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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Strain-driven oxygen vacancy ordering in LaNiO 3 thin films revealed by integrated differential phase contrast imaging in scanning transmission electron microscopy

Rare-earth nickelates, such as LaNiO 3 (LNO), exhibit complex electronic properties, with ordered oxygen vacancies (OOV) influencing conductivity and magnetic behavior. We investigate the structural stability of strain-induced OOV phases in LNO thin films grown on SrTiO 3 substrates and the impact of Ruddlesden–Popper (RP) faults. Using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and integrated differential phase contrast (iDPC) STEM imaging, we conducted atomic-scale structural and compositional analyses of OOV. Geometric phase analysis (GPA) was employed to measure the strain in fault-free and RP fault regions, while density functional theory (DFT) calculations explored different OOV arrangements in the LNO phase. Simulated iDPC-STEM imaging of energy-stabilized structures was performed to correlate with experimental results. Here, our findings reveal superstructure modulation in the chemical composition and atomic-scale lattice structure in LNO, primarily due to the formation of the OOV in Ni–O layers of the LaNiO 2.5 phase. The out-of-plane compressive strain of about 2% stabilizes this phase, reducing the strain, diminishing OOV, and transforming them into LNO.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Strengthening the Security of Operational Technology: Understanding Contemporary Bill of Materials

The evolution of cyber-physical infrastructure has made its security more challenging. The last few years have witnessed a convergence of hardware and software segments in various domains, including operational technology (OT) which is responsible for carrying out critical tasks such as monitoring and controlling power grids, nuclear plants, transportation, and emergency services. Both hardware and software encapsulate numerous open source and proprietary subcomponents, making it crucial for end-users to understand the composition of the products they are using. For example, wind turbines incorporate thousands of lines of code (software) used for the turbine's design, planning, operation, and analytics in addition to the numerous hardware subcomponents that construct it. Due to the highly complex nature of software and hardware, knowledge of the components and subcomponents is required to mitigate cyber vulnerabilities and defend against cyberattacks. There has also been a transformation from a traditional linear supply chain into a global, dynamic, diverse, and interconnected system. The digitization of the supply chain makes it easier to find and exploit vulnerabilities. Critical infrastructures (e.g., power grids, oil, natural gas, water, and wastewater) rely on OT to function, and if the OT is compromised, equipment damage and potential interruption of services could result. A significant security measure to protect OT systems from disruption is to develop a supply chain bill of materials (BoM) corresponding to the software and hardware used in OT, along with attestations amongst vendors and asset owners. A supply chain BoM is a proactive way to understand the inherent vulnerabilities in the system and mitigate them in advance of being exploited. BoMs bolster the trust placed in the digital infrastructure and enhance software supply chain security by sustaining the management of component obsolescence and compliance, along with the seclusion of unsafe segments of a specific product. Adopting BoM tools is becoming increasingly important across various government sectors, as evidenced by the recent U.S. executive order on cybersecurity (NIST 2021). This paper aims to classify BoMs based on structure, functionality, component type, and architecture. The work also discusses case studies to further highlight the benefits of BoMs. In addition, it identifies missing pieces in existing BoM implementations so that future research may identify bounds on where it could expect to make improvements and directly enable researchers to identify promising areas for exploration. Further, the authors provide valuable recommendations to tool developers, researchers, and standardizing organizations (policymakers), additionally benefitting critical infrastructure owners and government executives. This aids in paving a path for future work, thereby, providing suggestions to determine a tool for consumers that best suit their needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Synthesis and Characterization of Silver-Modified Nanoporous Silica Materials for Enhanced Iodine Removal

