Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “criticality benchmark”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

IRDFF-II: A New Neutron Metrology Library

High quality nuclear data is the most fundamental underpinning for all neutron metrology applications. This paper describes the release of version II of the International Reactor Dosimetry and Fusion File (IRDFF-II) that contains a consistent set of nuclear data for fission and fusion neutron metrology applications up to 60 MeV neutron energy. The library is intended to support: a) applications in research reactors; b) safety and regulatory applications in the nuclear power generation in commercial fission reactors; and c) material damage studies in support of the research and development of advanced fusion concepts. The paper describes the contents of the library, documents the thorough verification process used in its preparation, and provides an extensive set of validation data gathered from a wide range of neutron benchmark fields. The new IRDFF-II library includes 119 metrology reactions, four cover material reactions to support self-shielding corrections, five metrology metrics used by the dosimetry community, and cumulative fission products yields for seven fission products in three different neutron energy regions. In support of characterizing the measurement of the residual nuclei from the dosimetry reactions and the fission product decay modes, the present document lists the recommended decay data, particle emission energies and probabilities for 68 activation products. It also includes neutron spectral characterization data for 29 neutron benchmark fields for the validation of the library contents. Additional six reference fields were assessed (four from plutonium critical assemblies, two measured fields for thermal-neutron induced fission on 233U and 239Pu targets) but not used for validation due to systematic discrepancies in C/E reaction rate values or lack of reaction-rate experimental data. Another ten analytical functions are included that can be useful for calculating average cross sections, average energy, thermal spectrum average cross sections and resonance integrals. The IRDFF-II library and comprehensive documentation is available online at www-nds.iaea.org/IRDFF/. Evaluated cross sections can be compared with experimental data and other evaluations at www-nds.iaea.org/exfor/endf.htm. The new library is expected to become the international reference in neutron metrology for multiple applications.

IAEA IRDFF↗
DOE OSTI2025

A High-Fidelity Model of the Peach Bottom 2 Turbine-Trip Benchmark Using VERA

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type.

BWR↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Deciphering the Oxygen Absorption Pre-edge: A Caveat on its Application for Probing Oxygen Redox Reactions in Batteries

The pre-edges of oxygen-K X-ray absorption spectra have been ubiquitous in transition metal (TM) oxide studies in various fields, especially on the fervent topic of oxygen redox states in battery electrodes. However, critical debates remain on the use of the O-K pre-edge variations upon electrochemical cycling as evidences of oxygen redox reactions, which has been a popular practice in the battery field. This study presents an investigation of the O-K pre-edge of 55 oxides covering all 3d TMs with different elements, structures, and electrochemical states through combined experimental and theoretical analyses. It is shown unambiguously that the O-K pre-edge variation in battery cathodes is dominated by changing TM-d states. Furthermore, the pre-edge enables a unique opportunity to project the lowest unoccupied TM-d states onto one common energy window, leading to a summary map of the relative energy positions of the low-lying TM states, with higher TM oxidation states at lower energies, corresponding to higher electrochemical potentials. The results naturally clarify some unusual redox reactions, such as Cr 3+/6+ . This work provides a critical clarification on O-K pre-edge interpretation and more importantly a benchmark database of O-K pre-edge for characterizing redox reactions in batteries and other energy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dynamic cycling enhances battery lifetime

Laboratory aging campaigns benchmark and elucidate the complex degradation behavior of lithium-ion batteries, and are critical not only for developing new battery chemistries and cell designs but also for engineering reliable battery management systems. Critically, these laboratory experiments aim to quantify and capture realistic aging mechanisms. In this study, we systematically compare dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant-current profiles. Surprisingly, we discovered that dynamic discharge enhances lifetime substantially compared to constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile leads to an increase of up to 38 % in equivalent full cycles at end-of-life. Explainable machine learning reveals the importance of low-frequency current pulses as well as time-induced aging under these realistic discharge conditions. Our work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, and highlights the opportunities to revisit our understanding of aging mechanisms at the chemistry, materials, and cell levels.

