Results for “Importance sampling” — Page 74

DOE OSTI2023

pH dependence of brown-carbon optical properties in cloud water

Abstract. Light-absorbing organic species present in aerosols, collectively called brown carbon (BrC), have important but highly uncertain effects on climate. Clouds likely represent a significant medium for secondary BrC production and for bleaching reactions, though the relative importance of the formation and loss processes in clouds is unknown at present. The acidity (or pH) of atmospheric particles and clouds affects the optical properties of BrC and bleaching rates. Given the wide variability of pH in the atmosphere (pH in particles and clouds ranges from −1 to 8), the optical properties of BrC and its bleaching behavior are expected to vary significantly, and the link between pH and BrC is yet another uncertainty in attempts to constrain its climate forcing effects. In this work, we characterize the pH dependence of BrC optical properties – including light absorption at 365 nm (Abs365), the mass absorption coefficient (MAC365), and the absorption Ångström exponent (AAE) – in bulk cloud water sampled from the summit of Whiteface Mountain, NY. In all samples (n=17), Abs365 and MAC365 increased linearly with increasing pH, highlighting the importance of reporting pH in studies of BrC in aqueous media. There was strong variability in the sensitivity of Abs365 to pH, with normalized slopes that ranged from 5.1 % to 17.2 % per pH unit. The normalized slope decreased strongly with increasing cloud water [K+], suggesting that the non-biomass-burning BrC has optical properties that are more sensitive to pH than BrC associated with biomass burning. AAE also showed a distinct pH dependence as it was relatively flat between pH 1.5–5 and then decreased significantly above pH 5. The cloud water composition was used to inform thermodynamic predictions of aerosol pH upwind and/or downwind of Whiteface Mountain and the subsequent changes in BrC optical properties. Overall, these results show that, in addition to secondary BrC production, photobleaching, and the altitudinal distribution, the climate forcing of BrC is quite strongly affected by its pH-dependent absorption.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Testing and Characterization to Develop a Mechanistic Explanation for Unsaturated Drift of Fiber Optic Sensors during High-Dose Irradiation

The primary limitation for any optical fiber-based sensor for nuclear reactor applications is radiation-induced attenuation (RIA) of the transmitted and/or reflected signals. Based on several recent studies, RIA is tolerable for some fused silica optical fibers with the proper choice of sensing wavelength and fiber dopants. For extreme temperature applications (> 1000°C), sapphire optical fibers have been proposed; however, recent optical transmission measurements performed on bulk sapphire samples showed prohibitively large RIA. For some sensors, radiation-induced dimensional changes in the fiber materials can also cause significant drift. Moreover, the drift that was observed in numerous experiments performed in the High Flux Isotope Reactor (HFIR), the Advanced Test Reactor, the Massachusetts Institute of Technology Reactor, and other international facilities far exceeded what would be expected based on compaction of fused silica glass. Clearly, additional work is needed to better understand the origins of both RIA and radiation-induced drift in both silica and sapphire optical fiber-based sensors before these sensors can be reliably deployed for nuclear applications. This work evaluated the underlying mechanisms that may be responsible for RIA and drift in silica and sapphire materials. First, detailed characterization was performed on bulk fused silica glass samples that were previously irradiated to different neutron fluences at different temperatures to better understand the structural changes that drive radiation-induced drift in the absence of coating effects that are discussed later. Results show that the non-monotonic compaction that occurs with increasing neutron fluence continues up to fast neutron fluences approaching 10 22 n/cm 2 , which has important implications for physics-based models that may be used to compensate for the sensor drift. Initial Raman spectroscopy and synchrotron x-ray diffraction provide insights into the nature of the structural changes. Next, detailed characterizations were performed on silica fibers with various coatings that were subjected to several different thermal treatments. The hypothesis is that the coatings convert to carbon-rich materials that compact under irradiation, putting a large compressive strain on the fiber. Out-of-pile testing confirms that both polyimide and acrylate fiber coatings convert to glassy carbon (GC) materials when heated under inert conditions, and the degree of order (i.e., graphitization) increases with increasing temperature. The results provide increasingly strong evidence that the combination of polymeric coatings and inert (or vacuum) conditions render fiber optic sensors susceptible to significant radiation-induced drift that would not otherwise exist in uncoated fibers. Finally, transmission electron microscopy was performed on bulk sapphire samples that were irradiated to two neutron fluences at different temperatures to gain insights into the potential mechanisms driving the prohibitive RIA at higher neutron fluences and temperatures. Contrary to previous hypotheses, results show that scattering from radiation-induced voids cannot explain the observed RIA. Similarly, models for scattering losses from dislocation loops also do not agree with the experimental results. Instead, fitting to the experimental data shows that increased absorption from aluminum vacancy centers is the most likely explanation for the the prohibitively large RIA that was observed at high irradiation temperature and dose. In addition, the voids that formed in these single-crystal samples were found to align along the basal plane (a-axis) as opposed to that seen in previous observations of c-axis alignment in polycrystalline samples, which could have important implications for anisotropic swelling and other phenomena that could affect sensor performance at high neutron fluence.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Testing and Characterization to Develop a Mechanistic Explanation for Unsaturated Drift of Fiber Optic Sensors during High-Dose Irradiation

