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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 253 records · Page 14

Cryo-FIB for TEM Investigation of Soft Matter and Beam Sensitive Energy Materials

Primarily driven by structural biology, the rapid advances in cryogenic electron microscopy techniques are now being adopted and applied by materials scientists. Samples that inherently have electron transparency can be rapidly frozen (vitrified) in amorphous ice and imaged directly on a cryogenic transmission electron microscopy (cryo-TEM), however this is not the case for many important materials systems, which can consist of layered structures, embedded architectures, or be contained within a device. Cryogenic focused ion beam (cryo-FIB) lift-out procedures have recently been developed to extract intact regions and interfaces of interest, that can then be thinned to electron transparency and transferred to the cryo-TEM for characterization. Several detailed studies have been reported demonstrating the cryo-FIB lift-out procedure, however due to its relative infancy in materials science improvements are still required to ensure the technique becomes more accessible and routinely successful. Here, we review recent results on the preparation of cryo-TEM lamellae using cryo-FIB and show that the technique is broadly applicable to a range of soft matter and beam sensitive energy materials. We then present a tutorial that can guide the materials scientist through the cryo-FIB lift-out process, highlighting recent methodological advances that address the most common failure points of the technique, such as needle attachment, lift-out and transfer, and final thinning.

36 MATERIALS SCIENCE↗

Suppression of Collisionless Magnetic Reconnection in the High Ion β, Strong Guide Field Limit

Abstract In magnetic reconnection, the ion bulk outflow speed and ion heating have been shown to be set by the available reconnecting magnetic energy, i.e., the energy stored in the reconnecting magnetic field ( B r ). However, recent simulations, observations, and theoretical works have shown that the released magnetic energy is inhibited by upstream ion plasma beta β i —the relative ion thermal pressure normalized to magnetic pressure based on the reconnecting field—for antiparallel magnetic field configurations. Using kinetic theory and hybrid particle-in-cell simulations, we investigate the effects of β i on guide field reconnection. While previous works have suggested that guide field reconnection is uninfluenced by β i , we demonstrate that the reconnection process is modified and the outflow is reduced for sufficiently large β i > ( B r 2 + B g 2 ) / B r 2 . We develop a theoretical framework that shows that this reduction is consistent with an enhanced exhaust pressure gradient, which reduces the outflow speed as v out ∝ 1 / β i . These results apply to systems in which guide field reconnection is embedded in hot plasmas, such as reconnection at the boundary of eddies in fully developed turbulence like the solar wind or the magnetosheath as well as downstream of shocks such as the heliosheath or the mergers of galaxy clusters.

Giai, Carlos A.↗

Imaging of a van der Waals spin-orbit torque system using spin ensembles in hBN

Recently, optically active spin defects embedded in two-dimensional (2D) van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science. Taking advantage of excellent solid-state integrability, this new class of spin defects can be readily arranged in nanoscale proximity to target materials, showing great promise for realizing in-situ quantum sensing of microscopic spin and charge behaviors in vdW heterostructures. Here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching and electric current distributions in an all-vdW spin-orbit torque (SOT) system. By visualizing variations of nanoscale magnetic stray field profile of room-temperature 2D magnet Fe 3 GaTe 2 under different SOT conditions, we show how the magnetic switching evolves from deterministic to stochastic behavior due to the interplay between spin orientations, anisotropy and Joule heating. Micromagnetic simulations rationalize our results well, revealing the role of field-like SOT in inhibiting thermal fluctuation driven stochastic switching and chaotic multi-domain competition. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights into material design, testing, and performance evaluation of next-generation vdW spintronic devices.

Imaging techniques↗

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗

Voronoi cell analysis: The shapes of particle systems

Many physical systems can be studied as collections of particles embedded in space, often evolving in time. Natural questions arise concerning how to characterize these arrangements—are they ordered or disordered? If they are ordered, how are they ordered and what kinds of defects do they possess? Voronoi tessellations, originally introduced to study problems in pure mathematics, have become a powerful and versatile tool for analyzing countless problems in pure and applied physics. Here we explain the basics of Voronoi tessellations and the shapes that they produce and describe how they can be used to characterize many physical systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Embedding via the Exact Factorization Approach

We present a quantum electronic embedding method derived from the exact factorization approach to calculate static properties of a many-electron system. The method is exact in principle but the practical power lies in utilizing input from a low-level calculation on the entire system in a high-level method computed on a small fragment, as in other embedding methods. Here, the exact factorization approach defines an embedding Hamiltonian on the fragment. Further, various Hubbard models demonstrate that remarkably accurate ground-state energies are obtained over the full range of weak to strongly correlated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗

HybridSystemsSimulations.jl - Solving the Merchant Collocated Facilities with JuMP

