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Informing nuclear physics via machine learning methods with differential and integral experiments
Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.
Discovery of Nuclides Project
The Discovery of Nuclides project compiles detailed discovery information for all naturally occurring and artificially produced nuclides. The short abstracts for each nuclide include the authors, institution, country, year, method of discovery, and reference. The database contains over 3300 nuclides and is continuously updated as new nuclides are discovered. The work is performed at the Facility for Rare Isotope Beams (FRIB) and is supported by the U.S. Department of Energy, Office of Nuclear Physics under Grant No. DE-SC0016948 and Michigan State University. The FRIB Nuclear Data group is responsible for coordinating and updating the database.
GNDS v2.0 release and future developments [Slides]
This presentation begins by discussing the major changes in v2.0 of GNDS and that the GNDS-2.0 formatted libraries are now available. It also examines how code support for GNDS is growing with processing codes, APIs and transport codes, and evaluation/testing codes. The presentation states that while migrating to GNDS can be a major effort it worthwhile, especially for anyone who is just getting started as nuclear data creator or user. Additionally, this presentation outlines the established milestones that the EG-GNDS (the group in charge of GNDS specs) has for the next GNDS release and elaborates on the first goal, which is to migrate GNDS specifications to the new JSON schema language.
Uncertainty Quantification of Calculated Temperatures for the U.S. Capsules in the AGR-2 Experiment
A series of Advanced Gas Reactor (AGR) irradiation experiments are being conducted within the Advanced Reactor Technology (ART) Fuel Development and Qualification Program. The main objectives of the fuel experimental campaign are to provide the necessary data on fuel performance to support fuel process development, qualify a fuel design and fabrication process for normal operation and accident conditions, and support development and validation of fuel performance and fission product transport models and codes (PLN-3636). The AGR-2 test was inserted in the B-12 position in the Advanced Test Reactor (ATR) core at Idaho National Laboratory (INL) in June 2010 and successfully completed irradiation in October 2013, resulting in irradiation of the TRISO fuel for 559.2 effective full power days (EFPDs) during approximately 3.3 calendar years. The AGR-2 data, including the irradiation data and calculated results, were qualified and stored in the Nuclear Data Management and Analysis System (NDMAS) (Pham and Einerson 2014). To support the U.S. TRISO fuel performance assessment and to provide data for validation of fuel performance and fission product transport models and codes, the daily as-run thermal analysis has been performed separately on each of four AGR-2 U.S. capsules for the entire irradiation as discussed in (Hawkes 2014). The ABAQUS code’s finite element-based thermal model predicts the daily average volume-average fuel temperature and peak fuel temperature in each capsule. This thermal model involves complex physical mechanisms (e.g., graphite holder and fuel compact shrinkage) and properties (e.g., conductivity and density). Therefore, the thermal model predictions are affected by uncertainty in input parameters and by incomplete knowledge of the underlying physics leading to modeling assumptions. Therefore, alongside with the deterministic predictions from a set of input thermal conditions, information about prediction uncertainty is instrumental for the ART program decision-making. Well defined and reduced uncertainty in model predictions helps increase the quality of and confidence in the AGR technical findings.
