Disposal of commercial spent nuclear fuel in a geologic repository is studied. In situ heater experiments in underground research laboratories provide a realistic representation of subsurface behavior under disposal conditions. This study describes process model development and modeling analysis for a full-scale heater experiment in opalinus clay host rock. The results of thermal-hydrology simulation, solving coupled nonisothermal multiphase flow, and comparison with experimental data are presented. The modeling results closely match the experimental data.
In this work, the frequency response of a simplified shaft-bearing assembly is studied using numerical continuation. Roller-bearing clearances give rise to contact behavior in the system, and past research has focused on the nonlinear normal modes of the system and its response to shock-type loads. A harmonic balance method (HBM) solver is applied instead of a time integration solver, and numerical continuation is used to map out the system’s solution branches in response to a harmonic excitation. Stability analysis is used to understand the bifurcation behavior and possibly identify numerical or system-inherent anomalies seen in past research. Continuation is also performed with respect to the forcing magnitude, resulting in what are known as S-curves, in an effort to detect isolated solution branches in the system response.
Before residential photovoltaic (PV) systems are interconnected with the grid, various planning and impact studies are conducted on detailed models of the system to ensure safety and reliability are maintained. However, these model-based analyses can be time-consuming and error-prone, representing a potential bottleneck as the pace of PV installations accelerates. Data-driven tools and analyses provide an alternate pathway to supplement or replace their model-based counterparts. In this article, a data-driven algorithm is presented for assessing the thermal limitations of PV interconnections. Using input data from residential smart meters, and without any grid models or topology information, the algorithm can determine the nameplate capacity of the service transformer supplying those customers. The algorithm was tested on multiple datasets and predicted service transformer capacity with >98% accuracy, regardless of existing PV installations. This algorithm has various applications from model-free thermal impact analysis for hosting capacity studies to error detection and calibration of existing grid models.
Spatial navigation involves the formation of coherent representations of a map-like space, while simultaneously tracking current location in a primarily unsupervised manner. Despite a plethora of neurophysiological experiments revealing spatially-tuned neurons across the mammalian neocortex and subcortical structures, it remains unclear how such representations are acquired in the absence of explicit allocentric targets. Drawing upon the concept of predictive learning, we utilize a biologically plausible learning rule which utilizes sensory-driven observations with internally-driven expectations and learns through a contrastive manner to better predict sensory information. The local and online nature of this approach is ideal for deployment to neuromorphic hardware for edge-applications. We implement this learning rule in a network with the feedforward and feedback pathways known to be necessary for spatial navigation. After training, we find that the receptive fields of the modeled units resemble experimental findings, with allocentric and egocentric representations in the expected order along processing streams. These findings illustrate how a local and self-supervised learning method for predicting sensory information can extract latent structure from the environment.
To decarbonize the energy sector, there are international efforts to displace carbon-based fuels with renewable alternatives, such as hydrogen. Storage and transportation of gaseous hydrogen are key components of large-scale deployment of carbon-neutral energy technologies, especially storage at scale and transportation over long distances. Due to the high cost of deploying large-scale infrastructure, the existing pipeline network is a potential means of transporting blended natural gas-hydrogen fuels in the near term and carbon-free hydrogen in the future. Much of the existing infrastructure in North America was deployed prior to 1970 when greater variability existed in steel processing and joining techniques often leading to microstructural inhomogeneities and hard spots, which are local regions of elevated hardness relative to the pipe or weld. Hard spots, particularly in older pipes and welds, are a known threat to structural integrity in the presence of hydrogen. High-strength materials are susceptible to hydrogen-assisted fracture, but the susceptibility of hard spots in otherwise low-strength materials (such as vintage pipelines) has not been systematically examined. Assessment of fracture performance of pipeline steels in gaseous hydrogen is a necessary step to establish an approach for structural integrity assessment of pipeline infrastructure for hydrogen service. This approach must include comprehensive understanding of microstructural anomalies (such as hard spots), especially in vintage materials. In this study, fracture resistance of pipeline steels is measured in gaseous hydrogen with a focus on high strength materials and hardness limits established in common practice and in current pipeline codes (such as ASME B31.12). Elastic-plastic fracture toughness measurements were compared for several steel grades to identify the relationship between hardness and fracture resistance in gaseous hydrogen.