The development of new clean-generation technologies also leads to new plant-level architectures that combine several generation and storage assets behind the point of connection. These co-located generation resources (Hybrid Systems) primarily operate as merchant assets that employ automated market bidding models and internal Energy Management Systems (EMS) to comply with the operator's signals. Formulating an optimal bidding model requires embedding the EMS control model into the bidding algorithm, resulting in a bi-level optimization problem. In this presentation, we first showcase using JuMP to formulate and solve this problem effectively for multiple merchant systems and the bidding outcomes considering different model formulations. Second, the bidding outcomes are later integrated into a PowerSimulations.jl (also built with JuMP) simulation to study the system-level effects of the various merchant bidding and the interactions between market-clearing models and the embedded EMS model. We will showcase simulations conducted in the RTS system considering different levels of merchant hybrid systems participation. The presentation provides the following specific insights on JuMP usage: 1) the Formulation of specialized bi-level problems with custom cuts to solve the merchant hybrid system bidding problem; 2) the integration of a modular model within a complex simulation workflow supported by JuMP in PowerSimulations.jl; 3) Accelerating the solution of power systems operations simulation that employ agent optimization problems using JuMP.

energy markets↗

Effects of ZrN coating and heat treatment on U-Mo dispersion fuel systems under irradiation

The stability of U-Mo fuel particles embedded in an Al matrix under irradiation can be enhanced through ZrN coatings and/or heat treatment. Here, the present study investigates the irradiation behavior of fuel plates containing U-Mo fuel particles fabricated under various heat-treatment conditions and ZrN coating thicknesses. Different fission densities were also applied to each fuel plate to evaluate the effects of these variables. Results indicate that higher fission densities lead to more grain recrystallization and high burnup structure (HBS) development in the fuel particles. Heat treatment was found to mitigate the accumulation of fission gas bubbles in fuel particles at low fission densities by coarsening their grains. Fuel particles with ZrN coatings of a 1.2 μm thickness or above exhibited reduced formation of U-Mo/Al interaction layers, suggesting the existence of a critical ZrN coating thickness that minimizes the development of these layers. Fission gas bubbles were predominantly observed at grain boundaries of U-Mo fuel particles irradiated at low fission densities. Subgrain boundaries, which appeared to originate from the original grain boundaries containing fission gas bubbles or HBSs, were also observed, indicating the early stage of HBS propagation in the fuel particles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences

Quantum mechanical embedding methods hold the promise to transform not just the way calculations are performed, but to significantly reduce computational costs and improve scaling for macro molecular systems containing hundreds if not thousands of atoms. The field of embedding has grown increasingly broad with many approaches of different intersecting flavors. Here, we lay out the methods into two streams: QM:MM and QM:QM, showcasing the advantages and disadvantages of both. We provide a review of the literature, the underpinning theories including our contributions, and we highlight current applications with select examples spanning both materials and life sciences. Lastly, we conclude with prospects and future outlook on embedding, and our view on the use of universal test case scenarios for cross comparisons of the many available (and future) embedding theories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Numerical Study and Validation of Melting and Solidification in PCM Embedded Heat Exchangers with Straight Tube

Latent heat thermal energy storage (LHTES) systems have shown great potential to enable reliable use of renewable energy and load shifting. LHTES offer high storage density and release energy at near constant temperature because of its use of phase change materials (PCMs). The cylindrical PCM heat exchangers (PCMHX) are one of the most used technologies due to their simplicity. Numerical models for such PCMHX enable engineers to estimate their performance for different design parameters and operating conditions without having to test them all. However, modeling the phase change phenomena can be challenging. To better understand the difficulties involving accurate modeling of PCMHX, a cylindrical latent storage unit filled with PCM and water as in-tube heat transfer fluid (HTF) is numerically investigated. This paper presents a study based on a 2D-axisymmetric model of a straight tube embedded in PCM in a cylindrical container. CFD is used to study the charging (melting) and discharging (solidification) phenomena. The models are validated against experimental and numerical data from the literature. The predicted local PCM temperature profile over time agrees within 2K compared to the experimental values. The paper also presents a simple method to estimate the melting and solidification phase change temperature range from limited data provided by PCM manufacturers.

