Search NASA⌕ Search

SEARCH · Search NASA

Results for “controller information tool”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17

Investigating the Chinese postman problem on a quantum annealer

The recent availability of quantum annealers has fueled a new area of information technology where such devices are applied to address practically motivated and computationally difficult problems with hardware that exploits quantum mechanical phenomena. D-Wave annealers are promising platforms to solve these problems in the form of quadratic unconstrained binary optimization. Here we provide a formulation of the Chinese postman problem that can be used as a tool for probing the local connectivity of graphs and networks. We treat the problem classically with a tabu algorithm and simulated annealing, and using a D-Wave device. The efficiency of quantum annealing with respect to the simulated annealing has been demonstrated using the optimal time to solution metric. We systematically analyze computational parameters associated with the specific hardware. Our results clarify how the interplay between the embedding due to limited connectivity of the Chimera graph, the definition of logical qubits, and the role of spin-reversal controls the probability of reaching the expected solution

36 MATERIALS SCIENCE↗

Utility-Scale Operational Consequences for Solar Grid Services

This report delves into the critical aspects of grid services provided by solar inverter-based resources (IBRs), with an emphasis on the evolving landscape of microgrids, virtual power plants (VPPs), aggregators, and distributed energy resource management systems (DERMS). As the energy sector undergoes a transformative shift towards more decentralized and resilient grid architectures, understanding the multifaceted risks associated with these technologies becomes paramount. The report categorizes these risks into organizational, technical, and procedural domains, providing a thorough risk assessment framework that stakeholders can utilize to anticipate and mitigate potential issues. In addressing the increasing complexity of grid interconnections, the report highlights the importance of Cyber-Informed Engineering (CIE). By embedding engineering controls and cybersecurity measures into the early stages of system design, this approach aims to fortify grid infrastructure against emerging cyber threats. The analysis includes an exploration of best practices and strategies for integrating CIE principles to enhance grid security and resilience. To provide practical insights, the report conducts a detailed consequence analysis of various grid services and cyber mitigations that can be applied through the interconnection process. This analysis evaluates the potential impacts of different failure modes and vulnerabilities, offering a clear understanding of the consequences that could arise from disruptions within the energy grid. The findings are further enriched by a series of case studies that illustrate real-world scenarios and lessons learned from past incidents. Through this comprehensive examination of grid services and their criticality, the report aims to prepare industry professionals with the knowledge and tools necessary to navigate the complexities of modern energy systems. By providing a comprehensive approach that includes risk assessment, cybersecurity, and consequence analysis, solar stakeholders can more effectively guarantee the reliability, efficiency, and security of the energy grid.

14 SOLAR ENERGY↗

Evidence for the utility of quantum computing before fault tolerance

Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely accepted solution to this challenge is the implementation of fault-tolerant quantum circuits, which is out of reach for current processors. Here we report experiments on a noisy 127-qubit processor and demonstrate the measurement of accurate expectation values for circuit volumes at a scale beyond brute-force classical computation. We argue that this represents evidence for the utility of quantum computing in a pre-fault-tolerant era. These experimental results are enabled by advances in the coherence and calibration of a superconducting processor at this scale and the ability to characterize and controllably manipulate noise across such a large device. We establish the accuracy of the measured expectation values by comparing them with the output of exactly verifiable circuits. In the regime of strong entanglement, the quantum computer provides correct results for which leading classical approximations such as pure-state-based 1D (matrix product states, MPS) and 2D (isometric tensor network states, isoTNS) tensor network methods break down. These experiments demonstrate a foundational tool for the realization of near-term quantum applications.

97 MATHEMATICS AND COMPUTING↗

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗

Magnon-mediated qubit coupling determined via dissipation measurements

Controlled interaction between localized and delocalized solid-state spin systems offers a compelling platform for on-chip quantum information processing with quantum spintronics. Hybrid quantum systems (HQSs) of localized nitrogen-vacancy (NV) centers in diamond and delocalized magnon modes in ferrimagnets—systems with naturally commensurate energies—have recently attracted significant attention, especially for interconnecting isolated spin qubits at length-scales far beyond those set by the dipolar coupling. However, despite extensive theoretical efforts, there is a lack of experimental characterization of the magnon-mediated interaction between NV centers, which is necessary to develop such hybrid quantum architectures. Here, we experimentally determine the magnon-mediated NV–NV coupling from the magnon-induced self-energy of NV centers. Our results are quantitatively consistent with a model in which the NV center is coupled to magnons by dipolar interactions. This work provides a versatile tool to characterize HQSs in the absence of strong coupling, informing future efforts to engineer entangled solid-state systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗

