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DOE OSTI2022

In-beam γ-ray spectroscopy of 32 Mg via direct reactions

Background: The nucleus 32 Mg (N=20 and Z=12) plays a central role in the so-called “island of inversion,” where in the ground states sd-shell neutrons are promoted to the fp-shell orbitals across the shell gap, resulting in the disappearance of the canonical neutron magic number N=20. Purpose: The primary goals of this work are to extend the level scheme of 32Mg, provide spin-parity assignments to excited states, and discuss the microscopic structure of each state through comparisons with theoretical calculations. Method: In-beam γ-ray spectroscopy of 32 Mg was performed using two direct-reaction probes: one-neutron (two-proton) knockout reactions on 33 Mg ( 34 Si). Final-state exclusive cross sections and parallel momentum distributions were extracted from the experimental data and compared with eikonal-based reaction model calculations combined with shell-model overlap functions. Results: Owing to the remarkable selectivity of the one-neutron and two-proton knockout reactions, a significantly updated level scheme for 32 Mg, which exhibits negative-parity intruder and positive-parity normal states, was constructed. The experimental results were confronted with four different nuclear structure models. Conclusions: In some of these models, different aspects of 32 Mg and the transition into the island of inversion are well described. However, unexplained discrepancies remain, and, even with the help of these state-of-the-art theoretical approaches, the structure of this key nucleus is not yet fully captured.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Photodissociation of iso-propoxy (i-C 3 H 7 O) radical at 248 nm

Photodissociation of the i-C 3 H 7 O radical is investigated using fast beam photofragment translational spectroscopy. Neutral i-C 3 H 7 O radicals are produced through the photodetachment of a fast beam of i-C 3 H 7 O - anions and are subsequently dissociated at 248 nm (5.0 eV). Furthermore, the dominant product channels are CH 3 + CH 3 CHO and OH + C 3 H 6 with some contribution from H + C 3 H 6 O. The CH 3 and H loss channels are attributed to dissociation on the ground electronic state of i-C 3 H 7 O, but in a nonstatistical manner because calculated RRKM dissociation rates exceed the rate of energy randomization. Translational energy and angular distributions for OH loss are consistent with ground state dissociation, but the branching ratio for this channel is considerably higher than predicted from RRKM rate calculations. Additionally, i-C 3 H 7 O undergoes three-body fragmentation to CH 3 + CH 3 + HCO and CH 3 + CH 4 + CO. These three-body channels are attributed to dissociation of i-C 3 H 7 O to CH 3 + CH 3 CHO, followed by secondary dissociation of CH 3 CHO on its ground electronic state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Generative learning for slow manifolds and bifurcation diagrams

In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit. In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. Finally, the quantity of interest, which determines how the sampling is conditioned, is either known a priori or identified using manifold learning-based dimensionality reduction techniques applied to the training data.

Dynamical systems↗

DOE OSTI2023

Enabling residential heating decarbonization through hydronic low-temperature thermal distribution using forced-air assistive devices

Space heating represents approximately one-tenth of the United States’ energy use and has a breadth of potential for emission reduction. An element of space heating, hydronic heat distribution methods, use water supply temperatures up to 82.2 °C (180 °F). However, this operating temperature can be incompatible with high-efficiency heat generation systems, which typically provide heating temperatures of up to 60 °C (140 °F). In this work, a low-cost retrofit solution is developed using experimentally validated computational tools by incorporating an airflow distributor that preferentially directs the airflow from a fan to enhance heat transfer over the finned-tube heat exchanger found in conventional baseboards. The same heat output of traditional baseboard heat distribution systems operating at higher water supply temperatures (71.1 – 82.2 °C) can also be achieved at lower temperatures (≤60 °C). Specifically, results indicate that the technology can produce more than a 46.7% improvement in the heat transfer output at temperatures as low as 60 °C (140 °F), effectively matching the same output range achieved by 71.1 – 82.2 °C (160–180 °F) water supply temperatures. An important benefit of this solution is that it utilizes the existing infrastructure as opposed to requiring the replacement of the entire distribution system. Additionally, this technology enables existing building infrastructure to be coupled with newer, high-efficiency heating systems such as condensing boilers, solar-thermal systems, and geothermal/air-to-water heat pumps that may produce lower water supply temperatures (60 °C). A geospatially resolved techno-economic and environmental analysis is completed and presented to further understand the equivalent carbon footprint of enabling higher efficiency heat generation systems with the improved efficiency heat distribution system disclosed. Using 2021 grid-average emission factors, an emission reduction of up to a 67.5% decrease (2658 kgCO 2 /yr) for a single-family home, depending on state and climate region, could be realized by replacing a traditional natural gas-fired boiler with an air-to-water heat pump coupled to the same high-temperature heat distribution system along with the low-cost retrofit solution. A complete CO 2 emission reduction of 6688 kgCO 2 /yr, depending on state and climate region, could be realized if all electricity is further renewably sourced. Thus, this study provides a possible pathway towards enabling the reduction of operational emissions in space heating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Distribution of blackouts in the power grid and the Motter and Lai model