25 ENERGY STORAGE↗
DOE OSTI2024

Laser pulse-length dependent ablation and shock generation in silicon at 5 × 10 14 W/ cm 2 intensities

The effect of laser pulse duration on energy coupling into a planar silicon target is investigated in experiments at the OMEGA-EP facility by varying the laser pulse length τ —spanning 3 orders of magnitude from 100 ps to 10 ns—while maintaining a constant peak laser intensity, I 0 = 5 × 10 14 W/ cm 2 . In theoretical models, the ablation pressure primarily scales for a given material with laser intensity and wavelength, which are all fixed variables here, allowing us to explore the specific role of laser pulse duration. Two-dimensional radiation-hydrodynamics simulations benchmarked with optical probing of the expanding plasma show that the pulse duration is critical for the ablation pressure to reach a steady state. Moreover, the pulse duration impacts shock decay and multiple wave effects, which strongly dictate the evolving shock profile that propagates within the laser-shocked target as ultimately measured by rear-surface diagnostics. The shock velocities inferred from the theoretical model, after considering shock decay, impedance matching, and shock Hugoniot, are found to be in good agreement with velocimetry measurements. However, discrepancies are observed with simulations for the shorter (0.1 ns) and longer (10 ns) pulse durations, which are respectively attributed to unaccounted contributions of kinetic absorption mechanisms and instabilities in simulations. Published by the American Physical Society 2024

Bailly-Grandvaux, M. (ORCID:0000000175294013)↗
DOE OSTI2020

IER-519 CED-2: Final Design for Thermal/Epithermal eXperiments (TEX) with Absorbers to Provide Validation Benchmarks for Hanford Tank Farms

The Hanford tank farms contain 56 million gallons of waste across 177 tanks. The primary criticality safety concern for the waste is the plutonium inventory in waste solids – approximately 670 kg in total. Criticality safety analysis credits the absorption and dilution properties of the large quantities of other elements (aluminum, chromium, iron, manganese, nickel, silicon, sodium, and zirconium) present in the waste. Of these, iron and manganese are by far the most significant neutron absorbers, particularly for the waste compositions of highest criticality safety concern. The criticality safety analyses at the Hanford Waste Treatment and Vitrification Plant (WTP) and the Savannah River tank farms also credit iron and manganese as the primary neutron absorbers to demonstrate subcriticality.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

SoW: Energy-Dependent Fission Product Yields

The purpose of this LLNL subcontract is to capitalize on our already established long-term relationship with Duke University and Triangle Universities Nuclear Laboratory (TUNL) to study fission observables like Fission Product Yields (FPY), fission cross-section and other data relevant to our programmatic needs. LLNL with Duke university partnership, wants to further develop new experimental capabilities to measure critical nuclear data important for LLNL stakeholders. The new FPY data will provide important benchmarks for nuclear theory and evaluation currently developed by the nuclear data and theory group. The improved FPY data is of interest to many major laboratory programs, including nuclear chemistry, nuclear forensics, safeguard, and nonproliferation communities. The impact of new FPY data, obtained by a graduate student from Duke (Aitor Bracho), will result in the U.S. Nuclear Data Program.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Integral Experiment Request 523 CED-2 Report

This report documents the final design phase of the Critical Experiment Design (CED-2) conducted as part of integral experiment request (IER) 523. The purpose of IER 523 is to determine critical configurations of 35 weight percent (wt%) enriched uranium dioxide beryllium oxide (UO 2 -BeO) material driven by an annular ring of Seven Percent Critical Experiment (7uPCX) fuel rods at Sandia National Laboratories (Sandia). The experiments will provide benchmark data on water moderated, intermediately enriched UO2 systems as well as Be nuclear data. The experiment will also provide partial validation for the beryllium oxide (BeO) thermal neutron scattering law (TSL) in the thermal energy range. Experiment design concepts, neutronic analysis results, and proposed paths for continuing the CED process are presented. This report builds on the feasibility and justification of experimental need report (CED-0) and preliminary experiment design report (CED-1) completed in December 2021 and September 2023, respectively [1, 2].