ResStock™ v3.2.0 [SWR-19-15 and SWR-20-07]

The ResStock™ analysis tool was built on NREL's OpenStudio® platform, and is a project geared at modeling existing residential building stocks at national, regional, or local scales with a high-degree of granularity (e.g., one physics-based simulation model for every 200 dwelling units), using the EnergyPlus® simulation engine. Information about ComStock™, a sister tool for modeling the commercial building stock, can be found here: https://www.nrel.gov/buildings/comstock.html This repository contains: Housing characteristics of the U.S. residential building stock, in the form of conditional probability distributions stored as tab-separated value (.tsv) files. Comments at the bottom of each file document data sources and assumptions for each. A library of housing characteristic "options" that translate high-level characteristic parameters into arguments for OpenStudio measures, and which are referenced by the housing characteristic .tsv files and building energy upgrades defined in project definition files Project definition files: v2.3.0 and later: buildstockbatch YML files openable in any text editor v2.2.5 and prior: Project folder openable in PAT Unit-level OpenStudio Measures for automatically constructing OpenStudio Models of each representative dwelling unit model: v3.0.0 and later: OpenStudio-HPXML Measures v2.5.0 and prior: OpenStudio Measures Higher-level OpenStudio Measures for controlling simulation inputs and outputs This repository does not contain software for running ResStock simulations, which can be found as follows: Versions 2.3.0 and later only support the use of buildstockbatch for deploying simulations on high-performance or cloud computing. Version 2.3.0 also removed separate projects for single-family detached and multifamily buildings, in lieu of a combined project_national representing the U.S. residential building stock. See the changelog for more details. Versions 2.2.5 and prior support the use of the publicly available OpenStudio-PAT software as an interface for deploying simulations on cloud computing. Read the documentation for v2.2.5.

Horowitz, Scott↗

MINE: maximally informative next experiment—toward a new GWAS experimental design and methodology

Abstract The computational methodology of Genome Wide Association Studies (GWAS) currently has several limitations: (i) the number of observations (rows) on a quantitative trait tends to be smaller than the number of single nucleotide polymorphisms (SNPs) (columns) in the design matrix; (ii) each SNP is usually modeled separately, failing to acknowledge interaction between each other (ie epistasis); (iii) there is implicit linkage disequilibrium (LD) between neighboring SNPs due to their linkage. To overcome these issues, we developed a tool that uses ensemble methods to fit mixed linear models to GWAS data, and these ensemble methods include the development of a new experimental design approach in GWAS, which uses the resultant models and data to select the next informative experiment over time. This new adaptive and staged approach for GWAS experimental design was developed and tested in a 3 yr adaptive model-guided discovery experiment against a fixed classical design. In Sorghum bicolor a total of 79, 86, and 78 accessions were tested in years 1, 2, and 3, respectively out of 343 accessions available in the Bioenergy Association Panel (BAP) each identified for 232,303 SNPs, 1 every 2–3 kb in the genomes. We demonstrated the feasibility of MINE enacted with 8 people in the field per year over 3 yr vs in 1 large classical design enacted with 20 people in 1 yr. The MINE results for chromosomal regions identified controlling dry weight were confirmed against results from previous sorghum GWAS experiments and 1 large classical design for the BAP panel.

Genetics & Heredity↗

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .

59 BASIC BIOLOGICAL SCIENCES↗

Detection of photon statistics and multimode field correlations by Raman processes

Glauber’s g (2) -function provides a common measure of quantum field statistics through two-photon coincidence counting in Hanbury Brown–Twiss measurements. Here, we propose to use nonlinear optical signals as a tool for the characterization of quantum light. In particular, we show that Raman measurements provide an alternative direct probe for a different component of the four-point correlation function underlying the g (2) -function. We illustrate this capacity for a specific quantum state obtained from a frequency conversion process. Our work points out how the analysis of controlled optical nonlinear processes can provide an alternative window toward the analysis of quantum light sources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Guided Resilience Self Assessment Application

