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At least 487 records · Page 27

Source of Processable Vitrimer Viscosities: Swap Frequencies and Steric Factors

Vitrimers exhibit high, processable viscosities, where other polymers do not, and are among the most promising polymers for closed-loop material circularity. We sought to investigate the underlying chemical kinetic factors that result in high viscosities for vitrimers, which are crucial to designing vitrimers with tunable viscosity. To interrogate these factors, we achieved the first simulated predictions of real vitrimer viscosities, using a novel kinetic Monte Carlo molecular dynamics method, overcoming the time and length scale gaps to predict experimental bulk viscosities. The vitrimer architecture investigated is based on poly(dimethylsiloxane) chains and vinylogous urethane bond swaps. We probed the effects of the extent of free swapping groups, %F, the activation energy, E A , and the steric factor, ρ. The steric factor is related to the intrinsic reaction probability for molecules with sufficient energy. All three factors were found to be significant, but the role of ρ was found to be the biggest and also the most underappreciated. The results show that the inclusion of accurate ρ is of critical importance for viscosity predictions, with the evidence suggesting that the typical assumption of ρ = 1 is not valid for vitrimers and that, indeed, very low steric factors are present in bond-swap vitrimers such that values of ρ < 10 –10 may be typical. This greatly influences the bond exchange rates and, ultimately, the viscosities. Recognition of this result is necessary for the prediction of vitrimer viscosities from molecular simulations and to make vitrimers by design from molecular dynamics. We also investigated the effects that E A , ρ, and the number of free swapping groups have upon vitreous range temperatures, TV, with respect to achieving a specific viscosity (η V = 1 × 10 8 Pa·s), as well as for a commonly reported higher viscosity extrapolation (η V = 1 × 10 12 Pa·s). The evidence suggests that vitrimers may follow universal curves for E A vs T V , as a function of ρ. Finally, this study achieves the first of these comparisons of molecular simulations to experiments and reveals critical insights toward creating vitrimers by design, while providing a route for the prediction of T V from kinetic Monte Carlo molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

Ziabari, Amir↗

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

High fidelity simulations of contaminant dispersion in an urban environment with comparison to magnetic resonance imaging measurements

The dispersion of a contaminant in an urban environment has the potential to impact a large population of people. In this work, a complex urban canopy flow based on the Oklahoma City downtown business district circa 2003 is studied using Magnetic Resonance Imaging (MRI) and high-fidelity Large Eddy Simulations (LES). MRI is a novel experimental technique that can provide high-resolution measurements in four dimensions (three spatial and temporal) for lab scale models. The experiments and simulations use the same geometry and boundary conditions providing a one-to-one comparison of the two methods. Results are presented on the time-averaged velocity and concentration fields, the temporal dynamics of the concentration plumes for a transient release, and a novel Cloud Identification Algorithm that can separate plumes produced by periodic contaminant releases used for ensemble averaging over many releases. The MRI and LES datasets both include millions of measurement voxels and the comparisons highlight the complex 3D nature of the flow including strong vertical velocities in spanwise street canyons and flow acceleration in streamwise street canyons. The concentration fields are qualitatively similar albeit the LES shows larger dispersion. A quantitative analysis with performance measures compares the datasets pointwise and demonstrates that the two 3D datasets are similar with respect to many measures including a fractional bias of 0.02 (ideal=0.0), correlation coefficient of 0.87 (ideal = 1.0), and the fraction points within a factor of 2 is 0.98 (ideal = 1.0). Plume analysis compares the arrival and residence time of contaminant and is found to vary significantly with location within the urban environment with arrival times between 0 and 1.25 and differences within the contaminant cloud less than 10% at most locations.

54 ENVIRONMENTAL SCIENCES↗

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗

Charge lifetime improvement of the Continuous Electron Beam Accelerator Facility photogun with a biased anode

GaAs photocathodes in dc high-voltage photoguns are highly susceptible to ion back-bombardment, which reduces the photocathode quantum efficiency and limits the useful operating lifetime for producing polarized electron beams. This paper demonstrates that applying a small positive bias to the photogun anode can significantly suppress ion back-bombardment and increase charge lifetime. This technique was studied extensively using the Continuous Electron Beam Accelerator Facility photogun, where highly polarized electron beams created using a strained-superlattice GaAs / GaAsP photocathode were used and charge lifetimes improved by almost a factor of 2. A new simulation code was developed to model ion production and tracking in order to better understand and explain the factors that led to the performance improvement. Results of the experiments and simulations are discussed in detail. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

INL Report: Multiphysics Modeling and Simulation of Deimos, an Advanced Reactor Experiment