In aquatic environments, the presence of iodine species, including radioactive isotopes like 129 I and I 2 , poses significant environmental and health concerns. Iodine can enter water resources from various sources, including nuclear accidents, medical procedures, and natural occurrences. To address this issue, the use of natural occurring nanoporous minerals, such as zeolitic materials, for iodine removal will be explored. This study focuses on the adsorption of iodine by silver-modified zeolites (13X-Ag, 5A-Ag, Chabazite-Ag, and Clinoptilolite-Ag) and evaluates their performance under different conditions. All materials were characterized using scanning electron microscopey (SEM), energy-dispersive X-ray spectroscopy (EDS), powdered X-ray diffraction (P-XRD), Fourier-transform infrared spectrometry (FTIR), and nitrogen adsorption studies. The results indicate that Chabazite-Ag exhibited the highest iodine adsorption capacity, with an impressive 769 mg/g, making it a viable option for iodine removal applications. 13X-Ag and 5A-Ag also demonstrated substantial adsorption capacities of 714 mg/g and 556 mg/g, respectively, though their behavior varied according to different models. In contrast, Clinoptilolite-Ag exhibited strong pH-dependent behavior, rendering it less suitable for neutral to slightly acidic conditions. Furthermore, this study explored the impact of ionic strength on iodine adsorption, revealing that Chabazite-Ag is efficient in low-salinity environments with an iodine adsorption capacity of 51.80 mg/g but less effective in saline conditions. 5A-Ag proved to be a versatile option for various water treatments, maintaining its iodine adsorption capacity across different salinity levels. In contrast, Clinoptilolite-Ag exhibited high sensitivity to ionic competition, virtually losing its iodine adsorption ability at a NaCl concentration of 0.1 M. Kinetic studies indicated that the pseudo-second-order model best describes the adsorption process, suggesting chemisorption mechanisms dominate iodine removal. Chabazite-Ag exhibited the highest initial adsorption rate with a k 2 value of 0.002 mg g -1 h -1 , emphasizing its superior adsorption capabilities. Chabazite and Clinoptilolite, naturally occurring minerals, provide eco-friendly solutions for iodine adsorption. Chabazite superior iodine removal highlights its value in critical applications and its potential for addressing pressing environmental challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Probing the solidification of quasicrystals via joint experiment and simulation (Final Report)

Quasicrystals (QCs) possess long-range positional order but non-crystallographic orientational order. Their classically ‘forbidden’ symmetry has long attracted the interest of scientists worldwide. Despite their frequent observation in both metal alloys and soft matter structures in the 35 years since their discovery, little is known about how QCs evolve from a liquid, amorphous, or crystalline precursor. In this project, we sought to resolve the enigma of QC self-assembly through a combined experimental and computational program. The Shahani group employed in situ electron and synchrotron X ray imaging to peer into the growth dynamics of QCs in a liquid, covering a broad range of length scales and solidification pathways. Glotzer's team developed new simulation models incorporating phasonic defects — which are unique to quasicrystals — and used this model to investigate two grains growing together, closely mimicking the experimental conditions. While QCs remain exceptional structures, most compounds in the realm of intermetallics adopt non-trivial geometries. In fact, only around 6% of phases are comprised of the simplest sphere packings that researchers so readily associate with metals. State-of-the-art knowledge of phase transformations at the outset of this collaborative project did not encompass the remaining 94% of intermetallic compounds that possess complex and aperiodic structure types. Therefore, we expect that outcomes from this project will have immediate and profound impact on synthesis and processing science: our efforts will be used to not only explain the growth dynamics of QCs but also complex intermetallics more broadly.

36 MATERIALS SCIENCE↗
DOE OSTI2026

UNLOQ: UNderstanding coherence in Light-matter interfaces for Quantum Science (Final Technical Report)

The general goal of this project is to prepare next-generation quantum systems for novel quantum information science applications. Decades of research on quantum optics have provided revolutionary systems for manipulating atomic and photonic quantum coherence in transformative ways. Our hypothesis is that the next-generation quantum systems will come from nano-molecular quantum optics. Specifically, we are interested in coupling the electronic states of molecules or nanoparticles to the quantized radiation field inside an optical cavity to create a set of new photon-matter hybrid excitations, called polaritons. As opposed to atoms, the vibrational modes of molecules and nanoparticles provide new degrees of freedom to mediate the quantum transduction between electronic and photonic states, offering new ways to tune and ultimately control the quantum coherence of the integrated system. In this project we are interested in designing, fabricating, and characterizing the polaritons that arise from coupling CdSe nanoplatelets (NPLs) to a Fabry-Pérot optical cavity. Specific attention was paid to parameters of the system (cavity mode volume, quality factor, etc.) that would maximize the collective coupling strength. Through a combined theoretical and experimental approach, we were able to make important contributions to understanding NPL exciton-polariton photophysics including how to use cavity loss as a tunable parameter in order to manipulate the populations of the upper and lower polariton states. Finally, using state-of-the-art theoretical methods, we were able to show how the collective coupling of many molecular excitons in a cavity can protect polariton coherence from vibrationally-induced decoherence. In particular, for NPL-cavity polaritonic systems, the quantum coherence can be extended by over an order of magnitude at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Physics of parameter correlations around the solar-scale enhancement in neutrino theory with unitarity violation

