spatial discretizations also require significantly more floating point operations (FLOPs) per unknown compared to DD. Historically, neutron transport problems of interest have been able to be resolved sufficiently that DD is accurate enough and FLOPs have been the computational performance bottleneck controlling time to solution. Thus, in practice DD has been and remains a preferred spatial discretization for neutron transport simulations. Trends in computing architecture are leading to a new paradigm where data movement, not FLOPs will control time to solution. This presents an opportunity to improve neutron transport physics by reconsidering more FLOP intensive spatial discretization than DD without incurring a time to solution penalty. In this work, we consider the improved simple corner balance (ISCB) spatial discretization, a unique alternative to DD that has the same memory footprint as DD, and detail efforts to improve its computational performance. The ISCB spatial discretization is a more FLOP intensive spatial discretization than DD with the same memory footprint as DD. Though designed to maximize FLOP intensity, third party libraries such as LAPACK are not tuned for the unique, frequent, small linear algebra needs of neutron transport problems. While hand coding a sparse Gauss elimination is likely to be faster than a dense matrix solve (CM vs. HGE), were LAPACK suitable for small linear algebra problems, we would not have seen a performance increase with decreased numbers of LAPACK calls for the Schur complement solve (SC4 vs. SC1). Significant effort has reduced the relative compute time of ISCB to DD, but on current computer architectures DD is still faster than ISCB. Future work comparing the overall efficiency of ISCB to DD may reveal that while ISCB is slower than DD on current computers, ISCB is more accurate than DD by a factor greater than the compute time slowdown. As new computer architectures become available, we will test our hypothesis that neutron transport simulations will be bandwidth limited, ideally rendering the additional FLOPs required by ISCB to be 'free' [in terms of time to solution]. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

High-performance computing in water resources hydrodynamics

In this work, we present a vision of future water resources hydrodynamics codes that can fully utilize the strengths of modern high-performance computing. The advances to computing power, formerly driven by the improvement of central processing unit processors, now focus on parallel computing and, in particular, the use of graphics processing units (GPUs). However, this shift to a parallel framework requires refactoring the code to make efficient use of the data as well as changing even the nature of the algorithm that solves the system of equations. These concepts along with other features such as the precision for the computations, dry regions management, and input/output data are analyzed in this paper. A 2D multi-GPU flood code applied to a large-scale test case is used to corroborate our statements and ascertain the new challenges for the next-generation parallel water resources codes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Software and Computing for Small HEP Experiments

This white paper briefly summarized key conclusions of the recent US Community Study on the Future of Particle Physics (Snowmass 2021) workshop on Software and Computing for Small High Energy Physics Experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Solar-Forecast GridAPPS-D Application [SWR-18-36]

Persistent model serves as the baseline in the intra-hour forecasting of solar radiation due to the extensive computing efforts from the numerical weather prediction (NWP) models and the lack of surface observations in cloud as well as other atmospheric properties. The smart persistent model actively updates the extraterrestrial solar radiation with time and is an important improvement from the persistent model while the cloud conditions are assumed unchanged by giving constant clear-sky indexes. We developed a Physics-based Smart Persistent model for Intra-hour solar forecasting (PSPI) to further improve the accuracy of the smart persistent model. We used a cloud retrieval technique developed by Xie and Liu (2013) to estimate cloudy fraction and cloud albedo using surface-based observations of solar radiation. The solar radiation in future time steps is accurately given by actively computing individual cloud albedo related to the future solar position. Our evaluation studies indicated that the PSPI model significantly increased the accuracy in the short-term forecast of solar radiation, especially in thin cloud conditions or those covered by broken clouds.

Xie, Yu↗

DOE OSTI2025

LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages

The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the LLaMA 2-70B model in automating these tasks for scientific applications written in commonly used programming languages. Using representative test problems, we assess the model's capacity to generate code, documentation, and unit tests, as well as its ability to translate existing code between commonly used programming languages. Our comprehensive analysis evaluates the compilation, runtime behavior, and correctness of the generated and translated code. Additionally, we assess the quality of automatically generated code, documentation, and unit tests. Here, our results indicate that while LLaMA 2-70B frequently generates syntactically correct and functional code for simpler numerical tasks, it encounters substantial difficulties with more complex, parallelized, or distributed computations, requiring considerable manual corrections. We identify key limitations and suggest areas for future improvements to better leverage AI-driven automation in scientific computing workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

An evaluation of the variable-resolution CESM for modeling California's climate

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared to reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems

In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO 2 -emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