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The ADAQ framework: An integrated toolkit for data acquisition and analysis with real and simulated radiation detectors
Here, the ADAQ framework is a collection of software tools that is designed to streamline the acquisition and analysis of radiation detector data produced in modern digital data acquisition (DAQ) systems and in Monte Carlo detector simulations. The purpose of the framework is to maximize user scientific pro-ductivity by minimizing the effort and expertise required to fully utilize radiation detectors in a variety of scientific and engineering disciplines. By using a single set of tools to span the real and simulation domains, the framework eliminates redundancy and provides an integrated workflow for high-fidelity comparison between experimental and simulated detector performance. Built on the ROOT data analysis framework, the core of the ADAQ framework is a set of Cþþ and Python libraries that enable high-level control of digital DAQ systems and detector simulations with data stored into standardized binary ROOT files for further analysis. Two graphical user interface programs utilize the libraries to create powerful tools: ADAQAcquisition handles control and readout of real-world DAQ systems and ADAQAnalysis provides data analysis and visualization methods for experimental and simulated data. At present, the ADAQ framework supports digital DAQ hardware from CAEN S.p.A. and detector simulations performed in Geant4; however, the modular design will facilitate future extension to other manufacturers and simulation platforms.
Optimum Planning for Inductively Charged On-Demand Automated Electric Shuttles at Greenville, South Carolina
Wireless charging technology presents perfect fits for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear integer planning optimization problem is formulated. A comprehensive cost function representing the inductively charged SAESs is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs realizing charge sustaining operation. The planning platform incorporates representative simulated traffic data (drive cycles and routes) for four SAESs at Greenville project using the Simulation of Urban Mobility (SUMO) tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge (SOC) profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. The results indicated that implementing wireless charging at a few designated stops for fixed-route SAESs with proper design allows the vehicles realizing charge sustaining operation, infinite range and zero recharge downtime, with a significant reduction in the on-board battery (36 % ) and road coverage (69%), at minimum cost.
Predicting Local Material Thickness from Steady-State Ultrasonic Wavefield Measurements Using a Convolutional Neural Network
Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field, ultrasonic non-destructive evaluation (NDE) technique used to locate and characterize defects in plate-like structures. ASSESS generates a steady-state, single-tone ultrasonic excitation in a structure and a scanning laser Doppler Vibrometer (LDV) measures the resulting full-field surface velocity response. Traditional processing techniques for ASSESS data rely on wavenumber domain analysis. This paper presents the alternative use of a convolutional neural network (CNN), trained using simulated ASSESS data, to predict the local plate thickness at every pixel in the wavefield measurement directly. The defect detection accuracy of CNN-based thickness predictions are shown to improve for defects of greater size, and for defects with higher thickness reductions. The CNN demonstrates the ability to predict thickness accurately in regions where Lamb wave dispersion relations are complex or unknown, such as near the boundaries of a test specimen, so long as the CNN is trained on data that accounts for these regions. The CNN also shows generalizability to ASSESS experimental data, despite an entirely simulated training dataset.
Estimation of placental and lactational transfer and tissue distribution of atrazine and its main metabolites in rodent dams, fetuses, and neonates with physiologically based pharmacokinetic modeling
Atrazine (ATR) is a widely used chlorotriazine herbicide, a ubiquitous environmental contaminant, and a potential developmental toxicant. To quantitatively evaluate placental/lactational transfer and fetal/neonatal tissue dosimetry of ATR and its major metabolites, physiologically based pharmacokinetic models were developed for rat dams, fetuses and neonates. These models were calibrated using pharmacokinetic data from rat dams repeatedly exposed (oral gavage; 5 mg/kg) to ATR followed by model evaluation against other available rat data. Model simulations corresponded well to the majority of available experimental data and suggest that: (1) the fetus is exposed to both ATR and its major metabolite didealkylatrazine (DACT) at levels similar to maternal plasma levels, (2) the neonate is exposed mostly to DACT at levels two-thirds lower than maternal plasma or fetal levels, while lactational exposure to ATR is minimal, and (3) gestational carryover of DACT greatly affects its neonatal dosimetry up until mid-lactation. To test the model's cross-species extrapolation capability, a pharmacokinetic study was conducted with pregnant C57BL/6 mice exposed (oral gavage; 5 mg/kg) to ATR from gestational day 12 to 18. By using mouse-specific parameters, the model predictions fitted well with the measured data, including placental ATR/DACT levels. However, fetal concentrations of DACT were overestimated by the model (10-fold). This overestimation suggests that only around 10% of the DACT that reaches the fetus is tissue-bound. These rodent models could be used in fetal/neonatal tissue dosimetry predictions to help design/interpret early life toxicity/pharmacokinetic studies with ATR and as a foundation for scaling to humans. - Highlights: • We developed PBPK models for atrazine in rat dams, fetuses, and neonates. • We conducted pharmacokinetic (PK) study with atrazine in pregnant mice. • Model predictions were in good agreement with experimental rat and mouse PK data. • The fetus is exposed to atrazine/its main metabolite at levels similar to the dam. • The nursing neonate is exposed primarily to atrazine's main metabolite DACT.
