Publications

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.

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.

In general, multiple-input/multiple-output (MIMO) vibration testing utilizes a response-controlled test methodology where specifications are in the form of response quantities at various locations distributed on the device under test (DUT). There are some advantages to this approach, namely that DUT response could be measured in some field environment and directly used as MIMO specifications for subsequent MIMO vibration tests on similar DUTs. However, in some cases it may be advantageous to control the MIMO vibration test at the inputs rather than the responses. One such case is free-flight environments, where the DUT is unconstrained, and all loads come from aerodynamic pressures. In this case, the force-controlled test method is much more robust to system changes such as unit-to-unit variability as compared to a response-controlled test method. This could make force-controlled MIMO test specifications more generalizable and easier to derive. This is exactly akin to transfer path analysis, where pseudo-forces are applicable in special circumstances. This paper will explore the force-controlled test concept and demonstrate it with a numerical example, comparing performance under various conditions vs. the traditional response-controlled test method.

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.

Blind geothermal systems are believed to be common in the Basin and Range province and represent an underutilized source of renewable green energy. Their discovery has historically been by chance but more methodological strategies for exploration of these resources are being developed. One characteristic of blind systems is that they are often overlain by near-surface zones of low-resistivity caused by alteration of the overlying sediments to swelling clays. These zones can be imaged by resistivity-based geophysical techniques to facilitate their discovery and characterization. Here we present a side-by-side comparison of resistivity models produced from helicopter transient electromagnetic (HTEM) and ground-based broadband magnetotelluric (MT) surveys over a previously discovered blind geothermal system with measured shallow temperatures of ~100°C in East Hawthorne, NV. The HTEM and MT data were collected as part of the BRIDGE project, an initiative for improving methodologies for discovering blind geothermal systems. HTEM data were collected and modelled along profiles, and the results suggest the method can resolve the resistivity structure 300 - 500 m deep. A 61-station MT survey was collected on an irregular grid with ~800 m station spacing and modelled in 3D on a rotated mesh aligned with HTEM flight directions. Resistivity models are compared with results from potential fields datasets, shallow temperature surveys, and available temperature gradient data in the area of interest. We find that the superior resolution of the HTEM can reveal near-surface details often missed by MT. However, MT is sensitive to several km deep, can resolve 3D structures, and is thus better suited for single-prospect characterization. We conclude that HTEM is a more practical subregional prospecting tool than is MT, because it is highly scalable and can rapidly discover shallow zones of low resistivity that may indicate the presence of a blind geothermal system. Other factors such as land access and ground disturbance considerations may also be decisive in choosing the best method for a particular prospect. Resistivity methods in general cannot fully characterize the structural setting of a geothermal system, and so we used potential fields and other datasets to guide the creation of a diagrammatic structural model at East Hawthorne.

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.

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.

The primary goal of any laboratory test is to expose the unit-under-test to conservative realistic representations of a field environment. Satisfying this objective is not always straightforward due to laboratory equipment constraints. For vibration and shock tests performed on shakers over-testing and unrealistic failures can result because the control is a base acceleration and mechanical shakers have nearly infinite impedance. Force limiting and response limiting are relatively standard practices to reduce over-test risks in random-vibration testing. Shaker controller software generally has response limiting as a built-in capability and it is done without much user intervention since vibration control is a closed loop process. Limiting in shaker shocks is done for the same reasons, but because the duration of a shock is only a few milliseconds, limiting is a pre-planned user in the loop process. Shaker shock response limiting has been used for at least 30 years at Sandia National Laboratories, but it seems to be little known or used in industry. This objective of this paper is to re-introduce response limiting for shaker shocks to the aerospace community. The process is demonstrated on the BARBECUE testbed.

