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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Unsupervised, supervised and reinforced learning via spiking computation

The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module.

Modha, Dharmendra S.↗
DOE OSTI2024

HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters

Popular HPC network interconnection simulators such as SST/macro provide a variety of configurable parameters to explore the design space of hardware components such as network interface cards (NIC), switches, and links among them. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for matching configurations of the existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or intentionally omitted. In this work, we propose a framework to autotune the multiple network models’ simulation configurations within SST/macro using Tree-structured Parzen Estimator-based Bayesian optimization to observe the effect on simulation accuracy across different message regimes. These regimes consist of small to large message sizes and latency to bandwidth-bound messages. We provide a detailed analysis of the simulation error for four representative HPC systems. Our Bayesian optimization based autotuning framework for network models achieves a maximum of 5x improvement in accuracy over best-effort manual configurations based on available hardware specifications.

Simulation, autotuning↗
DOE OSTI2024

Automatic Extraction of Network Configurations for Realistic Simulation and Validation

Popular HPC network interconnection simulators such as SST Macro provide a variety of configurable parameters to explore the design space of hardware components such as network links and switches. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or are intentionally omitted. In this work, we provide a methodology to automatically tune the simulation configuration of the multiple network models running within SST Macro using Bayesian optimization. We perform this optimization in the context of multiple messaging regimes (i.e., small to large and latency to bandwidth-bound messages) and provide a detailed analysis of the simulation error for four systems. With our automated framework, we achieve a 5x improvement in accuracy over best-effort configurations based on available hardware specifications.

Suetterlein, Joshua D.↗
DOE OSTI2026

Architectural scaling tradeoffs in modular 3D bosonic quantum processors

We propose a modular three-dimensional bosonic quantum processor built from repeatable coupled-cavity modules linked by configurable interconnect networks. Using hardware-motivated graph-theoretic measures, we compare nearest-neighbor, hub-based, and hybrid architectures in terms of interconnect count, communication distance, resource concentration, and implementation complexity. Rather than identifying a universally optimal topology, our analysis shows how these architectures redistribute the costs of scaling, including wiring and port requirements, nonlocal communication distance, exposure to shared resources, routing bottlenecks, and scheduling overhead. Case studies of a \(3\times3\) processor and a larger hierarchical architecture further distinguish finite-size performance from asymptotic scaling. The resulting framework provides a systematic basis for evaluating modular three-dimensional bosonic processors and for identifying the device-level parameters required for quantitative hardware design.

Zhu, Shaojiang [Fermilab] (ORCID:0000000293180092)↗
DOE OSTI2020

Evaluating software defined networking solutions to reduce the digital attack surface of nuclear security systems

Most nuclear security systems used today were not designed for today’s threat environment. Systems that were intended to be stand alone are now interconnected. Devices that have a single purpose are built on multi-purpose platforms and communication protocols that, while effective, have no ability to authenticate authorized versus unauthorized commands. These attributes provide an attacker significant ability to affect the system, pivot throughout the interconnected networks, and remain undetected if he/she is able to compromise a single node. Software defined networking (SDN) has been used for years by information technology (IT) cloud service providers to quickly provision or remove servers or other systems to meet changing demand. The same concept has recently been applied to operational technology (OT) systems to enable very fast failover on critical systems that have stringent and deterministic (<5ms) transmit/receive times. By carefully engineering the communication flows through a network using preplanned routes and specific pathways it is possible to achieve deterministic and extremely reliable message delivery even when components fail. This engineering approach to network design has added security benefits including securing the networking control plane, eliminating network scanning and mapping, inhibiting ARP spoofing and host masquerading, eliminating unauthorized network pivoting and enabling greater situational awareness on the network. SDN in OT environments is new but early testing in electrical power and other critical infrastructure has shown it to be a very powerful tool for building reliable networks and reducing the digital attack surface of the network. The authors tested a software defined network switch on a simple physical protection system with components commonly found in nuclear security systems and found improved mitigations to denial of service attacks, lateral movement and network reconnaissance. The paper details the tests and their results.

Cyber security, Nuclear security, software defined↗
DOE OSTI2020

A temporal hierarchy underpins the transcription factor–DNA interactome of the maize UPR

