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DOE OSTI2022

Development of nuclear data processing code FRENDY version 2

Nuclear data processing is an important interface between an evaluated nuclear data library and neutronics calculation codes. JAEA has been developed the new nuclear data processing code FRENDY from 2013. FRENDY version 1 generates the ACE files used for the continuous-energy Monte Carlo codes including PHITS, Solomon, Serpent,and MCNP and it was released as the open-source software under the 2-clause BSD license in 2019.After we released FRENDY version 1, many functions, e.g., the multi-group neutron cross-section library generation, the statistical uncertainty quantification of the probability tables for unresolved resonance cross-section, the 'perturbation' of the ACE file for uncertainty quantification, and the modification of the ENDF-6 formatted nuclear data file, have been developed. We released FRENDY version 2 including these functions. The present paper gives an overview of FRENDY and presents features of new functions implemented in FRENDY version 2. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Quantifying Solar PV Variability at Multiple Timescales for Power Systems Studies: Preprint

Increasing penetration of solar photovoltaics (PV) has concerned power system operators about their ability to balance load and generation over multiple timescales and maintain acceptable frequency profile because of the variability and uncertainty associated with solar PV. This paper focuses on developing metrics to quantify solar PV variability at multiple temporal scales ranging from seconds to an hour to help power system operators and planners better understand and account for solar PV variability under high solar PV penetration. Fourier coefficients of solar irradiation spectrum and average size of solar irradiation ramps over a day are the two metrics that are used to quantity the variability. It is shown that by using the two metrics the days with highest and lowest solar irradiation variability can be identified, which is useful information for operations planning or long-term power system planning studies. The key findings of the paper are (i) single sensor-based variability quantification can significantly exaggerate the variability of a solar PV plant, particularly at seconds to 1-min temporal resolution, (ii) at 1-minute or lower temporal resolutions, single sensor-based variability quantification of an entire plant can be fairly accurate, with the accuracy increasing as the temporal resolution reduces from 1-minute to 1-hour, (iii) at each 1-minute and lower temporal resolutions, maximum solar irradiation ramps appear to be independent of the geographical location of the sensor, and (iv) significant reduction in solar PV plant variability over a region with multiple plants is possible at all temporal scales, but more high quality, high temporal resolution data is needed to quantify the reduction with high confidence.

photovoltaics↗

DOE OSTI2021

Modeling State Transition and Head-Dependent Efficiency Curve for Pumped Storage Hydro in Look-Ahead Dispatch

As one of the most widely installed utility-scale storage facilities, pumped storage hydro (PSH) plays an essential role in providing flexibility for power systems worldwide. Thus, to accurately quantify the flexibility of PSH units in operational optimization problems is important. The conventional PSH models in the literature rarely consider detailed state transitions due to their hourly-based settings. However, it becomes imperative for operational optimization on short-term intervals, especially with increasing shares of renewable energy in power systems. To this end, this paper presents a novel deterministic PSH model that considers the transition time and trajectory between three states of PSH units in look-ahead dispatch. Moreover, to better characterize the varying efficiency of PSH units with water head and flow rate, this paper proposes to model detailed headdependent efficiency curves (hereafter called input-output curves) in look-ahead dispatch, which encounters heavy computational burdens when short time intervals are applied. In this work, a zig-zag piece-wise linear approximation method is used for input-output curve modeling. This can enable an accurate quantification for variable efficiency and head dependence in a computationally effective manner. Here, numerical results are presented to show performances of the proposed PSH model in both flexibility quantification and computation time.

13 HYDRO ENERGY↗

DOE OSTI2022

Anthropogenically driven climate and landscape change effects on inland water carbon dynamics: What have we learned and where are we going?

