Publications

Plasma sprays can be used to melt particles, which may be deposited on an engineered surface to apply unique properties to the part. Because of the extreme temperatures (>>3000ºC) it is desirable to conduct the process in a way to avoid melting the parts to which the coatings are being applied. A jet of ambient gas is sometimes used to deflect the hot gases, while allowing the melted particles to impact and adhere to the substrate. This is known as a plume quench. While plume quenching is done in practice, to our knowledge there have not been any studies on how to apply a plume quench, and how it may affect the flows. We have recently adapted our fire simulation tool to simulate argon plasma sprays with a variety of metal particles. Two nozzle conditions are considered, with very different gas flow and power conditions. Two particle types are considered, Tantalum and Nickel. For the model, the k-epsilon turbulence model is compared to a more dynamic TFNS turbulence model. Limited data comparisons suggest the higher-fidelity TFNS model is significantly more accurate than the k-epsilon model. Additionally, the plume quench is found to have a noticeable effect for the low inlet flow case, but minimal effect on the high flow case. This suggests the effectiveness of a quench relates to the relative momentum of the intersecting gas jets.

High penetrations of residential solar PV can cause voltage issues on low-voltage (LV) secondary networks. Distribution utility planners often utilize model-based power flow solvers to address these voltage issues and accommodate more PV installations without disrupting the customers already connected to the system. These model-based results are computationally expensive and often prone to errors. In this paper, two novel deep learning-based model-free algorithms are proposed that can predict the change in voltages for PV installations without any inherent network information of the system. These algorithms will only use the real power (P), reactive power (Q), and voltage (V) data from Advanced Metering Infrastructure (AMI) to calculate the change in voltages for an additional PV installation for any customer location in the LV secondary network. Both algorithms are tested on three datasets of two feeders and compared to the conventional model-based methods and existing model-free methods. The proposed methods are also applied to estimate the locational PV hosting capacity for both feeders and have shown better accuracies compared to an existing model-free method. Results show that data filtering or pre-processing can improve the model performance if the testing data point exists in the training dataset used for that model.

Motion primitives (MPs) provide a fundamental abstraction of movement templates that can be used to guide and navigate a complex environment while simplifying the movement actions. These MPs, when utilized as an action space in reinforcement learning (RL), can allow an agent to learn to select a sequence of simple actions to guide a vehicle towards desired complex mission outcomes. This is particularly useful for missions involving high speed aerospace vehicles (HSAVs) (i.e., Mach 1 to 30) where near real time trajectory generation is needed but the computational cost and timeliness of trajectory generation remains prohibitive. This paperdemonstrates that when MPs are employed in conjunction with RL, the agent can learn to solve a wider range of problems for HSAV missions. To this end, using both a MP and and non-MP approach, RL is employed to solve the problem of an HSAV arriving at a non-maneuvering moving target at a constant altitude and with an arbitrary, but constant, velocity and heading angle. The MPs for HSAV consist of multiple pull (flight path angle) and turn (heading angle) commands that are defined for a specific duration based on mission phases; whereas the non-MP approach uses angle of attack and bank angle as action space for RL. The paper describes details on HSAV problem formulation to include equations of motion, observation space, telescopic reward function, RL algorithm and hyperparameters, RL curriculum, formation of the MPs, and calculation of time to execute the MP used for the problem. Our results demonstrate that the non-MP approach is unable to even train an agent that is successful in the base-case of the RL curriculum. The MP approach, however, can train an agent with success rate of 76.6% inarriving at a target moving with any heading angle with a velocity between 0 and 500 m/s.

This paper presents the uncertainty propagation of turbulent coefficients for the Spalart– Allmaras (SA) turbulence model using projection-based reduced-order models (ROMs). ROMs are used instead of Reynolds-averaged Navier–Stokes (RANS) solvers and stochastic collocation/ Galerkin and Monte Carlo methods because they are computationally inexpensive and tend to offer more accuracy than a polynomial surrogate. The uncertainty propagation is performed on two benchmark RANS cases documented on NASA’s turbulence modeling resource. Uncertainty propagation of the SA turbulent coefficients using a ROMis shown to compare well against uncertainty propagation performed using only RANS and using a Gaussian process regression (GP) model. The ROM is shown to be more robust to the size and spread of the training data compared to a GP model.

