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DOE OSTI2022

Radon-222 Charcoal Canister Steady State Model Calibrations Performed in a Highly Controlled Environmental Chamber and a Natural Indoor Environment

Charcoal canisters are a common method of 222 Rn screening. The calibrations of different batches of activated charcoal used in different canister designs are typically performed in large volume controlled environmental chambers with known and controlled radon concentration, temperature, and humidity. Radon screening could be facilitated in locations without ready access to environmental chambers if canisters could be accurately calibrated and undergo quality control in less controlled environments. This study compares charcoal canister calibrations from a highly controlled radon chamber with calibrations from a basement storage area experiencing temporally varying radon. In addition, the impacts of exposure time and spectral region of interest selection on calibration accuracy are investigated by comparing calibrations calculated using three different choices. Approximately 30 mo after calibrations were completed, groups of canisters were exposed for different durations of time to investigate measurement accuracy and calibration validity over an extended time. A digital simulation of charcoal canister kinetics was also performed to establish limits on how stable radon must be in a space for equilibrium-based calibration to be performed there. Overall, the accuracy of measurements using calibrations from each space differed by less than 10% after 2 d exposure time, showing that carefully controlled conditions are not necessary for the accurate calibration of charcoal canisters. Furthermore, measurement accuracy differed by less than 2% for different spectral region selections. Accuracy improved slightly with canister exposure duration. Simulations suggest that radon instability is most tolerable near the beginning of canister calibration exposures, but this merits further experimental study.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

nEXO: neutrinoless double beta decay search beyond 10 28 year half-life sensitivity

The nEXO neutrinoless double beta (0νββ) decay experiment is designed to use a time projection chamber and 5000 kg of isotopically enriched liquid xenon to search for the decay in 136 Xe. Progress in the detector design, paired with higher fidelity in its simulation and an advanced data analysis, based on the one used for the final results of EXO-200, produce a sensitivity prediction that exceeds the half-life of 10 28 years. Specifically, improvements have been made in the understanding of production of scintillation photons and charge as well as of their transport and reconstruction in the detector. The more detailed knowledge of the detector construction has been paired with more assays for trace radioactivity in different materials. In particular, the use of custom electroformed copper is now incorporated in the design, leading to a substantial reduction in backgrounds from the intrinsic radioactivity of detector materials. Furthermore, a number of assumptions from previous sensitivity projections have gained further support from interim work validating the nEXO experiment concept. Together these improvements and updates suggest that the nEXO experiment will reach a half-life sensitivity of 1.35 × 10 28 yr at 90% confidence level in 10 years of data taking, covering the parameter space associated with the inverted neutrino mass ordering, along with a significant portion of the parameter space for the normal ordering scenario, for almost all nuclear matrix elements. Furthermore, the effects of backgrounds deviating from the nominal values used for the projections are also illustrated, concluding that the nEXO design is robust against a number of imperfections of the model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Object detection with deep learning for rare event search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. Here, we present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Convolutional neural network↗
DOE OSTI2024

New Muon Campus Simulations for the Muon $g\textrm{-}2$ Experiment at Fermilab

The Muon $g\textrm{-}2$ Experiment (E989) at Fermilab conducted high-precision measurements of the muon anomalous magnetic moment $a_{\mu}$ using a storage ring from 2018 to 2023, achieving a remarkable precision of $200\:\mathrm{ppb}$ over Runs 1-3, with analyses for Runs 4-6 ongoing. A comprehensive understanding of the storage ring's beam dynamics and its accurate simulations are crucial for achieving the experiment's ambitious goals. One of the requirements for this effort is a very detailed knowledge of the phase space distribution of the beam. To address this requirement, we performed high-statistics simulations of the Muon $g\textrm{-}2$ Target Station (AP0) and the Muon Campus beamlines: M2 and M3, followed by the Delivery Ring, and then M4 and M5. The resulting muon distribution at the end of the M5 beamline from our previous $3\times10^{12}$ protons-on-target (PoT) simulation serves as an essential input for the storage ring simulations. In 2024, to facilitate the analyses of Runs 4-6, we have updated our Muon Campus models and re-optimised certain parameters to reflect the operational currents and wire chamber measurements of the beam. For these optimisations, we employed the heterogeneous island method, implemented in our evolutionary optimisation tool, glyfada. This key update addresses the need to use the best possible beam for the storage ring simulations and thus supports the experiment's overall precision. The Muon $g\textrm{-}2$ Experiment at Fermilab has successfully achieved its $70\:\mathrm{ppb}$ systematic uncertainty goal and collected 21 times more data than its predecessor at BNL. The updated and improved Muon Campus models and simulations not only facilitate the experiment's efforts to potentially resolve the current tension between experimental measurements and theoretical predictions of $a_{\mu}$, but also provide a basis for future simulations for the Mu2e Experiment (E-973), which utilises shared Muon Campus beamlines.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

