Performance assessment is an important tool to estimate the long-term safety for a nuclear waste repository. Performance assessment simulations are subject to multiple kinds of uncertainty including stochastic uncertainty, state of knowledge uncertainty, and model uncertainty. Task F1 of the DECOVALEX project involves comparison of the models and methods used in post-closure performance assessment of deep geologic repositories in fractured crystalline rock, providing an opportunity to compare the effects of different sources of uncertainty. A generic reference case for a mined repository in fractured crystalline rock was put together by participating teams, where each team was responsible for determining how best to represent and implement the model. This work presents the preliminary crystalline reference case results for the Department of Energy (DOE) team.
This paper describes and analyzes the Discrete Direct (DD) model calibration and uncertainty propagation approach for computational models calibrated to data from sparse replicate tests of stochastically varying phenomena. The DD approach consists of generating and propagating discrete realizations of possible calibration parameter values corresponding to possible realizations of the uncertain inputs and outputs of the experiments. This is in contrast to model calibration methods that attempt to assign or infer continuous probability density functions for the calibration parameters. The DD approach straightforwardly accommodates aleatory variabilities and epistemic uncertainties (interval and/or probabilistically represented) in system properties and behaviors, in input initial and boundary conditions, and in measurement uncertainties of experimental inputs and outputs. In particular, the approach has several advantages over Bayesian and other calibration techniques in capturing and utilizing the information obtained from the typically small number of replicate experiments in model calibration situations, especially when sparse realizations of random function data like force-displacement curves from replicate material tests are used for calibration. The DD approach better preserves the fundamental information from the experimental data in a way that enables model predictions to be more directly tied to the supporting experimental data. The DD methodology is also simpler and typically less expensive than other established calibration-UQ approaches, is straightforward to implement, and is plausibly more reliably conservative and accurate for sparse-data calibration-UQ problems. The methodology is explained and analyzed in this paper under several regimes of model calibration and uncertainty propagation circumstances.
We demonstrate an optical waveguide device capable of supporting the optical power necessary for trapping a single atom or a cold-atom ensemble with evanescent fields. Our photonic integrated platform successfully manages optical powers of ~30mW.
A novel method for fault classification and location is presented in this paper. This method is divided into an initial signal processing stage that is followed by a machine learning stage. The initial stage analyzes voltages and currents with a window-based approach based on the dynamic mode decomposition (DMD) and then applies signal norms to the resulting DMD data. The outputs for the signal norms are used as features for a random-forests for classifying the type of fault in the system as well as for fault location purposes. The method was tested on a small distribution system where it showed an accuracy of 100% in fault classification and a mean error of ~ 30 m when predicting the fault location.
Energy storage is an extremely flexible grid asset than can provide a wide range of services. Unfortunately, energy storage is often relatively expensive compared to other options. With the emphasis on decarbonization, energy storage is required to buffer the intermittency associated with variable renewable generation. This paper calculates the maximum potential revenue from an energy storage system engaged in day-ahead market arbitrage in the California Independent System Operator (CAISO) region and uses these results to estimate the distribution of break-even capital costs. Break-even cost data is extremely useful as it provides insight into expected market penetration given a target capital cost. This information is also valuable for setting policy related to energy storage incentives as well as for setting price targets for research and development initiatives. The potential annual revenue of a generic battery energy storage system (BESS) participating in the CAISO day-ahead energy market was analyzed for 2,145 nodes over a seven year period (2014-2020). This data was used to estimate the break-even capital cost for each node as well as the cost requirements for several internal rate of return scenarios. Based on the analysis, the capital costs of lithium-ion systems must be reduced by approximately 80% from current levels to enable arbitrage applications to have a reasonable rate of return.
