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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Thermally robust and highly stable method for splicing silica glass fiber to crystalline sapphire fiber

This research reports an advancement in splicing silica glass fiber to sapphire single-crystal optical fiber (SCF) using a specialized glass processing device, including data that demonstrate the thermal stability of the splice to 1000°C. A filament heating process was used to produce a robust splice between the dissimilar fibers. A femtosecond laser is used to inscribe a fiber Bragg gratings sensor into the SCF to measure the high-temperature capabilities and signal attenuation characteristics of the splice joint. The experimental results demonstrate that the proposed splicing method produces a splice joint that is robust, stable, repeatable, and withstands temperatures up to 1000°C with a low attenuation of 0.5 dB. Here, the proposed method allows placement of SCF-based sensors in the extreme environments encountered in various engineering fields, such as nuclear, chemical, aviation, and metals manufacturing, to enable improvements in process monitoring, product quality, and production efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean

Fundamental to biological decision-making is the ability to generate bimodal expression patterns where two alternate expression states simultaneously exist. Here in this study, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV’s fate decision between active replication and viral latency. We find that the HIV Tat protein manipulates the intrinsic toggling of HIV’s promoter, the LTR, to generate bimodal ON-OFF expression, and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit’s non-cooperative ‘non-latching’ feedback architecture is optimized to slow the promoter’s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that non-latching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean-expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV’s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Long-range, low-cost electric vehicles enabled by robust energy storage

ABSTRACT A variety of inherently robust energy storage technologies hold the promise to increase the range and decrease the cost of electric vehicles (EVs). These technologies help diversify approaches to EV energy storage, complementing current focus on high specific energy lithium-ion batteries. The need for emission-free transportation and a decrease in reliance on imported oil has prompted the development of EVs. To reach mass adoption, a significant reduction in cost and an increase in range are needed. Using the cost per mile of range as the metric, we analyzed the various factors that contribute to the cost and weight of EV energy storage systems. Our analysis points to two primary approaches for minimizing cost. The first approach, of developing redox couples that offer higher specific energy than state-of-the-art lithium-ion batteries, dominates current research effort, and its challenges and potentials are briefly discussed. The second approach represents a new insight into the EV research landscape. Chemistries and architectures that are inherently more robust reduce the need for system protection and enables opportunities of using energy storage systems to simultaneously serve vehicle structural functions. This approach thus enables the use of low cost, lower specific energy chemistries without increasing vehicle weight. Examples of such systems include aqueous batteries, flow cells, and all solid-state batteries. Research progress in these technical areas is briefly reviewed. Potential research directions that can enable low-cost EVs using multifunctional energy storage technologies are described.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Robust Importance Sampling for Bayesian Model Calibration with Spatio-Temporal Data

This paper addresses two challenges in Bayesian calibration: 1) computational speed of existing sampling algorithms, and 2) calibration with spatio-temporal responses. The commonly used Markov Chain Monte Carlo (MCMC) approaches require many sequential model evaluations making the computational expense prohibitive. This paper proposes an efficient sampling algorithm: iterative importance sampling with genetic algorithm (IISGA). While iterative importance sampling enables computational efficiency, the genetic algorithm enables robustness by preventing sample degeneration and avoids getting stuck in multimodal search spaces. An inflated likelihood further enables robustness in high-dimensional parameter spaces by enlarging the target distribution. Spatio-temporal data complicate both surrogate modeling, which is necessary for expensive computational models, and the likelihood estimation. In this work, singular value decomposition is investigated for reducing the high-dimensional field data to a lower-dimensional space prior to Bayesian calibration. Then the likelihood is formulated and Bayesian inference is performed in the lower-dimension, latent space. An illustrative example is provided to demonstrate IISGA relative to existing sampling methods, and then IISGA is employed to calibrate a thermal battery model with 26 uncertain calibration parameters and spatio-temporal response data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Using a 1D Radially Symmetric Coda Envelope Model for Robust Moment Magnitude ( M w ) Estimation in Iraq’s Tectonically Diverse Zones

Robust estimation of moment magnitude (M w ) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of M w for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Therefore, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.

