Publications

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 paper, we develop a nested chi-squared likelihood ratio test for selecting among shrinkage-regularized covariance estimators for background modeling in hyperspectral imagery. Critical to many target and anomaly detection algorithms is the modeling and estimation of the underlying background signal present in the data. This is especially important in hyperspectral imagery, wherein the signals of interest often represent only a small fraction of the observed variance, for example when targets of interest are subpixel. This background is often modeled by a local or global multivariate Gaussian distribution, which necessitates estimating a covariance matrix. Maximum likelihood estimation of this matrix often overfits the available data, particularly in high dimensional settings such as hyperspectral imagery, yielding subpar detection results. Instead, shrinkage estimators are often used to regularize the estimate. Shrinkage estimators linearly combine the overfit covariance with an underfit shrinkage target, thereby producing a well-fit estimator. These estimators introduce a shrinkage parameter, which controls the relative weighting between the covariance and shrinkage target. There have been many proposed methods for setting this parameter, but comparing these methods and shrinkage values is often performed with a cross-validation procedure, which can be computationally expensive and highly sample inefficient. Drawing from Bayesian regression methods, we compute the degrees of freedom of a covariance estimate using eigenvalue thresholding and employ a nested chi-squared likelihood ratio test for comparing estimators. This likelihood ratio test requires no cross-validation procedure and enables direct comparison of different shrinkage estimates, which is computationally efficient.

In many applications, one can only access the inexact gradients and inexact hessian times vector products. Thus it is essential to consider algorithms that can handle such inexact quantities with a guaranteed convergence to solution. An inexact adaptive and provably convergent semismooth Newton method is considered to solve constrained optimization problems. In particular, dynamic optimization problems, which are known to be highly expensive, are the focus. A memory efficient semismooth Newton algorithm is introduced for these problems. The source of efficiency and inexactness is the randomized matrix sketching. Applications to optimization problems constrained by partial differential equations are also considered.

A new Adaptive Mesh Refinement (AMR) keyword was added to the CTH¹ hydrocode developed at Sandia National Laboratories (SNL). The new indicator keyword, "ratec*ycle", allows the user to specify the minimum number of computational cycles before an AMR block is allowed to be un-refined. This option is designed to allow the analyst to control how quickly a block is un-refined to avoid introducing anomalous waves in their solution due to information propagating across mesh resolution changes. For example, in reactive flow simulations it is often desirable to accurately capture the expansion region behind the reaction front. The effect of this new option was examined using the XHVRB^{2, 3} model for XTX8003 to model the propagation of the detonation wave in explosives in small channels, and also for a simpler explosive model driving a steel case. The effect on computational cost as a function of this new option was also examined.

Rotational testbeds are ubiquitous in the test and evaluation of inertial sensors and systems. However, the use of rotational testbeds is typically restricted to static states employed over long integration windows to allow for data aggregation. These methods ignore the transitions between states, and data aggregation masks potentially useful signals. In this paper, we discuss the development of modular equations for the description of the inertial inputs to a test sensor using any rotational testbed. Implementing our equations in software, specific force and angular rates can be computed from idealized table motion or measured encoder data. Results are presented using simulated data and measured data. The measured data was acquired using a three axis rate table and a MEMS IMU. The experimental results validate our model equations and demonstrate the benefits of modeling sensor inputs at the sensor rates to compensate for testbed errors.

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.

Over the past few years, advancements in closed-loop geothermal systems (CLGS), also called advanced geothermal systems (AGS), have sparked a renewed interest in these types of designs. CLGS have certain advantages over traditional and enhanced geothermal systems (EGS), including not requiring in-situ reservoir permeability, conservation of the circulating fluid, and allowing for different fluids, including working fluids directly driving a turbine at the surface. CLGS may be attractive in environments where water resources are limited, rock contaminants must be avoided, and stimulation treatments are not available (e.g., due to regulatory or technical reasons). Despite these advantages, CLGS have some challenges, including limited surface area for heat transfer and requiring long wellbores and laterals to obtain multi-MW output in conduction-only reservoirs. CLGS have been investigated in conduction-only systems. In this paper, we explore the impact of both forced and natural convection on the levels of heat extraction with a CLGS deployed in a hot wet rock reservoir. We bound potential benefits of convection by investigating liquid reservoirs over a range of natural and forced convective coefficients. Additionally, we investigate the effects of permeability, porosity, and geothermal temperature gradient in the reservoir on CLGS outputs. Reservoir simulations indicate that reservoir permeabilities of at least ~100 mD are required for natural convection to increase the heat output with respect to a conduction-only scenario. The impact increases with increasing reservoir temperature. When subject to a forced convection flow field, Darcy velocities of at least 10-7 m/s are required to obtain an increase in heat output.