Structural materials used in combustion or power generation systems need to have both environmental and temperature resistance to ensure long-term performance. As the energy sector transitions to hydrogen, there is a need to ensure compatibility of highly-alloyed austenitic steels and nickel-based alloys with hydrogen over a range of temperatures. Hydrogen embrittlement of these alloy systems is often considered most detrimental near ambient temperatures and low temperatures, although there is some evidence in the literature that hydrogen can affect creep behavior at elevated temperature. In the intermediate temperature range (e.g., 100-400C), it is uncertain whether hydrogen degradation of mechanical properties will be of concern. In this study, three alloys (304L, IN625, Hastelloy X) commonly used in power generation systems were thermally precharged with hydrogen and subsequently tensile tested to failure in air at temperatures ranging from 20°C to 200°C. At 20°C, the hydrogen-precharged condition for all materials exhibited loss in ductility with relative reduction of area ranging between 32% and 57%. The three alloys exhibited different trends with temperature but, in general, the relative reduction of area improved with increasing temperature tending towards noncharged behavior. Tests were performed at a nominal strain rate of 2 x 10-3 s-1 in order to minimize loss of hydrogen during elevated temperature testing. Hydrogen contents from the grip sections were measured both before and after testing and remained within 10% of starting content for 100°C tests and within 8-23% for 200°C tests.

Quantum sensing utilizes the inherent sensitivity of a quantum system to external stimuli. Our goal is to leverage this sensitivity to develop a quantum sensor designed for the detection of ionizing radiation. Here we report on the design, fabrication, and measurement of a new quantum device for hard x-ray and gamma-ray detection. Our quantum device is based on a superconducting quantum bit (qubit) with superconducting tunnel junctions as the core device elements. We describe our experimental investigation directed toward the detection metrics of energy resolution, dynamic range, and active area. In contrast to existing superconducting detectors, the active area per qubit may be much larger than the physical area of the tunnel junctions or the physical area of the qubit device, due to the sensitivity of quantum coherence to ionizing radiation deposition within a radius on the millimeter or centimeter scale. Our experimental design enables an ionizing radiation source at room temperature to be detected by our quantum sensor at low temperature.

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.

Additive manufacturing has ushered in a new paradigm of bottom-up materials-by-design of spatially non-uniform materials. Functionally graded materials have locally tailored compositions to provide optimized global properties and performance. In this letter, we propose an opportunity for the application of graded magnetic materials as lens elements for charged particle optics. A Hiperco50/Hymu80 (FeCo-2 V/Fe-80Ni-5Mo) graded magnetic alloy was successfully additively manufactured via Laser Directed Energy Deposition with spatially varying magnetic properties. The compositional gradient is then applied using computational simulations to demonstrate how a tailored material can enhance the magnetic performance of a critical, image-forming component of a transmission electron microscope.

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.

The explosive BTF (benzotrifuroxan) is an interesting molecule for sub-millimeter studies of initiation and detonation. It has no hydrogen, thus no water in the detonation products and a subsequently high temperature in the reaction zone. The material has impact sensitivity that is comparable or less than that of PETN (pentaerythritol tetranitrate) and slightly greater than RDX, HMX, and CL-20. Physical vapor deposition (PVD) can be used to grow high-density films of pure explosives with precise control over geometry, and we apply this technique to BTF to study detonation and initiation behavior as a function of sample thickness. The geometrical effects on detonation and corner turning behavior are studied with the critical detonation thickness experiment and the micromushroom test, respectively. Initiation behavior is studied with the high-throughput initiation experiment. Vapor-deposited films of BTF show detonation failure, corner turning, and initiation consistent with a heterogeneous explosive. Scaling of failure thickness to failure diameter shows that BTF has a very small failure diameter.

Photovoltaic modules undergoing laboratory hail tests were observed using high speed video to analyze the key characteristics of impact-induced glass fracture, including crack onset time, initiation location relative to the impact site, and propagation trends. Fifteen commercially representative glass-glass thin-film modules were recorded at 300,000 frames per second during hail impacts which happened to cause glass fracture. Images were processed to identify the time between impact and first plausible glass crack appearance (average 126 μs, standard deviation 59 μs) along with the time to a confirmed crack (average 158 μs, standard deviation 77μs), during the ice ball impacts which had a median kinetic energy of 47 J delivered by 55 mm diameter balls. Limiting factors for identifying glass crack timings were ice ball fragmentation obscuring the impact site and indistinct initial crack appearance, which were inherent to the images and not improved with processing. Computational simulations corresponding to each impact event showed that glass stresses were still localized to the impact site during times with definitively identifiable fracture, and even impacts which did not induce failure created local stress magnitudes exceeding stress levels associated with static glass fracture. These observations confirm that impact-induced glass failure is a time-and rate-dependent phenomena. Results from this study provide baseline metrics for developing a glass fracture criterion to predict module damage during hail impact events, which in turn allows for analysis of design features that may affect damage susceptibility.