Adverse environmental conditions reduce crop productivity and often increase the load of unfolded or misfolded proteins in the endoplasmic reticulum (ER). This potentially lethal condition, known as ER stress, is buffered by the unfolded protein response (UPR), a set of signaling pathways designed to either recover ER functionality or ignite programmed cell death. Despite the biological significance of the UPR to the life of the organism, the regulatory transcriptional landscape underpinning ER stress management is largely unmapped, especially in crops. To fill this significant knowledge gap, we performed a large–scale systems–level analysis of the protein–DNA interaction (PDI) network in maize (Zea mays). Using 23 promoter fragments of six UPR marker genes in a high–throughput enhanced yeast one–hybrid assay, we identified a highly interconnected network of 262 transcription factors (TFs) associated with significant biological traits and 831 PDIs underlying the UPR. We established a temporal hierarchy of TF binding to gene promoters within the same family as well as across different families of TFs. Cistrome analysis revealed the dynamic activities of a variety of cis–regulatory elements (CREs) in ER stress–responsive gene promoters. By integrating the cistrome results into a TF network analysis, we mapped a subnetwork of TFs associated with a CRE that may contribute to UPR management. Lastly, we validated the role of a predicted network hub gene using the Arabidopsis system. The PDIs, TF networks, and CREs identified in our work are foundational resources for understanding transcription–regulatory mechanisms in the stress responses and crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Extending OpenSHMEM with Aggregation Support for Improved Message Rate Performance

OpenSHMEM is a highly efficient one-sided communication API that implements the PGAS parallel programming model, and is known for its low latency communication operations that can be mapped efficiently to RDMA capabilities of network interconnects. However, applications that use OpenSHMEM can be sensitive to point-to-point message rates, as many-to-many communication patterns can generate large amounts of small messages which tend to overwhelm network hardware that has predominantly been optimised for bandwidth over message rate. Additionally, many important emerging classes of problems such as data analytics are similarly troublesome for the irregular access patterns they employ. Message aggregation strategies have been proven to significantly enhance network performance, but their implementation often involves complex restructuring of user code, making them unwieldy. This paper shows how to combine the best qualities of message aggregation within the communication model of OpenSHMEM such that applications with small and irregular access patterns can improve network performance while maintaining their algorithmic simplicity. We do this by providing a path to a message aggregation framework called conveyors through a minimally intrusive OpenSHMEM extension introducing aggregation contexts that fit more naturally to the OpenSHMEM atomics, gets, and puts model. We test these extensions using four of the bale 3.0 applications which contain essential many-to-many access patterns to show how they can produce performance improvements of up to 65×.

Welch, Aaron↗
DOE OSTI2021

Shape Persistent, Highly Conductive Ionogels from Ionic Liquids Reinforced with Cellulose Nanocrystal Network

Abstract Shape‐persistent, conductive ionogels where both mechanical strength and ionic conductivity are enhanced are developed using multiphase materials composed of cellulose nanocrystals and hyperbranched polymeric ionic liquids (PILs) as a mechanically strong supporting network matrix for ionic liquids with an interrupted ion‐conducting pathway. The integration of needlelike nanocrystals and PIL promotes the formation of multiple hydrogen bonding and electrostatic ionic interaction capacitance, resulting in the formation of interconnected networks capable of confining a high amount of ionic liquid (≈95 wt%) without losing its self‐sustained shape. The resulting nanoporous and robust ionogels possess outstanding mechanical strength with a high compressive elastic modulus (≈5.6 MPa), comparable to that of tough, rubbery materials. Surprisingly, these rigid materials preserve the high ionic conductivity of original ionic liquids (≈7.8 mS cm −1 ), which are distributed within and supported by the nanocrystal network‐like rigid frame. On the one hand, such stable materials possess superior ionic conductivities in comparison to traditional solid electrolytes; on the other hand, the high compression resistance and shape‐persistence allow for easy handling in comparison to traditional fluidic electrolytes. The synergistic enhancement in ion transport and solid‐like mechanical properties afforded by these ionogel materials make them intriguing candidates for sustainable electrodeless energy storage and harvesting matrices.

Lee, Hansol↗
DOE OSTI2020

Slip Tendency Analysis of Fracture Networks to Determine Suitability of Candidate Testbeds for the EGS Collab Hydroshear Experiment

Experiment 2 of the EGS Collab project is aimed at testing stimulation by hydro-shearing of existing natural fractures, versus Experiment 1, which is focused on hydraulic fracturing a rock mass to enhance permeability. The main criterion for the testbed selection in Experiment 2 is the presence of an interconnected network of fractures, at least 10 meters in extent, and with orientations such that shear slip can be induced at injection pressures less than the minimum horizontal stress (Shmin). The feasibility analysis for this experiment requires a well-constrained stress state along with well-characterized fracture networks. The fracture systems at two candidate locations in the Sanford Underground Research Facility (SURF), the 4850 Level (number refers to depth below ground surface in feet) and the 4100 Level, have been characterized to different extents. The 4850 Level has a well-characterized Discrete Fracture Network (DFN) from borehole (drilled for an experimental test bed) and drift observations. In contrast, the 4100 level has a fracture network characterized only by observations from the drift wall, which provides little constraint on fracture extents. This paper will present assessments for the interpreted slip potential for the natural fractures at the two locations. Data uncertainties are addressed by performing a probabilistic analysis that takes into account the uncertainty in the stress state, uncertainty in the fracture properties, and preliminary borehole locations. There is strong evidence of shear stimulation of some natural fractures in Experiment 1 on the 4850 level. We use examples of natural fractures strongly linked to shear stimulation in Experiment 1 from multiple monitoring indicators to test the consistency of the stress model and guide the uncertainty interpretation. This analysis is intended to guide the site selection process for Experiment 2 by highlighting the fracture orientations that are likely to be shear-stimulated in a majority of modeled realizations.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2024

