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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗
DOE OSTI2020

Adaptive Power System Emergency Control using Deep Reinforcement Learning

Power system emergency control is generally regarded as the final safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenarios or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper proposes a novel adaptive emergency control scheme using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities DRL has for complex systems with high-dimensional variations. Furthermore, an open-source platform named DeepGrid has been designed for the first time to assist the DRL development and benchmarking processes in power system emergency control. Details of the platform, DRL, and emergency control schemes that use dynamic braking or under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

Deep reinforcement learning, emergency control, lo↗
DOE OSTI2018

Constant Rate Production: DOE Approach to Meeting NASA Needs for Radioisotope Power Systems for Nuclear-Enabled Launches

The use of radioisotope power systems (RPSs) for nuclear-enabled National Aeronautics and Space Administration (NASA) missions is made possible through a long-standing arrangement between the Department of Energy (DOE) and NASA. The requirements for the power system come from NASA, but DOE performs the procurement, fueling, testing, and delivery. A challenge has been interplay between the schedule for RPS availability from DOE versus the schedule for competitively selected missions. By mutual agreement, the actual operations to procure an RPS and prepare it for fueling have always been delayed until the final selection of a mission. The timeline for a New Frontiers–class mission leaves approximately 5 to 6 years from the time of final mission selection to the actual launch date. The number of RPSs used for a New Frontiers–class mission can be one to three units. If one or two units are needed, the timeline from the decision point to the launch date is a challenge, but it is achievable. The activities taking place include manufacturing the power system, producing the fuel, and performing the assembly/testing and delivery operations. If the mission selected requires three RPSs, the logistics of accomplishing all activities during the 5–6 years is problematic. The challenge involves obtaining the necessary resources for plutonium production, heat source production, and assembly/testing operations. Typically, the time between RPS-enabled missions requires staffing reduction down to 65%–75% of peak staffing levels to reduce costs. Coupling the ~2 year duration needed for hiring, training, and obtaining the appropriate security clearances for the required staff with the requirement for the RPS to arrive at Kennedy Space Center 6 months before the launch erodes much of the 5–6 years available to comfortably support the use of three RPSs. To provide better support for NASA RPS missions, a different approach for the production of heat sources was devised—constant rate production. This involves a higher level of base capability at DOE national laboratories to provide a stabilized workforce. This will enable 10–15 heat sources to be produced annually and placed into a stable intermediate form to enable storage for up to several years leading to quick production of general purpose heat source modules when a mission is selected. The upfront production of 238 Pu is maintained so material is constantly in the pipeline. Production of key specialized components is also maintained using this model.

42 ENGINEERING↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

IEA Bioenergy Task 40 Sustainable International Bioenergy Trade: Securing Supply and Demand (Country Report 2014—United States)

Logistical barrier are tied to feedstock harvesting, collection, storage and distribution. Current crop harvesting machinery is unable to selectively harvest preferred components of cellulosic biomass while maintaining acceptable levels of soil carbon and minimizing erosion. Actively managing biomass variability imposes additional functional requirements on biomass harvesting equipment. A physiological variation in biomass arises from differences in genetics, degree of crop maturity, geographical location, climatic events, and harvest methods. This variability presents significant cost and performance risks for bioenergy systems. Currently, processing standards and specifications for cellulosic feedstocks are not as well-developed as for mature commodities. Biomass that is stored with high moisture content or exposed to moisture during storage is susceptible to spoilage, rotting, spontaneous combustion, and odor problems. Appropriate storage methods and strategies are needed to better define storage requirements to preserve the volume and quality of harvested biomass over time and maintain its conversion yield. Raw herbaceous biomass is costly to collect, handle, and transport because of its low density and fibrous nature. Existing conventional, bale-based handling equipment and facilities cannot cost-effectively deliver and store high volumes of biomass, even with improved handling techniques. Current handling and transportation systems designed for moving woodchips can be inefficient for bioenergy processes due to the costs and challenges of transporting, storing, and drying high-moisture biomass. The infrastructure for feedstock logistics has not been defined for the potential variety of locations, climates, feedstocks, storage methods, processing alternatives, etc., which will occur at a national scale. When setting up biomass fuel supply chains, for large-scale biomass systems, logistics are a pivotal part in the system. Various studies have shown that long-distance international transport by ship is feasible in terms of energy use and transportation costs, but availability of suitable vessels and meteorological conditions (e.g., winter time in Scandinavia and Russia) need to be considered. However, local transportation by truck (both in biomass exporting and importing countries) may be a high-cost factor, which can influence the overall energy balance and total biomass costs.

