Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MgS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Bicontinuous nanoporous design induced homogenization of strain localization in metallic glasses

Bicontinuous nanoporous metallic glasses (MG) synergize the outstanding properties of MGs and open-cell nanoporous materials. The low-density and high-specific-surface-area of bicontinuous nanoporous structures have the potential to enhance the applicability of MGs in catalysis, sensors, and lightweight structural designs. In this work, we report molecular dynamics simulations of tensile loading deformation and failure of bicontinuous nanoporous Cu 64 Zr 36 MG with 55% porosity and 4.4 nm ligament size. Results indicate an anomalous mechanical behavior featuring delocalized plastic deformation preceding ductile failure. The deformation follows two mechanisms: i) Necking of ligaments aligned with the loading direction and ii) progressive alignment of randomly oriented ligaments. Failure occurs at 0.16 strain, following massive rupture of ligaments. This work indicates that a bicontinuous nanoporous design is able to effectively delocalize strain localization in a MG due to a combination of size effect on the ductility of MGs resulting in nano ligaments necking and progressive asynchronous alignment of ligaments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Continuous polyamorphic transition in high-entropy metallic glass

Polyamorphic transition (PT) is a compelling and pivotal physical phenomenon in the field of glass and materials science. Understanding this transition is of scientific and technological significance, as it offers an important pathway for effectively tuning the structure and property of glasses. In contrast to the PT observed in conventional metallic glasses (MGs), which typically exhibit a pronounced first-order nature, herein we report a continuous PT (CPT) without first-order characteristics in high-entropy MGs (HEMGs) upon heating. This CPT behavior is featured by the continuous structural evolution at the atomic level and an increasing chemical concentration gradient with temperature, but no abrupt reduction in volume and energy. The continuous transformation is associated with the absence of local favorable structures and chemical heterogeneity caused by the high configurational entropy, which limits the distance and frequency of atomic diffusion. As a result of the CPT, numerous glass states can be generated, which provides an opportunity to understand the nature, atomic packing, formability, and properties of MGs. Moreover, this discovery highlights the implication of configurational entropy in exploring polyamorphic glasses with an identical composition but highly tunable structures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Contribution of cryogenic thermal cycling to the atomic dynamics in a La-based bulk metallic glass with different initial states

Structural rejuvenation in metallic glasses (MGs) induced by cryogenic thermal cycling has been intensively studied. However, the effect of thermal cycling on the atomic dynamics in MGs is still missing. In this work, we present a systematic study on the atomic dynamics in a La-based MG affected by sub-Tg annealing and thermal cycling. We find that the thermal cycling has little effect on the atomic dynamics in both as-cast and annealed MGs although it could significantly increase the free volume and change mechanical properties of the as-cast MG sample. In contrast, both the structure and mechanical properties of the well-relaxed sample are almost unchanged by thermal cycling. Further, the increased open volume, mainly in the form of atomic vacancies, does not greatly promote the atomic dynamics in the as-cast MG sample, indicating that the free volume induced by thermal cycling is not the key factor in stimulating the β-relaxation and the boson peak in the studied MG.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Polyamorphism in a solute-lean Al–Ce metallic glass

Polyamorphism discovered in lanthanide-rich metallic glasses (MGs) has been attributed to the electronic transition of the lanthanide element as a solvent element. In this work, we report that pressure-induced polyamorphism still exists in a Ce-poor Al 93 Ce 7 binary MG where the 4f electron element serves as a solute and solute–solute avoidance is expected. The polyamorphic transition, observed by in situ high-pressure synchrotron x-ray diffraction, is accompanied by a volume collapse of ~0.78% and occurs over a narrow pressure range from ~0.8 to ~1.8 GPa. Further synchrotron Ce L 3 -edge x-ray absorption spectroscopy measurements reveal that pressure-induced 4f electron delocalization underlies the polyamorphic transition. Molecular dynamics simulations confirm that the Ce atoms in the MG are completely isolated by the solvent Al atoms. This result demonstrates that 4f element-bearing alloys with extremely dilute concentrations can also exhibit polyamorphic states originating from electronic transitions, extending the compositional space of polyamorphism of MGs into very dilute regions. Our work suggests that tunable properties under compressive stress could be achieved in MGs by even minor doping of elements prone to electronic transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MicroFisher: Fungal taxonomic classification for metatranscriptomic and metagenomic data using multiple short hypervariable markers