Reliable electricity access is vital for everything from hospitals to national security. When grid disturbances occur, it is crucial to minimize the amount of time it takes to recover. Grid resiliency considers both the preparation for and recovery from high-impact low-frequency disturbances (HILF) such as weather events and cyber attacks. The Department of Energy (DOE) Wind Energy Technologies Office (WETO) funds the multi-laboratory Microgrids, Infrastructure Resilience, and Advanced Control Launchpad (MIRACL) project to investigate technologies to support and grow distributed wind. Researchers at Idaho National Laboratory (INL) contribute to resilience and cybersecurity efforts for the project, and have produced a resilience framework to evaluate system resilience based on individual system specifications. In order to make this framework easy for anyone to apply, I have coded a web application that allows users to input their power system qualities, goals, and perceived risks and then receive suggestions on how to improve their grid resiliency. The web application follows the seven steps in the planning stage of the framework process, allowing the user to input information about their grid and select from common goals, metrics, and hazards, as well as add their own. The application currently provides information about how to complete each step, requiring minimal prior knowledge of the framework. Future work for this application will include allowing the user to submit line drawings of their system and incorporate modeling tools so that the hazard simulations of applying the framework is completed for the user.

97 MATHEMATICS AND COMPUTING↗

Hazard analysis for identifying common cause failures of digital safety systems using a redundancy-guided systems-theoretic approach

Replacing the existing aging analog instrumentation and control (I&C) systems with modern safety control and protection, digital technology offers one of the foremost means of performance improvements and cost reductions for the existing nuclear power plants (NPPs). However, the qualification of digital I&C systems remains a challenge, especially considering the issue of software common-cause failures (CCFs), which are difficult to address. With the application and upgrades of advanced digital I&C systems, software CCFs have become a potential threat to plant safety because most redundant designs use similar digital platforms or software in the operating and application systems. With complex designs of multilayer redundancy to meet the single-failure criterion, digital I&C safety systems (e.g., engineered safety-features actuation system [ESFAS]) are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. Here, this paper applies a modularized approach to conduct redundancy-guided systems-theoretic hazard analysis for an advanced digital ESFAS with multilevel redundancy designs. Systematic methods and risk-informed tools are incorporated to address both hardware and software CCFs, which provide guidance to eliminate the causal factors of potential single points of failure in the design of digital safety systems in advanced plant designs.

42 ENGINEERING↗

Cabin Thermal Management Analysis for SuperTruck II Next-Generation Hybrid Electric Truck Design

In this article, we present a multistage, coupled thermal management simulation approach, informed by physical testing where available, to aid design decisions for PACCAR's SuperTruck II hybrid truck cabin concept. Focus areas include cabin insulation, battery sizing, and sleeper curtain position, as well as heating, ventilating, and air-conditioning (HVAC) component and accessory configurations, to maintain or improve thermal comfort while saving energy. The authors analyzed weather data and determined the national vehicle miles traveled weighted temperature and solar conditions for long-haul trucks. Example weather day profiles were selected to approximate the 5th and 95th percentile weighted conditions. A daylong drive cycle was developed to impose appropriate external wind conditions during rest and driving periods. Using the National Renewable Energy Laboratory's vehicle HVAC modeling and simulation tool VTCab, HVAC load design trade-off studies for the new truck geometry concept were completed. Parameters analyzed included effects of paint color, insulation, glass transmissivity, and curtain location. Simulation results helped with early design material selections for efficient cabin climate control. A detailed three-dimensional computer-aided engineering (CAE), computational fluid dynamics (CFD), radiation, and human physiology co-simulation, referred to in this article as CAE Thermal-CFD, was used to evaluate thermal comfort and energy impacts of diffuser configurations and air supply settings in driving and hoteling modes. Analysis revealed that it is more difficult to heat the cabin in hoteling mode during the winter than to cool the space in the summer. This seasonal load profile drives the requirement of additional energy storage for heating comfort. To determine the battery capacity requirement, multiday HVAC operation drive cycle simulations were then completed, showing that a 15-kWh battery would be required for HVAC operation during hoteling. Results helped reduce cabin thermal loads, determine component sizing requirements, and improve occupant comfort to save fuel and contribute to the economic viability of the hybrid system.

33 ADVANCED PROPULSION SYSTEMS↗

Unconventional compute methods and future challenges for superconducting digital computing

Superconducting digital computing (SDC) based on Josephson junctions (JJs) offers significant potential for enhancing compute throughput and reducing energy consumption compared to conventional room-temperature CMOS-based approaches. Current superconducting logic families exhibit diverse characteristics in clocking strategies, power management, and information encoding techniques. This paper reviews recent advancements in unconventional computing methods specifically designed for superconducting digital circuits, emphasizing temporal computing and pulse-train representations. Notable techniques include race logic (RL), temporal pulse train computing (U-SFQ), and temporal multipliers, each offering unique performance and area advantages suited to superconducting implementations. Additionally, this paper reviews innovations in superconducting coarse-grain reconfigurable architectures (CGRA), superconducting-specific on-chip communication architectures, cryogenic sensor interfaces, and quantum computing control electronics. Finally, we highlight research challenges that should be addressed to facilitate the widespread adoption of superconducting digital computing.