Single-axis solar trackers are typically simulated under the assumption that all modules on a given section of torque tube are at a single orientation. In reality, various mechanical effects can cause twisting along the torque tube length, creating variation in module orientation along the row. Simulation of the impact of this on photovoltaic system performance reveals that the performance loss resulting from torque tube twisting is significant at twists as small as fractions of a degree per module. The magnitude of the loss depends strongly on the design of the photovoltaic module, but does not vary significantly across climates. Additionally, simple tracker control setting tweaks were found to substantially reduce the loss for certain types of twist.
Measurements of the oxidation rates of various forms of carbon (soot, graphite, coal char) have often shown an unexplained attenuation with increasing temperatures in the vicinity of 2000 K, even when accounting for diffusional transport limitations and gas-phase chemical effects (e.g. CO2 dissociation). With the development of oxy-fuel combustion approaches for pulverized coal utilization with carbon capture, high particle temperatures are readily achieved in sufficiently oxygen-enriched environments. In this work, a new semi-global intrinsic kinetics model for high temperature carbon oxidation is created by starting with a previously developed 5-step mechanism that was shown to reproduce all major known trends in carbon oxidation, except for its high temperature kinetic falloff, and incorporating a recently discovered surface oxide decomposition step. The predictions of this new model are benchmarked by deploying the kinetic model in a steady-state reacting particle code (SKIPPY) and comparing the simulated results against a carefully measured set of pulverized coal char combustion temperature measurements over a wide range of oxygen concentrations in N2 and CO2 environments. The results show that the inclusion of the spontaneous surface oxide decomposition reaction step significantly improves predictions at high particle temperatures. Furthermore, the simulations reveal that O atoms released from the oxide decomposition step enhance the radical pool in the near-surface region and within the particle interior itself. Incorporation of literature rates for O and OH reactions with the carbon surface results in a reduction in the predicted radical pool concentrations and a very minor enhancement of the overall carbon oxidation rate.
The development of multi-axis force sensing ca-pabilities in elastomeric materials has enabled new types of human motion measurement with many potential applications. In this work, we present a new soft insole that enables mobile measurement of ground reaction forces (GRFs) outside of a lab-oratory setting. This insole is based on hybrid shear and normal force detecting (SAND) tactile elements (taxels) consisting of optical sensors optimized for shear sensing and piezoresistive pressure sensors dedicated to normal force measurement. We develop polynomial regression and deep neural network (DNN) GRF prediction models and compare their performance to ground-truth force plate data during two walking experiments. Utilizing a 4-layer DNN, we demonstrate accurate prediction of the anterior-posterior (AP), medial-lateral (ML) and vertical components of the GRF with normalized mean absolute errors (NMAE) of <5.1 %, 4.1 %, and 4.5%, respectively. We also demonstrate the durability of the hybrid SAND insole construction through more than 20,000 cycles of use.
Multiple scattering is a common phenomenon in acoustic media that arises from the interaction of the acoustic field with a network of scatterers. This mechanism is dominant in problems such as the design and simulation of acoustic metamaterial structures often used to achieve acoustic control for sound isolation, and remote sensing. In this study, we present a physics-informed neural network (PINN) capable of simulating the propagation of acoustic waves in an infinite domain in the presence of multiple rigid scatterers. This approach integrates a deep neural network architecture with the mathematical description of the physical problem in order to obtain predictions of the acoustic field that are consistent with both governing equations and boundary conditions. The predictions from the PINN are compared with those from a commercial finite element software model in order to assess the performance of the method.
Interim dry storage of spent nuclear fuel involves storing the fuel in welded stainless-steel canisters. Under certain conditions, the canisters could be subjected to environments that may promote stress corrosion cracking leading to a risk of breach and release of aerosol-sized particulate from the interior of the canister to the external environment through the crack. Research is currently under way by several laboratories to better understand the formation and propagation of stress corrosion cracks, however little work has been done to quantitatively assess the potential aerosol release. The purpose of the present work is to introduce a reliable generic numerical model for prediction of aerosol transport, deposition, and plugging in leak paths similar to stress corrosion cracks, while accounting for potential plugging from particle deposition. The model is dynamic (changing leak path geometry due to plugging) and it relies on the numerical solution of the aerosol transport equation in one dimension using finite differences. The model’s capabilities were also incorporated into a Graphical User Interface (GUI) that was developed to enhance user accessibility. Model validation efforts presented in this paper compare the model’s predictions with recent experimental data from Sandia National Laboratories (SNL) and results available in literature. We expect this model to improve the accuracy of consequence assessments and reduce the uncertainty of radiological consequence estimations in the remote event of a through-wall breach in dry cask storage systems.
