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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 919 records · Page 51

Titrating Controlled Defects into Si-LTA Zeolite Crystals Using Multiple Organic Structure-Directing Agents

Controlling defects in zeolites is crucial for tuning their adsorption and catalytic properties. In this work, we have performed an integrated zeolite synthesis, spectroscopy, and density functional theory study to test the limit of F– as a charge-balancing agent that mitigates defects in siliceous zeolites. We focused on the synthesis of siliceous zeolite LTA at 150 °C with 1,2-dimethyl-3-(4-methylbenzyl) imidazolium as the primary organic structure-directing agent (OSDA) and tetramethyl ammonium (TMA) as the secondary OSDA. By varying the amount of TMA in the synthesis gel, positive charges were titrated into the resulting as-made Si-LTA. Surprisingly, we found that greater TMA concentration does not induce more F– to enter into the zeolite. 29Si solid-state MAS NMR, Raman spectroscopy, and density functional theory suggest that this system has surpassed its capacity for F– to balance OSDA charge, and additional positive charge is balanced by Si–O– framework defects. The number of defects in the as-made Si-LTA can be precisely titrated by the amount of TMA in the zeolite structures. For the Si-LTA synthesized without TMA, framework defects formed in the early stage of crystal growth were found to heal during later crystallization, leading to defect-free Si-LTA. However, for the Si-LTA synthesized with TMA, the defects formed in early stages do not heal. A DFT thermodynamic analysis explains that crowding of Si-LTA pores by TMA impedes defect healing; this prediction is corroborated by synthesis experiments at an elevated temperature (170 °C). These results indicate that F– can have a limited capacity to balance OSDA charge in zeolite synthesis, opening up a third route to zeolite synthesis intermediate between the fluoride and hydroxide routes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Plasma confinement state classification via FPP relevant microwave diagnostics

We present a parsimonious and robust machine learning approach for identifying plasma confinement states in fusion power plants (FPPs) where reliable identification of the low-confinement and high-confinement regimes is critical for safe and efficient operation. Unlike research-oriented devices, FPPs must operate with a severely constrained set of diagnostics. To address this challenge, we demonstrate that a minimalist model, using only electron cyclotron emission (ECE) signals, can achieve accurate and reliable state classification. ECE provides electron temperature profiles without the engineering or survivability issues of in-vessel probes, making it a primary candidate for FPP-relevant diagnostics. Our framework employs ECE as input, extracts features using radial basis functions, and applies a gradient boosting classifier, achieving a test accuracy of 96% (correct predictions). Robustness analysis and feature importance analyzes confirm the approach’s reliability. These results demonstrate that state-of-the-art performance is attainable from a restricted diagnostic set, paving the way for minimalist yet resilient plasma control architectures for FPPs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Design Considerations for High-Voltage Insulated Gate Drive Power Supply for 10-kV SiC MOSFET Applied in Medium-Voltage Converter

High-performance gate drive power supply (GDPS) plays a crucial role in ensuring the reliability and safety of the gate driver for power semiconductor devices. This article focuses on the design of a high-voltage-insulated GDPS for the 10-kV silicon carbide MOSFET in medium-voltage (MV) application. Design considerations, including insulation scheme, high-voltage-insulated transformer design, and load voltage regulation scheme, are proposed. In addition, the performance of the secondary-side-regulated (SSR) GDPS and that of the primary-side-regulated (PSR) GDPS are compared for several aspects, including interwinding capacitance, load voltage regulation rate, conversion efficiency, and hardware complexity. Finally, an SSR GDPS and a PSR GDPS, with an insulation voltage of 20 kV, are built in the lab. Furthermore, the test results demonstrate that the PSR GDPS is more preferable because of lower interwinding capacitance, lower load voltage regulation rate, higher conversion efficiency, and simpler control circuit.

42 ENGINEERING↗

Investigating the Impacts of Outdoor Lighting (Final Report)

This report documents the research associated with the Depart of Energy supported project “Investigating the Health Impact of Outdoor Lighting.” This project was developed to consider how alternative solid state lighting options, particularly the spectral power distribution, effected the hormone melatonin which ultimately could lead to potential health issues. The project also provides an opportunity to gather other secondary data with respect to visual performance in a controlled environment. These tasks were developed to consider how alternative solid state lighting applications impacted performance. This report is a complete documentation of the tasks undertaken as part of this project. The report includes a literature review and consideration of the background data which are part of this field of study as well as a documentation of the methods developed as part of the project. The melatonin results, the primary dependent variable are included in the attached peer-review paper that has been recent published (Gibbons et al., 2022). Our discussion of these results are included in that publication. Other results from this project will be documented in one or more future publications.