We discuss the physics of the three neutrino flavor transformation with non-unitary mixing matrix, with particular attention to the correlation between the $\nu$SM- and the $\alpha$ parameters which represent the effect of unitarity-violating (UV) new physics. Towards this goal, a new perturbative framework is created to illuminate the effect of non-unitarity in the region of the solar-scale enhanced oscillations. We refute the skepticism about the physical reality of the $\nu$Standard Model CP phase $\delta$–$\alpha$ parameter phase correlation by analysis with the SOL convention of $U_{{\tiny MNS}}$, in which $e^{\pm i \delta}$ is attached to $s_{12}$. Then, a comparative study between the solar- and atmospheric-scale oscillation regions allowed by the framework reveals a dynamical $\delta$–(blobs of the $\alpha$ parameters) correlation in the solar oscillation region, in sharp contrast to the “chiral”-type phase correlation $[e^{- i \delta} \bar{\alpha}_{\mu e},\ e^{- i \delta} \bar{\alpha}_{\tau e},\ \bar{\alpha}_{\tau \mu}]$ in the Particle Data Group convention seen in the atmospheric oscillation region. An explicit perturbative calculation to the first order in the $\nu_{\mu} \rightarrow \nu_{e}$ channel allows us to decompose the UV related part of the probability into the unitary evolution part and the genuine non-unitary part. We observe that the effect of non-unitarity tends to cancel between these two parts, as well as between the different $\alpha_{\beta \gamma}$ parameters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Neural network based emulation of galaxy power spectrum covariances: A reanalysis of BOSS DR12 data

We train neural networks to quickly generate redshift-space galaxy power spectrum covariances from a given parameter set (cosmology and galaxy bias). This covariance emulator utilizes a combination of traditional fully connected network layers and transformer architecture to accurately predict covariance matrices for the high redshift, north galactic cap sample of the BOSS DR12 galaxy catalog. We run simulated likelihood analyses with emulated and brute-force computed covariances, and we quantify the network’s performance via two different metrics: (1) difference in Χ 2 and (2) likelihood contours for simulated BOSS DR 12 analyses. We find that the emulator returns excellent results over a large parameter range. We then use our emulator to perform a reanalysis of the BOSS HighZ NGC galaxy power spectrum, and find that varying covariance with cosmology along with the model vector produces Ω m = $0.27⁢6$$^{+0.013}_{–0.015}$, H 0 = 70.2 ± 1.9 km/s/Mpc, and σ 8 = $0.67⁢4$$^{+0.058}_{–0.077}$. These constraints represent an average 0.46⁢σ shift in best-fit values and a 5% increase in constraining power compared to fixing the covariance matrix (Ω m = 0.293 ± 0.017, H 0 = 70.3 ± 2.0 km/s/Mpc, σ 8 = $0.70⁢2$$^{+0.063}_{–0.075}$). As a result, this work demonstrates that emulators for more complex cosmological quantities than second-order statistics can be trained over a wide parameter range at sufficiently high accuracy to be implemented in realistic likelihood analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Nonlinear Interface Reduction for Time-Domain Analysis of Hurty/Craig-Bampton Superelements with Frictional Contact

