Publications

Abstract not provided.

It is impossible in practice to comprehensively test even small software programs due to the vastness of the reachable state space; however, modern cyber-physical systems such as aircraft require a high degree of confidence in software safety and reliability. Here we explore methods of generating test sets to effectively and efficiently explore the state space for a module based on the Traffic Collision Avoidance System (TCAS) used on commercial aircraft. A formal model of TCAS in the model-checking language NuSMV provides an output oracle. We compare test sets generated using various methods, including covering arrays, random, and a low-complexity input paradigm applied to 28 versions of the TCAS C program containing seeded errors. Faults are triggered by tests for all 28 programs using a combination of covering arrays and random input generation. Complexity-based inputs perform more efficiently than covering arrays, and can be paired with random input generation to create efficient and effective test sets. A random forest classifier identifies variable values that can be targeted to generate tests even more efficiently in future work, by combining a machine-learned fuzzing algorithm with more complex model oracles developed in model-based systems engineering (MBSE) software.

The installation of digital sensors, such as advanced meter infrastructure (AMI) meters, has provided the means to implement a wide variety of techniques to increase visibility into the distribution system, including the ability to calibrate the utility models using data-driven algorithms. One challenge in maintaining accurate and up-to-date distribution system models is identifying changes and event occurrences that happen during the year, such as customers who have changed phases due to maintenance or other events. This work proposes a method for the detection of phase change events that utilizes techniques from an existing phase identification algorithm. This work utilizes an ensemble step to obtain predicted phases for windows of data, therefore allowing the predicted phase of customers to be observed over time. The proposed algorithm was tested on four utility datasets as well as a synthetic dataset. The synthetic tests showed the algorithm was capable of accurately detecting true phase change events while limiting the number of false-positive events flagged. In addition, the algorithm was able to identify possible phase change events on two real datasets.

We propose the use of balanced iterative reducing and clustering using hierarchies (BIRCH) combined with linear regression to predict the reduced Young's modulus and hardness of highly heterogeneous materials from a set of nanoindentation experiments. We first use BIRCH to cluster the dataset according to its mineral compositions, which are derived from the spectral matching of energy-dispersive spectroscopy data through the modular automated processing system (MAPS) platform. We observe that grouping our dataset into five clusters yields the best accuracy as well as a reasonable representation of mineralogy in each cluster. Subsequently, we test four types of regression models, namely linear regression, support vector regression, Gaussian process regression, and extreme gradient boosting regression. The linear regression and Gaussian process regression provide the most accurate prediction, and the proposed framework yields R2 = 0.93 for the test set. Although the study is needed more comprehensively, our results shows that machine learning methods such as linear regression or Gaussian process regression can be used to accurately estimate mechanical properties with a proper number of grouping based on compositional data.

We have extended the computational singular perturbation (CSP) method to differential algebraic equation (DAE) systems and demonstrated its application in a heterogeneous-catalysis problem. The extended method obtains the CSP basis vectors for DAEs from a reduced Jacobian matrix that takes the algebraic constraints into account. We use a canonical problem in heterogeneous catalysis, the transient continuous stirred tank reactor (T-CSTR), for illustration. The T-CSTR problem is modelled fundamentally as an ordinary differential equation (ODE) system, but it can be transformed to a DAE system if one approximates typically fast surface processes using algebraic constraints for the surface species. We demonstrate the application of CSP analysis for both ODE and DAE constructions of a T-CSTR problem, illustrating the dynamical response of the system in each case. We also highlight the utility of the analysis in commenting on the quality of any particular DAE approximation built using the quasi-steady state approximation (QSSA), relative to the ODE reference case.

Abstract not provided.

The precise estimation of performance loss rate (PLR) of photovoltaic (PV) systems is vital for reducing investment risks and increasing the bankability of the technology. Until recently, the PLR of fielded PV systems was mainly estimated through the extraction of a linear trend from a time series of performance indicators. However, operating PV systems exhibit failures and performance losses that cause variability in the performance and may bias the PLR results obtained from linear trend techniques. Change-point (CP) methods were thus introduced to identify nonlinear trend changes and behaviour. The aim of this work is to perform a comparative analysis among different CP techniques for estimating the annual PLR of eleven grid-connected PV systems installed in Cyprus. Outdoor field measurements over an 8-year period (June 2006-June 2014) were used for the analysis. The obtained results when applying different CP algorithms to the performance ratio time series (aggregated into monthly blocks) demonstrated that the extracted trend may not always be linear but sometimes can exhibit nonlinearities. The application of different CP methods resulted to PLR values that differ by up to 0.85% per year (for the same number of CPs/segments).