Inland waters serve as important hydrological connections between the terrestrial landscape and oceans but are often overlooked in global carbon (C) budgets and Earth System Models. Terrestrially derived C entering inland waters from the watershed can be transported to oceans but over 83% is either buried in sediments or emitted to the atmosphere before reaching oceans. Anthropogenic pressures such as climate and landscape changes are altering the magnitude of these C fluxes in inland waters. Here, we synthesize the most recent estimates of C fluxes and the differential contributions across inland waterbody types (rivers, streams, lakes, reservoirs, and ponds), including recent measurements that incorporate improved sampling methods, small waterbodies, and dried areas. Across all inland waters, we report a global C emission estimate of 4.40 Pg C/year (95% confidence interval: 3.95–4.85 Pg C/year), representing a 13% increase from the most recent estimate. In this work, we also review the mechanisms by which the most globally widespread anthropogenically driven climate and landscape changes influence inland water C fluxes. The majority of these drivers are expected to influence terrestrial C inputs to inland waters due to alterations in terrestrial C quality and quantity, hydrological pathways, and biogeochemical processing. We recommend four research priorities for the future study of anthropogenic alterations to inland water C fluxes: (1) before-and-after measurements of C fluxes associated with climate change events and landscape changes, (2) better quantification of C input from land, (3) improved assessment of spatial coverage and contributions of small inland waterbodies to C fluxes, and (4) integration of dried and drawdown areas to global C flux estimates. Improved measurements of inland water C fluxes and quantification of uncertainty in these estimates will be vital to understanding both terrestrial C losses and the “moving target” of inland water C emissions in response to rapid and complex anthropogenic pressures.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Quantitative far-infrared band strengths of iodine monochloride (ICl), a molten salt off-gas product

The gaseous (by)products generated from molten salt reactors need to be monitored to prevent the release of potentially toxic gases to the environment. In particular, 129I has a long half-life and its toxicity and persistence in the environment make iodine and iodine-containing compounds (such as ICl from chloride containing molten salt systems) of great concern. Optical spectroscopy tools, including Raman and Fourier-transform infrared (FTIR) spectroscopies, are ideal for monitoring and quantifying such off-gas products. Iodine (I2) has a strong and distinct Raman signature and the change in signal with a change in concentration can be used for quantification of these byproducts. Iodine monochloride (ICl) has distinct signatures in both the Raman and the infrared, and its spectrum can also potentially be used for quantitative measurement of this species. In this paper we discuss our recent results on the quantification of iodine monochloride using infrared spectroscopy, in particular first reports of the absolute infrared band strength of ICl.

infrared (IR) spectroscopy, Molten Salt Reactors, ↗

DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Targeted rRNA depletion enables efficient mRNA sequencing in diverse bacterial species and complex co-cultures

Bacterial mRNA sequencing is inefficient due to the abundance of ribosomal RNA that is challenging to deplete. While commercial kits target rRNA from common bacterial species, they are frequently inefficient when applied to divergent species, including those from environmental isolates. Similarly, other methods typically employ large probe sets that tile the entire length of rRNAs; however, such approaches are infeasible when applied to many species. Therefore, we present EMBR-seq+, which requires fewer than 10 oligonucleotides per rRNA by combining rRNA blocking primers with RNase H-mediated depletion to achieve rRNA removal efficiencies of up to 99% in diverse bacterial species. Furthermore, in more complex microbial co-cultures between Fibrobacter succinogenes strain UWB7 and anaerobic fungi, EMBR-seq+ depleted both bacterial and fungal rRNA, with a fourfold improvement in bacterial rRNA depletion compared with a commercial kit, thereby demonstrating that the method can be applied to non-model microbial mixtures. Notably, for microbes with unknown rRNA sequences, EMBR-seq+ enables rapid iterations in probe design without requiring to start experiments from total RNA. Finally, efficient depletion of rRNA enabled systematic quantification of the reprogramming of the bacterial transcriptome when cultured in the presence of the anaerobic fungi Anaeromyces robustus or Caecomyces churrovis. We observed that F. succinogenes strain UWB7 downregulated several lignocellulose-degrading carbohydrate-active enzymes in the presence of anaerobic gut fungi, suggesting close interactions between two cellulolytic species that specialize in different aspects of biomass breakdown. Thus, EMBR-seq+ enables efficient, cost-effective, and rapid quantification of the transcriptome to gain insights into non-model microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors

Hierarchical models with gamma hyperpriors provide a flexible, sparse-promoting framework to bridge L 1 and L 2 regularizations in Bayesian formulations to inverse problems. Despite the Bayesian motivation for these models, existing methodologies are limited to maximum a posteriori estimation. The potential to perform uncertainty quantification has not yet been realized. This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors. The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement. In addition, it lends itself naturally to conduct model selection for the choice of hyperparameters. Here, we illustrate the performance of our methodology in several computed examples, including a deconvolution problem and sparse identification of dynamical systems from time series data.