COMPUTATIONAL MODELING OF IGNITION AND PREMIXED FLAME PROPAGATION INITIATED BY A PRE-CHAMBER TURBULENT JET

Addressing the pressing need for reduced carbon emissions, Turbulent Jet Ignition (TJI) emerges as a promising technology for ultra-lean combustion, offering enhanced thermal efficiencies and minimized cyclic variability in spark-ignited engines. To facilitate rapid testing and integration of this technology, a robust computational modeling framework is crucial. This study delves into the predictive capabilities of computational models for main-chamber ignition and premixed flame propagation using a single-cycle TJI rig measured by Biswas et al. (Applied Thermal Engineering, vol 106, 2016). Employing an open-source compressible flow simulation solver with Large Eddy Simulation (LES) for turbulence modeling, the investigation integrates the conventional Laminar Finite Rate Chemistry (LFRC) model alongside the transported Probability Density Method (PDF) for turbulence-chemistry interaction. A fully-consistent Eulerian Monte-Carlo Fields (EMCF) method is utilized to approximate the transported PDF, while Interaction by Exchange with Mean is employed to close micro-mixing terms in stochastic differential equations. A reduced chemical reaction mechanism with 21 species and 84 reactions (DRM-19) is used for solving chemical kinetics, and a double Gaussian energy deposition model is used to approximate the spark ignition in the pre-chamber. An unstructured O-grid mesh with 0.3 million cells in the prechamber and 1 million cells in the main chamber is employed. Results are divided into two phases: pre-chamber initialization and full TJI simulations. Validation of the predicted pre-chamber flame propagation and the lean ignition in the main-chamber is carried out by using available experimental data. Under quiescent conditions, both the LFRC and transported PDF methods largely underestimate the flame speed and subsequent pressure growth in the pre-chamber. A linear momentum forcing technique is applied to investigate the impact of initial turbulence in the pre-chamber, demonstrating a notable influence on flame propagation. Fine-tuning of the forcing coefficient reproduces the sudden pressure growth observed in the experiment. The experimentally validated pre-chamber simulation serves as the initial condition for the full TJI simulations. It is found that the LFRC model fails to predict lean-ignition in the main-chamber, resulting in a misfiring event. Incorporation of turbulence-chemistry interaction using the transported PDF method substantially improves the prediction of the ignition event in the main-chamber, achieving fair qualitative agreement and quantitative validation of combustion parameters within ±10% of the reported experimental data. The rich simulation results consisting of a full set of statistical description of the thermo-chemical states enable us to gain deep insights into the ignition mechanisms in the main chamber, which is limited when done experimentally. A novel dual ignition phenomenon is revealed in the TJI rig for the first time. Initially, a primary ignition kernel is formed at a downstream location which eventually detaches from the main jet. As the jet momentum decreases, a secondary ignition event follows, this time at a more upstream location which eventually combines with the primary ignition kernel to form a single connected flame front. Investigation of these ignition sequences in chemical composition space reveal distinct differences between the two. The primary ignition event in the main-chamber is followed by a large concentration of active radicals from the pre-chamber jet, accelerating the chain-branching steps, characterizing what has been referred to as flame ignition. In contrast, the secondary ignition occurs in the absence of active radicals in the pre-chamber jet, hence characterized as jet ignition. Further analysis of the effect of pre-chamber jet characteristics on lean ignition in the main-chamber is conducted by setting up cases with different initial pressure ratios (por) between the two chambers, a non-dimensional parameter, ranging from 1.2 to 3.2. As the initial pressure ratio increases, jet momentum increases, with dual ignition observed in cases above por= 2.2. Case with por= 3.2 lead to misfiring. The effect of ignition sequence on global combustion characteristics of TJI is analyzed. Dual ignition events lead to non-monotonicity in combustion characteristics such as global reaction progress variable, flame penetration, and global heat release rate. In dual ignition events, although the rate of fuel consumption and global heat release rate is initially lower, the secondary ignition leads to a sudden increase in flame surface area, resulting in a sudden jump and promoting the overall performance of the TJI system.