Metal additive manufacturing allows for the fabrication of parts at the point of use as well as the manufacture of parts with complex geometries that would be difficult to manufacture via conventional methods (milling, casting, etc.). Additively manufactured parts are likely to contain internal defects due to the melt pool, powder material, and laser velocity conditions when printing. Two different types of defects were present in the CT scans of printed AlSi10Mg dogbones: spherical porosity and irregular porosity. Identification of these pores via a machine learning approach (i.e., support vector machines, convolutional neural networks, k-nearest neighbors’ classifiers) could be helpful with part qualification and inspections. The machine learning approach will aim to label the regions of porosity and label the type of porosity present. The results showed that a combination approach of Canny edge detection and a classification-based machine learning model (k-nearest neighbors or support vector machine) outperformed the convolutional neural network in segmenting and labeling different types of porosity.

This paper presents a run-to-run (R2R) controller for mechanical serial sectioning (MSS). MSS is a destructive material analysis process which repeatedly removes a thin layer of material and images the exposed surface. The images are then used to gain insight into the material properties and often to construct a 3-dimensional reconstruction of the material sample. Currently, an experience human operator selects the parameters of the MSS to achieve the desired thickness. The proposed R2R controller will automate this process while improving the precision of the material removal. The proposed R2R controller solves an optimization problem designed to minimize the variance of the material removal subject to achieving the expected target removal. This optimization problem was embedded in an R2R framework to provide iterative feedback for disturbance rejection and convergence to the target removal amount. Since an analytic model of the MSS system is unavailable, we adopted a data-driven approach to synthesize our R2R controller from historical data. The proposed R2R controller is demonstrated through simulations. Future work will empirically demonstrate the proposed R2R through experiments with a real MSS system.

With the increase in penetration of inverter-based resources (IBRs) in the electrical power system, the ability of these devices to provide grid support to the system has become a necessity. With standards previously developed for the interconnection requirements of grid-following inverters (GFLI) (most commonly photovoltaic inverters), it has been well documented how these inverters 'should' respond to changes in voltage and frequency. However, with other IBRs such as grid-forming inverters (GFMIs) (used for energy storage systems, standalone systems, and as uninterruptable power supplies) these requirements are either: not yet documented, or require a more in deep analysis. With the increased interest in microgrids, GFMIs that can be paralleled onto a distribution system have become desired. With the proper control schemes, a GFMI can help maintain grid stability through fast response compared to rotating machines. This paper will present an experimental comparison of commercially available GFMIand GFLI ' responses to voltage and frequency deviation, as well as the GFMIoperating as a standalone system and subjected to various changes in loads.

Frequent changes in penetration levels of distributed energy resources (DERs) and grid control objectives have caused the maintenance of accurate and reliable grid models for behind-the-meter (BTM) photovoltaic (PV) system impact studies to become an increasingly challenging task. At the same time, high adoption rates of advanced metering infrastructure (AMI) devices have improved load modeling techniques and have enabled the application of machine learning algorithms to a wide variety of model calibration tasks. Therefore, we propose that these algorithms can be applied to improve the quality of the input data and grid models used for PV impact studies. In this paper, these potential improvements were assessed for their ability to improve the accuracy of locational BTM PV hosting capacity analysis (HCA). Specifically, the voltage- and thermal-constrained hosting capacities of every customer location on a distribution feeder (1,379 in total) were calculated every 15 minutes for an entire year before and after each calibration algorithm or load modeling technique was applied. Overall, the HCA results were found to be highly sensitive to the various modeling deficiencies under investigation, illustrating the opportunity for more data-centric/model-free approaches to PV impact studies.