58 GEOSCIENCES↗
DOE OSTI2017

A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients

Identification of reliable predictive biomarkers and new therapeutic targets is a critical step for significant improvement in patient outcomes. Here, we developed a multi-step bioinformatics analytic strategy to mine large omics and clinical data to build a prognostic scoring system for predicting the overall survival (OS) of lung adenocarcinoma (LuADC) patients. In latter we first identified 1327 significantly and robustly deregulated genes, 600 of which were significantly associated with the OS of LuADC patients. Gene co-expression network analysis revealed the biological functions of these 600 genes in normal lung and LuADCs, which were found to be enriched for cell cycle-related processes, blood vessel development, cell-matrix adhesion and metabolic processes. Finally, we implemented a multiple resampling method combined with Cox regression analysis to identify a 27-gene signature associated with OS, and then created a prognostic scoring system based on this signature. This scoring system robustly predicted OS of LuADC patients in 100 sampling test sets and was further validated in four independent LuADC cohorts. In addition, in comparison to other existing prognostic gene signatures published in the literature, our signature was significantly superior in predicting OS of LuADC patients. In summary, our multiomics and clinical data integration study created a 27-gene prognostic risk score that can predict OS of LuADC patients independent of age, gender and clinical stage. This score could guide therapeutic selection and allow stratification in clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

FRIEDA: Flexible Robust Intelligent Elastic Data Management Framework

Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance, cost trade-offs, complex application choices and complexity associated with elasticity, failure rates in these environments. The different data access patterns for data-intensive scientific applications require a more flexible and robust data management solution than the ones currently in existence. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies in cloud environments. FRIEDA can manage storage and data lifecycle of applications in cloud environments. There are four different stages in the data management lifecycle of FRIEDA – (i) storage planning, (ii) provisioning and preparation, (iii) data placement, and (iv) execution. FRIEDA defines a data control plane and an execution plane. The data control plane defines the data partition and distribution strategy, whereas the execution plane manages the execution of the application using a master-worker paradigm. FRIEDA also provides different data management strategies, either to partition the data in real-time, or predetermine the data partitions prior to application execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Next Generation Robust Low Noise Seismometer for Nuclear Monitoring

Implementation of the proposed seismometers turned out to be much more challenging than anticipated. The noise levels achieved are indeed well below those ever featured by any electrochemical sensor and just very nearly miss reaching the original objectives. However, while noise-wise the instruments could still prove their usefulness, especially considering their robustness and no-maintenance operation, the implementation of the proposed noise-reduction concept resulted in much larger and heavier devices than originally expected. Moreover, these large dimensions relate only to single-component vertical sensors. While building similar horizontal component is possible, the resulting three-component instrument would be way too large and heavy to be of any practical use. The prototype instruments developed and built retained the inherent advantages of the electrochemical seismometers: no maintenance operation; ability to perform with large installation tilts; and, unfortunately, to a much lesser extent in terms of robustness.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2013

Efficient and robust gradient enhanced Kriging emulators.

%E2%80%9CNaive%E2%80%9D or straight-forward Kriging implementations can often perform poorly in practice. The relevant features of the robustly accurate and efficient Kriging and Gradient Enhanced Kriging (GEK) implementations in the DAKOTA software package are detailed herein. The principal contribution is a novel, effective, and efficient approach to handle ill-conditioning of GEK's %E2%80%9Ccorrelation%E2%80%9D matrix, RN%CC%83, based on a pivoted Cholesky factorization of Kriging's (not GEK's) correlation matrix, R, which is a small sub-matrix within GEK's RN%CC%83 matrix. The approach discards sample points/equations that contribute the least %E2%80%9Cnew%E2%80%9D information to RN%CC%83. Since these points contain the least new information, they are the ones which when discarded are both the easiest to predict and provide maximum improvement of RN%CC%83's conditioning. Prior to this work, handling ill-conditioned correlation matrices was a major, perhaps the principal, unsolved challenge necessary for robust and efficient GEK emulators. Numerical results demonstrate that GEK predictions can be significantly more accurate when GEK is allowed to discard points by the presented method. Numerical results also indicate that GEK can be used to break the curse of dimensionality by exploiting inexpensive derivatives (such as those provided by automatic differentiation or adjoint techniques), smoothness in the response being modeled, and adaptive sampling. Development of a suitable adaptive sampling algorithm was beyond the scope of this work; instead adaptive sampling was approximated by omitting the cost of samples discarded by the presented pivoted Cholesky approach.