The primary limitation for any optical fiber-based sensor for nuclear reactor applications is radiation-induced attenuation (RIA) of the transmitted and/or reflected signals. Based on several recent studies, RIA is tolerable for some fused silica optical fibers with the proper choice of sensing wavelength and fiber dopants. For extreme temperature applications (> 1000°C), sapphire optical fibers have been proposed; however, recent optical transmission measurements performed on bulk sapphire samples showed prohibitively large RIA. For some sensors, radiation-induced dimensional changes in the fiber materials can also cause significant drift. Moreover, the drift that was observed in numerous experiments performed in the High Flux Isotope Reactor (HFIR), the Advanced Test Reactor, the Massachusetts Institute of Technology Reactor, and other international facilities far exceeded what would be expected based on compaction of fused silica glass. Clearly, additional work is needed to better understand the origins of both RIA and radiation-induced drift in both silica and sapphire optical fiber-based sensors before these sensors can be reliably deployed for nuclear applications. This work evaluated the underlying mechanisms that may be responsible for RIA and drift in silica and sapphire materials. First, detailed characterization was performed on bulk fused silica glass samples that were previously irradiated to different neutron fluences at different temperatures to better understand the structural changes that drive radiation-induced drift in the absence of coating effects that are discussed later. Results show that the non-monotonic compaction that occurs with increasing neutron fluence continues up to fast neutron fluences approaching 10 22 n/cm 2 , which has important implications for physics-based models that may be used to compensate for the sensor drift. Initial Raman spectroscopy and synchrotron x-ray diffraction provide insights into the nature of the structural changes. Next, detailed characterizations were performed on silica fibers with various coatings that were subjected to several different thermal treatments. The hypothesis is that the coatings convert to carbon-rich materials that compact under irradiation, putting a large compressive strain on the fiber. Out-of-pile testing confirms that both polyimide and acrylate fiber coatings convert to glassy carbon (GC) materials when heated under inert conditions, and the degree of order (i.e., graphitization) increases with increasing temperature. The results provide increasingly strong evidence that the combination of polymeric coatings and inert (or vacuum) conditions render fiber optic sensors susceptible to significant radiation-induced drift that would not otherwise exist in uncoated fibers. Finally, transmission electron microscopy was performed on bulk sapphire samples that were irradiated to two neutron fluences at different temperatures to gain insights into the potential mechanisms driving the prohibitive RIA at higher neutron fluences and temperatures. Contrary to previous hypotheses, results show that scattering from radiation-induced voids cannot explain the observed RIA. Similarly, models for scattering losses from dislocation loops also do not agree with the experimental results. Instead, fitting to the experimental data shows that increased absorption from aluminum vacancy centers is the most likely explanation for the the prohibitively large RIA that was observed at high irradiation temperature and dose. In addition, the voids that formed in these single-crystal samples were found to align along the basal plane (a-axis) as opposed to that seen in previous observations of c-axis alignment in polycrystalline samples, which could have important implications for anisotropic swelling and other phenomena that could affect sensor performance at high neutron fluence.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling

When sampling for Bayesian inference, one popular approach in the computational field is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS), which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice when relying on computationally expensive forward models. We propose Latent Hamiltonian neural networks (L-HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, L-HNNs do not require numerical gradients of the target density during sampling, and hence numerous evaluations of the forward computational model. Moreover, L-HNNs satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose the integration of L-HNNs in an online error monitoring scheme, in which numerical gradients of the target density are used for a few samples whenever the L-HNNs prediction errors are large. This online error monitor scheme prevents sample degeneracy in regions of low probability density and ensures robust uncertainty quantification. We demonstrate L-HNNs in NUTS with online error monitoring on several analytical examples involving complex, heavy-tailed, and high-local-curvature probability densities. We then demonstrate the applicability of L-HNNs in NUTS to two computational case studies, namely the Allen-Cahn stochastic partial differential equation and an elliptic partial differential equation with 25 and 50 inference parameters, respectively. Overall, the L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. In conclusion, compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1–2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient (which is a measure of both the sampling quality and the computational expense) by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Status Update on the High Precision Isotopic Measurements on High Burnup LWR Fuel in 2020