An array of Wave Energy Converters (WEC) is required to supply a significant power level to the grid. However, the control and optimization of such an array is still an open research question. This paper analyzes two aspects that have a significant impact on the power production. First the spacing of the buoys in a WEC array will be analyzed to determine the optimal shift between the buoys in an array. Then the wave force interacting with the buoys will be angled to create additional sequencing between the electrical signals. A cost function is proposed to minimize the power variation and energy storage while maximizing the delivered energy to the onshore point of common coupling to the electrical grid.

Integrating recent advancements in resilient algorithms and techniques into existing codes is a singular challenge in fault tolerance - in part due to the underlying complexity of implementing resilience in the first place, but also due to the difficulty introduced when integrating the functionality of a standalone new strategy with the preexisting resilience layers of an application. We propose that the answer is not to build integrated solutions for users, but runtimes designed to integrate into a larger comprehensive resilience system and thereby enable the necessary jump to multi-layered recovery. Our work designs, implements, and verifies one such comprehensive system of runtimes. Utilizing Fenix, a process resilience tool with integration into preexisting resilience systems as a design priority, we update Kokkos Resilience and the use pattern of VeloC to support application-level integration of resilience runtimes. Our work shows that designing integrable systems rather than integrated systems allows for user-designed optimization and upgrading of resilience techniques while maintaining the simplicity and performance of all-in-one resilience solutions. More application-specific choice in resilience strategies allows for better long-term flexibility, performance, and - importantly - simplicity.

Femtosecond laser electronic excitation tagging (FLEET) is a powerful unseeded velocimetry technique typically used to measure one component of velocity along a line, or two or three components from a dot. In this Letter, we demonstrate a dotted-line FLEET technique which combines the dense profile capability of a line with the ability to perform two-component velocimetry with a single camera on a dot. Our set-up uses a single beam path to create multiple simultaneous spots, more than previously achieved in other FLEET spot configurations. We perform dotted-line FLEET measurements downstream of a highly turbulent, supersonic nitrogen free jet. Dotted-line FLEET is created by focusing light transmitted by a periodic mask with rectangular slits of 1.6 × 40 mm2 and an edge-to-edge spacing of 0.5 mm, then focusing the imaged light at the measurement region. Up to seven symmetric dots spaced approximately 0.9 mm apart, with mean full-width at half maximum diameters between 150 and 350 µm, are simultaneously imaged. Both streamwise and radial velocities are computed and presented in this Letter.

Communication-assisted adaptive protection can improve the speed and selectivity of the protection system. However, in the event, that communication is disrupted to the relays from the centralized adaptive protection system, predicting the local relay protection settings is a viable alternative. This work evaluates the potential for machine learning to overcome these challenges by using the Prophet algorithm programmed into each relay to individually predict the time-dial (TDS) and pickup current (IPICKUP) settings. A modified IEEE 123 feeder was used to generate the data needed to train and test the Prophet algorithm to individually predict the TDS and IPICKUP settings. The models were evaluated using the mean average percentage error (MAPE) and the root mean squared error (RMSE) as metrics. The results show that the algorithms could accurately predict IPICKUP setting with an average MAPE accuracy of 99.961%, and the TDS setting with a average MAPE accuracy of 94.32% which is sufficient for protection parameter prediction.

Abstract not provided.

We propose a two-stage scenario-based stochastic optimization problem to determine investments that enhance power system resilience. The proposed optimization problem minimizes the Conditional Value at Risk (CVaR) of load loss to target low-probability high-impact events. We provide results in the context of generator winterization investments in Texas using winter storm scenarios generated from historical data collected from Winter Storm Uri. Results illustrate how the CVaR metric can be used to minimize the tail of the distribution of load loss and illustrate how risk-Aversity impacts investment decisions.

OpenMP 5.0 added support for reductions over explicit tasks. This expands the previous reduction support that was limited primarily to worksharing and parallel constructs. While the scope of a reduction operation in a worksharing construct is the scope of the construct itself, the scope of a task reduction can vary. This difference requires syntactical means to define the scope of reductions, e.g., the task_reduction clause, and to associate participating tasks, e.g., the in_reduction clause. Furthermore, the disassociation of the number of threads and the number of tasks creates space for different implementations in the OpenMP runtime. In this work, we provide insights into the behavior and performance of task reduction implementations in GCC/g++ and LLVM/Clang. Our results indicate that task reductions are well supported by both compilers, but their performance differs in some cases and is often determined by the efficiency of the underlying task management.