59 BASIC BIOLOGICAL SCIENCES↗

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗

Tensorized Interior Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator

Building energy simulation is a critical tool for developing and testing advanced control strategies, such as Reinforcement Learning (RL), to provide demand flexibility and affordable energy costs. The recently introduced Smart Buildings Control Suite (sbsim) provides a lightweight, scalable, and data-calibrated simulation environment based on a 2D finite-difference model. However, the initial model primarily focused on conductive and convective heat transfer, neglecting the significant impact of long-wave radiative heat exchange between interior surfaces. This paper presents a significant extension to the sbsim framework by incorporating a physically-grounded model for interior radiative heat transfer. Our primary contribution is the development and integration of a fully tensorized radiative heat transfer module, which preserves the computational efficiency and scalability of the original simulator. This was achieved by developing a pipeline for view factor calculation, including an algorithm to identify directly seeing surfaces within complex floor plans, and formulating the net radiation equations for efficient execution on modern hardware accelerators. We validate the numerical accuracy of our tensorized implementation by comparing its results against a traditional iterative approach, demonstrating identical outcomes. This enhancement increases the physical fidelity of sbsim, enabling more accurate training of RL agents for building energy optimization.

Ham, Sang woo↗

Application of Electromagnetic Methods for Reservoir Monitoring with Emphasis on Carbon Capture, Utilization, and Storage

The Controlled-Source ElectroMagnetic (CSEM) method provides crucial information about reservoir fluids and their spatial distribution. Carbon dioxide (CO2) storage, enhanced oil recovery (EOR), geothermal exploration, and lithium exploration are ideal applications for the CSEM method. The versatility of CSEM permits its customization to specific reservoir objectives by selecting the appropriate components of a multi-component system. To effectively tailor the CSEM approach, it is essential to determine whether the primary target reservoir is resistive or conductive. This task is relatively straightforward in CO2 monitoring, where the injected fluid is resistive. However, for scenarios involving brine-saturated (water-wet) or oil-wet (carbon capture, utilization, and storage—CCUS) reservoirs, consideration must also be given to conductive reservoir components. The optimization of data acquisition before the survey involves analyzing target parameters and the sensitivity of multi-component CSEM. This optimization process typically includes on-site noise measurements and 3D anisotropic modeling. Based on our experience, subsequent surveys tend to proceed smoothly, yielding robust measurements that align with scientific objectives. Other critical aspects to be considered are using magnetotelluric (MT) measurements to define the overall background resistivities and integrating real-time quality assurance during data acquisition with 3D modeling. This integration allows the fine tuning of acquisition parameters such as acquisition time and necessary repeats. As a result, data can be examined in real-time to assess subsurface information content while the acquisition is ongoing. Consequently, high-quality data sets are usually obtained for subsequent processing and initial interpretation with minimal user intervention. The implementation of sensitivity analysis during the inversion process plays a pivotal role in ensuring that the acquired data accurately respond to the target reservoirs’ expected depth range. To elucidate these concepts, we present an illustrative example from a CO2 storage site in North Dakota, USA, wherein the long-offset transient electromagnetic method (LOTEM), a variation of the CSEM method, and the MT method were utilized. This example showcases how surface measurements attain appropriately upscaled log-scale sensitivity. Furthermore, the sensitivity of the CSEM and MT methods was examined in other case histories, where the target reservoirs exhibited conductive properties, such as those encountered in enhanced oil recovery (EOR), geothermal, and lithium exploration applications. The same equipment specifications were utilized for CSEM and MT surveys across all case studies.

Barajas-Olalde, César↗

Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century

Terrestrial vegetation removes CO 2 from the atmosphere; an important climate regulation service that slows global warming. This 119 Pg C per annum transfer of CO 2 into plants—gross primary productivity (GPP)—is the largest land carbon flux globally. While understanding past and anticipated future GPP changes is necessary to support carbon management, the factors driving long-term changes in GPP are largely unknown. In this study, we show that 1901 to 2010 changes in GPP have been dominated by anthropogenic activity. Our dual constraint attribution approach provides three insights into the spatiotemporal patterns of GPP change. First, anthropogenic controls on GPP change have increased from 57% (1901 decade) to 94% (2001 decade) of the vegetated land surface. Second, CO 2 fertilization and nitrogen deposition are the most important drivers of change, 19.8 and 11.1 Pg C per annum (2001 decade) respectively, especially in the tropics and industrialized areas since the 1970’s. Third, changes in climate have functioned as fertilization to enhance GPP (1.4 Pg C per annum in the 2001 decade). These findings suggest that, from a land carbon balance perspective, the Anthropocene began over 100 years ago and that global change drivers have allowed GPP uptake to keep pace with anthropogenic emissions.

