Publications

In this study, we develop an end-to-end deep learning-based inverse design approach to determine the scatterer shape necessary to achieve a target acoustic field. This approach integrates non-uniform rational B-spline (NURBS) into a convolutional autoencoder (CAE) architecture while concurrently leveraging (in a weak sense) the governing physics of the acoustic problem. By utilizing prior physical knowledge and NURBS parameterization to regularize the ill-posed inverse problem, this method does not require enforcing any geometric constraint on the inverse design space, hence allowing the determination of scatterers with potentially any arbitrary shape (within the set allowed by NURBS). A numerical study is presented to showcase the ability of this approach to identify physically-consistent scatterer shapes capable of producing user-defined acoustic fields.

The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) - probabilistic graphical models consisting of a set of variables and their conditional dependencies - for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof.

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

Modern Industrial Control Systems (ICS) attacks evade existing tools by using knowledge of ICS processes to blend their activities with benign Supervisory Control and Data Acquisition (SCADA) operation, causing physical world damages. We present Scaphy to detect ICS attacks in SCADA by leveraging the unique execution phases of SCADA to identify the limited set of legitimate behaviors to control the physical world in different phases, which differentiates from attacker's activities. For example, it is typical for SCADA to setup ICS device objects during initialization, but anomalous during process-control. To extract unique behaviors of SCADA execution phases, Scaphy first leverages open ICS conventions to generate a novel physical process dependency and impact graph (PDIG) to identify disruptive physical states. Scaphy then uses PDIG to inform a physical process-aware dynamic analysis, whereby code paths of SCADA process-control execution is induced to reveal API call behaviors unique to legitimate process-control phases. Using this established behavior, Scaphy selectively monitors attacker's physical world-targeted activities that violates legitimate process-control behaviors. We evaluated Scaphy at a U.S. national lab ICS testbed environment. Using diverse ICS deployment scenarios and attacks across 4 ICS industries, Scaphy achieved 95% accuracy & 3.5% false positives (FP), compared to 47.5% accuracy and 25% FP of existing work. We analyze Scaphy's resilience to futuristic attacks where attacker knows our approach.

Scientific discovery increasingly relies on interoperable, multimodular workflows generating intermediate data. The complexity of managing intermediate data may cause performance losses or unexpected costs. This paper defines an approach to composing these scientific workflows on cloud services, focusing on workflow data orchestration, management, and scalability. We demonstrate the effectiveness of our approach with the SOMOSPIE scientific workflow that deploys machine learning (ML) models to predict high-resolution soil moisture using an HPC service (LSF) and an open-source cloud-native service (K8s) and object storage. Our approach enables scientists to scale from coarse-grained to fine-grained resolution and from a small to a larger region of interest. Using our empirical observations, we generate a cost model for the execution of workflows with hidden intermediate data on cloud services.

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

We demonstrate coherent anti-Stokes Raman scattering (CARS) detection of the CO and N2 molecules in the reaction layer of a graphite material sample exposed to the 5000-6000 K plume of an inductively-coupled plasma torch operating on air. CO is a dominant product in the surface oxidative reaction of graphite and lighter weight carbon-based thermalprotection-system materials. A standard nanosecond CARS approach using Nd:YAG and a single broadband dye laser with ~200 cm-1 spectral width is employed for demonstration measurements, with the CARS volume located less than 1-mm from an ablating graphite sample. Quantitative measurements of both temperature and the CO/N2 ratio are obtained from model fits to CARS spectra that have been averaged for 5 laser shots. The results indicate that CARS can be used for space- and time-resolved detection of CO in high-temperature ablation tests near atmospheric pressure.

Sustainable use of water resources continues to be a challenge across the globe. This is in part due to the complex set of physical and social behaviors that interact to influence water management from local to global scales. Analyses of water resources have been conducted using a variety of techniques, including qualitative evaluations of media narratives. This study aims to augment these methods by leveraging computational and quantitative techniques from the social sciences focused on text analyses. Specifically, we use natural language processing methods to investigate a large corpus (approx. 1.8M) of newspaper articles spanning approximately 35 years (1982–2017) for insights into human-nature interactions with water. Focusing on local and regional United States publications, our analysis demonstrates important dynamics in water-related dialogue about drinking water and pollution to other critical infrastructures, such as energy, across different parts of the country. Our assessment, which looks at water as a system, also highlights key actors and sentiments surrounding water. Extending these analytical methods could help us further improve our understanding of the complex roles of water in current society that should be considered in emerging activities to mitigate and respond to resource conflicts and climate change.

