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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Discrete-Time Causal Control of a Wave Energy Converter With Finite Stroke in Stochastic Waves

We consider feedback control design for a wave energy converter (WEC) for which the power takeoff (PTO) system has a finite stroke limit. Stationary stochastic wave loading is assumed, with a known spectrum, and the plant dynamics are assumed to be linear. We develop a technique for the design of a discrete-time controller, which has three design stages. In the first stage, a linear controller is optimized while imposing a relaxation of the maximum stroke constraint on the design. In the second stage, a tandem nonlinear feedback loop is designed for the purpose of stroke protection. In the third stage, the two designs (linear and nonlinear) are fused in a manner that preserves the stability of the overall system. The technique is demonstrated in a simulation of a simple cylindrical buoy. We show that the controller may be tuned through the adjustment of scalar design parameters, which adjust the tradeoff between the mean generated power and the force levels required to protect the stroke.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Apple Bitter Rot: Biology, Ecology, Omics, Virulence Factors, and Management of Causal Colletotrichum Species

ABSTRACT Apple bitter rot is caused by various Colletotrichum spp. that threaten apple production globally resulting in millions of dollars in damage annually. The fungus causes a decline in fruit quality and yield, eventually rotting the fruit and rendering it inedible. The pathogen is difficult to keep out of orchards because of its broad host range and transmissibility by rain splash and insects. Once the disease manifests, pathogen identification is difficult due to evolving taxonomy and similar morphology between species. Current management strategies are threatened by an increase in fungicide resistance and regulations on many multisite fungicides, leading to a pressing need for new management options for control. This review aims to summarise the most current knowledge regarding the biology, virulence factors, ecology, omics and emerging management strategies for Colletotrichum species that cause apple bitter rot. Taxonomy Colletotrichum species—Domain Eukaryota, Kingdom Fungi, Phylum Ascomycota, Class Sordariomycetes, Order Glomerellales, Family Glomerellaceae, Genus Colletotrichum . Biology Hemibiotrophic pathogen with a wide host range that establishes a biotrophic interaction where it penetrates host plants using appressoria followed by a switch to necrotrophy causing rot symptoms. Toxins Cercosporin, colletotrichins, colletotric acid, ferricrocin. Host Range The host range varies by species but largely occurs on dicotyledonous plants and is less prevalent on monocots as well as gymnosperms, ferns, mosses and animals (e.g., insects). Disease Symptoms Symptoms often manifest as flat to sunken necrotic areas on fruit. Lesions on leaves and fruit can have concentric rings with abundant pathogen sporulation. Disease Control Colletotrichum spp. are primarily managed by single‐site quinone outside inhibitor (Qol), methyl benzimidazole carbamate (MBC), demethylation inhibitor (DMI) fungicides, and multisite dithiocarbamate and phthalimide fungicides. Susceptibility may vary with species, strain specificity, or geographic region. Other management options include clean stock production, cultural practices, resistance breeding, and biological control through the introduction of protective or competing microorganisms.

Boeckman, Nathanial J. [Plant Pathology Laboratory↗
DOE OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2016

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. Here we demonstrate that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SOLIDS PRECIPITATION EVENT IN MCU CAUSAL ANALYSIS AND RECOMMENDATIONS FROM SOLIDS RECOVERY TEAM

A process upset occurred in the Modular Caustic-Side Solvent Extraction Unit (MCU) facility on April 6th, 2014. During recovery efforts, a significant amount of solids were found in the Salt Solution Feed Tank (SSFT), Salt Solution Receipt Tanks (SSRTs), two extraction contactors, and scrub contactors. The solids were identified by Savannah River National Laboratory (SRNL) as primarily sodium oxalate and sodium alumina silicate (NAS) with the presence of some aluminum hydroxide. NAS solids have been present in the SSFT since simulant runs during cold chemical startup of MCU in 2007, and have not hindered operations since that time. During the process upset in April 2014, the oxalate solids partially blocked the aqueous outlet of the extraction contactors, causing salt solution to exit through the contactor organic outlet to the scrub contactors with the organic phase. This salt solution overwhelmed the scrub contactors and passed with the organic phase to the strip section of MCU. The partially reversed flow of salt solution resulted in a Strip Effluent (SE) stream that was high in Isopar™ L, pH and sodium. The primary cause of the excessive solids accumulation in the SSRTs and SSFT at MCU is attributed to an increase in the frequency of oxalic acid cleaning of the 512-S primary filter. Agitation in the SSRTs at MCU in response to cold weather likely provided the primary mechanism to transfer the solids to the contactors. Sources of the sodium oxalate solids are attributed to the oxalic acid cleaning solution used to clean the primary filter at the Actinide Removal Process (ARP) filtration at 512-S, as well as precipitation from the salt batch feed, which is at or near oxalate saturation. The Solids Recovery Team was formed to determine the cause of the solids formation and develop recommendations to prevent or mitigate this event in the future. A total of 53 recommendations were generated. These recommendations were organized into 4 focus areas: • Improve understanding of oxalate equilibrium and kinetics in salt solutions • Reduction/elimination of oxalic acid cleaning in 512-S • Flowsheet optimization • Improving diagnostic capability The recommendations implemented prior to resumption of MCU operations provide a risk mitigation or detection function through additional sampling and observation. The longer term recommendations provide a framework to increase the basic process knowledge of both oxalate chemistry and filtration behavior and then facilitate decisions that improve the salt flowsheet as a system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Causal Analysis For Occurrence Report: OR NA-SS-SNL-2000-2015-0005 Unexpected Type of Failure of Thermal Battery