EDA tools↗

The HUNTER Dynamic Human Reliability Analysis Tool: Coupling an External Plant Code

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) with the aim to develop standalone software to perform the dynamic HRA calculations. Within the HRA, human actions in nuclear power plants (NPPs) are predicated by plant states, and human actions influence the plant. In other words, plant operations are necessarily recursive, and it becomes challenging to model complex human-plant interactions. Consequently, we have linked two software simulations that complement those shortcomings. RELAP5-3D—the Reactor Excursion and Leak Analysis Program (RELAP; Aumiller, Tomlinson, and Bauer 2001) is the foundational thermal-hydraulic software used to model nuclear systems. Using RELAP5-3D, we have simulated the plant operations proceeding according to procedures developed to address emergent situations in NPPs. Plant operations include various actions such as the operator checking plant parameters, as well as actions that are continuously performed over time until a specific parameter reaches certain criteria. That means that HUNTER and RELAP5-3D exchange information with each other and should be carried out simultaneously over time. To simulate plant operations, which represent the actual operator checks of plant parameters and corresponding manual control actions, changes in plant status are identified through simulation and performed according to the criteria and order of the procedure. Thus, the goal of coupling HUNTER with RELAP5-3D is to facilitate synchronous coupling, where human and plant models provide iterative feedback loops that drive the course of actions. The advantage of coupling with RELAP5-3D to serve as the external environment module in HUNTER is the ability to customize the plant model and streamline for particular model applications. In this paper, we will address the key features of the coupling and the coupling structure built to perform the feedback loops.

99 GENERAL AND MISCELLANEOUS↗

Sticky roots--implications of widespread, cryptic, viral infection of plants in natural and managed ecosystems for soil carbon processing in the rhizosphere

Plants strongly influence soil properties through rhizodeposition, in which exudates diffuse from roots, additional secretions are actively released, and root cells are sloughed into the soil. This contribution by plants of carbon compounds belowground is at the core of soil health, water holding capacity, and the soil carbon storage that pulls carbon dioxide out of the atmosphere. Once in soil, organic matter can bind with minerals such as iron hydroxides, where it can be protected from microbial attack for millenia, preserving very large terrestrial soil carbon pools. However, those same compounds contributed by roots to soil may also destabilize the long-term protective associations of SOM with minerals, making that soil organic matter (SOM) more vulnerable to microbial attack and decomposition. Plant roots thus influence both the buildup and breakdown of soil carbon pools. DOE’s E3SM Land Model (ELM) includes a representation of soil carbon storage on minerals, but the potential vulnerability of SOM–mineral associations to effects of rhizodeposition is not yet represented in ELM. To begin testing for this effect of rhizodeposition on soil carbon storage and decomposition, we worked to develop a novel approach during this TES Exploratory project DE-SC0019142 – we harnessed the power of plant viral infection. We examined whether plant virus infection can serve as a tool to intensify rhizodeposition at the root surface, and therefore possibly intensify mobilization of SOM from minerals making it visible to our analytical techniques. Viral infection is widespread in terrestrial ecosystems; 25-70% of plants have virus infection, yet the influence of such infection on root traits and terrestrial soil carbon dynamics remains largely unexplored. We used two plant hosts: the annual Avena sativa (oats) and the genetically tractable, model grass Brachypodium distachyon. These grasses were infected with the broad host range virus Barley Yellow Dwarf Virus (BYDV) via aphids (Rhopalosiphum padi). BYDV infects at least 150 grass species in agricultural and natural ecosystems, and in previous experiments, oats infected with BYDV had roots that were very sticky to the touch, strongly suggesting that infection altered rhizodeposition. We developed this new experimental approach mostly in a one virus (Barley Yellow Dwarf Virus)–one plant (Avena sativa) system. (Several effects of infection in a Brachypodium-BYDV system were similar in nature to effects on Avena sativa, but were more variable.) In the BYDV-Avena system, we developed protocols for consistently infecting target plants (and avoiding infection of control plants) using aphid caging on leaves. We measured that infected plants exhibited reduced photosynthesis, plant (including root) biomass, and root:shoot ratio, as well as simplified root system architecture. We established procedures for sampling the organic compounds carried specifically in phloem (vascular tissue) of leaves and roots, using aphid stylectomy. We used FTICR-MS, Orbitrap GC-MS, and LC-MS/MS to analyze organic compounds in phloem, liquid around roots of plants grown hydroponically, and pore water around roots in soil, and found differences in the compounds in solution bathing roots when infected and uninfected plants were grown hydroponically. Finally, we synthesized isotopically-labeled mineral–organic matter (MAOM) associations in the lab and developed assays using them in solution and in soil. Assays quantified the extent and rate of mineralization of labeled MAOM that was mobilized by functionally distinct rhizodeposits and then attacked by microbes. Two mechanisms for MAOM mobilization emerged, with distinct dynamics. During “direct” mobilization, rhizodeposits such as the strong ligand oxalic acid could drive rapid dissolution of minerals, mobilizing MAOM. During “indirect” mobilization, rhizodeposits such as the simple sugar glucose did not attack minerals directly but instead intensified microbial activity, which led to mobilization via changes in e.g. pH, Eh, and microbial metabolite production (Li et al. 2021). Mechanistic understanding derived from these data and our ongoing experiments using these techniques will inform future development of ELM. Plant roots not only contribute newly fixed organic compounds to soils, but also root activities can drive mineralization of the carbon and nutrients mobilized off minerals via “indirect” or “direct” mechanisms. Using viral infection as a new tool, ongoing combined experimentation and modeling will explore the strength and larger-scale significance of the cascade of processes from rhizodeposition to MAOM mobilization for soil carbon storage and nutrient cycling in terrestrial ecosystems. And if viral infection leads quite generally to “sticky roots”, our perception of the potential importance of prevalent virus infection in terrestrial landscapes will be transformed.