Constraining RV variation using highly reddened Type Ia supernovae from the Pantheon+ sample
ABSTRACT Type Ia supernovae (SNe Ia) are powerful tools for measuring the expansion history of the Universe, but the impact of dust around SNe Ia remains unknown and is a critical systematic uncertainty. One way to improve our empirical description of dust is to analyse highly reddened SNe Ia [E(B − V) > 0.4, roughly equivalent to the fitted SALT2 light-curve parameter c > 0.3]. With the recently released Pantheon+ sample, there are 57 SNe Ia that were removed because of their high colour alone (with colours up to c = 1.61), which can provide enormous leverage on understanding line-of-sight RV. Previous studies have claimed that RV decreases with redder colour, though it is unclear if this is due to limited statistics, selection effects, or an alternative explanation. To test this claim, we fit two separate colour–luminosity relationships, one for the main cosmological sample (c < 0.3) and one for highly reddened (c > 0.3) SNe Ia. We find the change in the colour–luminosity coefficient to be consistent with zero. Additionally, we compare the data to simulations with different colour models, and find that the data prefers a model with a flat dependence of RV on colour over a declining dependence. Finally, our results strongly support that line-of-sight RV to SNe Ia is not a single value, but forms a distribution.
Effect of non-Gaussian lensing deflections on CMB lensing measurements
We investigate the impact of non-Gaussian lensing deflections on measurements of the CMB lensing power spectrum. We find that the false assumption of their Gaussianity significantly biases these measurements in current and future experiments at the percent level. The bias is detected by comparing CMB lensing reconstructions from simulated CMB data lensed with Gaussian deflection fields to reconstructions from simulations lensed with fully non-Gaussian deflection fields. The non-Gaussian deflections are produced by ray-tracing through snapshots of an N-body simulation and capture both the non-Gaussianity induced by non-linear structure formation and by multiple correlated deflections. We find that the amplitude of the measured bias is in agreement with analytical predictions by Bohm et al. 2016. The bias is largest in temperature-based measurements and we do not find evidence for it in measurements from a combination of polarization fields (EB;EB). We argue that the non-Gaussian bias should be even more important for measurements of cross-correlations of CMB lensing with low-redshift tracers of large-scale structure.
Impact Of Melter Internal Design On Off-Gas Flammability
The purpose of this study was to: (1) identify the more dominant design parameters that can serve as the quantitative measure of how prototypic a given melter is, (2) run the existing DWPF models to simulate the data collected using both DWPF and non-DWPF melter configurations, (3) confirm the validity of the selected design parameters by determining if the agreement between the model predictions and data is reasonably good in light of the design and operating conditions employed in each data set, and (4) run Computational Fluid Dynamics (CFD) simulations to gain new insights into how fluid mixing is affected by the configuration of melter internals and to further apply the new insights to explaining, for example, why the agreement is not good.