On 6/26/2015 at approximately 1445 in 894/136, a pulse thermal battery (approximately the size of a commercial size C cell) experienced an unexpected failure following an electrical performance test that is routinely conducted on thermal batteries. A dedicated tester for this operation was used and it ran the test until the nominal 28-volt output of the battery had dropped to 5 volts, usually indicative of the battery being spent and safe enough to move. The failure occurred while a test operator was transferring the battery from the testing primary containment box to another primary containment box within the same room; initial indications are that the battery experienced an over-pressurization failure which led to the battery's base plate being expelled and the operator receiving a non-recordable injury (bruising to the palm of the hand) from the pressure of the expulsion. The operator was wearing the prescribed PPE (safety glasses and high temperature glove) and was handling the battery appropriately with an open, flat hand. Pictures of the scene are below.

25 ENERGY STORAGE↗
DOE OSTI2018

Causal Analysis Report for NA-SS-SNL-8000-2018-0002 Electrical shock: Wind Turbine Nacelle Lubbock TX

A Sandia National Laboratories Member of the Workforce (MOW) experienced contact with electrical energy while performing work on an oil filter pump electrical system in the al wind turbine nacelle at Sandia National Laboratories Scaled Wind Turbine Facility (SWiFT). The Department of Energy (DOE) Sandia National Laboratories (SNL) Scaled Wind Farm Technology (SWiFT) facility does research and development (R&D) work in collaboration with Texas Tech University. The SWiFT facility includes three turbines for performing wind plant and turbine technology research in support of DOE's Wind Energy Technology Office. The current work ongoing at the site is primarily related to commissioning the three turbines to support ongoing DOE customer needs and requests. It was during the performance of commissioning tests of the hydraulic systems in the a1 turbine that a Sandia MOW experienced contact with electrical energy. There were multiple root and contributing causes that ultimately resulted in the electrical energy contact. These causes are summarized and aligned to their corrective actions in the corrective action plan table provided in Section 7(c). The purpose of this narrative section is to provide additional information and context

17 WIND ENERGY↗
DOE OSTI2018

Causal Analysis Report for "Demolition of Historic and Yet-to-be-Determined Radar Towers at TTR Without Completing Required NEPA Process"

Two radar towers at the Tonopah Test Range (TTR), Contraves Towers 22-00 and 12-00, were torn down prior to completion of the required National Environmental Policy Act (NEPA) process. National Technology and Engineering Solutions of Sandia, LLC, (NTESS) submission of the NEPA checklist triggered a Sandia Field Office (SFO) review of the proposed work scope and was to include a required consultation between SFO and the Nevada State Historic Preservation Office (SHPO) to assess any adverse effect(s) on historic properties or contributing element(s) to a historic district and, if so, determine a resolution. Following the consultation, SFO provides approval/denial of the proposed work detailed on the submitted NEPA checklist. Contraves Tower 22-00 is a historic structure, as formally determined by the SFO in consultation with the SHPO. The historical eligibility of Contraves Tower 12-00 is yet to be determined. On February 13, 2018, NTESS notified SFO that Contraves Tower 22-00 at TTR was torn down in May 2017 prior to completion of the required the NEPA process, including consultation with the SHPO. NTESS submitted two NEPA checklists to SFO in 2016 for the removal of several structures no longer required by NTESS. The first NEPA checklist listed three historic structures (one of which was Contraves Tower 22-00) and SFO determination on whether consultation with the SHPO is necessary as well as approval/denial of the NEPA checklist is pending. The second NEPA checklist listed five non-historic structures, one of which was Contraves Tower 12-00. SFO approved the NEPA checklist covering non historic structures except for the Contraves Tower 12-00, as the Contraves Tower 12-00 requires further consultation with the SHPO by SFO. Only after completion of the consultation would SFO determine approval/denial on the remaining property cited within the NEPA checklist. In late January 2018, NTESS personnel discovered that Contraves Tower 22-00 was no longer listed in the Facility Information Management System (FIMS). Upon further investigation, NTESS personnel verified that the Contraves Tower 22-00 had been removed in May 2017. During the initial extent of condition, it was discovered that Contraves Tower 12-00 had also been torn down in May 2017 as part of this same destruction and demolition (D&D) activity. Both Contraves Towers 22-00 and 12-00) had been torn down prior to completion of the NEPA process, including consultation with the SHPO.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