Carreras, Dobson, and colleagues have studied empirical data on the sizes of the blackouts in real grids and modeled them with computer simulations using the direct current approximation. They have found that the resulting blackout sizes are distributed as a power law and suggested that this is because the grids are driven to the self-organized critical state. In contrast, more recent studies found that the distribution of cascades is bimodal resulting in either a very small blackout or a very large blackout, engulfing a finite fraction of the system. Here we reconcile the two approaches and investigate how the distribution of the blackouts changes with model parameters, including the tolerance criteria and the dynamic rules of failure of the overloaded lines during the cascade. Finally, we study the same problem for the Motter and Lai model and find similar results, suggesting that the physical laws of flow on the network are not as important as network topology, overload conditions, and dynamic rules of failure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Impact of truck electrification on air pollution disparities in the United States

Electrifying heavy-duty trucks reduces on-road diesel emissions but shifts the burden of supplying energy to power-generation facilities. The combined effect of Inflation Reduction Act investments in grid decarbonization and truck electrification will alter the magnitude and distribution of air pollution burdens across the United States. These investments are intended to facilitate a just energy transition, with 40% of the benefits flowing to disadvantaged communities per the Justice40 Initiative. Here we evaluate the combined effects of Inflation Reduction Act grid decarbonization and truck electrification investments on a national scale to determine whether the air pollution benefits would meet this 40% goal for both disadvantaged communities and the most exposed racial–ethnic groups. We find that truck electrification and decarbonization reduce air-pollution-related premature mortality in disadvantaged communities. However, the relative disparity between disadvantaged and non-disadvantaged communities increases, suggesting that a disproportionate share of benefits accrue to non-disadvantaged communities. Whereas absolute disparity in grid emissions decreases over time for all racial–ethnic groups, relative disparity remains largely unchanged, with Black populations being the most exposed. Electrifying drayage corridors would result in comparatively large health benefits for disadvantaged communities, suggesting that increasing targeted electrification investments in short-haul routes near urban areas (for example, ports) could be promising.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Robust Real-Time Modeling of Distribution Systems with Data-Driven Grid-Wise Observability (Final Technical Report)

The overall objective of this project is to leverage existing and emerging sensor measurements to develop data-driven observability enhancement algorithms as well as robust state estimation and parameter identification techniques to enable real-time grid-wise monitoring and modeling of loads and distributed energy resources (DERs). The project resulted in a holistic framework with the following key components: 1) data-driven and machine learning-based grid-edge visibility enhancement, 2) robust branch-current-based state estimation (BCSE), and 3) robust real-time steady-state and dynamic-state modeling of loads and DERs. The research outcomes have provided utility companies better network visibility, higher-fidelity load/DER models, and more accurate assessment of DERs’ impacts, thus, facilitating the integration of renewable energy sources. The synergistic collaborative project among Iowa State University (ISU), Argonne National Laboratory (ANL), Electric Power Research Center (EPRC), SIEMENS Industry, Alliant Energy, Cedar Falls Utilities (CFU), and Maquoketa Valley Electric Cooperative (MVEC) to leverage the team’s extensive expertise and experience in power distribution systems, state estimation, online modeling and identification, and DER integrations. The proposed frameworks have been verified with industry adopted software and attempted integrated with existing tool wherever possible