The US Department of Energy (DOE) Office of Nuclear Energy (NE) is currently investigating the feasibility of directly disposing dual-purpose (storage and transportation) canisters (DPCs) in a spent nuclear fuel (SNF) repository. Criticality during the repository performance period (10,000 years or more) is one of the major concerns related to direct disposal of DPCs, specifically as the system undergoes degradation in the repository environment and timeframe. Oak Ridge National Laboratory (ORNL) is developing an as-loaded criticality analysis methodology using full (actinides + fission products) burnup credit that exploits the inherent criticality margin associated with actual canister-specific loading configuration. Burnup credit criticality analysis requires validation of the depletion/decay codes used to generate the burned isotopic inventory of an assembly by comparing the code-predicted inventory with experimentally determined isotopic data. Currently, isotopic measurement data for boiling water reactors (BWR) SNF are limited, and additional measurements will be highly beneficial for BWR burnup credit analysis, which is essential to demonstrate disposability of BWR DPCs. Moreover, new pressurized water reactor (PWR) samples of isotopic measurements will expand the PWR sample population and consequently will greatly improve sample statistics. In turn, this will reduce uncertainty in the computational determination of the isotopic composition of commercial SNF and eliminate additional penalties currently used for lack of data. As part of the isotopic analysis task, eight diverse, high burnup (HBU) samples from several PWR rods are being dissolved and isotopically analyzed to provide high-quality measurement data for PWR SNF to reducing the uncertainties associated with PWR isotopic depletion validation. The isotopics of interest focus on nuclides important to burnup credit, shielding, and decay heat in PWR SNF. Additionally, eight high burnup BWR fuel samples from the Limerick nuclear power plant have been identified for measurement to support reducing uncertainties associated with BWR isotopic depletion validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1979

Geochemical data synthesis and analysis

Data obtained at the Goddard Flight Center were collected for the purpose of completing analyses started at Goddard in order to maximize the scientific yield of the geochemistry program which was terminated in 1977. The major analytical task undertaken was to complete Gd analyses on a large number of samples already analyzed by mass spectrometry for other rare earth element abundances at Goddard. Gd values are important for pinning down the central part of the geochemically significant rare earth abundance pattern and are especially useful in the high precision definition of the utilitarian Eu anomaly. Isotope-dilution Gd abundances were obtained for 39 samples. The data are for 27 partition-coefficient samples, six Apollo 15 and 16 breccia samples, four terrestrial impactities, and associated rock standards.

Philpotts, J. A.↗

DOE OSTI2024

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials. However, MLIPs are only as accurate and robust as the data on which they are trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. This work paves the way for robust high-throughput development of MLIPs across any compositional complexity.

Qi, Ji (ORCID:0000000158089931)↗

NASA NTRS2019

Was the Sun a Slow Rotator? Sodium and Potassium Constraints from the Lunar Regolith

While the Earth and Moon are generally similar in composition, a notable difference between the two is the apparent depletion in moderately volatile elements in lunar samples. This is often attributed to the formation process of the Moon, and it demonstrates the importance of these elements as evolutionary tracers. Here we show that paleo space weather may have driven the loss of a significant portion of moderate volatiles, such as sodium and potassium, from the surface of the Moon. The remaining sodium and potassium in the regolith is dependent on the primordial rotation state of the Sun. Notably, given the joint constraints shown in the observed degree of depletion of sodium and potassium in lunar samples and the evolution of activity of solar analogs over time, the Sun is highly likely to have been a slow rotator. Because the young Sun's activity was important in affecting the evolution of planetary surfaces, atmospheres, and habitability in the early Solar System, this is an important constraint on the solar activity environment at that time. Finally, as solar activity was strongest in the first billion years of the Solar System, when the Moon was most heavily bombarded by impactors, evolution of the Sun's activity may also be recorded in lunar crust and would be an important well-preserved and relatively accessible record of past Solar System processes.