Virtual prototyping in engineering design relies today on modern numerical models of contacting structures with accurate resolution of interface mechanics, which strongly affect the system-level stiffness and energy dissipation due to frictional losses. High-fidelity modeling within the localized interfaces is required to resolve local quantities of interest that may drive design decisions. The high-resolution finite element meshes necessary to resolve inter-component stresses tend to be computationally expensive, particularly when the analyst is interested in response time histories. The Hurty/Craig-Bampton (HCB) transformation is a widely used method in structural dynamics for reducing the interior portion of a finite element model while having the ability to retain all nonlinear contact degrees of freedom (DOF) in physical coordinates. These models may still require many DOF to adequately resolve the kinematics of the interface, leading to inadequate reduction and computational savings. This study proposes a novel interface reduction method to overcome these challenges by means of system-level characteristic constraint (SCC) modes and properly orthogonal interface modal derivatives (POIMDs) for transient dynamic analyses. Both SCC modes and POIMDs are computed using the reduced HCB mass and stiffness matrices, which can be directly computed from many commercial finite element analysis software. Comparison of time history responses to an impulse-type load in a mechanical beam assembly indicate that the interface-reduced model correlates well with the HCB truth model. Localized features like slip and contact area are well-represented in the time domain when the beam assembly is loaded with a broadband excitation. The proposed method also yields reduced-order models with greater critical timestep lengths for explicit integration schemes.

42 ENGINEERING↗
DOE OSTI2022

Simultaneous Insight into Dissolution and Aggregation of Metal Sulfide Nanoparticles through Single-Particle Inductively Coupled Plasma Mass Spectrometry

Nanoparticles (NPs) and their colloidal aggregates are ubiquitous and play important roles in the transport and release of metals. Knowledge of their dissolution rates and aggregation behavior in solution are crucial for better prediction of their fate in biogeochemical cycling, ecotoxicity, and environmental remediation. There are however significant technical challenges to accurately obtain such information as a result of the heterogeneity and highly dynamic transformation exhibited by NPs, particularly at relatively low particle concentrations in aqueous systems. Here, we quantitatively examine the simultaneous dissolution and aggregation behavior of metal sulfide NPs using single-particle inductively coupled plasma mass spectrometry (spICP–MS). We focus on nickel sulfide (NiS), with additional data presented for copper sulfide (CuS) and cobalt sulfide (CoS). The kinetics of metal release (dissolution and disaggregation) of NiS was fastest under strongly oxidizing conditions (from 0.04 to >3 min –1 with H 2 O 2 ) and were slower under near-neutral (HEPES buffer/H 2 O) and acidic (1 mM HNO 3 ) conditions (≤0.006 min –1 ). Metal release kinetics in HNO 3 was not faster than in H 2 O or HEPES, suggesting that the solution pH has an influence over both the dissolution kinetics of individual particles and the NP aggregation states, which in combination affect metal release rates over time. Between different metal sulfides, the measured metal release rates were largely consistent with predictions based on the crystallinity, solubility products, and specific surface areas of the NPs, following an order of CoS > NiS > CuS. Here, the spICP–MS approach described here can be easily applied to the characterization of metal release and aggregation of other NPs at low concentrations (~10 5 particles/mL) typically found in natural environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Rapidity-dependent spin decomposition of the nucleon

We revisit the two-dimensional Fourier transform of generalized parton distributions (GPDs) at nonzero skewness. At 𝜂 = 0 it reduces to the standard impact-parameter density, while at 𝜂 ≠ 0 it is an off-forward amplitude that we interpret as a genuine parton–nucleon correlation. Its overall strength (the transverse-plane integral of the density) is fixed by the GPD at the kinematic point 𝑡 =−𝑐 𝜂 =−4⁢𝜂 2 ⁢𝑚$^{2}_{𝑁}$/(1 − 𝜂 2 ) and decreases monotonically with the rapidity gap Δ⁢𝑦 = ln⁡[(1+𝜂)/(1−𝜂)] = 2 artanh⁡(𝜂). This rapidity dependence implies rapidity-modified Ji identities that connect helicity, orbital, and total angular momenta of the correlation in closed form. To quantify these effects, we construct leading-twist quark and gluon GPDs in a string-based conformal framework: conformal moments are parametrized by linear open- and closed-string Regge trajectories with slopes constrained by parton distribution functions (PDFs), hadron/glueball spectroscopy, and form-factor data, and GPDs are reconstructed over the full (𝑥,𝜂,𝑡) domain by Mellin-Barnes inversion with next-to-leading order evolution. We find qualitative agreement (and fair quantitative agreement within quoted uncertainties) for several moments and selected nonsinglet 𝑥-space channels at 𝜇 = 2 GeV when compared with lattice QCD, while we also identify channels with visible tension and discuss likely sources (PDF priors and 𝑡-slope systematics).