In recent years we have been exploring a novel asynchronous, ballistic physical model of reversible computing, variously termed ABRC (Asynchronous Ballistic Reversible Computing) or BARC (Ballistic Asynchronous Reversible Computing). In this model, localized information-bearing pulses propagate bidi-rectionally along nonbranching interconnects between I/O ports of stateful circuit elements, which carry out reversible transformations of the local digital state. The model appears suitable for implementation in superconducting circuits, using the naturally quantized configuration of magnetic flux in the circuit to encode digital information. One of the early research thrusts in this effort involves the enumeration and classification, at an abstract theoretical level, of the distinct possible reversible digital functional behaviors that primitive BARC circuit elements may exhibit, given the applicable conservation and symmetry constraints in superconducting implementations. In this paper, we describe the motivations for this work, outline our research methodology, and summarize some of the noteworthy preliminary results to date from our theoretical study of BARC elements for bipolarized pulses, and having up to three I/O ports and two internal digital states.
Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting machine learning model requires significantly fewer data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on two different material datasets, where one experimental and one computational dataset is used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce and noisy or when the dimensionality is high, and monotonicity is where supported by strong physical reasoning.
As the legacy distance protection schemes are starting to transition from impedance-based to traveling wave (TW) time-based, it is important to perform diligent simulations prior to commissioning the TW relay. Since Control-Hardware-In-the-Loop (CHIL) simulations have recently become a common practice for power system research, this work aims to illustrate some limitations in the integration of commercially available TW relays in CHIL for transmission-level simulations. The interconnection of Frequency-Dependent (FD) with PI-modeled transmission lines, which is a common practice in CHIL, may lead to sharp reflections that ease the relaying task. However, modeling contiguous lines as FD, or the presence of certain shunt loads, may cover certain TW reflections. As a consequence, the fault location algorithm in the relay may lead to a wrong calculation. In this paper, a qualitative comparison of the performance of commercially available TW relay is carried out to show how the system modeling in CHIL may affect the fault location accuracy.
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.
Using neural networks to solve variational problems, and other scientific machine learning tasks, has been limited by a lack of consistency and an inability to exactly integrate expressions involving neural network architectures. We address these limitations by formulating a polynomial-spline network, a novel shallow multilinear perceptron (MLP) architecture incorporating free knot B-spline basis functions into a polynomial mixture-of-experts model. Effectively, our architecture performs piecewise polynomial approximation on each cell of a trainable partition of unity while ensuring the MLP and its derivatives can be integrated exactly, obviating a reliance on sampling or quadrature and enabling error-free computation of variational forms. We demonstrate hp-convergence for regression problems at convergence rates expected from approximation theory and solve elliptic problems in one and two dimensions, with a favorable comparison to adaptive finite elements.
As presented above, because similar existing DOE-managed SNF (DSNF) from previous reactors have been evaluated for disposal pathways, we use this knowledge/experience as a broad reference point for initial technical bases for preliminary dispositioning of potential AR SNF. The strategy for developing fully-formed gap analyses for AR SNF entails the primary step of first obtaining all the defining characteristics of the AR SNF waste stream from the AR developers. Utilizing specific and accurate information/data for developing the potential disposal inventory to be evaluated is a key principle start for success. Once the AR SNF waste streams are defined, the initial assessments would be based on comparison to appropriate existing SNF/waste forms previously analyzed (prior experience) to make a determination on feasibility of direct disposal, or the need to further evaluate due to differences specific to the AR SNF. Assessments of criticality potential and controls would also be performed to assess any R&D gaps to be addressed in that regard as well. Although some AR SNF may need additional treatment for waste form development, these aspects may also be constrained and evaluated within the context of disposal options, including detailed gap analysis to identify further R&D activities to close the gaps.
There are unique challenges associated with protection and self-healing of microgrids energized by multiple inverterbased distributed energy resources. In this study, prioritized undervoltage load shedding and undervoltage-supervised overcurrent (UVOC) for fault isolation are demonstrated using PSCAD. The PSCAD implementations of these relays are described in detail, and their operation in a self-healing microgrid is demonstrated.
Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are violated, the existing system security margins will be largely unknown. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of Reinforcement-based Machine Learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. The approach requires the training of a Machine Learning Agent to accomplish this task using modeled data and employs it as a decision support tool under severe, near-blackout conditions.
