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At least 109 records · Page 6

HYBRID Modeling of DETAIL Experimental Facility

Idaho National Laboratory continues to be at the forefront of advanced reactor systems and integrated energy systems research. Through the Thermal Energy Distribution System (TEDS) and Microreactor Agile Non-nuclear Test Bed (MAGNET) experiments in the Dynamic Energy Transport and Innovation Laboratory (DETAIL) within the Engineering Systems Laboratory, a mix of digital and physical testing systems can explore the operation of thermally and electrically integrated systems. Modeling of these systems has been done using the HYBRID modeling repository of the Integrated Energy Systems program. DETAIL also contains high temperature steam electrolysis (HTSE) units to produce hydrogen and generate the load imposed on MAGNET and TEDS. The dynamic modeling capabilities within the IES program describe the DETAIL components as-built and can be used to evaluate controls, process flows, and overall system conditions.

08 HYDROGEN↗

Photodynamic melting of phase-reversed charge stripes and enhanced condensation

The interplay between charge stripes and pairing has long been a subject of scrutiny in a broad class of unconventional superconductors, as in some cases it is unclear whether this interplay benefits the ensuing superfluidity. Experiments that explore the out-of-equilibrium dynamics of these systems aim to tip the balance toward one phase or the other by selectively coupling to relevant modes. Leveraging the fact that competition between stripes and pairing is not exclusive to fermionic systems, we explore the photoirradiation dynamics of interacting hardcore bosons in which density-wave phase-reversal melting leads to enhanced phase-coherent transport response, as quantified by the dynamic amplification of both the zero-momentum occupancy and the condensate fraction, as well as finite out-of-equilibrium charge stiffness and superfluid weight, for a given system size. Finally, our results, obtained using unbiased methods for an interacting system on a ladder geometry, demonstrate how one can engineer time-dependent perturbations to release suppressed orders, potentially providing insight into the underlying mechanism in related experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Plant-microbe interactions: from genes to ecosystems using Populus as a model system

Plant-microbe symbioses span a continuum from pathogenic to mutualistic with functional consequences for both organisms in the symbiosis. In order to increase sustainable food and fuel production in the future, it is imperative that we harness these symbioses. The tree genus Populus is an excellent model system for studies examining plant-microbe interactions due to the wealth of genomic information available and the molecular tools that have been developed to manipulate Populus-microbe symbioses. In this review, we highlight how Populus can serve as a model system to explore plant-microbe interactions. Specifically, highlighting research linking Populus-microbe interactions from the gene to the ecosystem level. We explore why Populus is an excellent model for perennial plant systems, the molecular underpinnings of Populus-microbe interactions, how host genetics influence microbial community composition, and how microbial communities vary at fine spatial scales and between Populus species. Further, we explore how the patterns of the microbiome may affect ecosystem level functions in managed and natural ecosystems. Understanding and manipulating these interactions in Populus has the potential to improve plant health and impact ecosystem sustainability and processes as Populus trees function as foundational species in many natural ecosystems and are also deployed in managed ecosystems for various agroforestry applications.

59 BASIC BIOLOGICAL SCIENCES↗

Data Center High-Temperature Liquid Cooling and Heat Reuse Techno-Economic Study: Preprint