Gauge-gravity dualities↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2025

R&D Effort of Geologic Hydrogen Production at the National Renewable Energy Lab (NREL)

Geologic hydrogen (geoH2) is an emerging technology with massive current market interest and distinct potential to change the paradigm of hydrogen production. Two major subsurface processes influence the amount of geoH2 that are available for energy extraction: 1) geochemical reactions of H2O and Fe2+-bearing rocks which can produce hydrogen in the subsurface environment, where 2) various active microbial communities consume hydrogen as an energy source before the hydrogen reaches the surface. The net gain of hydrogen from these two competing processes dictates the production rate of geoH2. A recent study (Templeton et al., 2024) suggested that for most natural geoH2 systems, five orders of magnitude of production rate enhancement are needed to make geoH2 production economical in the near term. Effective enhancement of the production rate requires an in-depth understanding of the two geoH2 processes, in order to promote the H2-generating geochemical processes and suppress the H2-consuming microbial processes. However, current significant knowledge gaps in these two processes hinders the efforts to formulate strategies to enhance geoH2 production. The National Renewable Energy Laboratory (NREL) is a U.S. Department of Energy National Laboratory with the core mission of leading research, innovation, and strategic partnership to deliver solutions for a clean energy based economy. NREL's extensive research portfolio in hydrogen, bioenergy, geothermal, industrial decarbonization, and energy analysis makes us well positioned to conduct interdisciplinary research and facilitate technology deployment in the geoH2 space. In this presentation, we will discuss ongoing geoH2 research and engagement efforts at NREL, including: 1) geochemical investigation to understand the reaction mechanisms and production rate and potential of different source minerals and rocks, 2) microbiological investigation to understand methanogenesis and acetogenesis in the subsurface geoH2 environment, and identify effective inhibitors for these microbial processes, and 3) preliminary analysis for geoH2 production in the State of Minnesota, where abundant Fe-rich rocks for stimulated geoH2 production and ample opportunity to utilize geoH2 in transforming iron and steel industries are currently available.

08 HYDROGEN↗
DOE OSTI2022

Fast flavor instability in hypermassive neutron star disk outflows

Here, we examine the effect of neutrino flavor transformation by the fast flavor instability (FFI) on long-term mass ejection from accretion disks formed after neutron star mergers. Neutrino emission and absorption in the disk set the composition of the disk ejecta, which subsequently undergoes r-process nucleosynthesis upon expansion and cooling. Here we perform 28 time-dependent, axisymmetric, viscous-hydrodynamic simulations of accretion disks around hypermassive neutron stars (HMNSs) of variable lifetime, using a 3-species neutrino leakage scheme for emission and an annular-lightbulb scheme for absorption. We include neutrino flavor transformation due the FFI in a parametric way, by modifying the absorbed neutrino fluxes and temperatures, allowing for flavor mixing at various levels of flavor equilibration, and also in a way that aims to respect the lepton-number preserving symmetry of the neutrino self-interaction Hamiltonian. We find that for a promptly-formed black hole (BH), the FFI lowers the average electron fraction of the disk outflow due to a decrease in neutrino absorption, driven primarily by a drop in electron neutrino/antineutrino flux upon flavor mixing. For a long-lived HMNS, the disk emits more heavy lepton neutrinos and reabsorbs more electron neutrinos than for a BH, with a smaller drop in flux compensated by a higher neutrino temperature upon flavor mixing. The resulting outflow has a broader electron fraction distribution, a more proton-rich peak, and undergoes stronger radiative driving. Disks with intermediate HMNS lifetimes show results that fall in between these two limits. In most cases, the impact of the FFI on the outflow is moderate, with changes in mass ejection, average velocity, and average electron fraction of order ~10%, and changes in the lanthanide/actinide mass fraction of up to a factor ~2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Modeling Framework for Bulk Electric Grid Impacts from HEMP E1 and E3 Effects (Tasks 3.1 Final Report)