54 ENVIRONMENTAL SCIENCES↗

Solubility-limited depolymerization kinetics in the glycolysis of carbonyl-containing polymers

Chemical recycling of condensation polymers is often rationalized on the basis of the intrinsic reactivity of ester and carbonate functional groups. However, under heterogeneous conditions relevant to plastic waste processing and environmental degradation, bulk depolymerization rates often diverge from trends predicted by homogeneous chemistry. Here, we investigate how polymer–solvent compatibility, catalyst strength, and phase behavior govern the heterogeneous glycolysis of carbonyl-containing polymers. Using poly(ethylene terephthalate) (PET), glycol-modified PET (PETG), and bisphenol-A polycarbonate (PC) as model systems, we examine depolymerization kinetics at 180 °C with ethylene glycol and bisphenol A as diols under both amphoteric organosalt (TBD : MSA) and strong base (TBD) catalysis. Despite substantial differences in crystallinity and glycol uptake, PET and PETG depolymerize at comparable rates under organosalt catalysis, while PC depolymerizes significantly more slowly under identical conditions. Time-resolved molecular weight analysis and thermal characterization demonstrate that these rate differences do not arise from crystallinity, swelling, or inherent carbonyl reactivity, but instead reflect solubility-limited kinetics that constrain the transition from heterogeneous to homogeneous reaction regimes. When polymer solubility is low, depolymerization remains heterogeneous and slow; when solubility is enhanced—either through increased polymer–diol compatibility or stronger base catalysis—rapid homogeneous depolymerization is observed, reversing apparent reactivity trends. These results establish solubility and phase behavior as primary determinants of depolymerization kinetics in heterogeneous polymer recycling systems. By demonstrating how catalyst selection and solvent compatibility can expose or overcome solubility limitations, this work provides mechanistic insight to design more energy-efficient and selective chemical recycling processes. More broadly, these findings suggest that polymers with limited solvent or water compatibility may resist chemical degradation in the environment, favoring fragmentation and persistence as micro- and nanoplastics. Understanding solubility-controlled depolymerization offers a pathway toward more sustainable polymer design and end-of-life chemical recovery.

Watson-Sanders, Shelby [Department of Chemistry, U↗

AA2024/SiC metal matrix composites simultaneously improve ductility and cracking resistance during elevated temperature deformation

Here this study uses the stir-casting technique to combine a semi-solid AA2024 alloy directly with finely-sized β-SiC p embedded as a powder or with mechanically alloyed granules as a delivery agent. Liquid-state primary fabrication tends to form agglomerates of reinforcement particles, whereas rolling better distributes the composite constituents. Sub-micron reinforcements of low volume fractions do not significantly increase the hardness of the composite materials. Uniaxial tensile testing at elevated temperatures over a wide range of strain rates showed simultaneous increases in the ductility and crack resistance of AA2024 + SiC p granules embedded as a powder when compared to the non-reinforced control material at lower strain rates, with the same toughness as the control material. The maximum engineering strain of 252.7 ± 19.2% was observed in AA2024/SiCp at a strain rate of 10 -4 s -1 . This improvement in properties is attributed to grain refinement in the MMCs, leading to pinning events during the straining and ductility increases. The resultant impediments to grain growth and crack propagation allow the fine-sized reinforcements to control dynamic microstructural changes during fatigue. Cube {001}<100> is a dominant texture component in AA2024, whereas the Goss {011}<100> and S {123}<634> components mainly represent the texture of the discontinuously reinforced aluminum matrix.