Dalbey, Keith R.↗
DOE OSTI2016

2016 KIVA-hpFE Development: A Robust and Accurate Engine Modeling Software

Los Alamos National Laboratory and its collaborators are facilitating engine modeling by improving accuracy and robustness of the modeling, and improving the robustness of software. We also continue to improve the physical modeling methods. We are developing and implementing new mathematical algorithms, those that represent the physics within an engine. We provide software that others may use directly or that they may alter with various models e.g., sophisticated chemical kinetics, different turbulent closure methods or other fuel injection and spray systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Development and Characterization of High Performance and Robust Mixed Conducting Cathodes Supportive of Lower Cost Solid Oxide Fuel Cells (Final Report)

The objective of the proposed research is to advance an existing partnership between the University of South Carolina (USC) and LG Fuel Cell Systems, Inc. (LGFCS) to research and develop mixed electronic and ionic conducting cathode materials for durable and robust low-cost solid oxide fuel cells (SOFCs). The first task is to develop mixed ionic and electronic conducting doped (Pr,Nd)NiO 4 (PNNO)-based cathodes. This task is built on a prior collaborative work with LG Fuel Cell System, which showed that doped PNNO could exhibit phase stability at high temperatures. The initial focus will be on investigating the durability and endurance of its electrochemical performance and the temporal evolution of microstructures. In-situ high temperature neutron diffraction and x-ray diffraction will be used to study the structures of these phase stable compounds. PNNO doped with Ca and transition metal elements will be synthesized. Physical properties of these materials with respect to coefficient of thermal expansion, electrical conductivity, diffusion coefficient, and nonstoichiometry will be measured. The benefits of dense versus porous barriers will be studied and the role of barrier layer will be identified and a guideline will be provided to choose the maximum porosity for durable and robust cathode performance with co-doped PNNO as the cathodes. LSCF6428 will be used to compare its power density with that of co-doped PNNO in the absence of Cr, but some studies comparing Cr tolerance will be performed towards the end of the project. The conditions at which preferred nickelate phases are stable will be defined, including sintering temperature, operating temperature, and composition range. The second task will focus on the investigation of the location of degradation by using differential relaxation time (DRT) analysis, coupled with least squares fitting and systematical post analyses. DRT analysis can provide (1) a direct access to the kinetic parameters of the underlying processes in both cathode and anode, and (2) a clearer picture of SOFC operation, which allows loss factors to the changes of microstructures or activities of either electrode to be distinguished, and a better target fuel cell development to be enabled. The third task aims at applying the accelerated testing protocol to investigate the microstructural evolution of the cathode candidates identified in this research. Technical approaches will be developed to mitigate and improve the durability of cathode materials by choosing the right chemistry and tuning cathode microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On the Robustness of Network Community Structure Under Addition of Edges

Communities represent important functional modules in networked systems. A key goal in preserving such communities is understanding their robustness under perturbations. Previous research has studied the impact of node removals and edge removals on the community structure. However, the impact of edge additions on the robustness of the community structure is relatively unknown. Edge additions or false positive edges may simulate measurement errors or external exceptional events that threaten the functionality of networked systems. Here, we study the impact of edge additions on the community structure using Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. We show that, for a fixed network size, the impact of edge additions is greater on networks with initially weak community structure than on networks with strongly clustered structures. In addition, we find that the perception of the impact is also dependent on the community detection algorithm used to uncover communities. In particular, we found that modularity-based methods such as Leiden and Louvain are less affected than information-theoretic and message passing-based methods such as Infomap and Label Propagation. Our results demonstrate that edge addition can (a) significantly impact the community structure of networks based on their initial conditions, and (b) the perception of the impact is dependent on the community detection algorithm used. We describe limitations, open challenges, and how this methodology can inform the design of resilient networked systems under edge additions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Regression Based Approach for Robust Finite Element Analysis on Arbitrary Grids. LDRD Final Report