Saxena, Prabal↗

NASA NTRS2019

Was the Sun a Slow Rotator? Sodium and Potassium Constraints from the Lunar Regolith

While the Earth and Moon are generally similar in composition, a notable difference between the two is the apparent depletion in moderately volatile elements in lunar samples. This is often attributed to the formation process of the Moon, and it demonstrates the importance of these elements as evolutionary tracers. Here we show that paleo space weather may have driven the loss of a significant portion of moderate volatiles, such as sodium and potassium, from the surface of the Moon. The remaining sodium and potassium in the regolith is dependent on the primordial rotation state of the Sun. Notably, given the joint constraints shown in the observed degree of depletion of sodium and potassium in lunar samples and the evolution of activity of solar analogs over time, the Sun is highly likely to have been a slow rotator. Because the young Sun's activity was important in affecting the evolution of planetary surfaces, atmospheres, and habitability in the early Solar System, this is an important constraint on the solar activity environment at that time. Finally, as solar activity was strongest in the first billion years of the Solar System, when the Moon was most heavily bombarded by impactors, evolution of the Sun's activity may also be recorded in lunar crust and would be an important well-preserved and relatively accessible record of past Solar System processes.

Prabal Saxena↗

NASA NTRSDate not supplied

Chronology of Accessory Phases in Lunar Meteorite Northwest Africa 12593

Lunar meteorites are complementary to the suite of rock and regolith samples collected by the Apollo missions. While the Apollo samples represent the most pristine material we have from the Moon, lunar meteorites represent a more global sampling of the lunar crust, albeit without geologic context [e.g. 1]. As such, lunar meteorites are important for understanding the composition of the lunar crust and the processes responsible for its formation and evolution. This work aims to understand the observed differences in accessory phase chronology and volatile chemistry between lunar meteorites and Apollo rocks. For example, apatite ages in Apollo samples range from ~3.83 to 4.28 Ga [e.g. 2, 3], while apatites in lunar meteorites range from ~3.00 to 4.44 Ga [e.g. 4, 5]. Additionally, a much larger range of Cl isotope compositions and concentrations is observed in lunar meteorites as compared to Apollo samples[e.g. 6]. It has recently been shown that impact metamorphism can influence both chronology and chemistry of phosphates [e.g. 7, 8]. Here we present the first chronologic results from a consortium study pairing chronology, volatile chemistry, and crystal structure of apatites in a lunar breccia meteorite. The results of this work will help inform interpretation of ongoing analyses of apatites separated from Apollo soils. Note: Extended abstract on document.

C. A. Crow↗

DOE OSTI2016

Investigation of Energy Selective Neutron Imaging at the PULSTAR Reactor

Neutron imaging is complementary to X-ray imaging where both are used to investigate the internal features of objects nondestructively. Historically, it only began three years after discovery of the neutron in 1932, however, neutron radiographs with reasonable quality were first made in the mid-fifties. Conventionally, the image is produced by placing the object between the neutron source and a two-dimensional position sensitive detector. The source commonly has a full energy spectrum (white beam) of thermal or cold neutrons to obtain high intensity in the sample and short exposure time. As a result, all neutron energies contribute to the obtained radiograph. Since the interaction of the neutron with matter is energy dependent, different energies produce different contrast on the radiographic image, hence, additional information about the object can be obtained. Neutron imaging that utilizes a narrow spectrum of energy instead of white beam is called energy selective neutron imaging. Many techniques have been explored to select a narrow band of neutron energy for the use in imaging such as the use velocity selectors, double crystal Monochromators, and time of flight techniques (at pulsed neutron sources). The PULSTAR reactor of North Carolina State University (1 MWth) currently has an operational neutron powder diffractometer (NPD) installed on beam tube no. 4, which was designed for neutron scattering experiments and can provide a monoenergetic beam of neutrons. The neutron energy could be selected by diffraction means using a Si bent perfect crystal as a Monochromator. In this work, an investigation of energy selective neutron imaging at the NPD is discussed in terms of the system performance and the ability of material discrimination on a test sample by observing the contrast variations of the neutron radiographs obtained using different neutron energies. Energy selective neutron imaging experiment was successfully tested at the PULSTAR reactor's NPD. Although, the uniform region is small and the exposure time is long in comparison with the NIF, both facilities exhibit similar spatial resolution which is a very important characteristic for any imaging facility. The contrast variation of neuron radiographs for the test sample obtained at different energies indicated a potential advantage for using energy selective neutron imaging over the white beam. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

Dealloying in Individual Nanoparticles and Thin Film Grains: A Bragg Coherent Diffractive Imaging Study