The features of neutronic calculations for fast reactors with hybrid cores on the basis of BFS-62-3A critical assembly experiments
The different (U-Pu) fuel compositions are considered for next generation of sodium fast breeder reactors. The considerable discrepancies in axial and radial neutron spectra for hybrid reactor systems compared to the cores with UO{sub 2} fuel cause increasing uncertainty of generating the group nuclear constants in those reactor systems. The calculation results of BFS-62-3A critical assembly which is considered as full-scale model of BN-600 hybrid core with steel reflector specify quite different spectra in local areas. For those systems the MCNP 5 calculations demonstrate significant sensitivity of effective multiplication factor K{sub eff} and spectral indices to nuclear data libraries. For {sup 235}U, {sup 238}U, {sup 239}Pu the results of calculated radial fission rate distributions against the reconstructed ones are analyzed. Comparative analysis of spectral indices, neutron spectra and radial fission rate distributions are performed using the different versions of ENDF/B, JENDL-3.3, JENDL-4, JEFF-3.1.1 libraries and BROND-3 for Fe, Cr isotopes. For analyzing the fission rate sensitivity to the plutonium presence in the fuel {sup 239}Pu is substituted for {sup 235}U (enrichment 90%) in the FA areas containing the plutonium. For {sup 235}U, {sup 238}U, {sup 239}Pu radial fission rate distributions the explanation of pick values discrepancies is based on the group fission constants analyses and possible underestimation of some features at the experimental data recovery method (Westcott factors, temperature dependence). (authors)
Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal
Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.
The value of archived data
The rapid progress of atomic and nuclear physics in the twentieth century altered the way scientific results are documented, preserved and disseminated. Boris Pritychenko discusses how atomic and nuclear data tables have become a central resource for the community.
Validation of Thermal Scattering Laws for Light Water at Elevated Temperatures with Diffusion Experiments
Light water is the most important neutron moderator in many reactor applications. Thermal neutron scattering kernels for H bound in H 2 O are represented by ENDF-format thermal scattering laws (TSLs) evaluated at discrete temperatures T . Nuclear data evaluations are conventionally validated utilizing a combination of critical benchmarks and experimental cross section data. Existing public critical benchmarks may be inadequate for testing water TSLs over the full range of T of interest in reactor applications, and experimental thermal scattering cross section data for water is sparse at elevated T . In this work, MC21 is used to simulate the decay of the equilibrium thermal neutron flux in light-water spheres of several radii. The fundamental-mode time eigenvalue $\mathcal{a}$ is calculated at 22 °C and 227 °C (at saturation pressure) for each sphere using three different H-H 2 O TSLs. Fitting $\mathcal{a}$ to a polynomial function of geometric buckling allows calculation of the thermal neutron diffusion length L . Extrapolation lengths are treated as a function of the transport mean free path and geometry. Experimental L data from 25 publications at 49 temperatures (from 10 °C to 295 °C), computed by several different time-dependent and space-dependent decay methods, is used to develop an empirical fit of L vs. T . The MC21-calculated L for the TSLs tested are within ±1% of the predicted values at 22 °C and 227 °C. This validation approach, which may be repeated at arbitrary T , constitutes an integral benchmark specific to and characterizing the detailed physics of the thermal scattering kernel applied.
Preliminary Development of a Nuclide Inventory Code for HTGR
Nuclide inventory calculation for a reactor core is the basis of radiation protection, shielding design and radioactive environmental impact assessment of the power plant. HTR-PM (high-temperature gas-cooled reactor pebble-bed module) under construction in China is one of the candidates for Gen-IV advanced nuclear power system.1 In the design stage of HTR-PM, KORIGEN has been utilized in the inventory calculation.2 However, the current version of KORIGEN was developed several decades ago. In order to improve the design capability of the analysis code package for HTR-PM, a new computer code for nuclide inventory calculation called NUIT (Nuclide Inventory Tool) is being developed based on the DEPTH3 code. The NUIT code tries to utilize accurate and efficient burnup solvers, incorporate well-validated and most recent nuclear data libraries, and provide user-friendly IO interface. This paper presents the preliminary work in the development of NUIT.
Information Files of 238 U(n,f)/ 235 U(n,f) NIFFTE fissionTPC Cross-sections for the Neutron Data Standards Project
This report documents input files, output files and underlying assumptions for an uncertainty estimate for nuclear data evaluations purposes of the normalized 238 U/ 235 U neutron-induced fission cross-section ratios, 238 U(n,f)/ 235 U(n,f), measured by the NIFFTE fission Time Projection Chamber (fissionTPC). This uncertainty estimate and the resulting files were based on information provided by fissionTPC experimentalists, R.J.Casperson, N.S. Bowden, L. Snyder and K.T. Schmitt, and an EXFOR file compiled by N. Otuka. These files were generated for the Neutron Data Standards project coordinated by the IAEA in order to simplify the incorporation of 238 U(n,f)/ 235 U(n,f) fissionTPC data into the standards database.