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 National Academy of Sciences, Engineering, and Medicine (NASEM) defines reproducibility as 'obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis,' and replicability as 'obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data' [1]. Due to an increasing number of applications of artificial intelligence and machine learning (AI/ML) to fields such as healthcare and digital medicine, there is a growing need for verifiable AI/ML results, and therefore reproducible research and replicable experiments. This paper establishes examples of irreproducible AI/ML applications to medical sciences and quantifies the variance of common AI/ML models (Artificial Neural Network, Naive Bayes classifier, and Random Forest classifiers) for tasks on medical data sets.
Research is presented for carbon emissions abatement utilizing concentrating solar power (CSP) heating for culinary industrial process heat applications of roasting peppers. For this investigation the Sandia National Laboratories (SNL) performed high-intensity flux profile heating, as high as approximately 12.2 W/cm2 roasting peppers near 615oC. This work also explores the suitability of culinary roasting as applied to different forms of CSP heating as well as techno-economic costs. Traditionally, chile pepper roasting has used propane gas source heating to achieve similar temperatures and food roasting profiles in batch style processing. Here, the investigators roasted peppers on the top level of the National Solar Thermal Test Facility (NSTTF) solar tower for multiple roasting trials, with and without water. For comparison, the team also performed roasting from a traditional propane gas heating source, monitoring the volume of propane being consumed over time to assess carbon emissions that were abated using CSP. Results found that roasting peppers with CSP facilitated approximately 26 MJ of energy that abated approximately 0.122 kg CO2/kg chile for a 10 kg bag. The team also determined that pre-wetting the peppers before roasting both under propane and CSP heat sources increased the roast time by approximately 3 minutes to achieve the same qualitative optimal roast state compared to dry peppers.
Residual stress is a contributor to stress corrosion cracking (SCC) and a common byproduct of additive manufacturing (AM). Here the relationship between residual stress and SCC susceptibility in laser powder bed fusion AM 316L stainless steel was studied through immersion in saturated boiling magnesium chloride per ASTM G36-94. The residual stress was varied by changing the sample height for the as-built condition and additionally by heat treatments at 600°C, 800°C, and 1,200°C to control, and in some cases reduce, residual stress. In general, all samples in the as-built condition showed susceptibility to SCC with the thinner, lower residual stress samples showing shallower cracks and crack propagation occurring perpendicular to melt tracks due to local residual stress fields. The heat-treated samples showed a reduction in residual stress for the 800°C and 1,200°C samples. Both were free of cracks after >300 h of immersion in MgCl2, while the 600°C sample showed similar cracking to their as-built counterpart. Geometrically necessary dislocation (GND) density analysis indicates that the dislocation density may play a major role in the SCC susceptibility.
This paper reports the experimental comparison of two silicon photomultipliers (SiPMs): the MicroFJ-30035 by ONSemi and the ASD-NUV3S-P by AdvanSiD, in terms of gain, dark count rate, and crosstalk probability. SiPMs are solid state photon detectors that enable high sensitivity light readout. They have low-voltage power requirements, small form factor, and are durable. For these reasons, they are being considered as replacements for vacuum photomultiplier tubes in some applications. However, their performance relies on several parameters, which need to be carefully characterized to enable their high-fidelity simulation and SiPM-based design of devices capable to operate in harsh environments. The parameters tend to vary between manufacturers and processing technologies. In this work, we have compared the MicroFJ and ASD SiPMs in terms of gain, dark count rate, and crosstalk probability. We found that the dark count rate of the MicroFJ was 16% higher than the ASD. Also, the gain of the MicroFJ is 3.5 times higher than the ASD. Finally, the crosstalk probability of the ASD 1.96 times higher than the MicroFJ. Our findings are in good agreement with manufacturer reported values.
The Cramér-Rao Lower Bound (CRLB) is used as a classical benchmark to assess estimators. Online algorithms for estimating modal properties from ambient data, i.e., mode meters, can benefit from accurate estimates of forced oscillations. The CRLB provides insight into how well forced oscillation parameters, e.g., frequency and amplitude, can be estimated. Previous works have solved the lower bound under a single-channel PMU measurement; thus, this paper extends works further to study CRLB under two-channel PMU measurements. The goal is to study how correlated/uncorrelated noise can affect estimation accuracy. Interestingly, these studies shows that correlated noise can decrease the CRLB in some cases. This paper derives the CRLB for the two-channel case and discusses factors that affect the bound.