Dealloying is a process whereby selective dissolution results in a porous, strained structure often with new properties. The process is of both intrinsic and applied interest, and recently has been used to make highly active catalysts. The porosity has been studied using electron microscopy while the dealloying-induced strain has been studied at the ensemble level using X-ray diffraction. Despite the importance of local, for example, at the individual particle or grain level, strain in controlling the properties of the dealloyed material, it remains unresolved due to the difficulty of imaging 3D strain distributions with nanometer resolution in reactive environments. This information could play an integral role in understanding and controlling lattice strain for a variety of applications. Here, 3D strain distributions in individual nanoparticles and thin film grains in silver-gold alloys undergoing nitric acid-induced dealloying are imaged by Bragg coherent diffractive imaging. Particles exhibit dramatic changes in their local strains due to dealloying but grains do not. Furthermore, the average lattice in both grains and particles contracts during dealloying. In general, the results reveal significant dealloying-induced strain heterogeneity at the nanoscale in both isolated and extended samples, which may be utilized to develop advanced nanostructures for a variety of important applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

Development of Bayesian analysis program for extraction of polarisation observables at CLAS

At the mass scale of a proton, the strong force is not well understood. Various quark models exist, but it is important to determine which quark model(s) are most accurate. Experimentally, finding resonances predicted by some models and not others would give valuable insight into this fundamental interaction. Several labs around the world use photoproduction experiments to find these missing resonances. The aim of this work is to develop a robust Bayesian data analysis program for extracting polarisation observables from pseudoscalar meson photoproduction experiments using CLAS at Jefferson Lab. This method, known as nested sampling, has been compared to traditional methods and has incorporated data parallelisation and GPU programming. It involves an event-by-event likelihood function, which has no associated loss of information from histogram binning, and results can be easily constrained to the physical region. One of the most important advantages of the nested sampling approach is that data from different experiments can be combined and analysed simultaneously. Results on both simulated and previously analysed experimental data for the K{sup +}Λ channel will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Fitness factors impacting survival of a subsurface bacterium in contaminated groundwater

Many factors contribute to the ability of a microbial species to persist when encountering complexly contaminated environments, including time of exposure, the nature and concentration of contaminants, availability of nutritional resources, and possession of a combination of appropriate molecular mechanisms needed for survival. Herein we sought to identify genes that are most important for survival of Gram-negative Enterobacteriaceae in contaminated groundwater environments containing high concentrations of nitrate and metals using the metal-tolerant Oak Ridge Reservation isolate, Pantoea sp. MT58 (MT58). Survival fitness experiments in which a randomly barcoded transposon insertion (RB-TnSeq) library of MT58 was exposed directly to contaminated Oak Ridge Reservation groundwater samples from across a nitrate and mixed metal contamination plume were used to identify genes important for survival with increasing exposure times and concentrations of contaminants, and availability of a carbon source. Genes involved in controlling and using carbon, encoding transcriptional regulators, and related to Gram-negative outer membrane processes were among those found to be important for survival in contaminated Oak Ridge Reservation groundwater. A comparative genomics analysis of 75 Pantoea genus strains allowed us to further separate the survival determinants into core and non-core genes in the Pantoea pangenome, revealing insights into the survival of subsurface microorganisms during contaminant plume intrusion.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Testing for the Photon Doppler Broadening Data Sampling Bug in MCNP5/X

MCNP5/X has the capability to perform photon Doppler broadening to incorporate the effect of bound electrons on photon scattering. This effect is generally important if energy spectra or detector responses are desired for photons in the tens to hundreds of keY energy range. In the Fall of 2011, a diligent MCNP user noticed that the sampling routines in MCNP were inconsistent with t he data for sampling which atomic shell t he electron is bound. The data are provided in probability density function (PDF) format, whereas MCNP sampled the data as if they are in a cumulative density function (CDF) format, leading to erroneous results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

An Improved Sampling and Baiting Method for Phytophthora tropicalis and P. heveae Detection in Macadamia integrifolia

Macadamia nuts are, economically, the second most important crop in the state of Hawai’i. A recent decline in yield and acreage has been attributed to insect damage and diseases such as Macadamia Quick Decline (MQD) caused by Phytophthora tropicalis and P. heveae. To develop an improved methodology for the diagnosis and treatment of MQD, investigations were undertaken to better understand the pathosystem of the disease. These investigations included sampling from multiple locations from sectioned trees utilizing two methods of tissue collection and isolations using two baiting techniques. The collection of tissue from the cambium and phloem of trees after scraping away the bark and in locations of recent or current sap exudation using a narrow diameter steel awl proved to be an efficient means for the molecular detection of the MQD pathogens from infected trees exhibiting MQD symptoms. In addition, a more efficient and cost-effective baiting method using apple puree was developed.

Plant Sciences↗

NASA NTRS1995

On-line estimation of error covariance parameters for atmospheric data assimilation

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Dee, Dick P.↗

DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