Publications

Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.

Alkene ozonolysis generates short-lived Criegee intermediates that are a significant source of hydroxyl (OH) radicals. This study demonstrates that roaming of the separating OH radicals can yield alternate hydroxycarbonyl products, thereby reducing the OH yield. Specifically, hydroxybutanone has been detected as a stable product arising from roaming in the unimolecular decay of the methyl-ethyl-substituted Criegee intermediate (MECI) under thermal flow cell conditions. The dynamical features of this novel multistage dissociation plus a roaming unimolecular decay process have also been examined with ab initio kinetics calculations. Experimentally, hydroxybutanone isomers are distinguished from the isomeric MECI by their higher ionization threshold and distinctive photoionization spectra. Moreover, the exponential rise of the hydroxybutanone kinetic time profile matches that for the unimolecular decay of MECI. A weaker methyl vinyl ketone (MVK) photoionization signal is also attributed to OH roaming. Complementary multireference electronic structure calculations have been utilized to map the unimolecular decay pathways for MECI, starting with 1,4 H atom transfer from a methyl or methylene group to the terminal oxygen, followed by roaming of the separating OH and butanonyl radicals in the long-range region of the potential. Roaming via reorientation and the addition of OH to the vinyl group of butanonyl is shown to yield hydroxybutanone, and subsequent C-O elongation and H-transfer can lead to MVK. A comprehensive theoretical kinetic analysis has been conducted to evaluate rate constants and branching yields (ca. 10-11%) for thermal unimolecular decay of MECI to conventional and roaming products under laboratory and atmospheric conditions, consistent with the estimated experimental yield (ca. 7%).

As a part of NASA's efforts in space, options are being examined for an Artemis moon base project to be deployed. This project requires a system of interconnected, but separate, DC microgrids for habitation, mining, and fuel processing. This in-place use of power resources is called in-situ resource utilization (ISRU). These microgrids are to be separated by 9-12 km and each contains a photovoltaic (PV) source, energy storage systems (ESS), and a variety of loads, separated by level of criticality in operation. The separate microgrids need to be able to transfer power between themselves in cases where there are generation shortfall, faults, or other failures in order to keep more critical loads running and ensure safety of personnel and the success of mission goals. In this work, a 2 grid microgrid system is analyzed involving a habitation unit and a mining unit separated by a tie line. A set of optimal controls that has been developed, including power flow controls on the tie line, dispatch of PV generation, and dispatch of non-critical loads, is analyzed, and validated in hardware on the Secure Scalable Microgrid Testbed (SSMTB). This testbed includes hardware emulators for a variety of energy sources, energy storage devices, pulsed loads, and other loads.

A wind rotor is disclosed that produces energy optimally for a given thrust overturning moment. By designing rotors with suboptimal aerodynamic efficiency, they can have optimal thrust performance, which will reduce the substructure cost and/or enable greater energy capture for a given substructure.

This report describes specific activities in the Fiscal Year (FY) 2023 associated with the Geologic Disposal Safety Assessment (GDSA) Repository Systems Analysis (RSA) work package funded by the Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy Office of Nuclear Energy (DOE-NE), Office of Spent Fuel and Waste Disposition (SFWD).

This is the presentation, that goes with a previously-approved, Information-Released abstract of the same title,

A critical mission need exists to develop new materials that can withstand extreme environments and multiple sequential threats. High entropy materials, those containing 5 or more metals, exhibit many exciting properties which would potentially be useful in such situations. However, a particularly hard challenge in developing new high entropy materials is determining a priori which compositions will form the desired single phase material. The project outlined here combined several modeling and experimental techniques to explore several structure-property-relationships of high entropy ceramics in an effort to better understand the connection between their compositional components, their observed properties, and stability. We have developed novel machine learning algorithms which rapidly predict stable high entropy ceramic compositions, identified the stability interplay between configurational entropy and cation defects, and tested the mechanical stability of high entropy oxides using the unique capabilities at the Dynamic Compression Sector facility and the Saturn accelerator.

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The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial.

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

This project pursued a novel strategy for incorporating multiple qubits per ion into ion-trap based quantum computing (ITQC) involving Ca⁺ and Ba⁺. By forming molecular complexes of these cations with molecular-scale cages, we hypothesized that molecular energy levels could be incorporated into quantum computing while retaining key properties of the atomic ions intact. We experimented with a variety of molecular cages and found that Na⁺, K⁺, Rb⁺, Ca²⁺, Sr²⁺, and Ba²⁺ could be captured and brought into the gas phase efficiently by imbedding them inside [2.2.2]-benzocryptand. IR and UV spectra of these cage complexes are sensitive to the size and charge state of the ion, reporting on the structures and binding properties of the cage complexes. UV photofragmentation of the Ba²⁺-Acetate^-1-BzCrypt complex produces Ba⁺-BzCrypt, the complex targeted for exploration in the original hypothesis. Follow-on funding is needed to pursue the spectroscopy of this complex as a target for ITQC.