36 MATERIALS SCIENCE↗

Impacts of Control, Penetration, and Distribution of Embedded Storage Network in Bulk Power System

The current shift in generation mix from fossil fuel plants towards variable and intermittent renewable energy sources is poised to create a future grid with reduced physical inertia and mismatch between generation and demand. Embedded storage, which is a concept of a coordinated network of storage units sited at the interface between the transmission and distribution system, is proposed as a mechanism to provide a buffer between generation and demand. This paper proposes an automated framework to model and integrate embedded storage in large-scale power systems with industry-grade grid-following (GFL) and grid-forming (GFM) control technologies. More importantly, the developed framework is used to explore the impacts of embedded storage control, penetration, location, and capacity in providing fast frequency response to the grid under contingency events such as generator trips and faults. The framework and study are conducted using the transient-stability simulation tool PSS/E and a realistic model of the Puerto Rico grid as a chosen test system. The simulation results show that GFL and GFM embedded storage, distributed throughout the system, with sufficient penetration and capacity, can effectively improve primary frequency response of the system under the studied contingency events.

Battery Energy Storage, embedded storage, grid-for↗

Heteroatom-Modified and Compacted Zeolite-Templated Carbons for Gas Storage

Methane, the primary component of natural gas, is an energy-rich fuel by weight but exhibits lower energy density by volume than diesel and gasoline; efforts to increase its “volumetric” energy density have been focused on the design and synthesis of a porous scaffold that can bind the methane favorably. Novel approaches typically invoke the highly designable class of materials known as metal-organic frameworks (MOFs) owing to the relative difficulty of controlling carbon structure and composition precisely, without the use of a metal-based node. In this project, we instead explore the concept of carbon templating to achieve a porous scaffold with atomistically thin walls separated at a distance of ~1.2 nm, ideal for the formation of precisely two layers of methane per pore. This bottom-up templating approach permits the use of any small molecular precursor to build up the carbon scaffold, begging the question: which other elements (besides carbon) can be used to tune the surface for optimal methane binding without compromising the structure of the overall material? Boron and nitrogen were both explored and nitrogen was identified as the ideal agent for increasing methane binding to the optimal strength for use at near room temperature. Densification of the resulting materials by mechanical compaction was then explored to produce free-standing pellets with the highest methane “delivery” capacities of any known material. Here we define the amount “delivered” as the amount stored minus the amount remaining in the tank at 73 psi, the lowest useful pressure for vehicular transportation applications. The key features of the resulting methane storage system are rapid discharging and refueling speeds, extremely long cycle life (infinite cycle life for high-purity methane), a near-constant heat of refueling (making the heat generated upon refueling easier to manage), and a low maximum pressure of operation (<1500 psi), enabling the use of all-metal Type I cylinders and significantly reducing the costs of conversion of existing vehicles to cleaner burning, abundantly available natural gas). The technical effectiveness of this approach was further improved by exploring several cost-reduction strategies and investigating the reliability of such storage systems in the presence of “real” natural gas mixtures which contain a mixture of gaseous species.

03 NATURAL GAS↗

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗

An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints

The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective function and deterministic constraints. Our main contribution is combining the augmented Lagrangian framework with adaptive sampling, resulting in an efficient optimization methodology validated with practical examples. To achieve the presented efficiency, here we consider inexact solutions for the augmented Lagrangian subproblems, and through an adaptive sampling mechanism, we control the variance in the gradient estimates. Furthermore, we analyze the theoretical performance of the proposed scheme by showing equivalence to a gradient descent algorithm on a Moreau envelope function, and we prove sublinear convergence for convex objectives and linear convergence for strongly convex objectives with affine equality constraints. The worst-case sample complexity of the resulting algorithm, for an arbitrary choice of penalty parameter in the augmented Lagrangian function, is $\mathscr{O}$(ϵ -3-δ ) , where ϵ > 0 is the expected error of the solution and δ > 0 is a user-defined parameter. If the penalty parameter is chosen to be $\mathscr{O}$(ϵ -1 ), we demonstrate that the result can be improved to $\mathscr{O}$(ϵ -2 ) , which is competitive with the other methods employed in the literature. Moreover, if the objective function is strongly convex with affine equality constraints, we obtain $\mathscr{O}$(ϵ -1 log(1/ϵ)) complexity. Finally, we empirically verify the performance of our adaptive sampling augmented Lagrangian framework in machine learning optimization and engineering design problems, including topology optimization of a heat sink with environmental uncertainty.