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

Bae, Jonghyun↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks

Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we trained Residual neural Networks (ResNet) on 22,528 x 22,528-pixel size images using a distributed spatial decomposition method on 2,304 GPUs on the Summit Supercomputer. We applied our method on a Whole Slide Imaging (WSI) dataset from The Cancer Genome Atlas (TCGA) database. WSI images can be in the size of 100,000 x 100,000 pixels or even larger, and in this work we studied the effect of image resolution on a classification task, while achieving state-of-the-art AUC scores. Moreover, our approach doesn't need pixel-level labels, since we're avoiding patching from the WSI images completely, while adding the capability of training arbitrary large-size images. This is achieved through a distributed spatial decomposition method, by leveraging the non-block fat-tree interconnect network of the Summit architecture, which enabled GPU-to-GPU direct communication. Finally, detailed performance analysis results are shown, as well as a comparison with a data-parallel approach when possible.

Tsaris, Aristeidis (aris)↗
DOE OSTI2025

Li 21 Ge 8 P 3 S 34 : New Lithium Superionic Conductor with Unprecedented Structural Type

Abstract Lithium superionic conductors are pivotal for enabling all‐solid‐state batteries, which aim to replace liquid electrolytes and enhance safety. Herein, we report the discovery of an unprecedented lithium superionic conductor, Li 21 Ge 8 P 3 S 34 , featuring a novel structural type and a new composition in the Li–Ge–P–S system. This material exhibits high lithium ionic conductivity of approximately 1.0 mS cm −1 at 303 K with a low activation energy of 0.20(1) eV. It's unique crystal structure was elucidated using three‐dimensional electron diffraction (3D ED) and further refined through combined powder X‐ray and neutron diffraction analyses. The structure consists of alternating two‐dimensional slabs: one of corner‐sharing GeS 4 tetrahedra and the other of isolated PS 4 tetrahedra, enabling efficient lithium‐ion transport through a tetrahedrally interconnected network of 1D, 2D, and 3D diffusion pathways. This distinctive structural motif provides a novel design strategy for next‐generation solid electrolytes, broadening the structural landscape of lithium superionic conductors. With further advancements in compositional tuning and interfacial engineering, Li 21 Ge 8 P 3 S 34 could contribute to the development of high‐performance all‐solid‐state batteries.

Chemistry↗
DOE OSTI2024

Hypereutectic Al-Ce-X (X=Mn, Cr, V, Mo, W) alloys fabricated by laser powder-bed fusion

Here, we characterize the microstructures and high-temperature mechanical properties of Al-2Ce and ternary Al-2Ce-1X (at.%) alloys fabricated by laser powder-bed fusion (LPBF), where X = Mn, Cr, V, Mo, and W are slow-diffusing transition metals. All ternary alloys show a hypereutectic microstructure in the as-LPBF state, containing an interconnected network of eutectic Al 11 Ce 3 phases (~10 vol.%) and an additional population of submicron, equiaxed Al 20 CeX 2 primary precipitates (~10 vol.%) which are isomorphous among these five alloys. Similar microstructures are present in arc-melted rods and atomized powders but are coarser due to the slower cooling rates in these processes. The hardness of the as-LPBF ternary Al-Ce-X alloys (1300–1400 MPa) is higher than that of the binary Al-Ce alloy (~1100 MPa) due to the higher volume fraction of strengthening phases. Furthermore, during exposure at 400 °C for up to three months, greater hardness retention is achieved in the ternary Al-Ce-X alloys (65–75%) than in the binary Al-Ce alloy (~55%), which is attributed to the extreme coarsening resistance of the Al 20 CeX 2 precipitates imparted by the very slow-diffusing ternary solute. These coarsening-resistant Al 20 CeX 2 precipitates also substantially improve alloy creep resistance, increasing the threshold stress for dislocation creep at 300°C from ~32 MPa for the binary Al-Ce alloy to ~77–100 MPa for the ternary Al-Ce-X alloys, and at 400°C from <10 MPa for the binary Al-Ce alloy to >40 MPa for the ternary Al-Ce-V alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of chloride ingress on self-healing recovery of smart cementitious composite incorporating crystalline admixture and MgO expansive agent