54 ENVIRONMENTAL SCIENCES↗

Microreactor Optimization Using Simulation And Economics (mouse)

Microreactor Optimization Using Simulation and Economics (MOUSE) is a tool that integrates both nuclear microreactor design and reactor economics to provide comprehensive evaluations and optimizations. This tool enables stakeholders to explore the interplay between technical and economic variables, guiding them towards effective and competitive microreactor solutions. For the reactor core simulations, MOUSE leverages the OpenMC Monte Carlo Particle Transport Code to perform detailed core simulations for various microreactor designs. The included OpenMC models are 2D core designs of a Liquid Metal Thermal Microreactor (LMTR), a Gas-Cooled TRISO-Fueled Microreactor (GCMR), and a Heat Pipe Microreactor. Beyond core design, MOUSE includes simplified calculations for: - Calculating the masses of heat exchangers within the system. - Mechanical power of pumps. - Estimating the area occupied by various buildings within the nuclear plant. For the economic analysis, MOUSE provides detailed bottom-up cost estimates, encompassing a wide range of costs including preconstruction costs, direct costs, indirect costs, training costs, financial costs, operation & maintenance (O&M) costs, and fuel costs. These cost estimations are developed using data from the MARVEL project and additional literature sources, enabling the calculation of total capital costs and levelized cost of energy for both first-of-a-kind and nth-of-a-kind microreactors. MOUSE also enables analysis of the cost drivers and competitiveness in the electricity market. MOUSE allows users to modify a wide array of technical and economic parameters to evaluate different scenarios and their impacts. Examples of these parameters include: Fuels, coolants, or reflector materials Enrichment levels Control drum materials and geometry Fuel pin geometry and materials Moderator pin geometry and materials Reactor core and reflector dimensions Packing factor for the TRISO particles Nuclear reactor power and reactor burnup Number of sensors Shielding thickness Reactor vessel and guard vessel dimensions Operational staff requirements Number of emergency shutdowns Levelization period Interest rate Construction duration Since MOUSE is powered by the WATTS toolkit, it supports optimization studies, parametric analyses, and uncertainty calculations/propagation. The optimization techniques enable users to identify optimal design and economic configurations. The parametric analysis tools allow users to explore the sensitivity of various parameters, while uncertainty propagation helps quantify the impact of uncertainties on overall performance and cost. User Interface and Workflow: Currently, MOUSE is a command-line-based tool. Users can input various reactor design or economic parameters, modify the designs, run simulations, and visualize results through comprehensive data visualization and reporting capabilities. The typical workflow involves setting up the reactor model, defining economic parameters, running simulations, and analyzing the results to make informed decisions. By combining advanced design calculations with detailed economic modeling, MOUSE provides a robust framework for optimizing nuclear microreactor technologies, enhancing their competitiveness, and guiding stakeholders towards innovative and cost-effective solutions.

Hanna, Botros [Idaho National Laboratory (INL), Id↗