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Research and Development Pathways for Powering the Blue Economy with Marine Renewable Energy

As marine renewable energy technology is adapted or designed for Blue Economy end-uses, challenges unique to operation at sea and in remote coastal areas must be addressed to meet demands distinct from utility grid electricity generation. Foundational research and development with this end goal is conducted to broadly enable the initiative of Powering the Blue Economy. Determination of distinct R\&D topics is informed by pathways including an engineering design customer-driven approach and feedback from the creation and administration of prize competitions in the Blue Economy technology space. Critical research outcomes, such as determining the performance of small wave energy converters and benchmarking the reliability for power systems at sea, are among the goals of ten foundational R\&D projects currently underway.

Cavagnaro, Robert J.↗
DOE OSTI2021

Research and Development Pathways for Powering the Blue Economy with Marine Renewable Energy

As marine renewable energy technology is adapted or designed for Blue Economy end-uses, challenges unique to operation at sea and in remote coastal areas must be addressed to meet demands distinct from utility grid electricity generation. Foundational research and development with this end goal is conducted to broadly enable the initiative of Powering the Blue Economy. Determination of distinct R&D topics is informed by pathways including an engineering design customer-driven approach and feedback from the creation and administration of prize competitions in the Blue Economy technology space. Critical research outcomes, such as determining the performance of small wave energy converters and benchmarking the reliability for power systems at sea, are among the goals of ten foundational R&D projects currently underway.

customer discovery↗
DOE OSTI2023

Benchmarking for AI for Science

AI has been instrumental for recent developments in a number of domains of the sciences. With several hundred machine learning (ML) algorithms and models, and numerous AI-specific hardware platforms, a common quest for all scientists working on AI for Science is around the selection of machine learning algorithm(s) to solve their domain-specific scientific problems. A number of different initiatives around AI Benchmarking have been set up and have been useful in understanding the benefits of different ML algorithms for different tasks.However, with the majority of these AI Benchmarking initiatives focusing on the conventional notions of benchmarking, where the focus is purely runtime performance (such as training time or inference time), their suitability for benchmarking different ML algorithms for solving scientific problems has been viewed as a performance problem even though both are hardly the same. To make reasonable, explainable, and justifiable advancements in science using AI, it is critical to focus on the merits of these algorithms in handling different domain science problems. In other words, more emphasis must be given on Benchmarking for AI for Science than AI Benchmarking. The vision of the former is not only to assess the performance of ML algorithms, but also to assess, and understand the benefits and merits of different ML algorithms in handling scientific problems. Benchmarking for AI for Science, instead of pure performance focused AI Benchmarking, has several benefits: (i) it has the potential to offer advances in the sciences, much more rapidly than through pure performance-based AI methods, (ii) it will encourage the community to focus on developing better domain-specific AI techniques, particularly given the provision for being able to benchmark different techniques, and (iii) it will encourage hardware manufacturers to focus on developing science-specific hardware subsystems.

Thiyagalingam, Jeyan↗
DOE OSTI2022

Benchmark evaluation of one dimensional array of HEU moderated and reflected by Lucite

The evaluation of two HEU-Lucite experiments is presented. The critical experiments were performed using the Planet Universal Critical Assembly Machine at Los Alamos National Laboratory in 2019. In these experiments, HEU foils were interleaved with Lucite in a column stack and were moderated and reflected from the square Lucite plates. The neutron data produced were used to approximate the multiplication of each measured configuration. The evaluation of the experiments using calculational techniques is presented in the paper. A Monte-Carlo computational model to be used with MCNP6 was developed for the determination of uncertainties and the effects of various uncertainties on the multiplication factor were studied. The evaluation of the benchmark was performed for comparison of calculation results with the experimental data and validation of calculation techniques in particular the newly available thermal scattering data for the Lucite material. The sensitivity studies include parametric variations of material composition and geometry. Experimental uncertainties were low, with the overall height of the HEU/Lucite stack being the largest contributor to the overall uncertainty in k{sub eff}. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Comparative Evaluation of Spectral Methods for Robust Reactor Noise Estimation

