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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Benchmark Modeling and Simulation of the FFTF LOFWOS Test #13 Using SAM

The Fast Flux Test Facility (FFTF) was a 400 MW thermal powered, oxide-fueled, liquid sodium cooled test reactor, built to assist development and testing of advanced fuels and materials for fast breeder reactors. In July 1986, a series of unprotected Loss of Flow Without Scram (LOFWOS) transients were performed in FFTF as part of the Passive Safety Testing (PST) program. The LOFWOS Test #13, which was initiated at 50% power and 100% flow with the pump pony motors left off, has been chosen as a benchmark case by IAEA to support collaborative efforts within international partnerships on the validation of simulation tools and models in the area of sodium fast reactor passive safety in an IAEA Coordinated Research Project (CRP), launched in October 2018. The System Analysis Module (SAM) is an advanced and modern system analysis tool under development at Argonne National Laboratory for advanced non-LWR safety analysis. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. To participate the IAEA CRP and enhance the SAM validation base for advanced reactor transient safety analysis, benchmark simulations of the FFTF LOFWOS Test #13 are performed using the SAM code. In this first phase of the validation effort, the thermal-hydraulic behavior of the reactor system is the focus and the reactor kinetics is not considered in the SAM FFTF model. Instead, the results of Argonne’s neutronics calculations are directly used, including the power shape of the active core region and the power history during the transient. The simulation results of FFTF at steady state agreed well with the measured data from the test. During the transient, reasonably good agreement were also obtained. Future work to improve the model will focus on introducing the reactivity predictions into the model, as well as better understanding or resolving the current discrepancies with the measured data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Medium-assisted enhancement of χ c 1 ( 3872 ) production from small to large colliding systems

Studies of exotic hadrons such as the χ c 1 ( 3872 ) state provide crucial insights into the fundamental force governing the strong interaction dynamics, with an emerging frontier to investigate their production in high energy collisions where a partonic medium is present. The latest experimental measurements from the Large Hadron Collider show an intriguing evolution pattern of the χ c 1 ( 3872 ) -to- ψ ( 2 S ) yield ratio from proton-proton collisions with increasing multiplicities toward proton-lead and lead-lead collisions. Here we propose a mechanism of medium-assisted enhancement for the χ c 1 ( 3872 ) production, which competes with the more conventional absorption-induced suppression and results in a nonmonotonic trend from small to large colliding systems. Realistic simulations from this model offer a quantitative description of all available data. Predictions are made for the centrality dependence of this observable in PbPb collisions as well as for its system-size dependence from OO and ArAr to XeXe and PbPb collisions. In both cases, a nonmonotonic behavior emerges as the imprint of the competition between enhancement and suppression and can be readily tested by future data. Published by the American Physical Society 2024

Physics↗
DOE OSTI2022

Assessment of BQ-9000 Biodiesel Properties for 2021

This is the fifth in a series of reports documenting the quality of biodiesel from U.S. and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry voluntary quality assurance program. Participants agreed to provide monthly data on critical quality parameters for calendar year 2021. The quality data was provided to a team of experts, who removed any identifying company information and provided anonymized and randomized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. The critical quality parameters analyzed were: sodium and potassium (Na+K); calcium and magnesium (Ca+Mg); phosphorus (P); flash point and alcohol control; water and sediment; cloud point; acid number; free and total glycerin; monoglycerides; sulfur; oxidation stability; and cold soak filterability test (CSFT). The data was not weighted for production volume.

09 BIOMASS FUELS↗
DOE OSTI2022

Toward Robust Autotuning of Noisy Quantum dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a “gatekeeper” system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9)% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Evaluation of a streaming readout solution for Jefferson Lab experiments