Standard High Solids Vessel Design Non-Newtonian Simulant Qualification
The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is working to develop a Standard High Solids Vessel Design process vessel. To support testing of this new design, WTP engineering staff requested that a non-Newtonian simulant be developed that would represent the Most Adverse Design Condition (MADC), denoted as MADC-4, with respect to mixing performance as specified by WTP. The majority of the simulant requirements are specified in 24590-PTF-RPT-PE-16-001, Rev. 0, and a discussion of suggested non-Newtonian rheological parameters for the simulant was provided in WTP RPT-241, Rev. 0. This document describes the simulant composition that will satisfy the basis requirement along with ancillary testing to assess the rheological behavior of the simulant depending on the formulation approach used, simulant aging, and the effects of dilution. From this data, a simulant recipe to meet the basis is recommended. The simulant recipe was designed to meet a target rheology (as expressed by Bingham model parameters) of 34.5 ± 1.5 Pa [yield stress] and 33.5 ±1.5 cP [consistency] at 25 °C.
Validation of time-dependent shift using the pulsed sphere benchmarks
The detailed behavior of neutrons in a rapidly changing time-dependent physical system is a challenging computational physics problem, particularly when using Monte Carlo methods on heterogeneous high-performance computing architectures. A small number of algorithms and code implementations have been shown to be performant for time-independent (fixed source and k-eigenvalue) Monte Carlo, and there are existing simulation tools that successfully solve the time-dependent Monte Carlo problem on smaller computing platforms. To bridge this gap, a time-dependent version of ORNL’s Shift code has been recently developed. Shift’s history-based algorithm on CPUs, and its event-based algorithm on GPUs, have both been observed to scale well to very large numbers of processors, which motivated the extension of this code to solve time-dependent problems. The validation of this new capability requires a comparison with time-dependent neutron experiments. Lawrence Livermore National Laboratory’s (LLNL) pulsed sphere benchmark experiments were simulated in Shift to validate both the time-independent as well as new time-dependent features recently incorporated into Shift. A suite of pulsed-sphere models was simulated using Shift and compared to the available experimental data and simulations with MCNP. Overall results indicate that Shift accurately simulates the pulsed sphere benchmarks, and that the new time-dependent modifications of Shift are working as intended. Validated exascale neutron transport codes are essential for a wide variety of future multiphysics applications.
MicroBooNE Public Data Sets: a Collaborative Tool for LArTPC Software Development
Among liquid argon time projection chamber (LArTPC) experiments MicroBooNE is the one that continually took physics data for the longest time (2015-2021), and represents the state of the art for reconstruction and analysis with this detector. Recently published analyses include oscillation physics results, searches for anomalies and other BSM signatures, and cross section measurements. LArTPC detectors are being used in current experiments such as ICARUS and SBND, and being planned for future experiments such as DUNE. MicroBooNE has recently released to the public two of its data sets, with the goal of enabling collaborative software developments with other LArTPC experiments and with AI or computing experts. These data sets simulate neutrino interactions on top of off-beam data, which include cosmic ray background and noise. The data sets are released in two formats: the native art/ROOT format used internally by the collaboration and familiar to other LArTPC experts, and the HDF5 format which contains reduced and simplified content and is suitable for usage by the broader community. This contribution presents the open data sets, discusses their motivation, the technical implementation, and the extensive documentation -- all inspired by FAIR principles. Finally, opportunities for collaborations are discussed.
Adding a MOAB Geometry Interface to SHARP Structural Mechanics
The authors briefly summarize the development of, and test experience with, an initial data interface between the structural mechanics code Diablo and the SHARP reactor simulation system data hub MOAB. That interface has been exercised both to write MOAB databases from Diablo, and then also to use such a database to read in part of a simulation definition for a subsequent Diablo execution. All enhancements are integrated into the central Diablo source repository. The SHARP software system for advanced simulation of nuclear reactors and power plant systems is sponsored by DOE's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SHARP has been architected as a federation of single-physics simulation tools to permit flexibility in programming languages and leveraging of past and on-going investments. Solution of multi-physics problems will be coordinated by, and data passed through, a central 'hub'. SHARP's hub implementation is utilizing MOAB: a Mesh-Oriented datABase. This same data hub approach is also intended to enable multi-resolution simulations, e.g, lower-dimension plant-scale simulations can be informed by high-fidelity 3D models of particular critical components.
