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Program calculates probability of defects at selected confidence levels from nondestructive evaluation data. It provides alternate method of grouping sample data to obtain reasonable value for lower confidence limit with small sample size.
This presentation is titled ORNL Nuclear Data Evaluation Contribution to NCSP (ND-2). This lecture includes resonance evaluations, SAMMY fitting results, and resonance evaluation features. The presentation finishes with evaluations in alkaline earth-metals and halogens.
The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.
To support the LLNL ARM Best Estimate Data Products (ARMBE) data development, the San Jose State University (SJSU)’s group will provide the LLNL team yearly data files (in a format consistent with ARMBE) that contain variables from the instruments defined in Table 1 for the ARM SGP site during the available period. The ARMBE-Land data product will blend multiple ARM observations including the Energy Balance Bowen Ratio Station (EBBR), Soil Water and Temperature System (SWATs) and CO2 Flux (CO2FLX) data sets. The SJSU group will process these datasets with stringent quality controls to remove suspicious data points and average quality-controlled data over a desired temporal resolution (one hour). A qc-flag will be assigned to each variable to indicate how many valid data points are used to get the hourly mean data, consistent with other ARMBE fileds.
Comparison of underflight data with satellite estimates of temperature revealed significant gain calibration errors. The source of the LANDSAT 5 band 6 error and its reproducibility is not yet adequately defined. The error can be accounted for using underflight or ground truth data. When underflight data are used to correct the satellite data, the residual error for the scene studied was 1.3K when the predicted temperatures were compared to measured surface temperature.
This presentation covers the use of Whisper-1.1 to guide improvements to nuclear data evaluations. This presentation provides a brief overview on what Whisper-1.1 is. Additionally covered is the benchmark selection: using benchmark and application correlations (ck) computed using nuclear data sensitivity profiles and covariance data. Next the presentation touches on compute bias and bias uncertainty based on selected benchmarks using Extreme Value Theory Finally the presentation shows the additional margin for nuclear data uncertainty estimated by GLLS.
Vehicle telemetry measurements for all stages of Skylab 2 are presented. The data sources for the launch vehicle and command service module are identified. The data evaluation guide format is described. The system for designating the components of the spacecraft is defined. A list of abbreviations for technical terms used in the reporting documents is included.
Along with the latest release of the general purpose Monte Carlo radiation transport code, MCNP6.2, came the first officially released version of Whisper, version 1.1, a statistical analysis code used for nuclear criticality safety (NCS) validation. The Whisper software package is primarily used to calculate the upper subcritical limit (USL) of a particular application case along with components of the margin of subcriticality (MOS). In the calculation of the MOS due to nuclear data uncertainties, Whisper uses the generalized linear-least-squares (GLLS) approach to obtain adjusted nuclear data uncertainties which are then propagated to estimate the nuclear data uncertainties in the k eff of the application. Due to the fact that Whisper contains the simulated and measured k eff values for over 1100 benchmarks, nuclear data sensitivity profiles for each benchmark along with the nuclear data covariance matrices, all of which are used in the GLLS method, it is possible to easily utilize these data available to help guide improvements to nuclear data evaluations. In the present work, the Whisper data and the GLLS method are used together to suggest an adjusted nuclear data cross section evaluation specifically for 233 U such that the agreement between the calculated and experimental k eff measurements of the relevant benchmarks are improved. With this adjusted nuclear data available, feedback to the theoretical and experimental nuclear data community is provided.
Along with the latest release of the general purpose Monte Carlo radiation transport code, MCNP6.2, came the first officially released version of Whisper, version 1.1, a statistical analysis code used for nuclear criticality safety (NCS) validation. The Whisper software package is primarily used to calculate the upper subcritical limit (USL) of a particular application case along with components of the margin of subcriticality (MOS). In the calculation of the MOS due to nuclear data uncertainties, Whisper uses the generalized linear-least-squares (GLLS) approach to obtain adjusted nuclear data uncertainties which are then propagated to estimate the nuclear data uncertainties in the k{sub eff} of the application. Due to the fact that Whisper contains the simulated and measured k{sub eff} values for over 1100 benchmarks, nuclear data sensitivity profiles for each benchmark along with the nuclear data covariance matrices, all of which are used in the GLLS method, it is possible to easily utilize these data available to help guide improvements to nuclear data evaluations. In the present work, the Whisper data and the GLLS method are used together to suggest an adjusted nuclear data cross section evaluation specifically for {sup 233}U such that the agreement between the calculated and experimental k{sub eff} measurements of the relevant benchmarks are improved. With this adjusted nuclear data available, feedback to the theoretical and experimental nuclear data community is provided. (authors)
This presentation covers a few key topics in relation to advancing the theory of nuclear data evaluations. First it targets and highlights the NCSP mission and vision along with the five-year plan. This lecture focuses upon the uncertainty quantification and SAMMY modernization. This presentation covers theory, and a framework is provided. The presentation concludes with goals, outlooks, and accomplishments.
The generalized form of the Bayes’ Theorem improves UQ for NCSP of differential and/or integral data evaluations and of applications. It is being implemented in an API for use by SCALE, SAMMY, etc. Extended R-matrix formalism improves evaluations of RRR including direct reactions, direct capture, and the Reich-Moore approx., doorway state reactions with applications to RPI data on lead isotopes. Extended R-Matrix formalism will be implemented into SAMMY post modernization.
This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.