A discussion of systems like ChatGPT, what their legal issues may be, how they are affecting society, and what the ethical considerations of their existence and use are.

Accurate simulation of the evolution of polar ice-sheets requires a massive amount of computational power. In order to take advantage of the newest generation of supercomputing clusters, the Albany Land Ice code has been modernized for performance portability across a variety of parallel architectures, with a focus on enabling end-to-end GPU capability. Albany uses a multigrid preconditioning approach for solving linear systems via performance portable smoothers from the Trilinos package Ifpack2. Since the Albany Land Ice code is constantly evolving and both Albany and Trilinos are in constant development, it is likely that the optimal choice of solver parameters will change over time. It is therefore critical to have an automatic performance tuning framework to ensure that the best possible performance is maintained. Toward this effect, we have developed an automatic performance tuning framework to determine the best fine- and coarse-grid smoothing algorithms and parameters. We treat the underlying performance model of the linear solve as a black box and use the python-based GPTune Bayesian optimization library to determine the optimal smoother choice and parameters. Using this approach, we have found smoothers and their corresponding parameters that result in, on average, 1.2 times faster, and up to 1.5 times faster solve-times than our manually-tuned parameters. We also show that the proposed auto-tuning approach produces reliably better parameters than naive black box optimization techniques like random search for a given function evaluation budget. By implementing our tuning framework in the Python-based workflow management tool parsl, we also ensure that we efficiently use available computing resources during the tuning process and avoid unnecessary long wait times in computing cluster job queues.

The ion velocity distribution functions of thermonuclear plasmas generated by spherical laser direct drive implosions are studied using deuterium-tritium (DT) and deuterium-deuterium (DD) fusion neutron energy spectrum measurements. A hydrodynamic Maxwellian plasma model accurately describes measurements made from lower temperature (<10 keV), hydrodynamiclike plasmas, but is insufficient to describe measurements made from higher temperature more kineticlike plasmas. The high temperature measurements are more consistent with Vlasov-Fokker-Planck (VFP) simulation results which predict the presence of a bimodal plasma ion velocity distribution near peak neutron production. These measurements provide direct experimental evidence of non-Maxwellian ion velocity distributions in spherical shock driven implosions and provide useful data for benchmarking kinetic VFP simulations.

Abstract not provided.

In this project, we experimented the focused ion beam (FIB) based fabrications of semiconductor quantum dots (QDs) by using metal nano particles (NPs) (e.g., Al) on semiconductor as a template and by means of the FIB induced direct metal-to-QD conversion. We have examined effect of the experimental conditions, including Ga⁺ ion energy and dose as well as substrate temperature. The results of experiments have shown AlGaSb QD formation on GaSb substrate can be achieved under certain conditions but there are many challenges about the techniques, including compositional nonuniformity of the QDs formed, partial conversion of the metal NP to QD, and high defect concentration in the QDs.

Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. Further, by acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. An extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

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The understanding of power flow plasmas is important as we look towards next generation pulsed power (NGPP) as current losses could prohibit the goals of that facility. Therefore, it is important to have accurate diagnostics of the plasma parameters on the current machines, which can be used to help inform and improve simulations. Having these plasma parameters will help validate models and simulations to provide confidence when they are expanded to conditions relevant to NGPP. One important plasma parameter that can be measured is the electron density, which can be measured by photonic Doppler velocimetry (PDV). A PDV system has several key advantages over other interferometers by measuring relatively low densities (> 1 × 10¹⁵ cm^-2) with both spatial and temporal resolution. Experiments were performed on the Mykonos pulsed power machine, which is a 1 MA sub scale machine in which recent platforms have been developed to explore current densities relevant to the inner magnetically insulated transmission line (MITL) on the Z machine. Experiments were performed on two different platforms, the thin foil platform and the Mykonos parallel plate platform (MP3). In addition, a combination of both single-point and multi-point measurements were used. The single-point measurements proved to be very promising, providing a clear increase in density at about 70 ns into the current rise on thin foil experiments up to about 5 × 10¹⁷ cm^-3 before the probe stopped providing signal. While we did also see returns from multi-point measurements on both platforms, the signals were not as easy to interpret due to strong background effects. However, they do show initial promise for this diagnostic to measure density at several points across a 1 mm gap. These measurements provide insights in how to improve the diagnostic so that it can provide useful information on power flow relevant experiments.

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Code verification plays an important role in establishing the credibility of computational simulations by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, the numerical solution to integral equations incurs multiple interacting sources of numerical error, as well as other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the combined-field integral equation. We demonstrate the effectiveness of these approaches for cases with and without coding errors.