When designing a novel reactor, nuclear experiments are essential to validate the predictive capability of modeling/simulation tools and to justify the investment of a full-scale prototype. Being able to accurately predict temperature reactivity coefficients is of high importance to reactor designers as it impacts the safety and performance of the system. Deimos is a proposed graphite-moderated, beryllium-reflected, high assay low-enriched uranium (HALEU) tri-structural isotropic (TRISO) fueled experiment for NCERC. Deimos is a valuable experiment for validating the predictive performance of modeling and simulation tools since it comprises a relatively thermal energy spectrum with 84% thermal neutrons causing fission, 15% epithermal neutrons causing fission, and 1% fast neutrons causing fission. Additionally, by electrically heating the experiment, reactivity can be validated at various temperatures. Deimos will serve as a testbed for future advanced reactor concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Drive-pressure optimization in ramp-wave compression experiments through differential evolution

Ramp-wave dynamic-compression experiments are used to examine quasi-isentropic loading paths in materials. The gradual and continuous increase in pressure created by ramp waves make these types of experiments ideal for studying nonequilibrium material behavior, such as solidification kinetics. In ramp-wave compression experiments, the input drive pressure to the experimental setup may be exerted through one of a number of different mechanisms (e.g., magnetic fields, gas-gun-driven impactors, or high-energy lasers) and is generally required for simulating such experiments. Yet, regardless of the specific mechanism, this drive pressure cannot be measured directly (measurements are generally taken at a location near the back of the experimental setup through a transparent window), leading to an inverse problem where one must determine the drive pressure at the front of the experimental setup (i.e., the input) that corresponds to the particle velocity (the output) measured near the back of the experimental setup. Furthermore, we solve this inverse problem using a heuristic optimization algorithm, known as differential evolution, coupled with a multiphysics, hydrodynamics code that simulates the compression of the experimental setup. By running many rounds of forward simulations of the experimental setup, our optimization process iteratively searches for a drive pressure that is optimized to closely reproduce the experimentally measured particle velocity near the back of the experimental setup. While our optimization methodology requires a significant number of hydrodynamics simulations to be conducted, many of these can be performed in parallel, which greatly reduces the time cost of our methodology. One novel aspect of our method for determining the drive pressure is that it does not require physical modeling of the drive mechanism and can thus be broadly applied to many types of ramp-compression experiments, regardless of the drive mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Analysis of AP1000 Small-Break Loss-of-Coolant Accident Using Reactor Transient Simulator

The Westinghouse Electric Company’s Advanced Passive Reactor (AP1000) is characterized by the incorporation of passive safety systems (PSSs) designed to ensure core cooling during transient events. The assessment of PSSs requires evaluation of their performance through a combination of experiments and simulations employing various thermal-hydraulic codes. In addition, detailed evaluation of PSSs for a specific reactor system transient analysis such as loss-of-coolant-accident analysis supports understanding representative integral effects test facility development and the further evolution model development and assessment process. Developing a reactor system code is a complex and time-consuming process that requires significant engineering expertise and effort. It can take several months to even years to complete in the early stages of reactor system design and analysis. However, this process can be expedited through the use of transient simulator models for similar reactor systems, which can be used for lesson learning and training purposes. This study uses the Personal Computer Transient Analyzer (PCTRAN) code. The main advantage of PCTRAN is its ease of use and ability to run faster than real time. This study presents the results obtained for a small-break loss-of-coolant accident (SBLOCA) for two breaks using the full version (licensed) of PCTRAN. The purpose of this investigation is to evaluate the overall system behavior during the postulated SBLOCA event as well as assess the capability of the PCTRAN code to reproduce the system response during transient events. The obtained results were compared with the Westinghouse NOTRUMP system code. The PCTRAN code proved to be reliable in predicting the qualitative behavior of the system in both transient cases. As for the system response, it was found that it is contingent on the activation time of the PSSs. The differences in reactor coolant system pressure between the two codes were attributed to the critical flow model and simplification of mass and energy balance. Despite PCTRAN’s limitations, it can still provide a reasonable prediction of various reactor parameters such as pressure, mass flow rate, and void fraction during a SBLOCA scenario. It is worth noting that PCTRAN currently employs a bulk approach similar to that of the Modular Accident Analysis Program (MAAP) and MELCOR codes. However, the upcoming version of PCTRAN will include an artificial intelligence–based detection and accident prevention system, as well as different models for different reactor components. Consequently, PCTRAN has the potential to be upgraded to match the system thermal-hydraulic codes of the U.S. Nuclear Regulatory Commission and become more widely used in cybersecurity to safeguard nuclear power plants from cyberattacks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Discovery of Stacking Heterogeneity, Layer Buckling, and Residual Water in COF-999-NH 2 and Implications on CO 2 Capture