Reactor noise analysis provides a noninvasive means to determine neutron kinetic parameters from stochastic fluctuations in detector signals. However, standard cross-power spectral density (CPSD) analyses can be sensitive to numerical processing choices, which may introduce processing-dependent systematic shifts in estimates of the prompt neutron decay constant (α) and limit reproducibility. This study uses a hybrid multitaper–Welch spectral estimator to analyze subcritical noise measurements from a fast-spectrum critical assembly. The decay constant α was extracted using three frequency-domain methods: the CPSD, the magnitude-squared coherence (MSC), and the generalized magnitude-squared coherence (GMSC). These coherence-based estimators normalize detector auto-spectral structure and are expected to reduce the sensitivity of fitted α values to processing parameters. A Sobol global sensitivity analysis identified which numerical inputs most strongly influence the fitted values of α. All estimators produced a linear dependence of α on inverse count rate, with delayed-critical extrapolations near 1.7 × 10 4 s −1 , in agreement within 8% of MCNP6.3 KOPTS benchmark calculations. Sensitivity results show that while the CPSD depends on both time-bin width and taper selection, the MSC and GMSC are dominated by time-bin width alone, indicating reduced parameter coupling and greater robustness to processing variability. These findings demonstrate the feasibility and practical value of coherence-based spectral estimators for extracting α from reactor noise and support their broader application to multi-detector and irregular datasets in subcritical system characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Towards philosophical reasoning with agentic LLMs: Socratic method for scientific assistance

As large language models (LLMs) become central tools in science, improving their reasoning capabilities is critical for meaningful and trustworthy applications. We introduce a Socratic agent for scientific reasoning, implemented through a structured system prompt that guides LLMs via classical principles of inquiry. Unlike typical prompt engineering or retrieval-based methods, our approach leverages definition, analogy, hypothesis elimination, and other Socratic techniques to generate more coherent, critical, and domain-aware responses. We evaluate the agent across diverse scientific domains and benchmark it on the abstraction and reasoning corpus challenge dataset, achieving 97.15% under a fixed prompting protocol and without fine-tuning or external tools. Expert evaluation shows improved reasoning depth, clarity, and adaptability over conventional LLM outputs, suggesting that structured prompting rooted in philosophical reasoning can improve the scientific utility of language models.

LLM reasoning↗
DOE OSTI2023

PYK-SubstitutionOME: an integrated database containing allosteric coupling, ligand affinity and mutational, structural, pathological, bioinformatic and computational information about pyruvate kinase isozymes

Interpreting changes in patient genomes, understanding how viruses evolve and engineering novel protein function all depend on accurately predicting the functional outcomes that arise from amino acid substitutions. To that end, the development of first-generation prediction algorithms was guided by historic experimental datasets. However, these datasets were heavily biased toward substitutions at positions that have not changed much throughout evolution (i.e. conserved). Although newer datasets include substitutions at positions that span a range of evolutionary conservation scores, these data are largely derived from assays that agglomerate multiple aspects of function. To facilitate predictions from the foundational chemical properties of proteins, large substitution databases with biochemical characterizations of function are needed. We report here a database derived from mutational, biochemical, bioinformatic, structural, pathological and computational studies of a highly studied protein family—pyruvate kinase (PYK). A centerpiece of this database is the biochemical characterization—including quantitative evaluation of allosteric regulation—of the changes that accompany substitutions at positions that sample the full conservation range observed in the PYK family. We have used these data to facilitate critical advances in the foundational studies of allosteric regulation and protein evolution and as rigorous benchmarks for testing protein predictions. We trust that the collected dataset will be useful for the broader scientific community in the further development of prediction algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hierarchical Division and Clustering of Group Structures

When using multigroup neutron transport models, it is important to choose a suitable group structure due to the impact on runtime and accuracy. Group structures at LANL have been largely pared down to a handful of commonly used group structures, such as the LANL-30 and LANL-70 group structures. By identifying the key boundaries for a given problem, more insight can be gained into why the commonly used group structures are accurate and when they are inaccurate. This work focuses on using hierarchical division and agglomeration methods to identify the most critical boundaries in these group structures, using the ICSBEP HEU-MET-FAST-001-001 benchmark as a case study.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