This report presents a framework to evaluate the impact of a high-altitude electromagnetic pulse (HEMP) event on a bulk electric power grid. This report limits itself to modeling the impact of EMP E1 and E3 components. The co-simulation of E1 and E3 is presented in detail, and the focus of the paper is on the framework rather than actual results. This approach is highly conservative as E1 and E3 are not maximized with the same event characteristics and may only slightly overlap. The actual results shown in this report are based on a synthetic grid with synthetic data and a limited exemplary EMP model. The framework presented can be leveraged and used to analyze the impact of other threat scenarios, both manmade and natural disasters. This report d escribes a Monte-Carlo based methodology to probabilistically quantify the transient response of the power grid to a HEMP event. The approach uses multiple fundamental steps to characterize the system response to HEMP events, focused on the E1 and E3 components of the event. 1) Obtain component failure data related to HEMP events testing of components and creating component failure models. Use the component failure model to create component failure conditional probability density function (PDF) that is a function of the HEMP induced terminal voltage. 2) Model HEMP scenarios and calculate the E1 coupled voltage profiles seen by all system components. Model the same HEMP scenarios and calculate the transformer reactive power consumption profiles due to E3. 3) Sample each component failure PDF to determine which grid components will fail, due to the E1 voltage spike, for each scenario. 4) Perform dynamic simulations that incorporate the predicted component failures from E1 and reactive power consumption at each transformer affected by E3. These simulations allow for secondary transients to affect the relays/protection remaining in service which can lead to cascading outages. 5) Identify the locations and amount of load lost for each scenario through grid dynamic simulation. This can be an indication of the immediate grid impacts from a HEMP event. In addition, perform more detailed analysis to determine critical nodes and system trends. 6) To help realize the longer-term impacts, a security constrained alternating current optimal power flow (ACOPF) is run to maximize critical load served. This report describes a modeling framework to assess the systemic grid impacts due to a HEMP event. This stochastic simulation framework generates a large amount of data for each Monte Carlo replication, including HEMP location and characteristics, relay and component failures, E3 GIC profiles, cascading dynamics including voltage and frequency over time, and final system state. This data can then be analyzed to identify trends, e.g., unique system behavior modes or critical components whose failure is more likely to cause serious systemic effects. The proposed analysis process is demonstrated on a representative system. In order to draw realistic conclusions of the impact of a HEMP event on the grid, a significant amount of work remains with respect to modeling the impact on various grid components.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Universality of Collective Density Fluctuations in Liquids at and away from Equilibrium – An Integrated Neutron Scattering, Theoretical, and Computational Study (Final Technical Report)

Liquids, ubiquitous on earth, are prototypical disordered condensed matter. As the mediate phase between non-structured gases and ordered solids, liquids p ossess intriguing complexities that are absent on either extreme counter phase, especially when they are driven away from equilibrium. Despite decades of studies, the physics of liquids still keeps surprising us and challenges our understanding of condensed matter, emergent phenomena, and complex systems. Among the many interesting properties, the nature of collective dynamics in liquids remains elusive because of both the presence of strong interactions and the absence of translational invariance. This project studied the universal features of collective density fluctuations (or collective modes), which include both phonon-like excitations and collective relaxations, in the Q-dependent generalized hydrodynamic regime in liquids and liquid-like matter both at and away from equilibrium. We performed systematic studies of the fast collective dynamics (including both phonon-like excitations and collective relaxations) of four classes of liquids characterized by different fragilities and interactions using synergistically integrated coherent neutron scattering experiments (Inelastic Neutron Scattering (INS), Quasi-Elastic Neutron Scattering (QENS), and Neutron Spin Echo (NSE)), a ViscoElastic Hydrodynamics theory we have been developing, andMolecular Dynamics (MD) simulations and our analysis package LiquidLib. The transformative knowledge of the collective dynamics in liquids is important to understand and ultimately control the transport of mass, energy, and charge in liquids, both at and away from thermal equilibrium, which may will likely impact a wide range of disciplines.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Solar Receiver with Integrated Thermal Storage for a Supercritical Carbon Dioxide Power Cycle