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.

Deep neural networks for automatic target recognition (ATR) have been shown to be highly successful for a large variety of Synthetic Aperture Radar (SAR) benchmark datasets. However, the black box nature of neural network approaches raises concerns about how models come to their decisions, especially when in high-stake scenarios. Accordingly, a variety of techniques are being pursued seeking to offer understanding of machine learning algorithms. In this paper, we first provide an overview of explainability and interpretability techniques introducing their concepts and the insights they produce. Next we summarize several methods for computing specific approaches to explainability and interpretability as well as analyzing their outputs. Finally, we demonstrate the application of several attribution map methods and apply both attribution analysis metrics as well as localization interpretability analysis to six neural network models trained on the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset to illustrate the insights these methods offer for analyzing SAR ATR performance.

Visualization of flow structures within post-detonation fireballs has been performed for benchmark validation of numerical simulations. Custom pressed PETN explosives with a 12-mm diameter hemispherical form factor were used to produce a spherically symmetric post-detonation flow with low soot yield. Hydroxyl-radical planar laser induce fluorescence (OH-PLIF) was employed to visualize the structure ranging from approximately 10μs to 35μs after shock breakout from the explosive pellet. Fireball simulations were performed using the HyBurn Computational Fluid Dynamics (CFD) package. Experimental OH-PLIF results were compared to synthetic OH-PLIF from post-processing of CFD simulations. From the comparison of experimental and synthetic OH-PLIF images, CFD is shown to replicate much of the flow structure observed in the experiments, revealing potential differences in turbulent length scales and OH kinetics. Results provide significant advancement in experimental resolution of these harsh turbulent combustion environments and validate physical models thereof.

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.

Our present electric power grid maximizes spinning inertia of fossil fuel generators (inherent energy storage) to meet stability and performance requirements. Our goal is to begin to investigate the replacement of the large spinning inertia of fossil fuel generators with energy storage systems (ESS) including information flow as a necessary part of the renewable energy sources (RES) and subject to certain criteria. General criteria metrics include: energy storage, information flow, estimation, communication links, central versus decentralized, etc. Our focus is on evaluating the Fisher Information Equivalency (FIE) metric as a multi-criteria trade-off cost function for the minimization of ESS options and information flow. This paper begins with a formal conceptual definition of an infinite bus. Then a simple example of a One Machine Infinite Bus (OMIB) system with a Unified Power Flow Controller (UPFC) to demonstrate the FIE-based approach to minimize the ESS. A second more detailed example of several spinning machines are included with representative power electronic and ESS for RES that are attached to the electric power grid. A simple trade-study begins to highlight requirements to support large penetration of RES. Keep in mind for a large scale high penetration of RES will require large investments in ESS which we want to minimize.

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.

Dendrites enable neurons to perform nonlinear operations. Existing silicon dendrite circuits sufficiently model passive and active characteristics, but do not exploit shunting inhibition as an active mechanism. We present a dendrite circuit implemented on a reconfigurable analog platform that uses active inhibitory conductance signals to modulate the circuit's membrane potential. We explore the potential use of this circuit for direction selectivity by emulating recent observations demonstrating a role for shunting inhibition in a directionally-selective Drosophila (Fruit Fly) neuron.

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.

Frequency stability issues are common in low-inertia microgrids due to the dominance of power electronics converter-based resources and comparatively low levels of inertia of synchronous generation. While many methods have been proposed for frequency support in these systems, it is challenging to ensure both stability and an adequate level of support without directly modeling the system. Perturbation-based extremum-seeking control (PESC) is a model-free adaptive control strategy that optimally sets the system's performance measure without requiring a mathematical system model. However, PESC offers poor transient performance due to design limitations, such as an averaging theory. Some modifications have been made to address these limitations; nevertheless, the modified design of PESC operates more as a model-based control. The Quasi-Newton method is a popular class of numerical optimizers attributed with a design procedure similar to model-free control that uses the gradient and an approximate inverse Hessian of the performance measure to run an optimization loop. This paper presents the design and compares the performance of the Quasi-Newton method and a model-based PESC for frequency support of microgrids. The simulation results illustrate the comparable performance of both control schemes and show the model-free control capability of the numerical optimization method for a class of nonlinear dynamic systems.

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.