AbstractProfiling the taxonomic and functional composition of microbes using metagenomic (MG) and metatranscriptomic (MT) sequencing is advancing our understanding of microbial functions. However, the sensitivity and accuracy of microbial classification using genome– or core protein-based approaches, especially the classification of eukaryotic organisms, is limited by the availability of genomes and the resolution of sequence databases. To address this, we propose the MicroFisher, a novel approach that applies multiple hypervariable marker genes to profile fungal communities from MGs and MTs. This approach utilizes the hypervariable regions of ITS and large subunit (LSU) rRNA genes for fungal identification with high sensitivity and resolution. Simultaneously, we propose a computational pipeline (MicroFisher) to optimize and integrate the results from classifications using multiple hypervariable markers. To test the performance of our method, we applied MicroFisher to the synthetic community profiling and found high performance in fungal prediction and abundance estimation. In addition, we also used MGs from forest soil and MTs of root eukaryotic microbes to test our method and the results showed that MicroFisher provided more accurate profiling of environmental microbiomes compared to other classification tools. Overall, MicroFisher serves as a novel pipeline for classification of fungal communities from MGs and MTs.

Wang, Haihua↗
DOE OSTI2023

Investigating Multi-Microgrid Black Start Methods Using Grid-Forming Inverters

This paper examines state-of-the-art microgrid (MG) black-start technologies with grid-forming (GFM) inverter-based resources (IBRs) and proposes black start and interconnection methods for 100% inverter-based MGs. A multiple-MG approach is proposed and compared to the existing methods in a 4-bus, 12-GFM inverter simulation test setup. This investigation involves intelligent synchronization units that enable the autonomous synchronization of multiple MGs based on their terminal measurements. MGs in this setup are held at different loading levels and comprise averaged models of GFM inverters. The results of the black-start techniques are compared, and conclusions are drawn to better prepare MG planners and distribution system operators for next-generation, multi-MG, GFM inverter-based, black-start procedures.

black start↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2023

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Singular Perturbation-Based Large-Signal Order Reduction of Microgrids for Stability and Accuracy Synthesis With Control

The increasing penetration of distributed energy resources (DERs) highlights the growing importance of microgrids (MGs) in enhancing power system reliability. Employing electromagnetic transient (EMT) analysis in MGs becomes crucial for controlling the rapid transients. However, this requires an accurate but high-order model of power electronics and their underlying control loops, complexifying the stability analysis from the viewpoint of a higher control level. To overcome these challenges, this paper proposes a large-signal order reduction (LSOR) method for MGs with considerations of external control inputs and the detailed dynamics of underlying control levels based on singular perturbation theory (SPT). Specially, we innovatively proposed and strictly proved a general stability and accuracy assessment theorem that allows us to analyze the dynamic stability of a full-order nonlinear system by only leveraging our derived reduced-order model (ROM) and boundary layer model (BLM). Furthermore, this theorem furnishes a set of conditions that determine the accuracy of the developed ROM. Lastly, by embedding such a theorem into the SPT, we propose a novel LSOR approach with guaranteed accuracy and stability analysis equivalence. Case studies are conducted on MG systems to show the effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Investigating Multi-Microgrid Black Start Methods Using Grid-Forming Inverters: Preprint

This paper examines state-of-the-art microgrid (MG) black-start technologies with high IBR penetrations and proposes black-start and interconnection methods for 100% grid-forming (GFM) inverter-based MGs. A multiple-microgrid approach is proposed and compared to the existing methods in a 4-bus, 12-GFM inverter simulation test setup. This investigation involves intelligent synchronization units called "synchrobreakers," which enable the synchronization of multiple MGs based on their terminal measurements. MGs in this setup are held at different loading levels and comprise averaged models of GFM inverters with ideal voltage sources. The results of the black-start techniques are compared, and conclusions are drawn to better prepare MG planners and DSOs for the next-generation GFM inverter-based multi-MG black-start procedures.

black start↗
DOE OSTI2024

Unveiling the Structural Origins of Dynamic Diversity in Pd-Based Metallic Glasses

The β -relaxation is one of the major dynamic behaviors in metallic glasses (MGs) and exhibits diverse features. Despite decades of efforts, the understanding of its structural origin and contribution to the overall dynamics of MG systems is still unclear. Here two palladium-based Pd—Cu—P and Pd—Ni—P MGs are reported with distinct different β -relaxation behaviors and reveal the structural origins for the difference using the advanced X-ray photon correlation spectroscopy and absorption fine structure techniques together with the first-principles calculations. The pronounced β -relaxation and fast atomic dynamics in the Pd—Cu—P MG mainly come from the strong mobility of Cu atoms and their locally favored structures. In contrast, the motion of Ni atoms is constrained by P atoms in the Pd—Ni—P MG, leading to the weakened β -relaxation peak and sluggish dynamics. Finally, the correlation of atomic dynamics with microscopic structures provides a way to understand the structural origins of different dynamic behaviors as well as the nature of aging in disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Harnessing graph convolutional neural networks for identification of glassy states in metallic glasses

