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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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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Tomato deploys defence and growth simultaneously to resist bacterial wilt disease

Abstract Plant disease limits crop production, and host genetic resistance is a major means of control. Plant pathogenic Ralstonia causes bacterial wilt disease and is best controlled with resistant varieties. Tomato wilt resistance is multigenic, yet the mechanisms of resistance remain largely unknown. We combined metaRNAseq analysis and functional experiments to identify core Ralstonia ‐responsive genes and the corresponding biological mechanisms in wilt‐resistant and wilt‐susceptible tomatoes. While trade‐offs between growth and defence are common in plants, wilt‐resistant plants activated both defence responses and growth processes. Measurements of innate immunity and growth, including reactive oxygen species production and root system growth, respectively, validated that resistant plants executed defence‐related processes at the same time they increased root growth. In contrast, in wilt‐susceptible plants roots senesced and root surface area declined following Ralstonia inoculation. Wilt‐resistant plants repressed genes predicted to negatively regulate water stress tolerance, while susceptible plants repressed genes predicted to promote water stress tolerance. Our results suggest that wilt‐resistant plants can simultaneously promote growth and defence by investing in resources that act in both processes. Infected susceptible plants activate defences, but fail to grow and so succumb to Ralstonia , likely because they cannot tolerate the water stress induced by vascular wilt.

Meline, Valerian↗
DOE OSTI2015

Networked Gamma Radiation System for Tactical Deployment

A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, Remote Sensing Laboratory to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2" × 4" × 16" sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart real-time localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We will test a collection of algorithms and analysis that centers on the problem of radiation detection with a distributed sensor network. We will study the basic characteristics of a radiation sensor network and focus on the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, will be examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain–affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. Further, the integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Data Integration Aggregated Model For Nuclear Deployment

The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often needed and is performed manually and on an as-needed basis. The integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data model contains objects that make up a given domain along with their attributes and relationships and could be used as a standard for exchange between applications for simple integration and data aggregation. After a survey of existing models, it was determined that a new model was required to meet the scope of this problem. DIAMOND has been developed in the form of an ontology to meet these needs. An ontology is simply a data model that also acts as a taxonomy for the domain, organizing the objects in a hierarchy with the most general objects at the top.

Browning, JerenM↗
DOE OSTI2022

An agent-based deployment decision-support system for electric vehicle services

METS-R ADDSEVS simulator is a high fidelity, parallel, agent-based evacuation simulator for multi-modal energy-optimal trip scheduling in real-time (METS-R) at transportation hubs. It consists of two modules. The first one is the traffic simulator module; the second one is the high-performance computing (HPC) module. More details can be found at https://umnilab.github.io/METS-R_doc/.

Lei, Zengxiang↗
DOE OSTI2022

gismo-cloud-deployment

Tools for executing time-consuming tasks with developer-defined custom code blocks in parallel on the AWS EKS platform.

Leu, Jimmy↗
DOE OSTI2023

SDOM (Storage Deployment Optimization Model) [SWR-21-73]

SDOM is designed to accurately represent the operation of energy storage across different timescales, including long-duration and seasonal applications, and the spatiotemporal diversity and complementarity among VRE sources. SDOM uses an hourly temporal resolution, a fine spatial resolution for VRE sources, and a 1-year optimization window. SDOM assumes that all builds of VRE are accompanied by sufficient additional transmission capacity to allow full utilization of these additional resources. Nuclear, hydropower, and other renewable generation (e.g., biomass and geothermal energy sources) are fixed based on operational data (time series) for a given year; thus, SDOM minimizes total system cost using conventional generators as balancing units and using VRE and storage technologies to achieve a user-defined carbon-free or renewable energy target. The total system cost includes capital costs, fixed operation-and-maintenance (FO&M) costs, variable operation-and-maintenance (VO&M) costs, and fuel cost for power generation and storage technologies.

Guerra Fernandez, Omar Jose↗
DOE OSTI2022

Deployment Optimization Tool (DOT)

This tool was developed to assist in the design of in-place RF monitoring systems. This tool uses a brute force method to find the optimal location for RF receivers in a location of interest. This tool currently depends on the commercial Wireless InSite software from Remcom. Currently available to US Government users only.

Clark, Trevor↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
DOE OSTI2024

DEPLOYING FAST CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES IN URBAN NETWORKS: AN ACTIVITY-BASED APPROACH

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users' daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Chain of Trips↗
DOE OSTI2023

Data for Climate vs Energy Security: Quantifying the Trade-offs of BECCS Deployment and Overcoming Opportunity Costs on Set-Aside Land

Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year–1 from the atmosphere, deliver up to 16 Tg CE year–1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.

Field Data↗
DOE OSTI2015

In-line chemical sensor deployment in a tritium plant

The Savannah River Tritium Plant (TP) relies on well understood but aging sensor technology for process gas analysis. The use of alternative sensing and detection technologies for in-line and real-time analysis would aid process control and optimization. The TP upgrading follows a 2-phase projects. In the first phase, TP sensing requirements were determined by a team of process experts. Meanwhile, Savannah River National Laboratory sensor experts identified candidate technologies and related them to the TP processing requirements. The resulting road-map links the candidate technologies to actual plant needs. In the second phase an instrument demonstration station was established within a TP glove box in order to provide accurate assessments of how a candidate sensor technology would perform in a contaminated process environment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