42 ENGINEERING↗
DOE OSTI2021

Unsupervised learning for identifying events in active target experiments

This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection Chamber (AT-TPC). The overarching goal is to group similar events in the early stages of the data analysis, thereby improving efficiency by limiting the computationally expensive processing of unnecessary events. The application of unsupervised clustering algorithms to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on 46 Ar is introduced. We explore the performance of autoencoder neural networks and a pre-trained VGG16 Simonyan and Zisserman (2015) convolutional neural network. We study clustering performance on both data from a simulated 46 Ar experiment, and real events from the AT-TPC detector. We find that a -means algorithm applied to simulated data in the VGG16 latent space forms almost perfect clusters. Additionally, the VGG16+-means approach finds high purity clusters of proton events for real experimental data. Here, we also explore the application of clustering the latent space of autoencoder neural networks for event separation. While these networks show strong performance, they suffer from high variability in their results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type.We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Measurement of Triple-Differential Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

We report the first measurement of the differential cross section $d^2σ(E_ν)/dcos(θ_µ)dP_µ$for inclusive muon neutrino charged-current scattering on argon. This measurement utilizes data from6.5×10 20 protons on target of exposure collected using the MicroBooNE liquid argon time projection chamber located on-axis in the Fermilab Booster Neutrino Beam with a mean neutrino energy of approximately 0.8 GeV. In this note, the mapping from reconstructed kinematics to truth quantities, particularly from reconstructed to true neutrino energy, is validated by comparing the distribution of reconstructed hadronic energy in data to that of the model prediction in different muon scattering angle bins after conditional constraint from the muon momentum distribution in data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well-modeled within its uncertainties in simulation, enabling the unfolding to an energy-dependent triple-differential measurement over muon momentum, muon scattering angle, and neutrino energy. The unfolded measurement covers an extensive phase space, providing a wealth of information useful for future LArTPC experiments measuring neutrino oscillations. Comparisons with model predictions show the best agreement with Neut 5.4.0.1 at low energy, and with NuWro 19.02.01 at higher energies, particularly at forward muon scattering angles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Clustering of electromagnetic showers and particle interactions with graph neural networks in liquid argon time projection chambers

Liquid argon time projection chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. In these images, the clustering of distinct particles into superstructures is of central importance to the current and future neutrino physics program. Electromagnetic (EM) activity typically exhibits spatially detached fragments of varying morphology and orientation that are challenging to efficiently assemble using traditional algorithms. Similarly, particles that are spatially removed from each other in the detector may originate from a common interaction. Graph neural networks (GNNs) were developed in recent years to find correlations between objects embedded in an arbitrary space. The graph particle aggregator (GrapPA) first leverages GNNs to predict the adjacency matrix of EM shower fragments and to identify the origin of showers, i.e., primary fragments. On the PILArNet public LArTPC simulation dataset, the algorithm achieves a shower clustering accuracy characterized by a mean purity of 99.4%, a mean efficiency of 99.6% and a primary identification accuracy of 99.8%. It yields a relative shower energy uncertainty of (4.1 + 1.4 / $\sqrt{\text{E(GeV)})}$% and a shower direction uncertainty of (2.1/ $\sqrt{\text{E(GeV)})}$°. Finally, the optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean purity of 99.8% and a mean efficiency of 99.5% for an interaction density of $\mathcal{O}(1)$ m –3 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Numerical study of advanced compression ignition and combustion in a gasoline direct injection engine