Covalent organic frameworks (COFs), with their modular architectures and tunable functionalities, provide a versatile platform to design sorbents for the direct capture of CO 2 from air. Here, for this work, we combined density functional theory, molecular dynamics, and grand canonical Monte Carlo simulations with experiment to understand structural factors for furthering COF-999-NH 2 ’s performance as the precursor to COF-999 for direct air CO 2 capture. Small energy differences among laterally shifted stackings suggest intrinsic stacking heterogeneity. The simulations show pronounced layer buckling coupled to extensive amine–nitrile hydrogen bonding and persistent pore water, which initiates undesired polymerization and undermines uptake. The predicted presence of water is confirmed by subsequent experiments. These insights point to a single, actionable design rule: exclude retained water by introducing hydrophobic pore environments to maximize the CO 2 capture efficiency.

adsorption↗

Wellbore injectivity response to step-rate CO 2 injection: Coupled thermo-poro-elastic analysis in a vertically heterogeneous formation

Safe and permanent carbon geological storages require an elaborate analysis of geomechanical stability. Subsurface injection of CO 2 changes the local temperature and pore pressure and, further, alters the stress due to thermo-poro-elastic responses. A permanent CO 2 storage testing was conducted in the water leg of Cranfield reservoir in Mississippi, USA, where hosted CO 2 enhanced-oil recovery activities. During CO 2 injection, the injection rate was ramped up twice but the bottom-hole pressure did not increase with the imposed injection rates as expected. This unexpected field observation suggests the possibility of an open-mode fracture development at the injector. However, the injector response and potential fracture development have not been rigorously interpreted with detailed near-wellbore temperature and pore pressure change upon the CO 2 injection. In this study, we performed history matching of CO 2 injection using coupled thermo-poro-elastic reservoir simulation to examine the possibility of fracturing. We built a reservoir model including vertical heterogeneity in both petrophysical and geomechanical properties estimated from well-logging analysis and laboratory experiments. The simulation results show that CO 2 injection changes stresses and support the hypothesis of development of an open-mode fracture at the injector during the Cranfield test. The near-injector region exhibits a large temperature reduction up to 55 °C with ensuing effective horizontal stress reduction up to 9.1 MPa. However, a caprock integrity issue is unlikely because of the horizontal stress contrast within layers and low hydraulic communication with the injection zone. This work indicates that the injectant temperature should be considered in the design of high-rate CO 2 injector.

58 GEOSCIENCES↗

Examining ICARUS Cosmic Muon Signal Shapes

In the search for new physics, such as sterile neutrinos, we must compare our experimental data to the case where this new physics does not exist, which is provided by simulations. However, our detectors and simulations are not perfect, so we need to be able to differentiate imperfections in our simulations, detector effects, and unknown unknowns from new physics. Thus, we need to quantify ICARUS detector systematic uncertainties; in other words, we need to know how much difference between experiment and simulation we can expect due to only detector systematics, so when we see differences greater than this, we can be confident they are due to new physics. To calculate this uncertainty, we study the signals produced in ICARUS by cosmic muons, since these muons are well understood. Ideally, we want to fit these waveforms as Gaussians and compare the fits from experimental data to fits from simulations to calculate the uncertainties, but first, we need to know how accurately these curve s can be described by Gaussians. We examined the peak and the full width at half maximum (FHWM) of signals from simulations, and we produced plots of the distribution of peaks and FHWMs for these signals. We further studied how the peaks and FHWMs varied depending on where the signal came from in the detector. Ultimately, by comparing the actual distribution of peaks and FHWMs to the distribution predicted by the Gaussian fits, we hope to determine how well these signals can be described as Gaussians.

Patino, Nicolas↗

Adaptive Computing and Multi-Fidelity Learning

We describe our ongoing research in adaptive computing. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. We will discuss a few application examples that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

97 MATHEMATICS AND COMPUTING↗

Density Functional Tight-Binding Simulations Reveal the Presence of Surface Defects on the Quartz (101)–Water Interface

Understanding the structure and reactivity of quartz–water interfaces is critical for numerous applications in the geological, environmental, and biological sciences. However, disagreements on the atomic-level structure of the interfaces between experiments and simulations are hampering our ability to predict the surface reactivity. Here, we used density functional tight-binding (DFTB)-based molecular dynamics to simulate a series of quartz (101) surfaces having different types and densities of surface defects in water and compared them with the structures determined by X-ray reflectivity measurements. The DFTB simulations are able to reproduce previous classical and quantum mechanical predictions of the pristine quartz (101)–water interface that disagree with experimental observations. To remedy this situation, a set of defective quartz surfaces having various surface silicon (Si) vacancies were built as indicated by recent experimental studies. We found that the rotation of surface [SiO 4 ] tetrahedra near Si vacancies can lead to outward displacements of Si atoms similar to those observed in the experiments. The presence of additional surface Si vacancies caused inward relaxations of terminal oxygens through the formation of hydrogen bonds. Finally, the overall results indicate that the quartz (101)–water interface may include a mixture of geminal (≡Si–(OH) 2 )- and vicinal (≡Si–OH)-type silanol groups together with the presence of surface Si vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