In this work, we evaluate the usefulness of nonsmooth basis functions for representing the periodic response of a nonlinear system subject to contact/impact behavior. As with sine and cosine basis functions for classical Fourier series, which have C∞ smoothness, nonsmooth counterparts with C0 smoothness are defined to develop a nonsmooth functional representation of the solution. Some properties of these basis functions are outlined, such as periodicity, derivatives, and orthogonality, which are useful for functional series applied via the Galerkin method. Least-squares fits of the classical Fourier series and nonsmooth basis functions are presented and compared using goodness-of-fit metrics for time histories from vibro-impact systems with varying contact stiffnesses. This formulation has the potential to significantly reduce the computational cost of harmonic balance solvers for nonsmooth dynamical systems. Rather than requiring many harmonics to capture a system response using classical, smooth Fourier terms, the frequency domain discretization could be captured by a combination of a finite Fourier series supplemented with nonsmooth basis functions to improve convergence of the solution for contact-impact problems.
Anelastic strain recovery, the process of measuring the time dependent recovered strain after a core is cut at depth was utilized to make a measure of the in-situ properties stresses at depth at the FORGE (Frontier Observatory for Research in Geothermal Energy) site in Milford Utah. Core was collected from a region of well 16B at approximately 4860-4870 ft. Core was instrumented with strain gages within 10 hours of the core being cut. The relaxation of the cores was measured for approximately one month, and the results analyzed, which showed that the principal stresses were slightly off vertical, and magnitudes are close to equal.
Battery systems are typically equipped with state of charge (SoC) estimation algorithms. Sensor measurements used to estimate SoC are susceptible to false data injection attacks (FDIAs) that aim to disturb state estimation and, consequently, damage the system. In this paper, SoC estimation methods are re-purposed to detect FDIAs targeting the current and voltage sensors of a battery stack using a combination of an improved input noise aware unscented Kalman filter (INAUKF) and a cumulative sum detector. The root mean squared error of the states estimated by the INAUKF was at least 85% lower than the traditional unscented Kalman filter for all noise levels tested. The proposed method was able to detect FDIA in the current and voltage sensors of a series-connected battery stack in 99.55% of the simulations.
A Marx generator module from the decommissioned RITS pulsed power machine from Sandia National Labs was modified to operate in an existing setup at Texas Tech University. This will ultimately be used as a testbed for laser triggered gas switching. The existing experimental setup at Texas Tech University consists of a large Marx tank, an oil-filled coaxial pulse forming line, an adjustable peaking gap, and load section along with various diagnostics. The setup was previously operated at a lower voltage than the new experiment, so electrostatic modeling was done to ensure viability and drive needed modifications. The oil tank will house the modified RITS Marx. This Marx contains half as many stages as the original RITS module and has an expected output of 1 MV. A trigger Marx generator consisting of 8 stages has been fabricated to trigger the RITS Marx. Charging and triggering of both Marx generators will be controlled through a fiber optic network. The output from the modified RITS Marx will be used to charge the oil-filled coaxial line acting as a low impedance pulse forming line (PFL). Once charged, the self-breaking peaking gap will close, allowing the compressed pulse to be released into the load section. For testing of the Marx module and PFL, a match 10 Ω water load was fabricated. The output pulsewidth is 55 nsec. Diagnostics include two capacitive voltage probes on either side of the peaking gap, a quarter-turn Rogowski coil for load current measurement, and a Pearson coil for calibrations purposes.
We demonstrate evanescently coupled waveguide integrated silicon photonic avalanche photodiodes designed for single photon detection for quantum applications. Simulation, high responsivity, and record low dark currents for evanescently coupled devices are presented.
Underground caverns in salt formations are promising geologic features to store hydrogen (H2) because of salt's extremely low permeability and self-healing behavior.Successful salt-cavern H2 storage schemes must maximize the efficiency of cyclic injection-production while minimizing H2 loss through adjacent damaged salt.The salt cavern storage community, however, has not fully understood the geomechanical behaviors of salt rocks driven by quick operation cycles of H2 injection-production, which may significantly impact the cost-effective storage-recovery performance.Our field-scale generic model captures the impact of combined drag and back stressing on the salt creep behavior corresponding to cycles of compression and extension, which may lead to substantial loss of cavern volumes over time and diminish the cavern performance for H2 storage.Our preliminary findings address that it is essential to develop a new salt constitutive model based on geomechanical tests of site-specific salt rock to probe the cyclic behaviors of salt both beneath and above the dilatancy boundary, including reverse (inverse transient) creep, the Bauschinger effect and fatigue.
The Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) project's main objective was collecting data for validation of aerodynamic and aeroelastic codes for large, flexible rotors. These data come from scanning lidars of the inflow and wake, met tower, profiling lidar, blade deflection from photogrammetry, turbine SCADA data (including root bending loads), and hub-mounted SpinnerLidar inflow measurements. The goal of the present work is to analyze various methods to align the SpinnerLidar inflow data in time and space with individual blade loading. These methods would prove a way of analyzing turbine response while estimating the flowfield at each blade and provide a way of improving turbine response understanding using field data in real time, not just from simulations. The hub-mounted SpinnerLidar measures the inflow in the rotor frame meaning the locations of the blades relative to the measurement pattern do not change. The present work outlines some methods for correlating the SpinnerLidar inflow measurements with root bending loads in the rotor frame of reference accounting for both changes in wind speed and rotor speed from the measurement location one diameter upstream to each blade.
Gallium nitride (GaN)-based nanoscale vacuum electron devices, which offer advantages of both traditional vacuum tube operation and modern solid-state technology, are attractive for radiation-hard applications due to the inherent radiation hardness of vacuum electron devices and the high radiation tolerance of GaN. Here, we investigate the radiation hardness of top-down fabricated n-GaN nanoscale vacuum electron diodes (NVEDs) irradiated with 2.5-MeV protons (p) at various doses. We observe a slight decrease in forward current and a slight increase in reverse leakage current as a function of cumulative protons fluence due to a dopant compensation effect. The NVEDs overall show excellent radiation hardness with no major change in electrical characteristics up to a cumulative fluence of 5E14 p/cm2, which is significantly higher than the existing state-of-the-art radiation-hardened devices to our knowledge. The results show promise for a new class of GaN-based nanoscale vacuum electron devices for use in harsh radiation environments and space applications.
The MACCS code was created by Sandia National Laboratories for the U.S. Nuclear Regulatory Commission and has been used for emergency planning, level 3 probabilistic risk assessments, consequence analyses and other scientific and regulatory research for over half a century. Specializing in modeling the transport of nuclear material into the environment, MACCS accounts for atmospheric transport and dispersion, wet and dry deposition, probabilistic treatment of meteorology, exposure pathways, varying protective actions for the emergency, intermediate and long-term phases, dosimetry, health effects (including but not limited to population dose, acute radiation injury and increased cancer risk), and economic impacts. Routine updates and recent enhancements to the MACCS code, such as the inclusion of a higher fidelity atmospheric transport and dispersion model, the addition of a new economic impact model, and the application of nearfield modeling, have continuously increased the codes capabilities in consequence analysis. Additionally, investigations of MACCS capabilities for advanced reactor applications have shown that MACCS can provide realistic and informative risk assessments for the new generation of reactor designs. Even so, areas of improvement as well as gaps have been identified that if resolved can increase the usefulness of MACCS in any application regarding a release of nuclear material into the environment.
Operation and control of a galvanically isolated three-phase AC-AC converter for solid state transformer applications is described. The converter regulates bidirectional power transfer by phase shifting voltages applied on either side of a high-frequency transformer. The circuit structure and control system are symmetrical around the transformer. Each side operates independently, enabling conversion between AC systems with differing voltage magnitude, phase angle, and frequency. This is achieved in a single conversion stage with low component count and high efficiency. The modulation strategy is discussed in detail and expressions describing the relationship between phase shift and power transfer are presented. Converter operation is demonstrated in a 3 kW hardware prototype.
We present a materials study of AlGaInP grown on GaAs leveraging deep-level optical spectroscopy and time resolved photoluminescence. Our materials may serve as the basis for wide-bandgap analogs of silicon photomultipliers optimized for short wavelength sensing.