Graph Convolutional Neural Networks (GCNNs) have emerged as powerful tools for analyzing materials. In this study, we employ GCNNs to examine structural characteristics of CuZr metallic glasses (MGs) and identify their states. We use molecular dynamics to simulate the quenching process of CuZr, using cooling rates ranging from 10 9 to 10 15 K/s, to produce six unique glassy states. For each state, we create a dataset comprising 1,800 distinct samples. We evaluate the effectiveness of various GCNNs, including Graph Attention Neural Network (GANN), Graph Sample and AggreGatE (GraphSAGE), Graph Isomorphism Network (GIN), and Relational Graph Convolutional Neural Network (RGCN). GANN and GraphSAGE demonstrate comparable performance, achieving an overall accuracy of 81% in classifying the MG states. Furthermore, these results underscore the potential of GCNNs to detect subtle structural variances in disordered materials and point to broader application of deep learning in the analysis of MGs and other amorphous substances.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reversible function switching of Ag catalyst in Mg/S battery with chloride-containing electrolyte

Rechargeable Mg/S batteries suffer from fast capacity decay because of the difficult re-oxidation of MgS. To tackle this problem, we used Ag catalyst in Mg/S cells with Cl- containing electrolyte, and achieved a greatly improved specific capacity of ~1200 mAh•g -1 and a long cycling life of 100 cycles. To understand the mechanism behind this improvement, we employed in-situ synchrotron radiation X-ray diffraction and in-situ X-ray absorption spectroscopy tools to study the reversible phase transitions during charge/discharge cycling. Additionally, the in-situ experiment results revealed that, at deeply charged state, Ag reacted with Cl- in the electrolyte to form the AgCl interfacial layer which prevented the physical contact of Ag with elemental S and avoided the formation of Ag 2 S; at early discharged state, AgCl transformed back to metallic Ag, which guaranteed its catalytic effectiveness for MgS decomposition. This reversible function switching mechanism of Ag catalyst is completely different from that in Mg/S cells using other catalysts or electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2021

Anomalous fast atomic dynamics in bulk metallic glasses

It is believed that the strong glass formers tend to be dense packing and have rather slow atomic dynamics. Here, we report that the anomalous fast atomic dynamics exist in an Au 55 Cu 20 Ag 5 Si 20 metallic glass (MG) with good glass forming ability (GFA) by combining X-ray photon correlation spectroscopy measurements with ab initio molecular dynamics simulations. The fast dynamics are mainly originated from the increased mobility of Cu atoms by Ag addition. Ag atoms are inclined to locate at Cu-centered <0, 2, 8, 1> motifs, which slow or accelerate the dynamics of shell Cu atoms, depending on whether Ag atoms appear at the first shells or not, leading to the formation of structural heterogeneity at the atomic level. The results enrich the understanding of atomic dynamics in Cue-Ag-containing MGs and will trigger more studies on the dynamics-GFA relationship in MGs.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Two-step annealing induced structural rejuvenation: A cause for memory effect in metallic glasses

The structural origin of the memory effect is still elusive in glassy materials. Here, in this letter, the memory effect in a series of metallic glasses (MGs) is observed by two step annealing using differential scanning calorimetry (DSC) and molecular dynamics simulations. It demonstrates that the Kovacs peak can be reflected from not only the system potential energy but also the atomic dynamics, showing the memory effect could originate from the formation of more loose packing configurations around mobile atoms by structural rejuvenation in the second step of high temperature annealing. In contrast, the high-low temperature annealing protocol can only induce the monotonic relaxation. Therefore, our results pave a pathway to better understand the origin of memory effect in MGs and other glassy materials from their dynamics and local atomic structure.

Memory effect↗
DOE OSTI2024

Anomalous temperature dependence of elastic limit in metallic glasses

Abstract Understanding the atomistic mechanisms of inelastic deformation in metallic glasses (MGs) remains challenging due to their amorphous structure, where local carriers of plasticity cannot be easily defined. Using molecular dynamics (MD) simulations, we analyzed the onset of inelastic deformation in CuZr MGs, specifically the temperature dependence of the elastic limit, in terms of localized shear transformation (ST) events. We find that although the ST events initiate at lower strain with increasing temperature, the elastic limit increases with temperature in certain temperature ranges. We explain this anomalous behavior through the framework of an energy-strain landscape (ESL) constructed from high-throughput strain-dependent energy barrier calculations for the ST events identified in the MD simulations. The ESL reveals that the anomalous behavior is caused by the transition of ST events from irreversible to reversible with increasing temperature. An analytical formulation is developed to predict this transition and the temperature dependence of the elastic limit.

36 MATERIALS SCIENCE↗