A comprehensive control strategy is necessary to safely and effectively operate particle based concentrating solar power (CSP) technologies. Particle based CSP with thermal energy storage (TES) is an emerging technology with potential to decarbonize power and process heat applications. The high-temperature nature of particle based CSP technologies and daily solar transients present challenges for system control to prevent equipment damage and ensure operator safety. An operational controls strategy for a tower based particle CSP system during steady state and transient conditions with safety interlocks is described in this paper. Control of a solar heated particle recirculation loop, TES, and a supercritical carbon dioxide (sCO2) cooling loop designed to reject 1 MW of thermal power are considered and associated operational limitations and their influence on control strategy are discussed.

The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.

A wind tunnel test from AEDC Tunnel 9 of a hypersonic turbulent boundary layer is analyzed using several fidelities of numerical simulation including Wall-Modeled Large Eddy Simulation (WMLES), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS). The DNS was forced to transition to turbulence using a broad spectrum of planar, slow acoustic waves based on the freestream spectrum measured in the tunnel. Results show the flow transitions in a reasonably natural process developing into turbulent flow. This is due to several 2nd mode wave packets advecting downstream and eventually breaking down into turbulence with modest friction Reynolds numbers. The surface shear stress and heat flux agree well with a transitional RANS simulation. Comparisons of DNS data to experimental data showreasonable agreement with regard to mean surface quantities aswell as amplitudes of boundary layer disturbances. The DNS does show early transition relative to the experimental data. Several interesting aspects of the DNS and other numerical simulations are discussed. The DNS data are also analyzed through several common methods such as cross-correlations and coherence of the fluctuating surface pressure.

Low-inertia microgrids experience significant frequency deviations compared to bulk-power systems. In such microgrids, energy storage systems (ESSs) can be a viable option to provide fast-frequency support to keep frequency variations within allowable bounds. A model predictive control (MPC)-based strategy is one of the efficient control strategies to enable fast-frequency support through ESSs. MPC provides the capability to explicitly incorporate physical constraints of the microgrid and the ESS into the control formulation while allowing signifi-cant operational flexibility. MPC allows near-optimal control by optimizing the system over a rolling horizon based on a predictive model of the system. However, the effectiveness of MPC relies on the accuracy of this predictive model. This paper proposes a data-driven system identification (SI) based approach to obtain an accurate yet computationally tractable predictive model for frequency support in microgrids. The proposed data-driven MPC is compared with the conventional MPC that utilizes a simplified transfer-function-based predictive model of the system. Results show that the data-driven MPC offers a better quality of service in terms of lower frequency deviations and rate-of-change of frequency (ROCOF).

Distribution systems may experience fast voltage swings in the matter of seconds from distributed energy resources, such as Wind Turbines Generators (WTG) and Photovoltaic (PV) inverters, due to their dependency on variable and intermittent wind speed and solar irradiance. This work proposes a WTG reactive power controller for fast voltage regulation. The controller is tested on a simulation model of a real distribution system. Real wind speed, solar irradiation, and load consumption data is used. The controller is based on a Reinforcement Learning Deep Deterministic Policy Gradient (DDPG) model that determines optimum control actions to avoid significant voltage deviations across the system. The controller has access to voltage measurements at all system buses. Results show that the proposed WTG reactive power controller significantly reduces system-wide voltage deviations across a large number of generation scenarios in order to comply with standardized voltage tolerances.

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware code-sign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

Terrain-relative autonomous navigation is a challenging task. In traditional approaches, an elevation map is carried onboard and compared to measurements of the terrain below the vehicle. These methods are computationally expensive, and it is impractical to store high-quality maps of large swaths of terrain. In this article, we generate position measurements using NeuroGrid, a recently-proposed algorithm for computing position information from terrain elevation measurements. We incorporate NeuroGrid into an inertial navigation scheme using a novel measurement rejection strategy and online covariance computation. Our results show that the NeuroGrid filter provides highly accurate state information over the course of a long trajectory.

Snow is a significant challenge for PV plants at northern latitudes, and snow-related power losses can exceed 30 % of annual production. Accurate loss estimates are needed for resource planning and to validate mitigation strategies, but this requires accurate snow detection at the inverter level. In this study, we propose and validate a framework for detecting snow in time-series inverter data. We identify four distinct snow-related power loss modes based on the inverter's operating points and electrical properties of the inverter and PV arrays. We validate these modes and identify their associated physical snow conditions using site images. Finally we examine relative frequencies of the snow power loss modes and their contributions to total power loss.