Jefferson Lab (JLab) operates the CEBAF accelerator that provides a high energy (up to 12 GeV) and high current (up to 150 µA) polarized electron beam for fixed target experiments. JLab's mission is to provide substantial progress in understanding the Quantum Chromo Dynamics (QCD), the current theory of the spectrum and the dynamics of hadronic matter. Given the broad scientific program a general-purpose detector, CLAS12, has been designed and constructed. Such a detector requires a sophisticated trigger and current experiments use an on-line FPGA-based system that relies upon custom firmware and electronics both of which are difficult reconfigure from one experiment to the next. To overcome these challenges transitioning to a streaming readout has been proposed since it would allow a more flexible, easier to debug, software trigger to be developed. To facilitate this JLab is gaining experience with streaming readout systems and is actively working to design and test a streaming data acquisition architecture. Significant work has already been done to make existing front-end electronics compatible with operation in streaming mode. Software-wise, much work remains to be done but an existing framework, TriDAS, which was developed for underwater neutrino telescopes can, in principle, be used with the existing JLab hardware. A test stand is already in place in the INDRA DAQ development lab at JLab. This paper will present the development of this TriDAS based test system and it?s operation in-beam using parts of the CLAS12 detector to evaluate streaming readout for a large general purpose detector.

Battaglieri, Marco↗
DOE OSTI2020

Availability of Shielding Benchmark Experiment Data in the ICSBEP Handbook

The International Criticality Safety Benchmark Evaluation Projects (ICSBEP) has over two decades of experience under the auspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) to identify, evaluate, and compile a comprehensive data set of criticality safety benchmark data [1]. The activities of the ICSBEP have led to the continued success of the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) [2]. The contents of the ICSBEP Handbook include benchmark specifications derived from nuclear facilities around the world, which have been utilized to support criticality safety efforts and also integral testing of nuclear data [3]. The 2020 edition of the ICSBEP Handbook will include data from 582 evaluations containing benchmark specifications for 5,053 critical, subcritical, or near-critical configurations. There are seven criticality-alarm-placement/shielding evaluations containing a total of 45 benchmark configurations, and ten fundamental physics evaluations containing a total of 237 measurements relevant to criticality safety applications. The purpose of this paper is to summarize those benchmarks relative to validation needs in shielding applications currently found within the ICSBEP Handbook. The current and future plans of shielding benchmarks with regards to the ICSBEP will also be discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The Argonne SuperGel for CBRN Decontamination - 20160

The Argonne SuperGel was developed between 2003 and 2015 to fill a gap in our nation's capability to quickly decontaminate important structures following a radiological or nuclear release event. Specifically, the decontamination technology was developed to minimize damage to monuments, high valued structures, and critical infrastructure while reducing environmental and health impacts. An important criterion during its development, common reagents were employed that could be easily acquired in order to minimize the timeline for its deployment. Its current formulation uses off-the-shelf super-absorbing hydrogels common to the food and agricultural industry and common salts. Over the years, two formulations of the Argonne SuperGel have been developed to specifically target radioactive cesium contaminations and then, more generically, actinide and fission product contaminations. A biodegradable derivative of phosphoric acid is used in small amounts to promote the removal of insoluble actinide species. The original order of unit operations for the SuperGel technology was 1) applying the wash solution to promote mobility of the contaminants on the surface, 2) applying the hydrogel to absorb the wash solution and contaminants, and 3) removing the hydrogel for disposal. During the course of our studies, we tested a new formulation of the gel technology that eliminated the need for a separate step to apply the wash solution without a compromise in decontamination factors. Greater than 70% and >95% of Cs-137 were removed from concrete and tile coupons, respectively, after two decontaminations, with the best results using wash solutions of 1.0 M KCl and 1.0 M NH{sub 4}Cl formulations. We found no statistical difference between results at 30 deg. C and 40 deg. C or at 70% and 90% relative humidity. We tested the gel under strong UV irradiation to simulate extreme environments. While a tropical noontime UV flux did not adversely affect the gel system decontamination of Cs-137 from concrete or tile coupons, the noontime flux resulted in significant dehydration of the gel after two hours of constant exposure. The rehydration of the gel by the humid air (at 90% humidity and 40 deg. C) was slow and may not be sufficient to offset dehydration by the sun. Americium decontamination from concrete was 70% after optimizing the phosphoric acid derivative and carbonate salt concentration in the wash solution. Since its development, we have had the opportunity to test the SuperGel in the removal of legacy contaminations in hot cell facilities and former glovebox facilities at Argonne. This has provided a unique opportunity to evaluate the SuperGel on a range of contaminants outside the original specifications for its use. We will report on the origin of the gel formulation, some highlighted experimental data including independent testing by the US EPA which has never been reported, and its efficacy for removing legacy alpha contaminations and its potential use for removal of chemical and biological hazardous agents. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Lens Model Accuracy in the Expected LSST Lensed AGN Sample

Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST survey, we anticipate using a sample of O(1000) lensed AGN for TDC. To prepare for this dataset and enable this measurement, we construct and analyze a realistic mock sample of 1300 systems drawn from the OM10 (Oguri & Marshall 2010) catalog of simulated lenses with AGN sources at $z<3.1$ in order to test a key aspect of the analysis pipeline, that of the lens modeling. We realize the lenses as power law elliptical mass distributions and simulate 5-year LSST i-band coadd images. From every image, we infer the lens mass model parameters using neural posterior estimation (NPE). Focusing on the key model parameters, $θ_E$ (the Einstein Radius) and $γ_{lens}$ (the projected mass density profile slope), with consistent mass-light ellipticity correlations in test and training data, we recover $θ_E$ with less than 1% bias per lens, 6.5% precision per lens and $γ_{lens}$ with less than 3% bias per lens, 8% precision per lens. We find that lens light subtraction prior to modeling is only useful when applied to data sampled from the training prior. If emulated deconvolution is applied to the data prior to modeling, precision improves across all parameters by a factor of 2. Finally, we combine the inferred lens mass models using Bayesian Hierarchical Inference to recover the global properties of the lens sample with less than 1% bias.

Venkatraman, Padmavathi [Illinois U., Urbana; KIPA↗
DOE OSTI2021

Failure in Confined Brazilian Tests on Sandstone

Strength of rocks in the confined tension region, where the minimum principal stress is tensile, has only infrequently been measured and is not well understood. Quasi-static confined Brazilian tests under a range of confining stresses (2.76 to 27.58 MPa) where used to determine the strength of sandstone in the confined tension region. The test results indicate that the strength in the confined tension region was a strong function of the intermediate principal stress: increasing the intermediate principal stress significantly increased the strength of the sandstone. The strength data were well fit by the Mogi–Coulomb criterion, which accounts for the intermediate principal stress. Unconfined Brazilian strength data were not well fit to the Mogi–Coulomb criterion derived from the confined Brazilian test data, consistent with a transition from tensile to shear processes dominating failure with increasing confining pressure. Observations of post-failure fracture surfaces reveal more indication of shear processes with increasing confining pressure. Numerical simulations from combined finite-discrete element method are compared to the experimental results and reflect similar conditions for failure compared to the experimental tests in the confined tension region.

42 ENGINEERING↗
DOE OSTI2020

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

Natural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

58 GEOSCIENCES↗
DOE OSTI2023

Maximizing Efficiency and Quality: Leveraging Automated Testing for Laboratory Commissioning

The traditional commissioning process for a building often implements sampling rates to select equipment for functional acceptance testing when large quantities of equipment are present. While this approach is generally effective in identifying wide-spread issues, it has several key shortfalls. (1) It provides a one-time validation of equipment operation in which the standard documentation is a simple checklist of pass/fail questions. (2) It fails to evaluate equipment not included in the sample rate selection. During the construction and commissioning process of the new Research and Innovation Laboratory (RAIL) in Golden, CO, the National Renewable Energy Laboratory team engaged Group14 Engineering to implement a Connected Commissioning process using fault detection and diagnostic software for automated functional acceptance testing. The goal of this presentation will be to highlight the advantages offered by automated functional testing in this critical laboratory setting. These are primarily (1) sampling 100% of BAS-connected equipment during functional testing, (2) testing results backed by data beyond the traditional pass/fail checklist, and (3) an automated test process that can be regularly executed by the building management team for ongoing commissioning throughout the life of the building. The presentation will also cover technical challenges associated with connected commissioning and the important conversations with the key stakeholders that need to occur well before functional acceptance testing in order to successfully implement the automated testing processes.

automated testing↗
DOE OSTI2024

Maximizing Efficiency and Quality: Leveraging Automated Testing for Laboratory Commissioning