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.

Abstract not provided.

We demonstrate for the first time waveguide integrated cascaded germanium photodetector arrays operated as photocells. We characterize several different array designs, and discuss their effects on voltage and photocurrent performance parameters.

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.

Vapor-deposited PETN films undergo significant microstructure evolution when exposed to elevated temperatures, even for short periods of time. This accelerated aging impacts initiation behavior and can lead to chemical changes as well. In this study, as-deposited and aged PETN films are characterized using scanning electron microscopy and ultra-high performance liquid chromatography and compared with changes in initiation behavior measured via a high-throughput experimental platform that uses laser-driven flyers to sequentially impact an array of small explosive samples. Accelerated aging leads to rapid coarsening of the grain structure. At longer times, little additional coarsening is evident, but the distribution of porosity continues to evolve. These changes in microstructure correspond to shifts in the initiation threshold and onset of reactions to higher flyer impact velocities.

Resonant plate shock testing techniques have been used for mechanical shock testing at Sandia for several decades. A mechanical shock qualification test is often done by performing three separate uniaxial tests on a resonant plate to simulate one shock event. Multi-axis mechanical shock activities, in which shock specifications are simultaneously met in different directions during a single shock test event performed in the lab, are not always repeatable and greatly depend on the fixture used during testing. This chapter provides insights into various designs of a concept fixture that includes both resonant plate and angle bracket used for multi-axis shock testing from a modeling and simulation point of view based on the results of finite element modal analysis. Initial model validation and testing performed show substantial excitation of the system under test as the fundamental modes drive the response in all three directions. The response also shows that higher order modes are influencing the system, the axial and transverse response are highly coupled, and tunability is difficult to achieve. By varying the material properties, changing thicknesses, adding masses, and moving the location of the fixture on the resonant plate, the response can be changed significantly. The goal of this work is to identify the parameters that have the greatest influence on the response of the system when using the angle bracket fixture for a mechanical shock test for the intent of tunability of the system.

For an Energy System to be truly equitable, it should provide affordable and reliable energy services to disadvantaged and underserved populations. Disadvantaged communities often face a combination of economic, social, health, and environmental burdens and may be geographically isolated (e.g., rural communities), which systematically limits their opportunity to fully participate in aspects of economic, social, and civic life.

This paper develops a new domain-decomposition method for solving the KKT system with heat-equation constraints.

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.

Determining the thermal response of energetic materials at high densities can be difficult when pressure dependent reactions occur within the interior of the material. At high temperatures, reactive components such as hexahydro-l,3,5-tri-nitro-l,3,5-triazine (RDX), ammonium perchlorate (AP), and hydroxyl-terminated polybutadiene (HTPB) decompose and interact. The decomposition products accumulate near defects where internal pressure ultimately causes mechanical damage with closed pores transitioning into open pores. Gases are no longer confined locally; instead, they freely migrate between open pores and ultimately escape into the surrounding headspace or vent. Recently we have developed a universal cookoff model (UCM) coupled to a micromechanics pressurization (MMP) model to address pressure-dependent reactions that occur within the interior of explosives. Parameters for the UCM/MMP model are presented for an explosive and two propellants that contain similar portions of both aluminum (Al) and a binder. The explosive contains RDX and the propellants contain AP with no RDX. One of the propellants contains small amounts of curing catalysts and a burn modifier whereas the other propellant does not. We found that the cookoff behavior of the two propellants behave similarly leading and conclude that small amounts of catalysts or burn modifiers do not influence cookoff behavior appreciably. Kinetic parameters for the UCM/MMP models were obtained from the Sandia Instrumented Thermal Ignition (SITI) experiment. Validation is done with data from other laboratories.