PCM, melting, solidification, CFD, heat exchanger↗

Data-Driven Adaptive Damping Controller for Wind Power Plants with Doubly-Fed Induction Generators

This paper presents an adaptive damping controller for wind power plants in which the turbines are equipped with doubly-fed induction generators. The controller is designed to respond to an input control signal that is triggered according to the system operating conditions. A processing unit continuously estimates the electromechanical modes of oscillation based on real-time streaming data acquired from a phasor measurement unit that is strategically positioned on the grid. The decision to trigger (or not trigger) the control signal is automatic, based on the relative damping of the dominant mode. The modes are estimated using the dynamic mode decomposition algorithm with time-delay embedding. Numerical simulations performed on the two-area system demonstrate that the proposed controller enhances the rotor angle stability for both small-signal and large disturbances, and is adaptive to changing grid conditions.

damping controller↗

Data-Driven Adaptive Damping Controller for Wind Power Plants with Doubly-Fed Induction Generators: Preprint

This paper presents an adaptive damping controller for wind power plants in which the turbines are equipped with doubly-fed induction generators. The controller is designed to respond to an input control signal that is triggered according to the system operating conditions. A processing unit continuously estimates the electromechanical modes of oscillation based on real-time streaming data acquired from a phasor measurement unit that is strategically positioned on the grid. The decision to trigger (or not trigger) the control signal is automatic, based on the relative damping of the dominant mode. The modes are estimated using the dynamic mode decomposition algorithm with time-delay embedding. Numerical simulations performed on the two-area system demonstrate that the proposed controller enhances the rotor angle stability for both small-signal and large disturbances, and is adaptive to changing grid conditions.

61 RADIATION PROTECTION AND DOSIMETRY↗

Active learning approach to simulations of strongly correlated matter with the ghost Gutzwiller approximation

Quantum embedding (QE) methods such as the ghost Gutzwiller approximation (gGA) offer a powerful approach to simulating strongly correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly correlated materials. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗

JTAG-based PLC memory acquisition framework for industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLC) are the embedded devices that directly control and monitor critical industrial infrastructure processes such as nuclear plants and power grid stations. Cyberattacks often target PLCs to sabotage a physical process. A memory forensic analysis of a suspect PLC can answer questions about an attack, including compromised firmware and manipulation of PLC control logic code and I/O devices. Given physical access to a PLC, collecting forensic information from the PLC memory at the hardware-level is risky and challenging. It may cause the PLC to crash or hang since PLCs have proprietary, legacy hardware with heterogeneous architecture. This paper addresses this research problem and proposes a novel JTAG (Joint Test Action Group)-based framework, Kyros, for reliable PLC memory acquisition. Kyros systematically creates a JTAG profile of a PLC through hardware assessment, JTAG pins identification, memory map creation, and optimizing acquisition parameters. It also facilitates the community of interest (such as ICS owners, operators, and vendors) to develop the JTAG profiles of PLCs. Further, we present a case study of Kyros implementation over Allen-Bradley 1756-A10/B to help understand the framework's application on a real-world PLC used in industry settings. The sample PLC memory dumps are shared with the research community to facilitate further research.

Rais, Muhammad Haris↗

Convective Momentum Transport and Its Impact on the Madden‐Julian Oscillation in E3SM‐MMF

Abstract Convective momentum transport (CMT) is the process of vertical redistribution of horizontal momentum by small‐scale turbulent flows from moist convection. Traditional general circulation models (GCMs) and their multiscale modeling framework (MMF) versions poorly represent CMT due to insufficient information of subgrid‐scale flows at each GCM grid. Here the explicit scalar momentum transport (ESMT) scheme for representing CMT is implemented in the Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) with embedded 2‐D cloud‐resolving models (CRMs), and verified against E3SM‐MMF simulations with 3‐D CRMs and observations. The results show that representing CMT by ESMT helps reduce climatological mean precipitation model bias over the western Pacific and the ITCZ regions, which is attributed to the weakened mean easterlies over the Pacific. Also, CMT from simulations with 2‐D and 3‐D CRMs impose a similar impact on Kelvin waves by reducing their variability and slowing down their phase speed, but opposite impacts on the Madden‐Julian Oscillation (MJO) variability. The ESMT scheme readily captures the climatological mean spatial patterns of the zonal and meridional components of CMT and their variability across multiple time scales, but shows some differences in estimating its magnitude. CMT mainly affects the MJO by decelerating its winds in the free troposphere, but accelerates its near‐surface winds. This study serves as a prototype for implementing CMT scheme in the MMF simulations, highlighting its crucial role in reducing model bias in mean state and spatiotemporal variability.

54 ENVIRONMENTAL SCIENCES↗

Topological superconductivity induced by a triple- q magnetic structure

Here we demonstrate that the recently discovered triple- q (3 q ) magnetic structure, when embedded in a magnet-superconductor hybrid (MSH) system, gives rise to the emergence of topological superconductivity. We investigate the structure of chiral Majorana edge modes at domain walls, and show that they can be distinguished from trivial in-gap modes through the spatial distribution of the induced supercurrents. Finally, we show that topological superconductivity in 3 q MSH systems is a robust phenomenon that does not depend on the relative alignment of the magnetic and superconducting layers, or on the presence of electronic degrees of freedom in the magnetic layer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