This paper applies sensitivity and uncertainty analysis to compare two model alternatives for fuel matrix degradation for performance assessment of a generic crystalline repository. The results show that this model choice has little effect on uncertainty in the peak 129I concentration. The small impact of this choice is likely due to the higher importance of uncertainty in the instantaneous release fraction and differences in epistemic uncertainty between the alternatives.
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.
We investigate the space complexity of two graph streaming problems: MAX-CUT and its quantum analogue, QUANTUM MAX-CUT. Previous work by Kapralov and Krachun [STOC 19] resolved the classical complexity of the classical problem, showing that any (2 - ?)-approximation requires O(n) space (a 2-approximation is trivial with O(log n) space). We generalize both of these qualifiers, demonstrating O(n) space lower bounds for (2 - ?)-approximating MAX-CUT and QUANTUM MAX-CUT, even if the algorithm is allowed to maintain a quantum state. As the trivial approximation algorithm for QUANTUM MAX-CUT only gives a 4-approximation, we show tightness with an algorithm that returns a (2 + ?)-approximation to the QUANTUM MAX-CUT value of a graph in O(log n) space. Our work resolves the quantum and classical approximability of quantum and classical Max-Cut using o(n) space.We prove our lower bounds through the techniques of Boolean Fourier analysis. We give the first application of these methods to sequential one-way quantum communication, in which each player receives a quantum message from the previous player, and can then perform arbitrary quantum operations on it before sending it to the next. To this end, we show how Fourier-analytic techniques may be used to understand the application of a quantum channel.
Large-scale deployment of energy storage systems is a pivotal step toward achieving the clean energy goals of the future. An accurate and publicly accessible database on energy storage projects can help accelerate deployment by providing valuable information and characteristic data to different stakeholders. The U.S. Department of Energy's Global Energy Storage Database (GESDB) aims at providing high-quality and accurate data on energy storage projects around the globe. This paper first provides an overview of the GESDB, briefly describing its features and overall usage. This is followed by a detailed description of the procedure used to validate the database. In doing so, the paper aims at improving the usability of the website while enhancing its value to the community. Furthermore, the presented validation procedure makes the underlying assumptions transparent to the public so that data misinterpretation can be minimized/avoided.
This work describes the development and testing of a carbon dioxide seeding system for the Sandia Hypersonic Wind Tunnel. The seeder injects liquid carbon dioxide into the tunnel, which evaporates in the nitrogen supply line and then condenses during the nozzle expansion into a fog of particles that scatter light via Rayleigh scattering. A planar laser scattering (PLS) experiment is conducted in the boundary layer and wake of a cone at Mach 8 to evaluate the success of the seeder. Second-mode waves and turbulence transition were well-visualized by the PLS in the boundary layer and wake. PLS in the wake also captured the expansion wave over the base and wake recompression shock. No carbon dioxide appears to survive and condense in the boundary layer or wake, meaning alternative seeding methods must be explored to extract measurements within these regions. The seeding system offers planar flow visualization opportunities and can enable quantitative velocimetry measurements in the future, including filtered Rayleigh scattering.
For the model-based control of low-voltage microgrids, state and parameter information are required. Different optimal estimation techniques can be employed for this purpose. However, these estimation techniques require knowledge of noise covariances (process and measurement noise). Incorrect values of noise covariances can deteriorate the estimator performance, which in turn can reduce the overall controller performance. This paper presents a method to identify noise covariances for voltage dynamics estimation in a microgrid. The method is based on the autocovariance least squares technique. A simulation study of a simplified 100 kVA, 208 V microgrid system in MATLAB/Simulink validates the method. Results show that estimation accuracy is close to the actual value for Gaussian noise, and non-Gaussian noise has a slightly larger error.
Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi-disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this and analogous resources. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data, in conjunction with new and preceding geoscientific measurements and observations, are being integrated and evaluated in this study for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings (i.e., whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs) will also be evaluated. This paper provides details and current updates on the course of this study in-progress.