97 MATHEMATICS AND COMPUTING↗

Tuning Point Defects by Elastic Strain Modulates Nanoparticle Exsolution on Perovskite Oxides

Exsolution generates stable and catalytically active metal nanoparticles via phase precipitation out of a host oxide. An ability to control the size and dispersion of the exsolution particles is desirable for design of nanostructured (electro)catalysts. In this work, we demonstrate that tuning point defects by lattice strain affects both the thermodynamics and the kinetics of iron (Fe 0 ) exsolution on La 0.6 Sr 0.4 FeO 3 (LSF) thin film model. By combining in situ surface characterization and ab initio defect modeling, we show oxygen vacancy and Schottky defects to be the primary point defects formed upon Fe0 exsolution. Lattice strain tunes the formation energy, and thus the abundance of these defects, and alters the amount and size of the resulting exsolution particles. In addition, we find that the density of exsolved nanoparticles matches the concentration of oxygen vacancy pairs, thus pointing to the surface oxygen vacancy pairs as preferential nucleation sites for exsolution. The tensile-strained LSF with a facile formation of these critical point defects results in a higher Fe 0 metal concentration, a larger density of nanoparticles, and a reduced particle size at its surfaces. These results provide important mechanistic insights and highlight the role of point-defect engineering in designing nanostructured catalysts in energy and fuel conversion technologies.

36 MATERIALS SCIENCE↗

Detonation Soot: A New Class of Ice Nucleating Particle

Abstract Temperatures and pressures from high explosive detonations far exceed atmospheric conditions in typical combustion reactions, and consequently, detonation soot forms with physiochemical properties distinct from soot formed by combustion. In this study, samples of detonation soot from two high explosives, PBX 9502 and Composition B‐3, were analyzed. Ice nucleation experiments on soot collected after controlled detonations were conducted in the laboratory to probe immersion and contact mode freezing. Samples nucleated ice at temperatures warmer than commercially available nanodiamonds, which has a mean nucleation temperature of −20.7°C. Ice nucleation rate coefficients increase rapidly by two to three orders of magnitude below −20°C for every sample. Size‐selected 137 μm diameter particles produced during detonation in an ambient air atmosphere yield bimodal distributions of freezing temperature with primary and secondary nucleation modes centered at −20°C and −13°C, respectively. The presence of a secondary mode allows for enhanced ice nucleation rate coefficients (one to two orders of magnitude greater than samples without a secondary mode) at temperatures outside the influence of the primary mode (>−17°C). Given the observed onset nucleation temperatures of −9.2°C, our results imply that detonation soot of the type studied here would only need to reach an altitude of approximately 4 km to facilitate ice formation.

54 ENVIRONMENTAL SCIENCES↗

Predictive Models and Novel Accelerated Tests for the Reliability of Cell Metallization and Solder Joints in Photovoltaic Modules

Interconnect, solder, and metallization-related failures are the primary reason for premature Si-based photovoltaic panel failure [1, 2] and can lead to a cascade of other failures, including thermal events. This combination of high severity and high occurrence means that understanding interconnect-related failures is key to the long-term health of the photovoltaic industry, and it is crucial that the industry adopt efficient and effective tests that aid in design-for-reliability and manufacturing quality control.

14 SOLAR ENERGY↗

Snowmass Neutrino Frontier: DUNE Physics Summary

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment with a primary physics goal of observing neutrino and antineutrino oscillation patterns to precisely measure the parameters governing long-baseline neutrino oscillation in a single experiment, and to test the three-flavor paradigm. DUNE's design has been developed by a large, international collaboration of scientists and engineers to have unique capability to measure neutrino oscillation as a function of energy in a broadband beam, to resolve degeneracy among oscillation parameters, and to control systematic uncertainty using the exquisite imaging capability of massive LArTPC far detector modules and an argon-based near detector. DUNE's neutrino oscillation measurements will unambiguously resolve the neutrino mass ordering and provide the sensitivity to discover CP violation in neutrinos for a wide range of possible values of $\delta_{CP}$. DUNE is also uniquely sensitive to electron neutrinos from a galactic supernova burst, and to a broad range of physics beyond the Standard Model (BSM), including nucleon decays. DUNE is anticipated to begin collecting physics data with Phase I, an initial experiment configuration consisting of two far detector modules and a minimal suite of near detector components, with a 1.2 MW proton beam. To realize its extensive, world-leading physics potential requires the full scope of DUNE be completed in Phase II. The three Phase II upgrades are all necessary to achieve DUNE's physics goals: (1) addition of far detector modules three and four for a total FD fiducial mass of at least 40 kt, (2) upgrade of the proton beam power from 1.2 MW to 2.4 MW, and (3) replacement of the near detector's temporary muon spectrometer with a magnetized, high-pressure gaseous argon TPC and calorimeter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