Comparison of source-location algorithms for atmospheric samplers.

Numerous algorithms have been developed to determine the source characteristics for an atmospheric radionuclide release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Organization Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. A model (now called BAYEST) developed at Pacific Northwest National Laboratory uses a Bayesian formulation (Eslinger et al., 2019, 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021a, 2021b). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing.

Nuclear explosion monitoring network design considerations.

Design of an efficient monitoring network requires information on the type and size of releases to be detected, the accuracy and reliability of the measuring equipment, and the desired network performance. This work provides a scientific basis for optimizing or minimizing networks of 133Xe samplers to achieve a desired performance level for different levels of release. The approach of this work varies the density of sampling locations to find optimal location subsets, and to explore the properties of variations of those subsets - how crucial is a specific subset; are substitutions problematic? The choice of possible station locations is arbitrary but constrained to some extent by the location of islands, land masses, difficult topography (mountains, etc.) and the places where infrastructure exists to run and support a sampler. Performance is evaluated using hypothetical releases and atmospheric transport models that cover an entire year. Three network performance metrics are calculated: the probability of detecting the releases, the expected number of stations to detect the releases, and the expected number of samples that detect the releases. The quantitative measures support picking optimal or near-optimal network of a specific station density. If a detection probability of 90% (high) was desired for a design basis release of 1014 Bq (1% of 133Xe production from a 1 kt explosion), then a very high density would be required using today's sampling and measurement technology. If the design basis release were raised to 1015 Bq, then the station density could be lowered by a factor of 3. To achieve a location goal of three station detections on average, posited here for the first time, would also require very high station density for a release of 1014 Bq.

An open database of computed bulk ternary transition metal dichalcogenides.

We present a dataset of structural relaxations of bulk ternary transition metal dichalcogenides (TMDs) computed via plane-wave density functional theory (DFT). We examined combinations of up to two chalcogenides with seven transition metals from groups 4-6 in octahedral (1T) or trigonal prismatic (2H) coordination. The full dataset consists of 672 unique stoichiometries, with a total of 50,337 individual configurations generated during structural relaxation. Our motivations for building this dataset are (1) to develop a training set for the generation of machine and deep learning models and (2) to obtain structural minima over a range of stoichiometries to support future electronic analyses. We provide the dataset as individual VASP xml files as well as all configurations encountered during relaxations collated into an ASE database with the corresponding total energy and atomic forces. In this report, we discuss the dataset in more detail and highlight interesting structural and electronic features of the relaxed structures.

An experimental, computational, and uncertainty analysis study of the rates of iodoalkane trapping by DABCO in solution phase organic media.

NMR spectroscopy was used to measure the rates of the first and second substitution reactions between iodoalkane (R = Me, 1-butyl) and DABCO in methanol, acetonitrile and DMSO. Most of the reactions were recorded at three different temperatures, which permitted calculation of the activation parameters from Eyring and Arrhenius plots. Additionally, the reaction rate and heat of reaction for 1-iodobutane + DABCO in acetonitrile and DMSO were also measured using calorimetry. To help interpret experimental results, ab initio calculations were performed on the reactant, product, and transition state entities to understand structures, reaction enthalpies and activation parameters. Markov chain Monte Carlo statistical sampling was used to determine a distribution of kinetic rates with respect to the uncertainties in measured concentrations and correlations between parameters imposed by a kinetics model. The reactions with 1-iodobutane are found to be slower in all cases compared to reactions under similar conditions for iodomethane. This is due to steric crowding around the reaction centre for the larger butyl group compared to methyl which results in a larger activation energy for the reaction.

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms.

Optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC.

Enabling probabilistic retrospective transport modeling for accurate source detection.

Predicting source or background radionuclide emissions is limited by the effort needed to run gas/aerosol atmospheric transport models (ATMs). A high-performance surrogate model is developed for the HYSPLIT4 (NOAA) ATM to accelerate transport simulation through model reduction, code optimization, and improved scaling on high performance computing systems. The surrogate model parameters are a grid of short-duration transport simulations stored offline. The surrogate model then predicts the path of a plume of radionuclide particles emitted from a source, or the field of sources which may have contributed to a detected signal, more efficiently than direct simulation by HYSPLIT4. Termed the Atmospheric Transport Model Surrogate (ATaMS), this suite of capabilities forms a basis to accelerate workflows for probabilistic source prediction and estimation of the radionuclide atmospheric background.

Source term estimation using multiple xenon isotopes in atmospheric samples.

Algorithms that estimate the location and magnitude of an atmospheric release using remotely sampled air concentrations typically involve a single chemical or radioactive isotope. A new Bayesian algorithm is presented that makes discrimination between possible types of releases (e.g., nuclear explosion, nuclear power plant, or medical isotope production facility) an integral part of the analysis for samples that contain multiple isotopes. Algorithm performance is demonstrated using synthetic data and correctly discriminated between most release-type hypotheses, with higher accuracy when data are available on three or more isotopes.