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Regulating surface potential maximizes voltage in all-perovskite tandems

The open circuit voltage (V OC ) deficit in perovskite solar cells (PSCs) is greater in wide bandgap (>1.7 eV) cells than in ~1.5 eV perovskites. Quasi-Fermi level splitting (QFLS) measurements reveal V OC -limiting recombination at the electron transport layer (ETL) contact. This, we find, stems from inhomogeneous surface potential and poor perovskite-ETL energetic alignment. Common monoammonium surface treatments fail to address this; instead we introduce diammonium molecules to modify the perovskite surface states and achieve a more uniform spatial distribution of surface potential. Using 1,3-propane diammonium (PDA), QFLS increases by 90 meV, enabling 1.79 eV PSCs with a certified 1.33 V V OC , and > 19% power conversion efficiency (PCE). Incorporating this layer into a monolithic all-perovskite tandem, we report a record V OC of 2.19 V (89% of the Detailed Balance V OC limit) and > 27% PCE (26.3% certified quasi-steady-state). Furthermore, these tandems retain more than 86% of their initial PCE after 500 hrs operation.

14 SOLAR ENERGY↗

DOE OSTI2023

Characterization of the In Vivo Deuteration of Native Phospholipids by Mass Spectrometry Yields Guidelines for Their Regiospecific Customization

Customization of deuterated biomolecules is vital for many advanced biological experiments including neutron scattering. However, because it is challenging to control the proportion and regiospecificity of deuterium incorporation in live systems, often only two or three synthetic lipids are mixed together to form simplistic model membranes. This limits the applicability and biological accuracy of the results generated with these synthetic membranes. Despite some limited prior examination of deuterating Escherichia coli lipids in vivo, this approach has not been widely implemented. In this report an extensive mass spectrometry-based profiling of E. coli phospholipid deuteration states with several different growth media was performed, and a computational method to describe deuterium distributions with a one-number summary is introduced. The deuteration states of 36 lipid species were quantitatively profiled in 15 different growth conditions, and tandem mass spectrometry was used to reveal deuterium localization. Regressions were employed to enable the prediction of lipid deuteration for untested conditions. Small-angle neutron scattering was performed on select deuterated lipid samples, which validated the deuteration states calculated from the mass spectral data. Based on these experiments, guidelines for the design of specifically deuterated phospholipids are described. This unlocks even greater capabilities from neutron-based techniques, enabling experiments that were formerly impossible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Angular Distribution of Characteristic Radiation Following the Excitation of He-Like Uranium in Relativistic Collisions

In this paper, we present an experimental and theoretical study of excitation processes for the heaviest stable helium-like ion, that is, He-like uranium occurring in relativistic collisions with hydrogen and argon targets. In particular, we concentrate on angular distributions of the characteristic Kα radiation following the K → L excitation of He-like uranium. We pay special attention to the magnetic sub-level population of the excited 1s2lj states, which is directly related to the angular distribution of the characteristic Kα radiation. We show that the experimental data can be well described by calculations taking into account the excitation by the target nucleus as well as by the target electrons. Moreover, we demonstrate for the first time an important influence of the electron-impact excitation process on the angular distributions of the Kα radiation produced by excitation of He-like uranium in collisions with different targets.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Insulation Coordination Design for Grid-Connected Solid-State Transformers

The deployment of solid-state transformers (SSTs) in medium-voltage (MV) distribution systems is facing various challenges, especially the challenge of insulation coordination design against grid-originated lightning impulses. In this article, two challenges in existing insulation coordination designs for grid-connected SSTs are identified. One challenge is the mismatch between metal-oxide varistor (MOV) protective levels and SST insulation strength, the other challenge is the incompatibility of standard impulse test on SST protective structures. To address the MOV selection challenge, a novel lightning protection scheme is designed to protect a single-stage SST where the semiconductor modules are directly exposed to external lightning impulses. Here, the in-lab lightning impulse tests are performed to verify the overvoltage attenuation performance of the prototyped lightning protection scheme. To address the impulse test challenge, the surge withstand capability of the protected SST is comprehensively evaluated with a complete set of insulation coordination design procedures beyond the basic insulation level (BIL) test. After these two challenges are addressed, a discussion is presented on substituting conventional transformers with the protected SSTs into insulation-coordinated distribution systems to facilitate the field deployment of SSTs.