Data centers are energy-intensive facilities with growing demands for efficiency and cost-effective operations. Smaller, more distributed edge inference data centers are expected to proliferate as AI applications require low latency closer to the user of AI tools, which presents a growing opportunity to explore the systems implications of liquid cooling on water and energy use. This study analyzes the implementation of high-temperature liquid cooling systems in a prototypical inference 1-MW data center and explores the potential for heat reuse across varying climates with a goal to optimize energy efficiency, reduce capital and operational costs, and identify opportunities for high-performance cooling and water use reduction infrastructure. This analysis evaluated configurations utilizing a peak day hourly sizing and systems performance spreadsheet to evaluate design and operational conditions from which component sizes, installed cost, operational cost, and performance metrics were determined for the Base case and the Elevated case. The techno-economic analysis included heat reuse applications across a range of heat recovery temperatures and heat rejection options. The analysis shows that high-temperature liquid cooling allows for improved energy efficiency, lower water consumption, and lower capital costs compared to traditional cooling approaches. Transitioning to elevated water inlet/outlet temperatures (50 degrees C/60 degrees C) eliminates the need for chillers, cooling towers, and heat recovery equipment in many scenarios across three distinct climate zones. This results in up to 75% capital cost savings for the cooling and heat recovery equipment, and with significantly reduced water consumption, especially in non-heat reuse applications. Heat generated from data centers can also be repurposed for space heating, domestic hot water, and other applications, and is most cost-effective when data center outlet temperatures exceed 55-60 degrees C.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗

Using mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system

Complex social-ecological systems can be difficult to study and manage. Simulation models can facilitate exploration of system behavior under novel conditions, and participatory modeling can involve stakeholders in developing appropriate management processes. Participatory modeling already typically involves qualitative structural validation of models with stakeholders, but with increased data and more sophisticated models, quantitative behavioral validation may be possible as well. In this study, we created a novel agent-basedmodel applied to a specific context: Zimbabwean non-governmental organization the Muonde Trust has been collecting data on their agro-pastoral system for the last 35 years and had concerns about land-use planning and the effectiveness of management interventions in the face of climate change. We collaboratively created an agent-based model of their system using their data archive, qualitatively calibrating it to the observed behavior of the real system without tuning any parameters to match specific quantitative outputs. We then behaviorally validated the model using quantitative community-based data and conducted a sensitivity analysis to determine the relative impact of underlying parameter assumptions, Indigenous management interventions, and different rainfall variation scenarios. We found that our process resulted in a model which was successfully structurally validated and sufficiently realistic to be useful for Muonde researchers as a discussion tool. The model was inconsistently behaviorally validated, however, with some model variables matching field data better than others. We observed increased model system instability due to increasing variability in underlying drivers (rainfall), and also due to management interventions that broke feedbacks between the components of the system. Interventions that smoothed year-to-year variation rather than exaggerating it tended to improve sustainability. The Muonde trust has used the model to successfully advocate to local leaders for changes in land-use planning policy that will increase the sustainability of their system.

54 ENVIRONMENTAL SCIENCES↗

A Community Energy Operations and Planning System: Concept, Use cases, Metrics, and Benefits

Community and city leaders are interested in achieving sustainability goals, providing resilient energy infrastructure, and improving economic competitiveness. Community-level data acquisition and analysis can provide energy and associated benefits that are not possible at the single building level. However, there is a lack of organizational structure, common semantic data models, interoperable systems, and methods to support data-driven decision-making for community-scale energy supply and demand systems. We explored the need and opportunity for a Community Energy Operations and Planning System (Community EOPS), a potential data exchange platform. We conducted “customer discovery” interviews, and reviewed literature, public tools, and technology platforms to identify key energy data “users” and use cases in communities. The key users of the Community EOPS could be developers of mixed-use districts, corporate, defense and university campus energy managers, and city managers of cities that own their energy utility. The value could be for community planning and reporting (for energy data-integrated land use planning and community infrastructure investments in microgrids, storage, district heating and cooling), energy efficiency (leveraging optimizations for community scale energy supply and demand), flexible load management (grid-edge load management to offset, shift, and flatten loads for multiple buildings and EV fleets), cost savings and revenue generation (participating in grid services), and social benefits such as energy resilience, equity, and awareness. We developed a conceptual Community EOPS architecture with recommendations for streamlined and prioritized data acquisition, sharing, and integration driven by prioritized use cases, common metrics, and actionable visualizations that can provide value to a community’s users.