Bayesian shrinkage↗

DOE OSTI2021

In Situ Transmission Electron Microscopy of High-Temperature Inconel-625 Corrosion by Molten Chloride Salts

This paper describes an approach to monitor high temperature molten chloride (MgCl 2 -NaCl-KCl) salt corrosion of Inconel-625 alloy in real time at high spatial resolution. The approach is based on a micro-environmental-cell assembly integrated into a transmission-electron-microscope goniometer to examine in situ the salt-alloy interface during corrosion, employing real time electron diffraction and imaging. It establishes procedures to minimize incorporation of H 2 O or O 2 from atmosphere in the chloride salts during sample fabrication and corrosion, which is critical to understanding the fundamental corrosion mechanisms. A clustering algorithm and a 2D Gaussian fit function are used to determine diffraction spot intensities in in situ diffraction patterns, to quantify alloy corrosion. This facilitates quantitative observation of the evolution of individual grains, in contrast to conventional macroscopic corrosion rate quantification. The isothermal corrosion rate of Inconel-625 in an anhydrous, unoxidized salt-stack is 220 ± 30 μm year -1 at 700 °C and 350 ± 20 μm year -1 at 800 °C. However, the corrosion rate at 700 °C increases five-fold to 1000 ± 170 μm year -1 when the salt stack is air-exposed, indicating the dominant effects of hydrated or oxidized impurities on corrosion acceleration. Furthermore, real time imaging of the microstructure evolution suggests that corrosion is initiated at grain boundaries.

14 SOLAR ENERGY↗

DOE OSTI2025

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗

DOE OSTI2026

Design of a Molten Salt Flow Cell for Combined Absorbance and Laser-Induced Breakdown Spectroscopy for Online Measurements

A novel flow cell allowing for multiple optical spectroscopy measurements on flowing molten salts was designed, and demonstrative calibrations of impurities in aqueous samples were performed. Online compositional measurements of molten salts are of high interest to monitor the state of relevant solar and nuclear systems. Here, the Spectroscopic Configuration for Optical Real-Time Characterization of High-Temperature (SCORCH) fluids cell was designed to meet this need by providing optical access to a high-temperature molten salt sample stream without physical contact between the sample and window materials. Laser-induced breakdown spectroscopy (LIBS) was utilized to quantify Li, Cr, Fe, Ni, Sr, and Pr at concentrations ranging nominally from 0 to 315 mmol L −1 . Laser power, frequency, and plasma position were optimized to mitigate challenges associated with sample splashing. Univariate calibration models were built with R 2 > 0.98, percent root mean square error of cross-validation (%RMSECV) as low as 2.7%, and limits of quantification (LOQs) down to 4.1 mmol L −1 . Simultaneously, absorbance calibrations were developed for the applicable analytes (Cr, Ni, and Pr) using Beer’s law with a pathlength of 4.41 ± 0.10 mm. These models provide excellent quantification performance with R 2 > 0.999, %RMSECV as low as 0.6%, and LODs down to 0.08 mmol L −1 . Although these calibrations were performed for each spectroscopic technique separately, the two methods may be combined in the future through multivariate modeling and sensor fusion to provide more robust models with the benefits of both techniques (e.g., absorbance: oxidation state concentrations, LIBS: elemental concentration). Additionally, optimized spectrometers may be deployed to enhance sensitivity.

absorbance spectroscopy↗

DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗

DOE OSTI2024

Comparing Designed Training Sets to Optimize Multivariate Regression Models for Pr, Nd, and Nitric Acid Using Spectrophotometry

Chemometric regression models were developed for the quantification of praseodymium (Pr, 0–1000 µg/mL), neodymium (Nd, 0–1000 µg/mL), and nitric acid (HNO 3 , 0.1–5 M) using spectrophotometry. Designed calibration sets were composed of 20 samples each: 10 model points and 10 lack-of-fit (LOF) points. The D-optimal designs effectively minimized the number of samples required to build models, and each design resulted in similar prediction performance, suggesting that statistical design of experiments can provide a reliable framework for selecting training set samples in three-variable systems. Partial least squares regression (PLSR) models were validated against a one-factor-at-a-time validation set composed of 125 samples (three variables, five levels). The top PLS-1 models resulted in average percent root mean square error of prediction error values of 3.5%, 1.7%, and 1.2% for Pr(III), Nd(III), and HNO 3 , respectively. Power set augmentations of the model and LOF samples were investigated to optimize the number of training set samples. PLSR models built using just required model points (10) had similar predictive capabilities as models including the LOF points (20) but with fewer samples. The number of validation samples was also varied systematically to learn how many samples are needed to validate regression models. This work addresses long-standing questions in the field of chemometrics to help make this approach amenable to the near-real-time quantification of hazardous species in remote settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Experimental and theoretical insights into the effects of pH on catalysis of bond-cleavage by the lignin peroxidase isozyme H8 from Phanerochaete chrysosporium