Multiple Input Multiple Output (MIMO) vibration testing provides the capability to expose a system to a field environment in a laboratory setting, saving both time and money by mitigating the need to perform multiple and costly large-scale field tests. However, MIMO vibration test design is not straightforward oftentimes relying on engineering judgment and multiple test iterations to determine the proper selection of response Degree of Freedom (DOF) and input locations that yield a successful test. This work investigates two DOF selection techniques for MIMO vibration testing to assist with test design, an iterative algorithm introduced in previous work and an Optimal Experiment Design (OED) approach. The iterative-based approach downselects the control set by removing DOF that have the smallest impact on overall error given a target Cross Power Spectral Density matrix and laboratory Frequency Response Function (FRF) matrix. The Optimal Experiment Design (OED) approach is formulated with the laboratory FRF matrix as a convex optimization problem and solved with a gradient-based optimization algorithm that seeks a set of weighted measurement DOF that minimize a measure of model prediction uncertainty. The DOF selection approaches are used to design MIMO vibration tests using candidate finite element models and simulated target environments. The results are generalized and compared to exemplify the quality of the MIMO test using the selected DOF.

The widespread adoption of residential solar PV requires distribution system studies to ensure the addition of solar PV at a customer location does not violate the system constraints, which can be referred to as locational hosting capacity (HC). These model-based analyses are prone to error due to their dependencies on the accuracy of the system information. Model-free approaches to estimate the solar PV hosting capacity for a customer can be a good alternative to this approach as their accuracies do not depend on detailed system information. In this paper, an Adaptive Boosting (AdaBoost) algorithm is deployed to utilize the statistical properties (mean, minimum, maximum, and standard deviation) of the customer's historical data (real power, reactive power, voltage) as inputs to estimate the voltage-constrained PV HC for the customer. A baseline comparison approach is also built that utilizes just the maximum voltage of the customer to predict PV HC. The results show that the ensemble-based AdaBoost algorithm outperformed the proposed baseline approach. The developed methods are also compared and validated by existing state-of-the-art model-free PV HC estimation methods.

Spectrally shaped forms of random frequency modulation (RFM) radar waveforms have been experimentally demonstrated for a variety of implementation approaches and applications. Of these, the continuous-wave (CW) perspective is particularly interesting because it enables the prospect of very high signal dimensionality and arbitrary receive processing from a range/Doppler perspective, while also mitigating range ambiguities by avoiding repetition. Here we leverage a modification to the constant-envelope orthogonal frequency division multiplexing (CE-OFDM) framework, which was originally proposed for power-efficient communications, to realize a nonrepeating FMCW radar signal that can be represented with a compact parameterization, thereby circumventing memory constraints that could arise for some applications. Experimental loopback and open-air measurements are used to demonstrate this waveform type.

We demonstrate the use of low-temperature grown GaAs (LT-GaAs) metasurface as an ultrafast photoconductive switching element gated with 1550 nm laser pulses. The metasurface is designed to enhance a weak two-step photon absorption at 1550 nm, enabling THz pulse detection.

As the electric grid becomes increasingly cyber-physical, it is important to characterize its inherent cyber-physical interdepedencies and explore how that characterization can be leveraged to improve grid operation. It is crucial to investigate what data features are transferred at the system boundaries, how disturbances cascade between the systems, and how planning and/or mitigation measures can leverage that information to increase grid resilience. In this paper, we explore several numerical analysis and graph decomposition techniques that may be suitable for modeling these cyber-physical system interdependencies and for understanding their significance. An augmented WSCC 9-bus cyber-physical system model is used as a small use-case to assess these techniques and their ability in characterizing different events within the cyber-physical system. These initial results are then analyzed to formulate a high-level approach for characterizing cyber-physical interdependencies.

Reinforcement learning (RL) may enable fixedwing unmanned aerial vehicle (UAV) guidance to achieve more agile and complex objectives than typical methods. However, RL has yet struggled to achieve even minimal success on this problem; fixed-wing flight with RL-based guidance has only been demonstrated in literature with reduced state and/or action spaces. In order to achieve full 6-DOF RL-based guidance, this study begins training with imitation learning from classical guidance, a method known as warm-staring (WS), before further training using Proximal Policy Optimization (PPO). We show that warm starting is critical to successful RL performance on this problem. PPO alone achieved a 2% success rate in our experiments. Warm-starting alone achieved 32% success. Warm-starting plus PPO achieved 57% success over all policies, with 40% of policies achieving 94% success.

Due to their increased levels of reliability, meshed low-voltage (LV) grid and spot networks are common topologies for supplying power to dense urban areas and critical customers. Protection schemes for LV networks often use highly sensitive reverse current trip settings to detect faults in the medium-voltage system. As a result, interconnecting even low levels of distributed energy resources (DERs) can impact the reliability of the protection system and cause nuisance tripping. This work analyzes the possibility of modifying the reverse current relay trip settings to increase the DER hosting capacity of LV networks without impacting fault detection performance. The results suggest that adjusting relay settings can significantly increase DER hosting capacity on LV networks without adverse effects, and that existing guidance on connecting DERs to secondary networks, such as that contained in IEEE Std 1547-2018, could potentially be modified to allow higher DER deployment levels.