09 BIOMASS FUELS↗
DOE OSTI2025

Opportunities for iron and steel industrial wastewater treatment and reuse in the United States

Concerns around water security in the United States have heightened the interest in industrial water treatment and reuse to improve water efficiency and operational reliability. Alongside nationwide efforts to expand industrial capacity, primary manufacturing sectors are adopting more resource-efficient technologies. This transition is expected to shift industrial water consumption patterns, driving the need for improved treatment and reuse practices. This study investigates opportunities for water use, treatment, and reuse in the iron and steel sector through a review of academic and industry literature and interviews with industry representatives. It identifies key challenges in water and wastewater management and outlines the conditions under which innovative treatment technologies could be deployed. Based on these insights, the study presents a practical water management action plan. Furthermore, it assesses water quality targets across different process operations, evaluates existing treatment technologies, and highlights challenges and opportunities for improvement relative to future performance expectations. Although water is often perceived as a low-cost commodity, industry feedback suggests that improvements in water use and treatment efficiency are typically prioritized only when they also reduce energy use, carbon emissions, or costs. This study advocates for a direct two-way partnership between industry and research audiences to bring their attention toward sustainable industrial water use, treatment, and reuse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Safe Reinforcement Learning for Emergency Load Shedding of Power Systems

The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid’s security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening in the grid. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)- based control techniques, RL control usually cannot guarantee the safety of the power systems. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulation on the IEEE 39-bus testcase is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

reinforcement learning, emergency control, power g↗
DOE OSTI2015

OPNET/Simulink Based Testbed for Disturbance Detection in the Smart Grid

The important backbone of the smart grid is the cyber/information infrastructure, which is primarily used to communicate with different grid components. A smart grid is a complex cyber physical system containing a numerous and variety number of sources, devices, controllers and loads. Therefore, the smart grid is vulnerable to grid related disturbances. For such dynamic system, disturbance and intrusion detection is a paramount issue. This paper presents a Simulink and Opnet based co-simulated platform to carry out a cyber-intrusion in cyber network for modern power systems and the smart grid. The IEEE 30 bus power system model is used to demonstrate the effectiveness of the simulated testbed. The experiments were performed by disturbing the circuit breakers reclosing time through a cyber-attack. Different disturbance situations in the considered test system are considered and the results indicate the effectiveness of the proposed co-simulated scheme.

Cyber Attacks↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Modelling and control coordination scheme of a wind‐to‐hydrogen set for future renewable‐based power systems

New challenges regarding system stability and efficiency arise when power systems operate with a high penetration level of inverter‐based renewable sources (IBRSs) and few synchronous generators. Since IBRSs have been on the rise, to secure the stable operation of future power systems, IBRSs will be required to support systems without having to rely on remaining synchronous generators. Also, to efficiently manage the uncertainty of renewable production, power‐to‐gas technology can provide the required flexibility. This study proposes modelling and a control coordination scheme (CCS) of a wind‐to‐hydrogen (W2H) set to optimise electricity production from a variable‐speed wind turbine generator (WTG) while helping balance between supply and demand in a system. To achieve this, a grid‐forming (GFM) inverter‐based WTG is modelled and a set of electrolyser and fuel cell is integrated at the DC circuit of a GFM‐WTG to be coordinated. Furthermore, the CCS offers an opportunity to reduce the investment cost for deploying a W2H set by utilising the control capabilities of a WTG and reducing the need for an additional device. The performance of the proposed W2H set with the CCS was verified considering the variations in system load and wind speed by using Power System Computer Aided Design (PSCAD)/ElectroMagnetic Transients including Direct Current (EMTDC).

Kim, Jinho↗
DOE OSTI2023

Evaluating Recursive Blind Forecast Against API and Baseline: A Puerto Rican Case Study on Solar Irradiance for Normal and Extreme Weather

This paper leverages ongoing work in a community microgrid in Adjuntas, Puerto Rico to forecast global horizontal irradiance (GHI) and compare performance in normal and extreme weather. Given a positive correlation of 0.98 between GHI and PV power, forecasting GHI can be an effective, indirect forecast of photovoltaic (PV) power, especially in microgrids where the end-users, owners, operators, or other stakeholders are reluctant to share data for training or validation due to privacy and security concerns. A recursive one-shot (termed as "blind") forecast is, hence, formulated, wherein a gradient-boosted regression tree (GBR) is built to forecast GHI for a 7-day horizon in normal weather, and a 2-day horizon in extreme weather. To demonstrate its resilience, the architecture is trained on normal and hurricane weather GHI from 2002-2022. It is generalized on February 9-16, 2023, and on the landfall of Hurricane Nicole (Nov 4-5, 2022), respectively. Forecasts from GBR are compared against that from a satellite-based API resource and three baselines: persistence, averaging, and exponential smoothing. Results show GBR and persistence outperform sophisticated API in both types of weather for this case study.