FLUKA: Predictive power for cosmogenic backgrounds
The next generation of experiments searching for rare physics events with increased sensitivity will require precise predictions of cosmogenic backgrounds. Recent high quality deep underground measurements for cosmogenic neutrons in large liquid scintillator targets were used to study the FLUKA simulation package for this purpose. The results and conclusions drawn from a detailed benchmark comparison with data from the Borexino experiment were reported recently. In general, good agreement between data and simulation results were found with some identified discrepancies. Improved physics models already implemented in the current version of the FLUKA code, which will be publicly available with the upcoming code release, address the more important identified issues. A careful evaluation of the improved predictions is ongoing. However, the agreement between preliminary FLUKA simulation results and the Borexino experimental data are excellent. The preliminary findings will be discussed.
Semi-supervised graph neural networks for pileup noise removal
Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.
Simulation of the transport, vertical distribution, optical properties and radiative impact of smoke aerosols with the ALADIN regional climate model during the ORACLES-2016 and LASIC experiments
Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent ( ~0.7 over continental sources and ~0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m -2 over the SAO (20 ° S–10 ° N and 10 ° W–20 ° E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes ( -25 W m -2 ) and surface land temperature ( -1.5 ° C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ~200 m.
Unsupervised learning of representative local atomic arrangements in molecular dynamics data
Molecular dynamics (MD) simulations present a data-mining challenge, given that they can generate a considerable amount of data but often rely on limited or biased human interpretation to examine their information content. By not asking the right questions of MD data we may miss critical information hidden within it. Here we combine dimensionality reduction (UMAP) and unsupervised hierarchical clustering (HDBSCAN) to quantitatively characterize prevalent coordination environments of chemical species within MD data. By focusing on local coordination, we significantly reduce the amount of data to be analyzed by extracting all distinct molecular formulas within a given coordination sphere. We then efficiently combine UMAP and HDBSCAN with alignment or shape-matching algorithms to partition these formulas into structural isomer families indicating their relative populations. The method was employed to reveal details of cation coordination in electrolytes based on molecular liquids.
Optimal constraints on primordial gravitational waves from the lensed CMB
We demonstrate how to obtain optimal constraints on a primordial gravitational wave component in lensed Cosmic Microwave Background (CMB) data under ideal conditions. We first derive an estimator of the tensorto- scalar ratio, r, by using an error-controlled close approximation to the exact posterior, under the assumption of Gaussian primordial CMB and lensing deflection potential. This combines fast internal iterative lensing reconstruction with optimal recovery of the unlensed CMB. We evaluate its performance on simulated lownoise polarization data targeted at the recombination peak. We carefully demonstrate our r-posterior estimate is optimal and shows no significant bias, making it the most powerful estimator of primordial gravitational waves from the CMB. We compare these constraints to those obtained from B-mode band-power likelihood analyses on the same simulated data, before and after map-level quadratic estimator delensing, and iterative delensing. Internally, iteratively delensed band powers are only slightly less powerful on average (by less than 10%), promising close-to-optimal constraints from a stage IV CMB experiment.
In-situ strain behavior and BISON simulations of Zircaloy cladding subjected to temperature cycling separate-effects tests in a steam environment
Understanding fuel system performance during anticipated transients without scram (ATWSs) in boiling water reactors (BWRs) is necessary for refining current and future safety limits. High-fidelity material models and simulations are fundamental to rigorous assessment of zirconium-based cladding performance. However, experimental thermomechanical data during simulated ATWSs to validate these modes are limited. To provide relevant in-situ data, Zircaloy-4 cladding was subjected to cyclic heating in a steam environment to simulate an out-of-pile BWR ATWS. Digital image correlation was used to capture the cladding strain behavior in-situ for comparison against simulations using the BISON finite element code. Conventional high-temperature models were compared using multiple schemes to gain a better understanding of the applicability of three BISON models to BWR ATWS: (1) the default combination of creep models in BISON, (2) the high-temperature Erbacher model alone, and (3) the low-temperature Limback-Andersson model alone. The cases run with the Limback-Andersson model alone produced the lowest root mean square error (RMSE). The lowest RMSE for the Limback-Andersson model alone was 0.659%, and the highest RMSE reported was 4.22%. A data gap within the model in the temperature regime of interest was also identified, and to account for this gap, the current model in BISON is linearly interpolated between two separate datasets. In conclusion, this evaluation highlights the need to either develop a new model or to improve the existing model to capture transient creep effects resulting from a cyclic temperature transient.