The Information Harm Triangle (IHT) is an approach that seeks to simplify the defense-in-depth design of digital instrumentation and control (I&C) systems. The IHT provides a novel framework for understanding how cyber-attacks targeting digital I&C systems can harm the physical process. The utility of the IHT arises from the decomposition of cybersecurity analysis into two orthogonal vectors: data harm and physical information harm. Cyber-attacks on I&C systems can only directly cause data harm. Data harm is then transformed into physical information harm by unsafe control actions (UCAs) identified using Systems-Theoretic Process Analysis (STPA). Because data harm and physical information harm are orthogonal, defense-in-depth can be achieved by identifying control measures that independently limit data harm and physical information harm. This paper furthers the development of the IHT by investigating the defense-in-depth design of cybersecurity measures for sequences of UCAs. The effects of the order and timing of UCAs are examined for several case studies to determine how to represent these sequences using the IHT. These considerations are important for the identification of data harm and physical information harm security measures, and they influence the selection of efficient measures to achieve defense-in-depth. This research enables the benefits of the IHT's simple approach to be realized for increasingly complex cyber-attack scenarios.

With increasing penetration of variable renewable generation, battery energy storage systems (BESS) are becoming important for power system stability due to their operational flexibility. In this paper, we propose a method for determining the minimum BESS rated power that guarantees security constraints in a grid subject to disturbances induced by variable renewable generation. The proposed framework leverages sensitivity-based inverse uncertainty propagation where the dynamical responses of the states are parameterized with respect to random variables. Using this approach, the original nonlinear optimization problem for finding the security-constrained uncertainty interval may be formulated as a quadratically-constrained linear program. The resulting estimated uncertainty interval is utilized to find the BESS rated power required to satisfy grid stability constraints.

Carbonaceous particulate produced by a diesel engine and turbojet engine combustor are analyzed by transmission electron microscopy (TEM) for differences in nanostructure before and after pulsed laser annealing. Soot is examined between low/high diesel engine torque and low/high turbojet engine thrust. Small differences in nascent nanostructure are magnified by the action of high-temperature annealing induced by pulsed laser heating. Lamellae length distributions show occurrence of graphitization while tortuosity analyses reveal lamellae straightening. Differences in internal particle structure (hollow shells versus internal graphitic ribbons) are interpreted as due to higher internal sp3 and O-atom content under the higher power conditions with hypothesized greater turbulence and resulting partial premixing. TEM in concert with fringe analyses reveal that a similar degree of annealing occurs in the primary particles in soot from both diesel engine and turbojet engine combustors—despite the aggregate and primary size differences between these sources. Implications of these results for source identification of the combustion particulate and for laser-induced incandescence (LII) measurements of concentration are discussed with inter-instrument comparison of soot mass from both diesel and turbojet soot sources.

As the path towardsUrban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). This paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.

Renewable microgrids are sustainable, resilient solutions to mitigate and adapt to climate change. Making electric loads nearly 100% available (i.e., power remains on) during outages increases cost. Near 100% availability is required when human life or high-cost assets are involved, but availability can be reduced for less consequential loads leading to lower costs. This study analyses costs for photo-voltaic and lithium-ion battery microgrids with availability ranging from 0–99%. We develop a methodology to analyse three Puerto Rican coastal communities. We consider power outage effects for hurricanes, earthquakes, and everyday outages. The results show cost versus availability from 0–99%. There is 27–31% cost reduction at 80% availability in comparison to 99% availability. A regression model of microgrid availability versus three ratios: 1) the annual generation to demand ratio, 2) storage to interruption energy ratio, and 3) peak storage to load ratio produced a coefficient of determination of 0.99949 with 70% of the data used for training and 30% for testing. The results can therefore be extended to other coastal Puerto Rican communities of varying sizes that have ratios within the ranges analysed in this study. This can empower decision makers to rapidly analyse designs that have availabilities well below 100%.

As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.

This presentation describes a new effort to better understand insulator flashover in high current, high voltage pulsed power systems. Both experimental and modeling investigations are described. Particular emphasis is put upon understand flashover that initiate in the anode triple junction (anode-vacuum-dielectric).

Sandia National Laboratories (SNL) and Oak Ridge National Laboratory (ORNL) have collaborated to develop a capability to test the epithermal/intermediate cross sections of materials at the SNL critical experiments facility using the Seven Percent Critical Experiment (7uPCX) fuel. The Sandia Critical Experiments Program provides a specialized facility for performing water moderated and reflected critical experiments with UO₂ fuel rod arrays. The facility offers the ability to modify the core configuration and reactor tank to evaluate various reactor cores for pitch, moderator characteristics, and other criteria. A history of safe operations and flexibility in reactor core configuration has resulted in the completion of nine sets of critical benchmark experiments that have been documented in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The experiment described here is expected to be evaluated for inclusion in the 2024 edition of the ICSBEP Handbook.