Glass wedges are used increase the dimensionality of various optical measurements. Light refracted through the wedges can be focused to closely spaced points, lines or planes as shown in the applications herein.

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.

The ability to accurately predict the structure and dynamics of pool fires using computational simulations is of great interest in a wide variety of applications, including accidental and wildland fires. However, the presence of physical processes spanning a broad range of spatial and temporal scales poses a significant challenge for simulations of such fires, particularly at conditions near the transition between laminar and turbulent flow. In this study, we examine the transition to turbulence in methane pool fires using high-resolution simulations with multi-step finite rate chemistry, where adaptive mesh refinement (AMR) is used to directly resolve small-scale flow phenomena. We perform three simulations of methane pool fires, each with increasing diameter, corresponding to increasing inlet Reynolds and Richardson numbers. As the diameter increases, the flow transitions from organized vortex roll-up via the puffing instability to much more chaotic mixing associated with finger formation along the shear layer and core collapse near the inlet. These effects combine to create additional mixing close to the inlet, thereby enhancing fuel consumption and causing more rapid acceleration of the fluid above the pool. We also make comparisons between the transition to turbulence and core collapse in the present pool fires and in inert helium plumes, which are often used as surrogates for the study of buoyant reacting flows.

The importance of user-accessible multiple-input/multiple-output (MIMO) control methods has been highlighted in recent years. Several user-created control laws have been integrated into Rattlesnake, an open-source MIMO vibration controller developed at Sandia National Laboratories. Much of the effort to date has focused on stationary random vibration control. However, there are many field environments which are not well captured by stationary random vibration testing, for example shock, sine, or arbitrary waveform environments. This work details a time waveform replication technique that uses frequency domain deconvolution, including a theoretical overview and implementation details. Example usage is demonstrated using a simple structural dynamics system and complicated control waveforms at multiple degrees of freedom.

The lack of large, relevant and labeled datasets for synthetic aperture radar (SAR) automatic target recognition (ATR) poses a challenge for deep neural network approaches. In the case of SAR ATR, transfer learning offers promise where models are pre-trained on either synthetic SAR, alternatively collected SAR, or non-SAR source data and then fine-tuned on a smaller target SAR dataset. The concept being that the neural network can learn fundamental features from the more abundant source domain resulting in high accuracy and robust models when fine-tuned on a smaller target domain. One open question with this transfer learning strategy is how to choose source datasets that will improve accuracy of a target SAR dataset when the model is fine-tuned. Here, we apply a set of model and dataset transferability analysis techniques to investigate the efficacy of transfer learning for SAR ATR. In particular, we examine Optimal Transport Dataset Distance (OTDD), Log Maximum Evidence (LogMe), Log Expected Empirical Prediction (LEEP), Gaussian Bhattacharyya Coefficient (GBC), and H-Score. These methods consider properties such as task relatedness, statistical analysis of learned embedding properties, as well as distribution distances of the source and target domains. We apply these transferability metrics to ResNet18 models trained on a set of Non-SAR as well as SAR datasets. Overall, we present an investigation into quantitatively analyzing transferability for SAR ATR.

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

We use complete polarization tomography of photon pairs generated in semiconductor metasurfaces via spontaneous parametric down-conversion to show how bound states in the continuum resonances affect the polarization state of the emitted photons.

Multi-axis testing has become a popular test method because it provides a more realistic simulation of a field environment when compared to traditional vibration testing. However, field data may not be available to derive the multi-axis environment. This means that methods are needed to generate “virtual field data” that can be used in place of measured field data. Transfer path analysis (TPA) has been suggested as a method to do this since it can be used to estimate the excitation forces on a legacy system and then apply these forces to a new system to generate virtual field data. This chapter will provide a review of using TPA methods to do this. It will include a brief background on TPA, discuss the benefits of using TPA to compute virtual field data, and delve into the areas for future work that could make TPA more useful in this application.