Multi-mode combustion strategies may provide a considerable thermal efficiency improvement targeted at part-load operating conditions for light-duty spark-ignition (SI) engines. The extension from boosted SI mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing compression ratio and utilizing intake air heating. In order to enable an accurate control of intake charge condition for ACI control and rapid mode-switches, it is essential to gain fundamental insight into the autoignition process with regard to thermal and fuel-air mixture stratification in the combustion chamber. In this work, a computational fluid dynamics (CFD) study is carried out to reveal some degrees of correlation between the mixture and thermal stratifications induced by the cylinder wall temperature and combustion-phasing across varying engine load conditions. The computational analysis begins with a calibrated simulation setup best matching the engine experiments, and subsequently evaluates the baseline setup for an extended range of engine load conditions for two excess-air ratio cases. The present study emphasizes the dominance of thermal stratifications for autoignition process due to wall temperature effects depending on the engine load conditions of interest. In addition, this study also aims to provide insight into the impact of mixture stratification on cyclic variability, especially at colder wall engine conditions (e.g., cold start). Observations herein provide highlight of the propensity of autoignition in correlation with mixture reactivity space.

Advanced Compression Ignition (ACI) engine↗
DOE OSTI2024

New constraints on ultraheavy dark matter from the LZ experiment

Searches for dark matter with liquid xenon time projection chamber experiments have traditionally focused on the region of the parameter space that is characteristic of weakly interacting massive particles, ranging from a few GeV / c 2 to a few TeV / c 2 . Models of dark matter with a mass much heavier than this are well motivated by early production mechanisms different from the standard thermal freeze-out, but they have generally been less explored experimentally. In this work, we present a reanalysis of the first science run of the LZ experiment, with an exposure of 0.9 tonne × yr , to search for ultraheavy particle dark matter. The signal topology consists of multiple energy deposits in the active region of the detector forming a straight line, from which the velocity of the incoming particle can be reconstructed on an event-by-event basis. Zero events with this topology were observed after applying the data selection calibrated on a simulated sample of signal-like events. New experimental constraints are derived, which rule out previously unexplored regions of the dark matter parameter space of spin-independent interactions beyond a mass of 10 17 GeV / c 2 . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Detection prospects for the second-order weak decays of 124 Xe in multi-tonne xenon time projection chambers

We investigate the detection prospects for two-neutrino and neutrinoless second-order weak decays of 124 Xe – double-electron capture (0/2νECEC), electron capture with positron emission (0/2νECβ + ) and double-positron emission (0/2νβ + β + ) – in multi-tonne xenon time projection chambers. We simulate the decays in a liquid xenon medium and develop a reconstruction algorithm which uses the multi-particle coincidence in these decays to separate signal from background. This is used to compute the expected detection efficiencies as a function of position resolution and energy threshold for planned experiments. In addition, we consider an exhaustive list of possible background sources and find that they are either negligible in rate or can be greatly reduced using our topological reconstruction criteria. In particular, we draw two conclusions: First, with a theoretical half-life of $T^{2νECβ^+}_{1/2}$= (1.7 ± 0.6) ∙10 23 year, the 2νECβ + decay of 124 Xe will likely be detected in upcoming Dark Matter experiments (e.g. XENONnT or LZ), and their major background will be γ-rays from detector construction materials. Second, searches for the 0νECβ + decay mode will likely be background-free, and new parameter space may be within reach. To this end we investigate two different scenarios of existing experimental constraints on the effective neutrino mass. The necessary 500 kg-year exposure of 124 Xe could be achieved by the baseline design of the DARWIN observatory, or by extracting and using the 124 Xe from the tailings of the nEXO experiment. We demonstrate how a combination of 124 Xe results with those from 0νβ - β - searches in 136 Xe could help to identify the neutrinoless decay mechanism.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗
DOE OSTI2020