solid-state transformer (SST)↗

DOE OSTI2024

Techno-Economic Simulation Results Using dGeo for EGS-Based District Heating in the Northeastern United States

This dataset presents the results of techno-economic simulations performed using the Distributed Geothermal Market Demand Model (dGeo) to evaluate the feasibility of Enhanced Geothermal Systems (EGS)-based district heating in the Northeastern United States. Developed by the National Renewable Energy Laboratory (NREL), dGeo is a geospatially resolved, bottom-up modeling framework designed to explore the deployment potential of geothermal distributed energy resources. The dataset, created as part of the Cornell EGS Ground-Truthing Project, provides census tract-level data that includes inputs and outputs such as thermal demand, road length, energy prices, geothermal system sizing, annual energy contributions from geothermal and natural gas peaking boilers, system capital costs (CAPEX), operation and maintenance costs (OPEX), and the levelized cost of heat (LCOH). Key simulation parameters include geothermal gradients, measured well depths, production temperatures, and district heating piping lengths based on S1400 neighborhood road lengths. The simulations assume a target bottom hole temperature of 80C and the development of new district heating networks in each census tract.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Effectively Considering the Distribution System in Integrated Resource Plans

While electricity planning practices vary by state and utility based on utility type and market structure, integrated resource planning (IRP) remains a prominent vehicle — even in states with centrally-organized wholesale electricity markets. IRP focuses on meeting forecasted long-term electricity needs. Typically, utilities have not considered impacts of design and operation of the low-voltage distribution network in IRP. With advanced capabilities of grid-edge technologies to generate and store electricity and provide load flexibility, and large utility investments in distribution systems, it's increasingly important to consider at least some distribution planning elements in IRP. This report considers the value proposition for doing so, such as reducing utility costs through resource co-optimization and strategic siting of grid-edge resources, and idenfities the most important touchpoints between planning for bulk power and distribution systems and provide a range of tactics for integrating these two processes.

Relf, Grace [Lawrence Berkeley National Laboratory↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

DOE OSTI2020

Measurement of the energy distribution of electrons escaping confinement from an electron cyclotron resonance ion source

Production of high charge state ions in electron cyclotron resonance ion sources (ECRISs) is dependent on the electron energy distribution (EED) within the source plasma. In order to better understand the EED, a measurement of electrons escaping axially from an ECRIS device has been performed at the National Superconducting Cyclotron Laboratory. Electrons were measured escaping from the Superconducting Source for Ions, driven at 18 GHz. Dependencies of the observed EED on the confining magnetic field strength and injected microwave power are reported. This paper focuses on large peaks of electrons in the 400–1200 keV energy range. Measurements of the axial bremsstrahlung spectrum have been simultaneously carried out to provide a direct comparison between both techniques. A comparison between the energy associated with the peak of the electron distribution and the spectral temperature of the bremsstrahlung distribution is shown.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Atomic autoionization in the photo-dissociation of super-excited deuterated water molecules fragmenting into D + + O + + D

We present the relaxation dynamics of deuterated water molecules via autoionization, initiated by the absorption of a 61 eV photon, producing the very rare D + + O + + D breakup channel. We employ the COLd target recoil ion momentum spectroscopy method to measure the 3D momenta of the ionic fragments and emitted electrons from the dissociating molecule in coincidence. We interpret the results using the potential energy surfaces extracted from multi-reference configuration interaction calculations. The measured particle energy distributions can be related to a super-excited monocationic state located above the double ionization threshold of D 2 O. The autoionized electron energy shows a sharp distribution centered around 0.5 eV, which is a signature of the atomic oxygen autoionization occurring in the direct and sequential dissociation processes of D 2 O + * at a large internuclear distance. In this way, an O + radical fragment and a low-energy electron are created, both of which can trigger secondary reactions in their environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