Sundararajan, Aditya↗
DOE OSTI2024

Planning for Material Control and Accountancy at Liquid Fueled Molten Salt Reactors

The purpose of this report is to provide molten salt reactor (MSR) developers and future US Nuclear Regulatory Commission (NRC) license applicants with recommendations for developing an effective and practical material control and accounting (MC&A) plan, focused primarily on MSR designs that use circulating liquid fuel. Because of the breadth of MSR designs, there is no single, generic, detailed MC&A plan that will work for every design. The wide variation of fresh fuel salts, the method and frequency of loading fresh fuel, the reactor system design components (e.g., tanks, filtration systems, chemical processing streams), and waste streams will determine the specific measurement locations and instrumentation that can best meet MC&A objectives throughout an MSR facility. Additionally, MSR designs are rapidly evolving, and new design features and deployment scenarios that will affect MC&A are being explored and pursued. This report defines a generic MC&A approach that was developed for terrestrial (as opposed to maritime) deployments to meet the intent of NRC domestic safeguards and MC&A. MSR license applicants should consider nuclear safeguards (both domestic and international) and security throughout the design, as early as the preconceptual design phase. MC&A of special nuclear material (SNM) is an aspect of the NRC’s domestic safeguards program, alongside physical protection. Because liquid-fueled MSRs are reactors with SNM in nondiscrete (or item) form, it is likely that the NRC may require liquid-fueled MSR license applicants to submit a formal MC&A plan as a part of their license application. Currently, the NRC licensing protocol presents a challenge because the NRC MC&A regulations have not been updated to accommodate advanced reactors, including types of MSRs. Because no liquid-fueled MSR has been licensed for operation at the time of this report, no template or precedence for a successfully licensed MSR MC&A plan exists. However, the MSR license applicant can take advantage of the NRC’s published commitments to performance-based regulations. The authors recommend that the license applicant, or MSR designers, develop an MC&A plan throughout the design lifecycle and plan to submit a detailed MC&A program description, or MC&A plan, to the NRC as a part of a license application. No MC&A plan template or guidance exists that is specific to liquid-fueled MSRs. The authors recommend that license applicants discuss the topic of MC&A during preapplication engagement. Because of the uniqueness of MC&A for liquid fueled MSRs, the authors recommend that liquid fueled MSR developers engage with the NRC on the topic of MC&A in the early phases of its design development and follow up any time there are significant modifications in design plans that would affect MC&A. For example, topics like modifications in fuel handling processes, changes in uranium enrichment, or additional chemical processing streams added to the design could be discussed with the NRC specifically on the topic of MC&A.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Computational model for predicting the performance of fluid-powered longiudinal piston modules and motors.

A Matlab-based computational model line code has been developed for predicting the performance of fluid-powered longitudinal piston modules and motors. A system of equations has been developed based upon applying the conservation of mass to a control volume defined by a reciprocating piston. The method results in a differential equation that predicts the pressure gradient in the control volume as a function of rotor rotation angle. The model uses an integration scheme to predict the pressure state in the control volume. The orifice equation is used to predict the flowrate in and out of the control volume. From the resulting solution the torque and speed characteristics of the motor configuration can be predicted, optimized, or tailored for a specific requirement.SAND2019-13983 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Taylor, Michael↗
DOE OSTI2015

Designing interchain and intrachain properties of conjugated polymers for latent optical information encoding

Building molecular-design insights for controlling both the intrachain and the interchain properties of conjugated polymers (CPs) is essential to determine their characteristics and to optimize their performance in applications. However, most CP designs have focused on the conjugated main chain to control the intrachain properties, while the design of side chains is usually used to render CPs soluble, even though the side chains critically affect the interchain packing. Here, we present a straightforward and effective design strategy for modifying the optical and electrochemical properties of diketopyrrolopyrrole-based CPs by controlling both the intrachain and interchain properties in a single system. The synthesized polymers, P1, P2 and P3, show almost identical optical absorption spectra in solution, manifesting essentially the same intrachain properties of the three CPs having restricted effective conjugation along the main chain. However, the absorption spectra of CP films are gradually tuned by controlling the interchain packing through the side-chain design. Here, based on the tailored optical properties, we demonstrate the encoding of latent optical information utilizing the CPs as security inks on a silica substrate, which reveals and conceals hidden information upon the reversible aggregation/deaggregation of CPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Optimization of tomographic reconstruction workflows on geographically distributed resources

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Furthermore, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

On the Effectiveness of Recurrent Neural Networks for Live Modeling of Cyber-Physical Systems