HL-2A's ELM cycle simulations by integrating BOUT++'s drift MHD and transport code

A new integrating model has been developed to couple tokamak edge multiscale magnetohydrodynamic (MHD) events and transport simulations, such as edge-localized mode (ELM) cycles. As a proof of principle, we first start from a set of three-field two-fluid model equations, which includes the pressure, current, and vorticity. Here, the equations are separated into the slowly evolving part of the axisymmetric component by taking a time average of the axisymmetric component. The time-averaged fluxes, which are quadratic in fluctuating quantities, act as driven terms for the time-averaged axisymmetric quantities that determine the plasma transport, and therefore the large-scale evolution of the plasma profiles. Then the HL-2A's ELM cycles are simulated using the model. Good agreements of ELM size and pedestal recovery time have been achieved for the solutions obtained from the coupled simulation compared with experiment. For one ELM cycle simulation, the coupled code can achieve a speedup of a factor of up to 30 over standalone code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Effects of Different Types of Entrances on Natural Ventilation in a Subway Station

In this study, the natural ventilation of a horizontal entrance of a typical subway station is investigated based on numerical simulations and experiments. In addition, a renormalization group k-e model (RNG k-e model) is applied to compute both the internal and external airflow patterns. Computational fluid dynamics (CFD) simulations are validated based on the experimental results. Furthermore, a multiple variable regression model is employed to study how the different parameters affect the internal airflow rates of the subway station statistically, according to the experimental results. For this typical model, the parameter importance can be ranked as follows: (1) outdoor wind speed; (2) flow resistance of the subway station; (3) height of the wind catcher; and (4) length of the wind catcher. To understand the detailed pressure distribution of the horizontal entrance of the subway station with and without the wind catcher, 3D numerical simulations are conducted for different scenarios. We attempt to alter the size of the wind catcher (including the length and height) to study the characteristics of the pressure on the horizontal entrance of the subway station under outdoor wind-driven conditions. The pressure distributions for the entrance for different scenarios are compared and analyzed. Finally, the interactions between the internal and external flows are investigated by changing the resistance of the subway station. When the internal flow resistance is changed, the pressure coefficient (Cp) of the entrance is different as well. Hence, the Cp is not only affected by the outdoor environment, but is also influenced by the internal airflows.

CFD↗

Understanding the Surprising Ionic Conductivity Maximum in Zn(TFSI) 2 Water/Acetonitrile Mixture Electrolytes

Aqueous electrolytes composed of 0.1 M zinc bis-(trifluoromethyl-sulfonyl)-imide (Zn-(TFSI) 2 ) and acetonitrile (ACN) were studied using combined experimental and simulation techniques. The electrolyte was found to be electrochemically stable when the ACN V% is higher than 74.4. In addition, it was found that the ionic conductivity of the mixed solvent electrolytes changes as a function of ACN composition, and a maximum was observed at 91.7 V% of ACN although the salt concentration is the same. This behavior was qualitatively reproduced by molecular dynamics (MD) simulations. Detailed analyses based on experiments and MD simulations show that at high ACN composition the water network existing in the high water composition solutions breaks. As a result, the screening effect of the solvent weakens and the correlation among ions increases, which causes a decrease in ionic conductivity at high ACN V%. Furthermore, this study provides a fundamental understanding of this complex mixed solvent electrolyte system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Comparison of Supervised and Un-Supervised Machine Learning Algorithms for Threat Detection and Scintillator Performance for Radiation Portal Monitoring

Following the events of September 11, 2001, international border crossing have been equipped with radiation portal monitors (RPMs) to identify illicit radioactive material. Polyvinyl toluene (PVT) scintillators are commonly used due to their low cost and reasonable maintainability, however they offer low spectral resolution. Despite the fact that over twenty years has transpired since this event, radioisotopes are still typically identified by hand-crafted classification algorithms, e.g., total counts or energy windowing, and exhibit relatively poor performance in detecting threats at the low false alarm rates required to support the stream of commerce. While some improvement to performance has been realized via the use of supervised machine learning, these classification algorithms typically utilize simulations in lieu of real data due to the sparsity of data for one or more classes. Accordingly, the performance of these algorithms is somewhat less than optimal when examining experiments or simulations with model mismatch. Consequently, in this work, we examine the application of a number of unsupervised machine learning, anomaly detection based algorithms, to circumvent the inverse crime when analyzing spectroscopy data for RPMs. We also compare anomaly detection results with those obtained via the use of supervised classification detection ML algorithms when model mismatch is introduced between the simulated threat items utilized for training/testing. Finally, we compared the performance of the PVT scintillators to those obtained with higher resolution detectors using both anomaly detection and supervised classification algorithms.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