Singh, Reshma↗

CO 2 Uptake and Stability Enhancement in Vinyltrimethoxysilane‐Treated SBA‐15 Solid Amine‐Based Sorbents

Abstract Silica‐supported amine absorbents, including materials produced by tethering aminosilanes or infusion of poly(ethyleneimine), represent a promising class of materials for CO 2 capture applications, including direct air and point source capture. Various silica surface treatments and functionalization strategies are explored to enhance stability and CO 2 uptake in amine‐based solid sorbent systems. Here, the synthesis and characterization of novel vinyltrimethoxysilane‐treated Santa Barbara Amorphous‐15 (SBA‐15) supports and the corresponding enhancement in CO 2 uptake compared to various SBA‐15‐based control supports are presented. The relationship between CO 2 diffusion and amine efficiency in these systems is explored using a previously reported kinetic model. The synthesized materials are characterized with CO 2 and H 2 O isotherms, diffuse reflectance infrared Fourier transform spectroscopy, 1 H T 1 – T 2 relaxation correlation NMR, and rapid thermal cycling experiments. The novel support materials are shown to enable high amine efficiencies, approaching a fourfold improvement over standard SBA‐15‐supported amines, while simultaneously exhibiting excellent stability when cycled rapidly under humid conditions. As the poly(ethyleneimine) loadings are held constant across the various samples, enhancements in CO 2 uptake are attributed to differences in the way the poly(ethyleneimine) interacts with the support surface.

Vallace, Anthony↗

Computing Free Energies with Fluctuation Relations on Quantum Computers

As a central thermodynamic property, free energy enables the calculation of virtually any equilibrium property of a physical system, allowing for the construction of phase diagrams and predictions about transport, chemical reactions, and biological processes. Thus, methods for efficiently computing free energies, which in general is a difficult problem, are of great interest to broad areas of physics and the natural sciences. The majority of techniques for computing free energies target classical systems, leaving the computation of free energies in quantum systems less explored. Recently developed fluctuation relations enable the computation of free energy differences in quantum systems from an ensemble of dynamic simulations. While performing such simulations is exponentially hard on classical computers, quantum computers can efficiently simulate the dynamics of quantum systems. Here, we present an algorithm utilizing a fluctuation relation known as the Jarzynski equality to approximate free energy differences of quantum systems on a quantum computer. In this work we discuss under which conditions our approximation becomes exact, and under which conditions it serves as a strict upper bound. Furthermore, we successfully demonstrate a proof of concept of our algorithm using the transverse field Ising model on a real quantum processor. As quantum hardware continues to improve, we anticipate that our algorithm will enable computation of free energy differences for a wide range of quantum systems useful across the natural sciences.

97 MATHEMATICS AND COMPUTING↗

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. Furthermore, these outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10 3 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

13 HYDRO ENERGY↗

Prediction of dynamic behavior of a system undergoing an exchange of components using a transmission simulator method

In systems with modular design, it is of interest to perform component exchanges to upgrade components and explore new system configurations. If the dynamics of the components are known, then dynamic substructuring can be used to obtain a prediction of system performance for the new configuration. The transmission simulator method is a substructuring technique that utilizes an additional fixture to apply an elastic boundary condition to a test article such that an appropriate dynamic response can be obtained for a given substructuring application, and the technique has been used widely for performing coupling and decoupling operations. Here, in this work, a modified transmission simulator method is proposed that allows a direct exchange of components to be performed. In a numerical demonstration, the proposed method is shown to accurately predict the dynamics of an assembly that has undergone a component exchange in which the exchanged components have different material properties and different geometric properties. This approach can be applied to dynamic qualification frameworks in which structures undergo modifications or upgrades rather than complete redesigns.