Nonlinear force appropriation is an extension of its linear counterpart where sinusoidal excitation is applied to a structure with a modal shaker and phase quadrature is achieved between the excitation and response. While a standard practice in modal testing, modal shaker excitation has the potential to alter the dynamics of the structure under test. Previous studies have been conducted to address several concerns, but this work specifically focuses on a shaker-structure interaction phenomenon which arises during the force appropriation testing of a nonlinear structure. Under pure-tone sinusoidal forcing, a nonlinear structure may respond not only at the fundamental harmonic but also potentially at sub- or superharmonics, or it can even produce aperiodic and chaotic motion in certain cases. Shaker-structure interaction occurs when the response physically pushes back against the shaker attachment, producing non-fundamental harmonic content in the force measured by the load cell, even for pure tone voltage input to the shaker. This work develops a model to replicate these physics and investigates their influence on the response of a nonlinear normal mode of the structure. Experimental evidence is first provided that demonstrates the generation of harmonic content in the measured load cell force during a force appropriation test. This interaction is replicated by developing an electromechanical model of a modal shaker attached to a nonlinear, three-mass dynamical system. Several simulated experiments are conducted both with and without the shaker model in order to identify which effects are specifically due to the presence of the shaker. The results of these simulations are then compared to the undamped nonlinear normal modes of the structure under test to evaluate the influence of shaker-structure interaction on the identified system's dynamics.
Tuned mass dampers are a common method implemented to control structure’s vibrations. Most tuned-mass dampers only transfer the mechanical energy of the primary system to a secondary system, but it is desirable to convert the primary systems’ mechanical energy into usable electric energy. This study achieves this by using a piezoelectric energy harvester as a tuned-mass damper. Additionally, this study focuses on improving the amount of energy harvested by including amplitude stoppers. Mechanical stoppers have been investigated to sufficiently widen the response of piezoelectric energy harvesters. Furthermore, magnetic stoppers are compared to the mechanical stopper’s response. A nonlinear reduced-order model using Galerkin discretization and Euler-Lagrange equations is developed. The goal of this study is to maximize the energy harvested from the absorber without negatively affecting the control of the primary structure.
Many, if not all, Waste Management Organisation programs will include criticality safety. As criticality safety in the long-term, i.e. considered over post-closure timescales in dedicated disposal facilities, is a unique challenge for geological disposal there is limited opportunity for sharing of experience within an individual organization/country. Therefore, sharing of experience and knowledge between WMOs to understand any similarities and differences will be beneficial in understanding where the approaches are similar and where they are not, and the reasons for this. To achieve this benefit a project on Post-Closure Criticality Safety has been established through the Implementing Geological Disposal - Technology Platform with the overall aim to facilitate the sharing of this knowledge. This project currently has 11 participating nations, including the United States and this paper presents the current position in the United States.
This work presents a high-speed laser-absorption-spectroscopy diagnostic capable of measuring temperature, pressure, and nitric oxide (NO) mole fraction in shock-heated air at a measurement rate of 500 kHz. This diagnostic was demonstrated in the High-Temperature Shock Tube (HST) facility at Sandia National Laboratories. The diagnostic utilizes a quantum-cascade laser to measure the absorbance spectra of two rovibrational transitions near 5.06 µm in the fundamental vibration bands (v" = 0 and 1) of NO in its ground electronic state (X2 Π1/2 ). Gas properties were determined using scanned-wavelength direct absorption and a recently established fitting method that utilizes a modified form of the time-domain molecular free-induction-decay signal (m-FID). This diagnostic was applied to acquire measurements in shock-heated air in the HST at temperatures ranging from approximately 2500 to 5500 K and pressures of 3 to 12 atm behind both incident and reflected shocks. The measurements agree well with the temperature predicted by NASA CEA and the pressure measured simultaneously using PCB pressure sensors. The measurements presented demonstrate that this diagnostic is capable of resolving the formation of NO in shock-heated air and the associated temperature change at the conditions studied.
Multi-model Monte Carlo methods have been illustrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation amongst the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC.
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.
Using the power balance method we estimate the maximum electric field on a conducting wall of a cavity containing an interior structure supporting eccentric coaxial modes in the frequency regime where the resonant modes are isolated from each other.