Highlights: • Crack closure under wet/dry cycles in chloride solution is higher than the one in distilled water. • Element analysis shows that calcium is vital for crack healing either in water or chloride solution. • AFm is consumed by chloride ion to form Fs, and Fs were decomposed to AH{sub 3} by carbonation. • Soluble reactive silica reacts with calcium hydrate to form C-S-H gel to heal concrete cracks. • Expansion of magnesium hydroxide facilitates crack self-healing by interconnected network. In this study, the effect of chloride environment containing various concentrations of chloride ion (Cl{sup −}) on self-healing performance of pre-cracked cementitious composite containing crystalline admixture (CA) and MgO expansive agent (MEA) was investigated under wet-dry cycles in chloride solutions. The results revealed that the Cl{sup −} changed the mineralogy of self-healing products, and consequently, affected the crack closure ratio and mechanical strength recovery. When self-healing occurred in distilled water, a large amount of ettringite (AFt) were detected, whereas in Cl{sup −} solution, monosulfate (AFm) was consumed by Cl{sup −} to form Friedel's salt (Fs), and then the Fs was decomposed to Al(OH){sub 3}(AH{sub 3}) due to carbonation. During the multiphase conversion process, hydroxide (OH{sup −}) was released into crack solution, therefore the dissolved carbon dioxide (CO{sub 2}) concentration was increased. The carbonation of the crystals formed in cracks was accelerated with the volume expansion, which achieved rapid crack sealing but contributed little to the mechanical performance recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Carbonation, strength development, and characterization of calcined limestone as a potential construction material

The emergence of the modern Portland cement has led to the downfall of lime as a construction material. Consequentially, its use has been limited to repair and restoration of historical buildings. Yet, its lower calcination temperature (~950 °C) and the ability to gain strength through carbonation implies its potential as a sustainable alternative to cement. In this study, calcined limestone was studied concerning its carbonation and strength development. Superplasticizer, raw limestone, and different carbonation regimes were explored as techniques to improve the composite strength development, a shortfall that hinders its re-adoption. The results indicated that the raw limestone as a partial replacement of the calcined limestone significantly improved the carbonation degree and mechanical properties. The average compressive strength reached 20 MPa under 20% CO{sub 2} due to the formation of an interconnected network of calcite as the dominant carbonation product. Supercritical CO{sub 2}, in contrast, formed disjoint particles or agglomerates of carbonates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microstructure and creep properties of cast near-eutectic Al–Ce–Ni alloys

This study investigates the as-cast and aged microstructures, thermal stability, ambient temperature strengthening, and creep resistance of three ternary Al–Ce–Ni alloys (wt%): near-eutectic Al–10Ce–5Ni (with both eutectic and hypoeutectic regions), hypoeutectic Al-7.5Ce-3.75Ni (with numerous primary Al dendrites), and hypereutectic Al-12.5Ce-6.25Ni (with coarse, blocky primary Al3Ni and Al11Ce3 precipitates and some primary Al dendrites). Depending on the alloy composition and local solidification conditions, the following eutectic morphologies are found: (i) coarse Al–Ce eutectic colonies where Al 11 Ce 3 is in the form of “Chinese script”, (ii) intermingled regions of binary Al–Ce and Al–Ni eutectic colonies, with finer Al 3 Ni and Al 11 Ce 3 fibers, (iii) large ternary eutectic colonies, where the binary Al 3 Ni and Al 11 Ce 3 phases are alternating or intertwining within the individual, fine fibers (diameters of ~60–170 nm, depending on solidification rates), and (iv) ternary eutectic zones (between primary Al dendrites), where fine Al 3 Ni and Al 11 Ce 3 build up a 3D-interconnected network. The high volume fraction of intermetallic phases and extremely fine eutectic spacing/fiber diameter both contribute to high ambient strengthening (higher as-cast microhardness than binary Al–Ce or Al–Ni), and also provide enhanced creep resistance at 300 and 350 °C. Additionally, the alloys are coarsening-resistant up to 425 °C for extended periods, with a gradual decrease in microhardness. The alloys aged at 400 °C to 1050 h show fiber fragmentation and coarsening of the resulting particles, with the faster-diffusing Ni driving more rapid coarsening of the Al 3 Ni particles which engulf finer, more stable Al 11 Ce 3 particles. Severe overaging (performed at 590 °C for 24 h) leads to Al 3 Ni and Al 11 Ce 3 spheroids which remain submicron-sized in eutectic colonies, but micron-sized at colony boundary and at Al dendrite-eutectic interface. Creep resistance at 300 °C of overaged Al–10Ce–5Ni remains substantial, consistent with load-transfer based composite strengthening being an important strengthening mechanism in these alloys, making them excellent candidates for replacement of heavier steel or titanium parts operating under stress up to 300 °C.

36 MATERIALS SCIENCE↗