Abstract Background Lignin peroxidases catalyze a variety of reactions, resulting in cleavage of both β-O-4′ ether bonds and C–C bonds in lignin, both of which are essential for depolymerizing lignin into fragments amendable to biological or chemical upgrading to valuable products. Studies of the specificity of lignin peroxidases to catalyze these various reactions and the role reaction conditions such as pH play have been limited by the lack of assays that allow quantification of specific bond-breaking events. The subsequent theoretical understanding of the underlying mechanisms by which pH modulates the activity of lignin peroxidases remains nascent. Here, we report on combined experimental and theoretical studies of the effect of pH on the enzyme-catalyzed cleavage of β-O-4′ ether bonds and of C–C bonds by a lignin peroxidase isozyme H8 from Phanerochaete chrysosporium and an acid stabilized variant of the same enzyme. Results Using a nanostructure initiator mass spectrometry assay that provides quantification of bond breaking in a phenolic model lignin dimer we found that catalysis of degradation of the dimer to products by an acid-stabilized variant of lignin peroxidase isozyme H8 increased from 38.4% at pH 5 to 92.5% at pH 2.6. At pH 2.6, the observed product distribution resulted from 65.5% β-O-4′ ether bond cleavage, 27.0% C α -C 1 carbon bond cleavage, and 3.6% C α -oxidation as by-product. Using ab initio molecular dynamic simulations and climbing-image Nudge Elastic Band based transition state searches, we suggest the effect of lower pH is via protonation of aliphatic hydroxyl groups under which extremely acidic conditions resulted in lower energetic barriers for bond-cleavages, particularly β-O-4′ bonds. Conclusion These coupled experimental results and theoretical explanations suggest pH is a key driving force for selective and efficient lignin peroxidase isozyme H8 catalyzed depolymerization of the phenolic lignin dimer and further suggest that engineering of lignin peroxidase isozyme H8 and other enzymes involved in lignin depolymerization should include targeting stability at low pH.

09 BIOMASS FUELS↗

DOE OSTI2023

A microCT-based platform to quantify drug targeting

Abstract Background Heterotopic ossification (HO) is a frequent and debilitating complication of traumatic musculoskeletal injuries and orthopedic procedures. Prophylactic dosing of botulinum toxin type A (BTxA) holds potential as a novel treatment option if accurately distributed throughout soft-tissue volumes where protection is clinically desired. We developed a high-resolution, microcomputed tomography (microCT)-based imaging strategy to assess drug distribution and validated this platform by quantifying distribution achieved via a prototype delivery system versus a single-bolus injection. Methods We injected an iodine-containing contrast agent (iodixanol 320 mg I/mL) into dissected rabbit musculature followed by microCT imaging and analysis. To contrast the performance of distributed versus bolus injections, a three-dimensional (3D) 64-cm 3 -printed soft-tissue holder was developed. A centered 2-cm 3 volume of interest (VOI) was targeted with a single-bolus injection or an equal volume distributed injection delivered via a 3D-printed prototype. VOI drug coverage was quantified as a percentage of the VOI volume that was < 1.0 mm from the injected fluid. Results The microCT-based approach enabled high-resolution quantification of injection distribution within soft tissue. The distributed dosing prototype provided significantly greater tissue coverage of the targeted VOI (72 ± 3%, mean ± standard deviation) when compared to an equal volume bolus dose (43 ± 5%, p = 0.031) while also enhancing the precision of injection targeting. Conclusions A microCT-based imaging technique precisely quantifies drug distribution within a soft-tissue VOI, providing a path to overcome a barrier for clinical translation of prophylactic inhibition of HO by BTxA. Relevance statement This platform will facilitate rapid optimization of injection parameters for clinical devices used to effectively and safely inhibit the formation of heterotopic ossification. Key points • MicroCT provides high-resolution quantification of soft-tissue drug distribution. • Distributed dosing is required to maximize soft-tissue drug coverage. • Imaging platform will enable rapid screening of 3D-printed drug distribution prototypes. Graphical Abstract

Ausk, Brandon J. (ORCID:0000000335338303)↗

DOE OSTI2026

SCALE HTR-PROTEUS Benchmark Model

This dataset contains input and result files of computational simulations of HTR-PROTEUS benchmark with the latest version of SCALE code system. The simulations cover criticality control rod worth calculations as well as sensitivity analysis and uncertainty quantification. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Constellation's best estimate alternate source term methodology overview