The traditional commissioning process uses sampling to select equipment for functional acceptance testing when large quantities of equipment are present. Although this approach is generally effective in identifying wide-spread issues, it has several shortcomings: it fails to evaluate equipment not included in the sample, provides only a one-time validation of equipment operation, and the standard documentation is a simple checklist of pass/fail questions. During the construction and commissioning process of the new Research and Innovation Laboratory (RAIL) in Golden, CO, the National Renewable Energy Laboratory team engaged Group14 Engineering to implement a Connected Commissioning process using fault detection and diagnostic software for automated functional acceptance testing. This presentation highlights the advantages offered by automated functional testing in this critical laboratory setting: (1) sampling 100% of BAS-connected equipment during functional testing, (2) testing results backed by data beyond the traditional pass/fail checklist, and (3) an automated test process that can be regularly executed by the building management team for ongoing commissioning throughout the life of the building. The presentation will also cover technical challenges associated with Connected Commissioning and the important conversations with key stakeholders that need to occur well before functional acceptance testing in order to successfully implement the automated testing processes.

automated testing↗
DOE OSTI2020

Discriminating Underground Nuclear Explosions Leading To Late-Time Radionuclide Gas Seeps

Utilizing historical data from the U.S. nuclear test program and freely available barometric pressure data, we performed an analytical barometric-pumping efficiency analysis to determine factors resulting in late-time radionuclide gas seeps from underground nuclear explosions. In this work, we considered 16 underground nuclear explosions with similar geology and test setup, of which five resulted in the measurement of late-time radionuclide gas concentrations at the ground surface. Additionally, the factors we considered include barometric frequency and amplitude, depth of burial, air-filled porosity, intact-rock permeability, fracture aperture, and fracture spacing. The analysis indicates that the best discriminators of late-time radionuclide gas seeps for these explosions are barometric frequency and amplitude and air-filled porosity. While geologic information on fracture aperture and spacing is not available for these explosions, the sensitivity of barometric-pumping efficiency to fracture aperture indicates that it would likely also be a good discriminator.

58 GEOSCIENCES↗
DOE OSTI2024

Testing hadronic-model predictions of depth of maximum of air-shower profiles and ground-particle signals using hybrid data of the Pierre Auger Observatory

We test the predictions of hadronic interaction models regarding the depth of maximum of air-shower profiles, X max , and ground-particle signals in water-Cherenkov detectors at 1000 m from the shower core, S ( 1000 ) , using the data from the fluorescence and surface detectors of the Pierre Auger Observatory. The test consists of fitting the measured two-dimensional ( S ( 1000 ) , X max ) distributions using templates for simulated air showers produced with hadronic interaction models pos-, et--04, 2.3d and leaving the scales of predicted X max and the signals from hadronic component at ground as free-fit parameters. The method relies on the assumption that the mass composition remains the same at all zenith angles, while the longitudinal shower development and attenuation of ground signal depend on the mass composition in a correlated way. The analysis was applied to 2239 events detected by both the fluorescence and surface detectors of the Pierre Auger Observatory with energies between 10 18.5 eV to 10 19.0 eV and zenith angles below 60°. We found, that within the assumptions of the method, the best description of the data is achieved if the predictions of the hadronic interaction models are shifted to deeper X max values and larger hadronic signals at all zenith angles. Given the magnitude of the shifts and the data sample size, the statistical significance of the improvement of data description using the modifications considered in the paper is larger than 5 σ even for any linear combination of experimental systematic uncertainties. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Sister Rod Destructive Examinations (FY20) Appendix A: Full Length Rod Heat Treatments (FHT)

As a part of the US DOE-NE High Burnup Spent Fuel Data Project, ORNL is performing destructive examinations (DEs) of high burnup (HBU) (>45 gigawatt days per metric ton uranium) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called the “sister rods,” are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4, low-tin Zircaloy-4, ZIRLO®, and M5®. The DEs are being conducted to obtain a baseline of the HBU rod’s condition prior to dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and empty cladding are being tested. While the data generated can be used for multiple purposes, a primary goal for obtaining the post-irradiation examination data will support SNF dry storage licensing and relicensing activities.This report documents the status of the ORNL Phase 1 DE activities related to full length rod heat treatments (FHT) applied to selected sister rods in Phase 1 of the sister rod test program. The results of this work and detailed examinations are summarized in the Sister Rod DE Status Report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