Neutronics Analysis of Cold Critical KRUSTY Experiments using MCNP and Serpent

Kilowatt Reactor Using Stirling Technology (KRUSTY) is a prototype designed as a proof of concept for NASA’s Kilo power program, which was funded in order to create a small reactor for various space applications such as providing power for a colony on Mars or the moon and eventually as a possible option for powering rockets on deep space missions. KRUSTY was tested at the National Criticality Experiments Research Center (NCERC) located at the Nevada National Security Site (NNSS) from November 2017, through March 2018. The experimental campaign was conducted in four phases: com ponent critical experiments, cold critical experiments, warm critical runs, and high temperature demonstration. The component critical and cold critical experiments were used to determine the worth of the beryllium oxide (BeO) reflector rings and the boron carbide (B4C) control rod disks under different conditions. These conditions were altered by the addition and subtraction of other components in the assembly. The component critical phase was performed at atmospheric pressure and had four solid support rods spanning the center core; whereas the cold critical phase had the center core placed in a vacuum chamber and eight sodium-filled heat pipes spanning the center core region. This summary discusses the static neutronic analysis completed on the cold critical phase of the campaign using MCNP ® and Serpent. These two codes were used to assess any potential differences or biases in the simulation results. Understanding these potential biases is a key step toward the final goal of being able to fully analyze the transient experiments performed during phases 3 and 4 of the campaign.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

AMORE-Isoprene v1.0: a new reduced mechanism for gas-phase isoprene oxidation

Abstract. Gas-phase oxidation of isoprene by ozone (O3) and the hydroxyl (OH) and nitrate (NO3) radicals significantly impacts tropospheric oxidant levels and secondary organic aerosol formation. The most comprehensive and up-to-date chemical mechanism for isoprene oxidation consists of several hundred species and over 800 reactions. Therefore, the computational expense of including the entire mechanism in large-scale atmospheric chemical transport models is usually prohibitive, and most models employ reduced isoprene mechanisms ranging in size from ∼ 10 to ∼ 200 species. We have developed a new reduced isoprene oxidation mechanism using a directed-graph path-based automated model reduction approach, with minimal manual adjustment of the output mechanism. The approach takes as inputs a full isoprene oxidation mechanism, the environmental parameter space, and a list of priority species which are protected from elimination during the reduction process. Our reduced mechanism, AMORE-Isoprene (where AMORE stands for Automated Model Reduction), consists of 12 species which are unique to the isoprene mechanism as well as 22 reactions. We demonstrate its performance in a box model in comparison with experimental data from the literature and other current isoprene oxidation mechanisms. AMORE-Isoprene's performance with respect to predicting the time evolution of isoprene oxidation products, including isoprene epoxydiols (IEPOX) and formaldehyde, is favorable compared with other similarly sized mechanisms. When AMORE-Isoprene is included in the Community Regional Atmospheric Chemistry Multiphase Mechanism 1.0 (CRACMM1AMORE) in the Community Multiscale Air Quality Model (CMAQ, v5.3.3), O3 and formaldehyde agreement with Environmental Protection Agency (EPA) Air Quality System observations is improved. O3 bias is reduced by 3.4 ppb under daytime conditions for O3 concentrations over 50 ppb. Formaldehyde bias is reduced by 0.26 ppb on average for all formaldehyde measurements compared with the base CRACMM1. There was no significant change in computation time between CRACMM1AMORE and the base CRACMM. AMORE-Isoprene shows a 35 % improvement in agreement between simulated IEPOX concentrations and chamber data over the base CRACMM1 mechanism when compared in the Framework for 0-D Atmospheric Modeling (F0AM) box model framework. This work demonstrates a new highly reduced isoprene mechanism and shows the potential value of automated model reduction for complex reaction systems.