This study presents a method for constructing machine learning-based reduced order models (ROMs) that accurately simulate nonlinear contact problems while quantifying epistemic uncertainty. These purely non-intrusive ROMs significantly lower computational costs compared to traditional full order models (FOMs). The technique utilizes adversarial training combined with an ensemble of Barlow twins reduced order models (BT-ROMs) to maximize the information content of the nonlinear reduced manifolds. These lower-dimensional manifolds are equipped with Gaussian error estimates, allowing for quantifying epistemic uncertainty in the ROM predictions. The effectiveness of these ROMs, referred to as UQ-BT-ROMs, is demonstrated in the context of contact between a rigid indenter and a hyperelastic substrate under finite deformations. The ensemble of BT-ROMs improves accuracy and computational efficiency compared to existing alternatives. The relative error between the UQ-BT-ROM and FOM solutions ranges from approximately 3% to 8% across all benchmarks. Remarkably, this high level of accuracy is achieved at a significantly reduced computational cost compared to FOMs. For instance, the online phase of the UQ-BT-ROM takes only 0.001 seconds, while a single FOM evaluation requires 63 seconds. Furthermore, the error estimate produced by the UQ-BT-ROMs reasonably captures the errors in the ROMs, with increasing accuracy as training data increases. The ensemble approach improves accuracy and computational efficiency compared to existing alternatives. The UQ-BT-ROMs provide a cost-effective solution with significantly reduced computational times while maintaining a high level of accuracy.

We demonstrate piezo-optomechanical phase control in a c-band silicon-photonic resonator using CMOS-compatible AlN microactuators. We achieve a frequency tuning response of 26.91 ± 0.77 MHz/V DC, operating at picowatt to nanowatt power levels.

A high altitude electromagnetic pulse (HEMP) or other similar geomagnetic disturbance (GMD) has the potential to severely impact the operation of large-scale electric power grids. By introducing low-frequency common-mode (CM) currents, these events can impact the performance of key system components such as large power transformers. In this work, a solid-state transformer (SST) that can replace susceptible equipment and improve grid resiliency by safely absorbing these CM insults is described. An overview of the proposed SST power electronics and controls architecture is provided, a system model is developed, and the performance of the SST in response to a simulated CM insult is evaluated. Compared to a conventional magnetic transformer, the SST is found to recover quickly from the insult while maintaining nominal ac input/output behavior.

Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. In this manuscript, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. We also demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.

A quantum-cascade-laser-absorption-spectroscopy (QCLAS) diagnostic was used to characterize post-detonation fireballs of RP-80 detonators via measurements of temperature, pressure, and CO column pressure at a repetition rate of 1 MHz. Scanned-wavelength direct-absorption spectroscopy was used to measure CO absorbance spectra near 2008.5 cm−1 which are dominated by the P(0,31), P(2,20), and P(3,14) transitions. Line-of-sight (LOS) measurements were acquired 51 and 91 mm above the detonator surface. Three strategies were employed to facilitate interpretation of the LAS measurements in this highly nonuniform environment and to evaluate the accuracy of four post-detonation fireball models: (1) High-energy transitions were used to deliberately bias the measurements to the high-temperature outer shell, (2) a novel dual-zone absorption model was used to extract temperature, pressure, and CO measurements in two distinct regions of the fireball at times where pressure variations along the LOS were pronounced, and (3) the LAS measurements were compared with synthetic LAS measurements produced using the simulated distributions of temperature, pressure, and gas composition predicted by reactive CFD modeling. The results indicate that the QCLAS diagnostic provides high-fidelity data for evaluating post-detonation fireball models, and that assumptions regarding thermochemical equilibrium and carbon freeze-out during expansion of detonation gases have a large impact on the predicted chemical composition of the fireball.

Soft magnetic composites (SMCs) offer a promising alternative to electrical steels and soft ferrites in high performance motors and power electronics. They are ideal for incorporation into passive electronic components such as inductors and transformers, which require a non-permanent magnetic core to rapidly switch magnetization. As a result, there is a need for materials with the right combination of low coercivity, low magnetic remanence, high relative permeability, and high saturation magnetization to achieve these goals. Iron nitride is an attractive soft magnetic material for incorporation into an amine/epoxy resin matrix. This permits the synthesis of net-shaped SMCs using a “bottom-up” approach for overcoming the limitations of current state-of-the-art SMCs made via conventional powder metal processing techniques. In this work we present the fabrication of various net-shaped, iron nitride-based SMCs using two different amine/epoxy resin systems and their magnetic characterization. The maximum volume loading of iron nitride reached was ∼77% via hot pressing, which produced SMCs with a saturation magnetic polarization (Js) of ∼0.9 T, roughly 2–3 times the Js of soft ferrites.

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.