Attention to cyber security of cyber-physical systems (CPS) has led to the development of innovative cyber-resilient methodologies to ensure early detection and mitigation of cyber anomalies and threats. The concept of Digital Twin (DT) has recently emerged as one of the approaches to achieve the objective of resilience. In the approach using DT, a software-based live model of a target CPS is used to continuously monitor, surveil and verify the correctness of the target CPS operation. In this paper, we empirically study the effectiveness of Recurrent Neural Network (RNN)-based models as the basis of DT-based resilience. We uncover the important characteristics of an RNN-based solution with experimentation on a lab-scale Canal Lock CPS emulator with live validations and attack scenarios. For the first time, we demonstrate actual, real-time use of a RNN-based model as a DT for performing live analysis on an operational CPS. Based on the observed results, we highlight the importance of a DT model's training interval, prediction interval and CPS polling interval in the process of anomaly detection. We uncover the limitations in anomaly detection due to real-time synchronization needs of the RNN-based DT. We highlight this uncovered tug of war between synchronization and anomaly detection is inherent in any complex CPS that is monitored and synchronized by relying on the same sensor streams of ground truth for both synchronization as well as anomaly detection.

Yoginath, Srikanth↗
DOE OSTI2024

Plant Disease Detection Technology Assessment

Visual inspections by US Customs and Border Protection agriculture specialists have identified approximately 20,000 regulated, quarantined pests each year in agricultural products entering the United States. Most of these pests identified in the Agriculture Quarantine and Inspection (AQI) program are insects. Many pathogens are difficult to detect in agricultural products, particularly in early stages of infection. New technologies can help to detect plant pathogens and the diseases that they cause. This technology assessment was performed for the US Department of Homeland Security Science and Technology Directorate (DHS S&T) through the Food, Agriculture, and Veterinary Defense (FAV-D) program to identify emerging technologies that could address this hard problem. These emerging technologies differ in their diagnostic sensitivities and specificities, as well as in their measurement time and training requirements. New instruments that detect volatile organic compounds characteristic of plant disease or pathogens could provide a less invasive inspection method. Dogs, which can successfully detect many concealed agricultural products, have also been trained to detect some plant pests and pathogens. Simple immunological tests offer sensitive and specific detection of many pathogens at the point of use. Advanced imaging methods that use AI to sort fruits and vegetables and recognize anomalies at high speeds could be used in cooperation with exporters to improve food quality and reduce pests. Advances in nucleic acid–based detection methods that have become gold standards for confirmatory diagnostics are now making those methods available for faster, point-of-use detection. These new methods should be developed in the context of AQI operational requirements, which apply risk-based sampling protocols to protect agriculture and facilitate commerce and passenger transit.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Land Use Manager Application Ensures Protectiveness Following Remediation on the Oak Ridge Reservation - 13355

Long-term stewardship is the set of activities necessary to return contaminated land to safe and beneficial use. The activities include physical and legal controls to prevent inappropriate exposure to contamination left in place at a site. It is the longest phase of the Department of Energy's Environmental Management Program and ensures the protection of human health and the environment for varied end uses. At the Department of Energy's Oak Ridge Reservation an automated program has been developed and implemented that tracks the multitude of long-term stewardship activities. The Oak Ridge Reservation is a large site that currently has over 50 actions requiring long-term stewardship activities. The Oak Ridge Reservation consists primarily of three plant sites, and long-term stewardship will enable these sites to be leased to private entities (East Tennessee Technology Park), modernized for an evolving national security mission (Y-12 National Security Complex), and revitalized to continue multi-disciplinary research (Oak Ridge National Laboratory). The varied site end uses of the individual plant sites coupled with the multitude of controls required by leaving waste in place presents challenges. A single remedial action may include surveillance and maintenance activities, media monitoring, property record notices as well as physical controls such as fences and signs. Thus, the array of long-term stewardship activities is complex and intermingled (over 200 inspections each year at various frequencies are required currently) and requires an effective tracking program, termed the Land Use Manager. The Land Use Manager is a web-based data management application for use by personnel responsible for implementing, maintaining, and verifying engineering and land use controls on the Oak Ridge Reservation. The program is a data entry and tracking tool, as well as a notification tool. The status and performance of engineering and land use controls are checked annually for evaluation in the required Remediation Effectiveness Report, and the automated Land Use Manager collects, maintains, tracks, notifies, monitors, and manages the information necessary to perform this evaluation. Land Use Manager tracks site information including type of contamination, regulatory requirements, locates land use controls; provides information on inspections, certification, and reporting; and provides reports. Most data access features, e.g., view, print, query, and download, are available to all users; however, data input, updating, and editing are restricted to the personnel directly responsible for monitoring and inspection. The Land Use Manager application was developed for the Department of Energy Oak Ridge Office by URS - CH2M Oak Ridge LLC, Restoration Services Incorporated, and MIJARA Corporation to meet the specific needs of long-term stewardship tracking on the Oak Ridge Reservation. The successful implementation of long-term stewardship enables the future government and private activities being planned on the Oak Ridge Reservation to proceed. (authors)

54 ENVIRONMENTAL SCIENCES↗