58 GEOSCIENCES↗
DOE OSTI2020

Accelerated Aging Humidity Chamber for Nuclear Grade HEPA Filter Media

A unique accelerated aging humidity chamber for simulating the natural aging process of nuclear grade high efficiency particulate air (HEPA) filter media is being designed, implemented, and tested. The nuclear industry currently lacks information regarding the shelf life and service life of HEPA filter media. The Department of Energy recommends disposing of HEPA filters 10 years after the manufacture date for filters operated under dry conditions. Studies have shown the tensile strength and water repellency of HEPA filter media diminish with age. The chamber's principal function is to sustain an elevated relative humidity condition for an extended duration specified by the Arrhenius aging model. To accomplish this, various design parameters were established: (1) consistently achieve uniform humidity conditions within the chamber to equally expose media sheets, (2) ensure the chamber is sufficiently sealed to mitigate the uncontrolled infiltration of air, (3) confidently secure the media sheets to avert the introduction of unnecessary stressing/creasing, (4) fabricate the chamber from clear polycarbonate material to allow inspection of the media sheets, (5) provide a safe and ergonomic design for personnel, (6) ensure the chamber design is replicable and relatively easy to fabricate/assemble. A humidity source, fan, PID controller, humidity and temperature transmitters, and data loggers are required for proper operation and control of the chamber. The humidity source must accommodate target values up to 95% relative humidity throughout the chamber. The three humidity and temperature transmitters are strategically spaced throughout the chamber to ensure uniform conditions. The 10 cubic feet per minute (CFM) fan selected intends to accomplish the desired air change rate inside the chamber of approximately 20 air changes per hour (ACH). The 2 plenums on the upper and lower portions of the chamber utilize a perforated design to uniformly distribute air, promote air mixing, and control the air velocity entering the chamber. Industrial blueprint hanging clamps are being retrofitted to be seated within the chamber drawers and firmly secure the nuclear grade filter media. The aged media will be evaluated using autopsy methodology, and the results obtained intend to help clarify the useful life of nuclear grade HEPA filters. Nuclear grade high efficiency particulate air (HEPA) filters are defined as disposable, extended-media, dry-type filters with a rigid casing enclosing the full depth of the pleats and have a minimum particle removal efficiency of 99.97%. [1] - HEPA filters are constantly exposed to stressors and subsequently have been shown to degrade as the filter ages. Properties such as tensile strength and water repellency, along with others, are negatively impacted. [2] - Due to the scarcity of naturally aged HEPA filters available, it is crucial to develop accelerated aging methods which effectively mimic the natural degradation effects. - After accelerated aging, the filters are subject to testing in the Axial Flow Large Scale Test Stand (ALSTS) and other analysis techniques such as the same qualification tests performed directly after being manufactured. - Quality data is necessary for future decisions regarding the lifetime of nuclear grade HEPA filters. - The objective is to develop a prototype accelerated aging humidity chamber for nuclear grade HEPA filter media. The chamber should expose the media to commonly occurring stressors to expedite the degradation process caused by aging. PID yields reliable and reproducible results: - CFD model correlates well with actual performance; - Further analysis is needed to complete characterization; - Future work for the chamber is still to be completed. The data shown in Figure 5 and Table 1 indicate the system is functioning as designed. As can be determined from the system response evaluation in Table 1, the PID controller is yielding reliable and reproducible results at each set point. As the set point increases, the time constant and 20% settling time increase accordingly. From the results in Table 1, it stands to reason that the CFD model correlates well with the actual performance of the system. Each system response parameter for the 90%RH* CFD model is slightly lower than the parameters collected from the physical model tested at an equivalent set point. This is to be expected considering the inlet in the CFD model is a constant supply of water vapor, whereas the physical model uses a PID controller to monitor the amount of water vapor needed and reacts accordingly. More analysis is still to be done on the prototype chamber. A pressure test will be conducted in order to determine the hourly leak rate of the chamber. This test will involve constant air flow into the chamber until the desired pressure is reached. A uniformity test will also be conducted in order to ensure each filter medium is being exposed to equivalent relative humidity levels. This test will involve an array of humidity meters strategically oriented inside the chamber to record any possible gradients. Future work for the chamber includes dehumidification abilities, tests while fully loaded with filters, and temperature control. Once completed, the autopsy team at Institute of Clean Energy Technology will conduct necessary accelerated aging studies as needed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Overview of the MAST Upgrade physics programme: testing novel concepts at low aspect ratio to inform future devices