An IAEA Coordinated Research Project (CRP) on Updated Decay Data Library for Actinides was implemented during the period 2005-2012. The author participated in the CRP, as a representative of the Horia Hulubei National Institute of Physics and Nuclear Engineering (IFIN-HH), the Radionuclide Metrology Laboratory. Decay data for five actinide nuclides were evaluated by the author, according to the procedures and rules of the international cooperation Decay Data Evaluation Project (DDEP): {sup 236}U, {sup 234}Th, {sup 228}Ra, {sup 211}Bi and {sup 211}Po. The most important results, conclusions and some recommendations of the evaluator are presented. The IFIN-HH involvement in several new international and national research projects in the field is briefly mentioned; new evaluations and experimental determination of some nuclear decay data (photon absolute emission probability, half-life) for nuclear medicine applications are foreseen.
EMPIRE is a modular system of nuclear reaction codes, comprising various nuclear models, and designed for calculations over a broad range of energies and incident particles. The system can be used for theoretical investigations of nuclear reactions as well as for nuclear data evaluation work. Photons, nucleons, deuterons, tritons, helions (3He), α’s, and light or heavy ions can be selected as projectiles. The energy range starts just above the resonance region in the case of a neutron projectile, and extends up to few hundred MeV for heavy ion induced reactions. The code accounts for the major nuclear reaction models, such as optical model, Coupled Channels and DWBA (ECIS06 and OPTMAN), Multi-step Direct (ORION + TRISTAN), NVWY Multi-step Compound, exciton model (PCROSS), hybrid Monte Carlo simulation (DDHMS), and the full featured Hauser-Feshbach model including width fluctuations and the optical model for fission. Heavy ion fusion cross section can be calculated within the simplified coupled channels approach (CCFUS). A comprehensive library of input parameters based on the RIPL-3 library covers nuclear masses, optical model parameters, ground state deformations, discrete levels and decay schemes, level densities, fission barriers, and γ-ray strength functions. Effects of the dynamic deformation of a fast rotating nucleus can be taken into account in the calculations (BARFIT, MOMFIT). The results can be converted into the ENDF-6 format using the accompanying EMPEND code. Modules of the ENDF Utility Codes and the ENDF Pre-Processing codes are applied for ENDF file verification. The package contains the full EXFOR library of experimental data in computational format C4 that are automatically retrieved during the calculations. EMPIRE contains the resonance module that retrieves data from the electronic version of the Atlas of Neutron Resonances by Mughabghab (not provided with the EMPIRE distribution), to produce resonance section and related covariances for the ENDF-6 formatted files. EMPIRE can be used to determine covariances of the calculated data using either sensitivity matrices along with the KALMAN code or employing Monte Carlo approach to produce model generated covariances. In both cases experimental data can be taken into account, either directly (KALMAN) or by feeding the EMPIRE calculated Monte Carlo modelling covariance as a prior to the least square fitting GANDR system. Publication quality graphs can be obtained using the powerful and flexible plotting package ZVView. Interactive plots with ZVView comparing experimental results with calculations can be produced with ENDVER modules. The backbone of the EMPIRE system are bash-shell UNIX scripts that provide for seamless console operation of EMPIRE on Linux, Mac OS X, and Microsoft Windows with GNU gfortran compiler installed. Additionally, the graphical interface, provides for an easy operation of the system on Linux, Mac OS X and virtual Linux machines running on Microsoft Windows.
Minimal updates to current capabilities (SAMMY/AMPX) and to the ENDF format are proposed to include threshold reaction for outgoing charged-particle channels in the URR formalism. If average resonance parameters are reported, probability tables for newly defined reactions channels may be needed, implemented, and tested. Nuclear data evaluation of n+ 35 Cl reactions can be considered the best case to apply the proposed updates.
We present calculations of neutron thermal cross sections, Westcott factors, resonance integrals, Maxwellian-averaged cross sections and astrophysical reaction rates for 843 ENDF materials using data from the major evaluated nuclear libraries and European activation file. Extensive analysis of newly-evaluated neutron reaction cross sections, neutron covariances, and improvements in data processing techniques motivated us to calculate nuclear industry and neutron physics quantities, produce s-process Maxwellian-averaged cross sections and astrophysical reaction rates, systematically calculate uncertainties, and provide additional insights on currently available neutron-induced reaction data. Nuclear reaction calculations are discussed and new results are presented. Due to space limitations, the present paper contains only calculated Maxwellian-averaged cross sections and their uncertainties. The complete data sets for all results are published in the Brookhaven National Laboratory report.
We present calculations of neutron thermal cross sections, Westcott factors, resonance integrals, Maxwellian-averaged cross sections, astrophysical reaction rates, and solar system r-process abundances using the latest data from the major evaluated nuclear libraries for 849 ENDF target materials. The recent release of ENDF/B-VIII.1 library, progress in 252 Cf(SF) evaluation, extensive analysis of newly-evaluated neutron reaction cross sections, neutron covariances, and improvements in data processing techniques motivated us to calculate the nuclear industry and neutron physics parameters, produce s-process Maxwellian-averaged cross sections and astrophysical reaction rates, extract r-process abundances, systematically calculate uncertainties, and provide additional insights on currently available neutron-induced reaction data.
The thermally anisotropic building envelope (TABE) is an active building envelope that redistributes thermal loads in response to weather conditions and building energy demand. Conductive layers throughout the TABE distribute low-grade heat among hydronic loops, altering heat flow direction and intensity. Finite element models of TABE roof and wall panels were developed and calibrated using field evaluation data. The calibration results showed that heat flux differences between the experimental data and finite element models averaged –0.42% and 3.57%, with a maximum mean square error of 1.78 and 3.96 for roof and wall panels, respectively. A reduction in heat flux from the environment to the building living space over the entire testing period (weeks in July/August) was found to be 85% for roof panels and 335% (load reversed) for wall panels. Finally, these results indicate TABE can effectively harness low-grade thermal energy sources to achieve high energy efficiency and promote demand-side management.