Abstract not provided.

Applications such as counterfeit identification, quality control, and non-destructive material identification benefit from improved spatial and compositional analysis. X-ray Computed Tomography is used in these applications but is limited by the X-ray focal spot size and the lack of energy-resolved data. Recently developed hyperspectral X-ray detectors estimate photon energy, which enables composition analysis but lacks spatial resolution. Moving beyond bulk homogeneous transmission anodes toward multi-metal patterned anodes enables improvements in spatial resolution and signal-to-noise ratios in these hyperspectral X-ray imaging systems. We aim to design and fabricate transmission anodes that facilitate confirmation of previous simulation results. These anodes are fabricated on diamond substrates with conventional photolithography and metal deposition processes. The final transmission anode design consists of a cluster of three disjoint metal bumps selected from molybdenum, silver, samarium, tungsten, and gold. These metals are chosen for their k-lines, which are positioned within distinct energy intervals of interest and are readily available in standard clean rooms. The diamond substrate is chosen for its high thermal conductivity and high transmittance of X-rays. The feature size of the metal bumps is chosen such that the cluster is smaller than the 100 m diameter of the impinging electron beam in the X-ray tube. This effectively shrinks the X-ray focal spot in the selected energy bands. Once fabricated, our transmission anode is packaged in a stainless-steel holder that can be retrofitted into our existing X-ray tube. Innovations in anode design enable an inexpensive and simple method to improve existing X-ray imaging systems.

This study presents a method that can be used to gain information relevant to determining the corrosion risk for spent nuclear fuel (SNF) canisters during extended dry storage. Currently, it is known that stainless steel canisters are susceptible to chloride-induced stress corrosion cracking (CISCC). However, the rate of CISCC degradation and the likelihood that it could lead to a through-wall crack is unknown. This study uses well-developed computational fluid dynamics and particle-tracking tools and applies them to SNF storage to determine the rate of deposition on canisters. The deposition rate is determined for a vertical canister system and a horizontal canister system, at various decay heat rates with a uniform particle size distribution, ranging from 0.25 to 25 µm, used as an input. In all cases, most of the dust entering the overpack passed through without depositing. Most of what was retained in the overpack was deposited on overpack surfaces (e.g., inlet and outlet vents); only a small fraction was deposited on the canister itself. These results are provided for generalized canister systems with a generalized input; as such, this technical note is intended to demonstrate the technique. This study is a part of an ongoing effort funded by the U.S. Department of Energy, Nuclear Energy Office of Spent Fuel Waste Science and Technology, which is tasked with doing research relevant to developing a sound technical basis for ensuring the safe extended storage and subsequent transport of SNF. This work is being presented to demonstrate a potentially useful technique for SNF canister vendors, utilities, regulators, and stakeholders to utilize and further develop for their own designs and site-specific studies.

Area efficient self-correcting flip-flops for use with triple modular redundant (TMR) soft-error hardened logic are implemented in a 12-nm finFET process technology. The TMR flip-flop slave latches self-correct in the clock low phase using Muller C-elements in the latch feedback. These C-elements are driven by the two redundant stored values and not by the slave latch itself, saving area over a similar implementation using majority gate feedback. These flip-flops are implemented as large shift-register arrays on a test chip and have been experimentally tested for their soft-error mitigation in static and dynamic modes of operation using heavy ions and protons. We show how high clock skew can result in susceptibility to soft-errors in the dynamic mode, and explain the potential failure mechanism.

Abstract not provided.

Centered on modern C++ and the SYCL standard for heterogeneous programming, Data Parallel C++ (dpc++) and Intel's oneAPI software ecosystem aim to lower the barrier to entry for the use of accelerators like FPGAs in diverse applications. In this work, we consider the usage of FPGAs for scientific computing, in particular with a multigrid solver, MueLu. We report on early experiences implementing kernels of the solver in DPC++ for execution on Stratix 10 FPGAs, and we evaluate several algorithmic design and implementation choices. These choices not only impact performance, but also shed light on the capabilities and limitations of DPC++ and oneAPI.

Abstract not provided.