The research programme performed on the Mega Amp Spherical Tokamak (MAST) Upgrade experiment has made significant advances in developing the physics understanding of low aspect ratio tokamaks in support of the operation of ITER and design of fusion powerplants. High performance plasma scenarios have been developed to facilitate a broad programme of experiments, in which confinement is constrained by the presence of m/n = 2/1 modes that cause substantial losses of fast ions. The onset of these modes coincides with the q = 2 surface residing in a local minimum in the toroidal current density profile. The maximum electron temperature at the pedestal top, T e,ped is limited with gas fuelling to ∼350 eV to maintain regular ELMs; higher T e,ped results in a transition to a non-stationary ELM-free regime. The operational space of spherical tokamaks has been expanded into small and ELM-free regimes. Strong shaping of the last closed flux surface can induce a transition from large to small ELMs, and ELM suppression with resonant magnetic perturbations has been observed for the first time in a low aspect ratio tokamak. Negative triangularity shaping has induced a transition from ELMy H-mode to a high-performance L-mode regime for the first time in a low aspect ratio tokamak. In studies of fast ion confinement, losses of fast particles due to Global Alfvén Eigenmodes have been identified. Interactions between fast ions generated by off-axis neutral beam injection and thermal neutrals can result in significant losses of fast ions. Experiments with on- and off-axis neutral beam injection exhibit a flux pumping mechanism, where the central safety factor is held to ∼1 in the absence of sawteeth. In studies of pedestal physics, it has been found that elevated main chamber neutral pressures result in an increase in the electron density and reduction in the temperature at the pedestal top. Advances in understanding plasma exhaust include the integration of a high-performance plasma core with detached outer divertors in the X-point target configuration. A newly commissioned lower divertor cryopump reduces the lower divertor neutral pressure by up to 50%, with minimal effect on the main chamber or upper divertor. New measurements and SOLPS-ITER simulations emphasise the importance of plasma–neutral interactions on divertor detachment in the conditions accessible in experiments. Real-time control of the ionisation front location in both divertor chambers independently has been demonstrated in double null experiments, enabled by the tightly baffled divertor chambers.

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DOE OSTI2022

Light and Dark in Liquid Argon Time Projection Chamber Neutrino Detectors

Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors have emerged as a detector technology poised to perform measurements of neutrino interactions with unprecedented precision and as a result answer some of the largest open questions in neutrino physics in the coming decade. This thesis describes methods developed to tackle the computational challenges faced as LArTPC detectors increase in scale and complexity moving towards the multi-kiloton DUNE detectors. These include a new, approximated, model that enables rapid simulation of scintillation light in very large scale detectors, as well as the first demonstration of running the LArSoft software framework on a high performance computer. The high precision of LArTPC detectors designed for next-generation neutrino measurements enables them to also search for Beyond the Standard Model physics produced in high-energy proton--fixed-target collisions in neutrino beams. This thesis presents searches for two dark-sector models performed with the ArgoNeuT experiment: Heavy Neutral Leptons and Heavy QCD Axions. Between them, these models can provide solutions for various unresolved puzzles including neutrino mass generation, the baryon asymmetry of the universe, dark matter and the strong CP problem. In both cases, the dark-sector particles could be produced in the NuMI neutrino beam and can then decay to a pair of oppositely charged muons observable in ArgoNeuT and the downstream MINOS near detector. Both measurements required the development of novel experimental selection techniques and enabled new constraints to be set on the existence of these particles in previously unexplored parameter-space. These searches are both the first of their kind in LArTPC neutrino detectors and pave the way for searches at future neutrino facilities.Date of Award31 Dec 2022Original languageEnglishAwarding InstitutionThe University of ManchesterSupervisorStefan Soldner-Rembold (Supervisor) & Justin Evans (Supervisor)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