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. In this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

Event-based sensors are a novel sensing technology which capture the dynamics of a scene via pixel-level change detection. This technology operates with high speed (>10 kHz), low latency (10 µs), low power consumption (<1 W), and high dynamic range (120 dB). Compared to conventional, frame-based architectures that consistently report data for each pixel at a given frame rate, event-based sensor pixels only report data if a change in pixel intensity occurred. This affords the possibility of dramatically reducing the data reported in bandwidth-limited environments (e.g., remote sensing) and thus, the data needed to be processed while still recovering significant events. Degraded visual environments, such as those generated by fog, often hinder situational awareness by decreasing optical resolution and transmission range via random scattering of light. To respond to this challenge, we present the deployment of an event-based sensor in a controlled, experimentally generated, well-characterized degraded visual environment (a fog analogue), for detection of a modulated signal and comparison of data collected from an event-based sensor and from a traditional framing sensor.

Holography is an effective diagnostic for the three-dimensional imaging of multiphase and particle-laden flows. Traditional digital inline holography (DIH), however, is subject to distortions from phase delays caused by index-of-refraction changes. This prevents DIH from being implemented in extreme conditions where shockwaves and significant thermal gradients are present. To overcome this challenge, multiple techniques have been developed to correct for the phase distortions. In this work, several holography techniques for distortion removal are discussed, including digital off-axis holography, phase conjugate digital in-line holography, and electric field techniques. Then, a distortion cancelling off-axis holography configuration is implemented for distortion removal and a high-magnification phase conjugate system is evaluated. Finally, both diagnostics are applied to study extreme pyrotechnic igniter environments.

A high bandwidth piezoelectric transducer technology for high data rate communications across metallic barriers is presented and discussed. To properly characterize the channel, a linear time-invariant (LTI) model of the device is obtained using frequency fitting methods on the S-parameter measurements of the communication network. The corresponding impulse response of the channel is derived from the poles and residues used to fit the frequency data. A recursive formulation of the impulse response of complex poles is advanced and analyzed. The channel characteristics were used to estimate the trans-barrier data rate employing orthogonal frequency division multiplex (OFDM) as used in a powerline communication (PLC) standard. An off-the-shelf PLC system is used to communicate through a metallic barrier and data rates exceeding 70 Mbps were achieved as predicted by the model. The methods described here are useful for estimating the physical data rate achievable by trans-barrier communication systems using piezoelectric transducers.

A medium-scale (30 cm diameter) methanol pool fire was simulated using Sandia National Laboratories’ Sierra/Fuego low-Mach number multi-physics turbulent reacting flow code. Large Eddy Simulation (LES) with subgrid turbulent kinetic energy closure was used as the turbulence model. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was modeled using the gray-gas approximation. This paper details analysis done to support a validation study for the fire model. In this analysis, integral quantities were primarily examined. The radiant fraction was computed and used as a model calibration parameter. Integrated buoyancy flux was calculated and compared to an engineering correlation. Entrainment rate was computed with and without a mixture fraction threshold filter and compared to engineering correlations. Turbulent kinetic energy was computed and the effect of mesh size on the subgrid and total turbulent kinetic energy was examined. Flame height was calculated using an intermittency definition with two input parameters. A sensitivity study was then conducted to determine the sensitivity of the estimated flame height to the input parameters. This analysis aided in achieving the primary validation study objectives by providing model calibration and expanding the scope of the validation effort. In addition, the range of physics examined was increased, enhancing the understanding of the model's overall performance and of the relationship between phenomena.

This paper introduces a new microprocessor-based system that is capable of detecting faults via the Traveling Wave (TW) generated from a fault event. The fault detection system is comprised of a commercially available Digital Signal Processing (DSP) board capable of accurately sampling signals at high speeds, performing the Discrete Wavelet Transform (DWT) decomposition to extract features from the TW, and a detection algorithm that makes use of the extracted features to determine the occurrence of a fault. Results show that this inexpensive fault detection system's performance is comparable to commercially available TW relays as accurate sampling and fault detection are achieved in a hundred and fifty microseconds. A detailed analysis of the execution times of each part of the process is provided.

TFLN/silicon photonic modulators featuring active silicon photonic components are reported with a Vπ of 3.6 Vcm. This hybrid architecture utilizes the bottom of the buried oxide as the bonding surface which features minimum topology.

Abstract not provided.

A comprehensive study of the mechanical response of a 316 stainless steel is presented. The split-Hopkinson bar technique was used to evaluate the mechanical behavior at dynamic strain rates of 500 s−1, 1500 s−1, and 3000 s−1 and temperatures of 22 °C and 300 °C under tension and compression loading, while the Drop-Hopkinson bar was used to characterize the tension behavior at an intermediate strain rate of 200 s−1. The experimental results show that the tension and compression flow stress are reasonably symmetric, exhibit positive strain rate sensitivity, and are inversely dependent on temperature. The true failure strain was determined by measuring the minimum diameter of the post-test tension specimen. The 316 stainless steel exhibited a ductile response, and the true failure strain increased with increasing temperature and decreased with increasing strain rate.