Sandia National Laboratories (SNL) has completed a comparative evaluation of three design assessment approaches for a 2-liter (2L) capacity containment vessel (CV) of a novel plutonium air transport (PAT) package designed to survive the hypothetical accident condition (HAC) test sequence defined in Title 10 of the United States (US) Code of Federal Regulations (CFR) Part 71.74(a), which includes a 129 meter per second (m/s) impact of the package into an essentially unyielding target. CVs for hazardous materials transportation packages certified in the US are typically designed per the requirements defined in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (B&PVC) Section III Division 3 Subsection WB “Class TC Transportation Containments.” For accident conditions, the level D service limits and analysis approaches specified in paragraph WB-3224 are applicable. Data derived from finite element analyses of the 129 m/s impact of the 2L-PAT package were utilized to assess the adequacy of the CV design. Three different CV assessment approaches were investigated and compared, one based on stress intensity limits defined in subparagraph WB-3224.2 for plastic analyses (the stress-based approach), a second based on strain limits defined in subparagraph WB-3224.3, subarticle WB-3700, and Section III Nonmandatory Appendix FF for the alternate strain-based acceptance criteria approach (the strain-based approach), and a third based on failure strain limits derived from a ductile fracture model with dependencies on the stress and strain state of the material, and their histories (the Xue-Wierzbicki (X-W) failure-integral-based approach). This paper gives a brief overview of the 2L-PAT package design, describes the finite element model used to determine stresses and strains in the CV generated by the 129 m/s impact HAC, summarizes the three assessment approaches investigated, discusses the analyses that were performed and the results of those analyses, and provides a comparison between the outcomes of the three assessment approaches.
We investigate the kinetics and report the time-resolved concentrations of key chemical species in the oxidation of tetrahydrofuran (THF) at 7500 torr and 450-675 K. Experiments are carried out using high-pressure multiplexed photoionization mass spectrometry (MPIMS) combined with tunable vacuum ultraviolet radiation from the Berkely Lab Advanced Light Source. Intermediates and products are quantified using reference photoionization (PI) cross sections, when available, and constrained by a global carbon balance tracking approach at all experimental temperatures simultaneously for the species without reference cross sections. From carbon balancing, we determine time-resolved concentrations for the ROO˙ and ˙OOQOOH radical intermediates, butanedial, and the combined concentration of ketohydroperoxide (KHP) and unsaturated hydroperoxide (UHP) products stemming from the ˙QOOH + O2 reaction. Furthermore, we quantify a product that we tentatively assign as fumaraldehyde, which arises from UHP decomposition via H2O or ˙OH + H loss. The experimentally derived species concentrations are compared with model predictions using the most recent literature THF oxidation mechanism of Fenard et al., (Combust. Flame, 2018, 191, 252-269). Our results indicate that the literature mechanism significantly overestimates THF consumption and the UHP + KHP concentration at our conditions. The model predictions are sensitive to the rate coefficient for the ROO˙ isomerization to ˙QOOH, which is the gateway for radical chain propagating and branching pathways. Comparisons with our recent results for cyclopentane (Demireva et al., Combust. Flame, 2023, 257, 112506) provide insights into the effect of the ether group on reactivity and highlight the need to determine accurate rate coefficients of ROO˙ isomerization and subsequent reactions.
Tabulated chemistry models are widely used to simulate large-scale turbulent fires in applications including energy generation and fire safety. Tabulation via piecewise Cartesian interpolation suffers from the curse-of-dimensionality, leading to a prohibitive exponential growth in parameters and memory usage as more dimensions are considered. Artificial neural networks (ANNs) have attracted attention for constructing surrogates for chemistry models due to their ability to perform high-dimensional approximation. However, due to well-known pathologies regarding the realization of suboptimal local minima during training, in practice they do not converge and provide unreliable accuracy. Partition of unity networks (POUnets) are a recently introduced family of ANNs which preserve notions of convergence while performing high-dimensional approximation, discovering a mesh-free partition of space which may be used to perform optimal polynomial approximation. We assess their performance with respect to accuracy and model complexity in reconstructing unstructured flamelet data representative of nonadiabatic pool fire models. Our results show that POUnets can provide the desirable accuracy of classical spline-based interpolants with the low memory footprint of traditional ANNs while converging faster to significantly lower errors than ANNs. For example, we observe POUnets obtaining target accuracies in two dimensions with 40 to 50 times less memory and roughly double the compression in three dimensions. We also address the practical matter of efficiently training accurate POUnets by studying convergence over key hyperparameters, the impact of partition/basis formulation, and the sensitivity to initialization.