This project endeavors to design and demonstrate an integrated CSP system that meets the Department of Energy’s 6¢/kW-hr LCOE goal [1]. This design is based on the 10 MWe Supercritical Transformational Electric Power (STEP) engine specifications, but adaptable to any sCO 2 power module up to 10 MW e . The enabling technology to be tested is a new class of metal hydride based thermal energy storage materials which can store thermal energy in the hydrogen bonds between the metal hydride and hydrogen. This thermochemical energy storage solution provides a much greater energy density than sensible and latent energy storage enabling an integrated system design that closely linked the metal hydride to the power block. This allows the entire system to be mounted up tower to provide cost reductions. A novel metal hydride, capable of operating above 760 °C, was developed during the course of this project and thoroughly tested at a lab scale. The material’s hydrogen capacity, reaction enthalpy, thermal conductivity, and cyclic stability were measured. A demonstration test article was built that can store 2kWh of thermal energy and was coupled to Brayton Energy’s CO 2 loop where the performance of the metal hydride under commercial conditions was measured. An integrated model was created that uses the inputs from all the different sub-models in order to return an overall system LCOE using SAM. This project greatly advanced metal hydride based thermochemical energy storage technology and demonstrated its use. Previously there were no metal hydrides which were capable of operating at the proposed temperature limits (760°C), there is now a high temperature metal hydride with documented performance/stability and a path for low-cost production. Additionally, the metal hydride was tested at a scale >650x larger than what had previously been done in the lab. While not at commercial scale, it was an excellent demonstration of the technology and provided knowledge that can be leveraged for other applications.

08 HYDROGEN↗
DOE OSTI2023

Elucidating Mechanisms of Rust Pathogenesis for Engineering Resistance in Poplar

The overall objective of this research was to elucidate the molecular basis for the virulence of Melampsora larici-populina towards Populus spp. in order to address the formidable challenge of engineering durable resistance in poplar against leaf rust. Our original research plan was built on the technically innovative approach of using dual host systems and known host defense proteins and pathogen avirulent effectors along with genome-wide approaches to identify key pathogenesis effectors of Melampsora larici-populina that target poplar defense and nutrient acquisition. Our efforts focused on developing high-throughput experimental approaches and systems to enable us to address our overarching hypothesis that rust effectors with homology to other fungal effectors will either bypass the poplar immune system and/or suppress effector-triggered immunity through conserved mechanisms, and that an analysis of these interaction networks would provide for new approaches to develop rust-resistant poplar. Three main objectives were originally proposed: (1) mine key rust effectors that suppress host immunity; (2) elucidate interaction networks in poplar targeted by key rust effectors; (3) generate transgenic poplar resources for identification of genes involved in pathogen infection or host defense triggered by rust. Generally, we worked towards all three objectives by developing and applying complementary molecular, genomic, biochemical and plant transformation approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hierarchical-embedding autoencoder with a predictor as efficient architecture for learning time-evolution in multi-scale turbulent flows

We introduce a scale-aware, data-driven deep learning modeling framework for accurately predicting the time evolution of multi-scale turbulent plasma and liquid flows. The approach is motivated by the idea of scale separation. Structures of vastly different length scales emerge in these systems, and interactions between these structures occur only locally. To exploit this structure, the flow state is transformed by a hierarchical, fully convolutional autoencoder, not into a single embedding layer as in conventional convolutional surrogate models, but into a series of embedding layers. A stepwise training strategy ensures that fine-scale features are encoded on a high-resolution grid, while larger structures are represented on progressively coarser layers. The time evolution predictor advances all embedding layers in sync, capturing local interactions between features at the same scale as well as between all scales. This approach enables efficient modeling of multi-scale systems since negligible interactions between distant, small-scale structures do not need to be directly modeled. Our hierarchical-embedding autoencoder with a predictor framework is evaluated on canonical examples of multi-scale turbulence: two-dimensional Kolmogorov flow and Hasegawa–Wakatani plasma turbulence. In both cases, the proposed framework significantly improves predictive accuracy relative to conventional convolutional network architectures. A significant improvement in prediction accuracy was observed for crucial statistical characteristics of the Hasegawa–Wakatani plasma as well as for individual trajectories of the Kolmogorov flow turbulence. Importantly, the model's rollout for the Hasegawa–Wakatani problem demonstrates a four-order-of-magnitude speedup compared to traditional numerical solvers.

Khrabry, Alexander I. [Princeton Univ., NJ (United↗