Performance and safety parameters for the high flux isotope reactor
A Monte Carlo depletion model for the High Flux Isotope Reactor (HFIR) Cycle 400 and its use in calculating parameters of relevance to the reactor performance and safety during the reactor cycle are presented in this paper. This depletion model was developed to serve as a reference for the design of a low-enriched uranium (LEU) fuel for an ongoing study to convert HFIR from high-enriched uranium (HEU) to LEU fuel; both HEU and LEU depletion models use the same methodology and ENDF/B-VII nuclear data as discussed in this paper. The calculated HFIR Cycle 400 parameters, which are compared with measurement data from critical experiments performed at HFIR, data included in the HFIR Safety Analysis Report (SAR), or data reported by previous calculations, provide a basis for verification or updating of the corresponding SAR data. (authors)
Implementation of the direct S ( α , β ) method in the KENO Monte Carlo code
The Monte Carlo code KENO contains thermal scattering data for a wide variety of thermal moderators. These data are processed from Evaluated Nuclear Data Files (ENDF) by AMPX and stored as double differential probability distribution functions. The method examined in this study uses S(α,β) probability distribution functions derived from the ENDF data files directly instead of being converted to double differential cross sections. This allows the size of the cross section data on the disk to be reduced substantially amount. KENO has also been updated to allow interpolation in temperature on these data so that problems can be run at any temperature. Results are shown for several simplified problems for a variety of moderators. In addition, benchmark models based on the KRITZ reactor in Sweden were run, and the results are compared with the previous versions of KENO without the direct S(α,β) method. Results from the direct S(α,β) method compare favorably with the original results obtained using the double differential cross sections. Finally, sampling the data increases the run-time of the Monte Carlo calculation, but memory usage is decreased substantially.
Verification of the Re-Released ENDF/B VIII.0 Based Thermal Scattering Libraries
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The Transmutation of Nuclear Waste in the Two-Zone Subcritical System Driven by High- Intensity Neutron Generator - 12098
The main problems of transmutation of high-level radioactive waste (minor actinides and long-lived fission products) are considered in our work. The range of radioactive waste of nuclear power is analyzed. The conditions under which the transmutation of radioactive waste will be most effective are analyzed too. The modeling results of a transmutation of the main radioactive isotopes are presented and discussed. The transmutation of minor actinides and long-lived fission products are modeled in our work (minor actinides - Np-237, Am-241, Am-242, Am-243, Cm-244, Cm-245; long-lived fission products - I-129, Tc-99). The two-zone subcritical system is calculated with help of different neutron-physical codes (MCNP, Scale, Montebarn, Origen). The ENDF/B-VI nuclear data library used in above calculations. Thus, radioactive wastes can be divided into two main groups that need to be transmuted. The minor actinides form the first group and the long-lived fission products form the second one. For the purpose of effective transmutation these isotopes must be extracted from the spent nuclear fuel with the help of either PUREX technology or pyrometallurgical technology. The two-zone reactor system with fast and thermal regions is more effective for nuclear waste transmutation than the one-zone reactor. Modeling results show that nearly all radioactive wastes can be transmuted in the two-zone subcritical system driven by a high-intensity neutron generator with the external neutron source strength of 1.10{sup 13} n/sec. Obviously, transmutation rate will increase with a rise of the external neutron source strength. From the results above we can also see that the initial loading of radioactive isotopes into the reactor system should exceed by mass those isotopes that are finally produced. (authors)
Statistical (n,$$\gamma $$) cross section model comparison for short-lived nuclei