Safety analyses for a nuclear power plants need to consider postulated accidents that results in at risk of accidental release of radiation. The regulations require plant specific safety analysis reports to include an evaluation of the requirements of 10CFR50.67. Such a safety analysis report mandates limits such that calculated radiological consequences relative to certain dose locations do not exceed a total effective dose equivalent (TEDE) limits following a postulated release of radioactivity. Calculations are performed to estimate the radiological consequences, in terms of dose, to people and equipment to ensure the estimated doses are within the prescribed limits. Analysis should demonstrate, with reasonable assurance, that these prescribed limits are complied with. Conventional methodologies utilize conservative approaches to address lack of uncertainty quantification in the utilized approaches, methods, and/or inputs. These built-in excess conservatisms often result in compounding effects and hence overly conservative results in the estimated radiological consequences, which leads to inaccurate margin evaluation for operation and accident mitigation. Therefore, evaluating accurate dose consequences is needed for both operational and safety reasons. The research documented in this paper provides an overview of Constellation's Best Estimate Alternate Source Term (BEAST) Methodology. BEAST methodology relies upon the use of realistic yet bounding input distributions for key analysis parameters, which replaces use of conservative deterministic singular inputs that bound overall analysis domain. This approach enables evaluating a more accurate accident analysis response, while enabling a bounding licensing basis envelope via more accurate quantification of uncertainty in the application. Therefore, built-in margin for a given scenario is more accurately evaluated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Initial Efforts Organizing WPNCS SG-8: Preservation of Expert Knowledge and Judgement Applied to Criticality Benchmarks

The Working Party on Nuclear Criticality Safety (WPNCS) under the guidance of the Organization for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) has over 20 years of experience addressing concerns related to static and transient configurations encountered within the nuclear fuel cycle: fuel fabrication, transportation, reprocessing, storage, and geological disposal. One of the cornerstone activities of the WPNCS is the International Criticality Safety Benchmark Evaluation Project (ICSBEP), which was established to identify a comprehensive set of criticality benchmark data, evaluate the data, including quantification of overall uncertainties; compile the data into a standardized format, perform sample calculations utilizing modern nuclear data sets and codes utilized in nuclear criticality safety, and formally document the work into a single source of verified benchmark data. Annually, members of the ICSBEP Technical Review Group (TRG) contribute evaluated benchmark data that undergoes comprehensive technical review prior to publication in the ICSBEP Handbook. In the years since the ICSBEP was established, there has been much work to prepare benchmark data to support validation activities in nuclear criticality safety. The 2020 edition of the ICSBEP Handbook contains acceptable benchmark specifications for 5,053 critical, subcritical, or near-critical configurations in 582 benchmark evaluations. Modern benchmark development benefits from decades of experienced international participants, a well-established handbook format, supplementary guides to deal with uncertainty quantification, and a comprehensive review process based upon independent reviews from international experts. The ICSBEP Handbook also contains 838 configurations deemed unacceptable to support criticality safety efforts. They are recorded, with the reasoning for their rejection, to preserve the experimental data, prevent reevaluation of data that are incomplete or contain known errors, and/or to potentially allow future reevaluation of the experiment pending the identification of sufficient data to resolve identified inconsistencies and errors. Users of the ICSBEP Handbook today might notice that the rigor and quality of modern criticality safety benchmarks is much greater than those prepared within the initial decade of the project. Benchmarks with 1s uncertainties in k eff greater than 1% were traditionally rejected unless they were identified as unique experiment types that encompassed materials, fuels, or designs not available from other benchmark experiments. However, benchmarks developed using modern experimental techniques and practices typically have uncertainties on the order of a few tenths of a percent. There have been ongoing efforts to improve the overall quality of previously published benchmark evaluations. Seventy-eight evaluations, containing approximately 600 configurations, have been revised just within the past decade. An additional eleven benchmarks are under revision for updated release in the 2020 edition of the ICSBEP Handbook. If some of the historic benchmarks were resubmitted in their current form to the TRG today, they would be rejected due to lack of data, missing components in the uncertainty analysis, or incomplete benchmark model development. The use of historic criticality safety benchmarks that underestimate the total uncertainty, lack properly quantified biases, or provide inadequate benchmark specifications do not sufficiently support modern criticality safety and nuclear data efforts. Although the ICSBEP Handbook is recognized by regulating bodies to support criticality safety, users are required to justify their reasons to ignore historic benchmark data and include additional safety margins within their designs. Discussions were held at the WPNCS 23rd Annual Meeting in September 2019 regarding the aforementioned issues. The resultant decision was to establish Subgroup 8 (SG-8): Preservation of Expert Knowledge and Judgement Applied to Criticality Benchmarks. The current activities of SG-8 are discussed herein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