Component exchange↗

An adaptive discontinuous Petrov-Galerkin method for the Grad-Shafranov equation

In this work, we propose and develop an arbitrary-order adaptive discontinuous Petrov--Galerkin (DPG) method for the nonlinear Grad--Shafranov equation. An ultraweak formulation of the DPG scheme for the equation is given based on a minimal residual method. The DPG scheme has the advantage of providing more accurate gradients compared to conventional finite element methods, which is desired for numerical solutions to the Grad--Shafranov equation. The numerical scheme is augmented with an adaptive mesh refinement approach, and a criterion based on the residual norm in the minimal residual method is developed to achieve dynamic refinement. Nonlinear solvers for the resulting system are explored and a Picard iteration with Anderson acceleration is found to be efficient to solve the system. Finally, the proposed algorithm is implemented in parallel on MFEM using a domain-decomposition approach, and our implementation is general, supporting arbitrary order of accuracy and general meshes. Furthermore, numerical results are presented to demonstrate the efficiency and accuracy of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗

Scoping studies for low-temperature melting ZnO–Bi 2 O 3 –(B 2 O 3 , SiO 2 ) binder glass

In this work, low melting point glass systems were explored as candidates for binders of solid oxide nuclear wastes which include volatile elements such as radioactive iodine. The base glass systems considered were ZnO-Bi 2 O 3 -B 2 O 3 and ZnO-Bi 2 O 3 -SiO 2 , with specific compositions selected for synthesis trials based on available phase diagrams and a desire to explore the whole glass-forming region. Ten compositions were synthesized by melt quenching. Promising candidate glass binders were selected based on absence of crystallization and favorable thermal properties, including low glass transition temperature (T g ) combined with a high crystallization onset temperature. These thermal properties indicate stable glass forming which would lead to sintering behavior needed for glass binding. Compositional effects include: higher Bi 2 O 3 content lead to lower Tg values, high ZnO content generally led to crystallization on quench, and high SiO 2 content resulted in requirements for excessively high melting temperature. 25ZnO-15Bi 2 O 3 -60B 2 O 3 was down-selected for some detailed crystallization versus time and temperature studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Nuclear Power's Future Role in a Decarbonized U.S. Electricity System

This study explores the potential future role of nuclear energy in a decarbonized U.S. electricity system through a multi-model comparison approach. We employ four state-of-the-art CEMs with native and harmonized input assumptions, layered with different policy and technology trajectories. Comparing outputs across models, technology assumptions, and policy scenarios informs model understanding, interpretation, and development decisions. Under current policies, models differ in their projections for nuclear retirements, but nuclear power plants consistently run with high capacity factors and new builds only occur in scenarios with very low nuclear costs. String power sector carbon policies drive models to align in keeping existing nuclear capacity and employing nuclear plant flexibility, but they may not be enough to bring new nuclear capacity online in the absence of significant cost declines. Therefore, significant economic deployment of new nuclear capacity requires both a stringent electric sector CO2 policy and very low cost assumptions for new nuclear. While these scenarios should not be interpreted as predictions, they are informative for understanding differing model assessments of the relative competitiveness of nuclear energy under a range of policy and technology conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Ultrastrong magnon-magnon coupling and chiral spin-texture control in a dipolar 3D multilayered artificial spin-vortex ice

Strongly-interacting nanomagnetic arrays are ideal systems for exploring reconfigurable magnonics. They provide huge microstate spaces and integrated solutions for storage and neuromorphic computing alongside GHz functionality. These systems may be broadly assessed by their range of reliably accessible states and the strength of magnon coupling phenomena and nonlinearities. Increasingly, nanomagnetic systems are expanding into three-dimensional architectures. This has enhanced the range of available magnetic microstates and functional behaviours, but engineering control over 3D states and dynamics remains challenging. Here, we introduce a 3D magnonic metamaterial composed from multilayered artificial spin ice nanoarrays. Comprising two magnetic layers separated by a non-magnetic spacer, each nanoisland may assume four macrospin or vortex states per magnetic layer. This creates a system with a rich 16 N microstate space and intense static and dynamic dipolar magnetic coupling. The system exhibits a broad range of emergent phenomena driven by the strong inter-layer dipolar interaction, including ultrastrong magnon-magnon coupling with normalised coupling rates of $\frac{Δf}{v}$ = 0.57, GHz mode shifts in zero applied field and chirality-control of magnetic vortex microstates with corresponding magnonic spectra.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