Abstract Neutron-capture cross sections of neutron-rich nuclei are calculated using a Hauser–Feshbach model when direct experimental cross sections cannot be obtained. A number of codes to perform these calculations exist, and each makes different assumptions about the underlying nuclear physics. We investigated the systematic uncertainty associated with the choice of Hauser-Feshbach code used to calculate the neutron-capture cross section of a short-lived nucleus. The neutron-capture cross section for $$^{73}\hbox {Zn}$$ 73 Zn (n, $$\gamma $$ γ ) $$^{74}\hbox {Zn}$$ 74 Zn was calculated using three Hauser-Feshbach statistical model codes: TALYS, CoH, and EMPIRE. The calculation was first performed without any changes to the default settings in each code. Then an experimentally obtained nuclear level density (NLD) and $$\gamma $$ γ -ray strength function ( $$\gamma \hbox {SF}$$ γ SF ) were included. Finally, the nuclear structure information was made consistent across the codes. The neutron-capture cross sections obtained from the three codes are in good agreement after including the experimentally obtained NLD and $$\gamma \hbox {SF}$$ γ SF , accounting for differences in the underlying nuclear reaction models, and enforcing consistent approximations for unknown nuclear data. It is possible to use consistent inputs and nuclear physics to reduce the differences in the calculated neutron-capture cross section from different Hauser-Feshbach codes. However, ensuring the treatment of the input of experimental data and other nuclear physics are similar across multiple codes requires a careful investigation. For this reason, more complete documentation of the inputs and physics chosen is important.
Estimation of average burnup of damaged fuels loaded in Fukushima Dai-ichi reactors by using the {sup 134}Cs/{sup 137}Cs ratio method
Average burnup of damaged fuels loaded in Fukushima Dai-ichi reactors is estimated, using the {sup 134}Cs/{sup 137}Cs ratio method for measured radioactivities of {sup 134}Cs and {sup 137}Cs in contaminated soils within the range of 100 km from the Fukushima Dai-ichi nuclear power plants. As a result, the measured {sup 134}Cs/{sup 137}Cs ratio from the contaminated soil is 0.996{+-}0.07 as of March 11, 2011. Based on the {sup 134}Cs/{sup 137}Cs ratio method, the estimated burnup of damaged fuels is approximately 17.2{+-}1.5 [GWd/tHM]. It is noted that the numerical results of various calculation codes (SRAC2006/PIJ, SCALE6.0/TRITON, and MVP-BURN) are almost the same evaluation values of {sup 134}Cs/ {sup 137}Cs ratio with same evaluated nuclear data library (ENDF-B/VII.0). The void fraction effect in depletion calculation has a major impact on {sup 134}Cs/{sup 137}Cs ratio compared with the differences between JENDL-4.0 and ENDF-B/VII.0. (authors)
The Paucity Problem: Where Have All the Space Reactor Experiments Gone?
The Handbooks of the International Criticality Safety Benchmark Evaluation Project (ICSBEP) and the International Reactor Physics Experiment Evaluation Project (IRPhEP) together contain a plethora of documented and evaluated experiments essential in the validation of nuclear data, neutronics codes, and modeling of various nuclear systems. Unfortunately, only a minute selection of handbook data (twelve evaluations) are of actual experimental facilities and mockups designed specifically for space nuclear research. There is a paucity problem, such that the multitude of space nuclear experimental activities performed in the past several decades have yet to be recovered and made available in such detail that the international community could benefit from these valuable historical research efforts. Those experiments represent extensive investments in infrastructure, expertise, and cost, as well as constitute significantly valuable resources of data supporting past, present, and future research activities. The ICSBEP and IRPhEP were established to identify and verify comprehensive sets of benchmark data; evaluate the data, including quantification of biases and uncertainties; compile the data and calculations in a standardized format; and formally document the effort into a single source of verified benchmark data. See full abstract in attached document.