This report summarizes the work performed under a one-year LDRD project aiming to enable accurate and robust numerical simulation of partial differential equations for meshes that are of poor quality. Traditional finite element methods use the mesh to both discretize the geometric domain and to define the finite element shape functions. The latter creates a dependence between the quality of the mesh and the properties of the finite element basis that may adversely affect the accuracy of the discretized problem. In this project, we propose a new approach for defining finite element shape functions that breaks this dependence and separates mesh quality from the discretization quality. At the core of the approach is a meshless definition of the shape functions, which limits the purpose of the mesh to representing the geometric domain and integrating the basis functions without having any role in their approximation quality. The resulting non-conforming space can be utilized within a standard discontinuous Galerkin framework providing a rigorous foundation for solving partial differential equations on low-quality meshes. We present a collection of numerical experiments demonstrating our approach in a wide range of settings: strongly coercive elliptic problems, linear elasticity in the compressible regime, and the stationary Stokes problem. We demonstrate convergence for all problems and stability for element pairs for problems which usually require inf-sup compatibility for conforming methods, also referring to a minor modification possible through the symmetric interior penalty Galerkin framework for stabilizing element pairs that would otherwise be traditionally unstable. Mesh robustness is particularly critical for elasticity, and we provide an example that our approach provides a greater than 5x improvement in accuracy and allows for taking an 8x larger stable timestep for a highly deformed mesh, compared to the continuous Galerkin finite element method. The report concludes with a brief summary of ongoing projects and collaborations that utilize or extend the products of this work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A simple levelset contact algorithm for large overlap removal and robust preloads

A simple approach to simulate contact between deformable objects is presented which relies on levelset descriptions of the Lagrangian geometry and an optimization-based solver. Modeling contact between objects remains a significant challenge for computational mechanics simulations. Common approaches are either plagued by lack of robustness or are exceedingly complex and require a significant number of heuristics. In contrast, the levelset contact approach presented herein is essentially heuristic free. Furthermore, the presented algorithm enables resolving and enforcing contact between objects with a significant amount of initial overlap. Examples demonstrating the feasibility of this approach are shown, including the standard Hertz contact problem, the robust removal of overlap between two overlapping blocks, and overlap-removal and pre-load for a bolted configuration.

42 ENGINEERING↗
DOE OSTI2022

Integrated Design of a Robust System for Inertial Fusion Energy

For inertial fusion energy (IFE) to be successful, designs need to work robustly including both target physics and engineering considerations. For a power plant to be reliable and have a high operating capacity factor, the integrated design of the target, driver, target fabrication, target injection system, and chamber all need to work together in way that is robust and repeatable to expected variations. One of the lesson’s learned from our experience at NIF is that designs that are sensitive have not performed as expected – even in single shot mode. The lesson’s learned from NIF should be folded into creating integrated designs for IFE and for evaluating the tradeoffs between the different parts of the IFE system. To do so, we propose using the NIF 1.3 MJ yield shot/design (N210808) as a starting point of a study of the feasibility of indirect drive designs for IFE. If the hohlraum inefficiency precludes indirect drive, the study would include direct drive or fast ignition versions of this design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Robust In-Situ Strain Measurements to Monitor CO 2 Storage

The goal of this project was to develop and demonstrate robust instrumentation to monitor the in-situ strain tensor in order to improve the reliability and security of CO 2 storage in geologic formations. We met the original goals of the project and the major overarching accomplishment is the advancement of strain tensor monitoring from an intriguing concept to a commercially available technology with a solid foundation of novel instruments supported by theoretical analyses and validation experiments. The main accomplishments of the project are summarized below. We designed, built and evaluated nine new optical fiber strainmeters and tiltmeters using Michelson interferometers to measure deformation with ultra-high resolution at both shallow and deep point locations in the subsurface. These are the robust strainmeters that motivated the title of the project. We designed, built and evaluated a novel method of measuring distributed strain in optical fibers with nanostrain resolution, and cm-scale location, and sampling into the seismic band. The new method is called Coherence-length-gated Microwave Photonics Interfereometry (CMPI). CMPI technology has advantages over existing commercial DAS and DSS methods. We developed and demonstrated capabilities to deploy instruments in the field and used them to measure strain caused by ambient signals like barometric pressure and tides, as well as induced signals like surface loading and pore pressure changes from pumping tests. We deployed a working strainmeter at 1,700 ft depth, slightly above an active reservoir. This is to our knowledge the greatest depth a strainmeter has been deployed and the techniques we used can readily be extended to greater depths. Optical fiber borehole tensor strainmeter techology was advanced from a TRL 4 at the start, to a TRL of 7 at the conclusion of the project. The project included advances in simulations and theoretical analyses. We developed and demonstrated a computational workflow that uses machine learning to reduce the computational requirements and make it practical to use Bayesian inversion to solve large numerical poroelastic analyses needed to interpret strain tensor field data. We evaluated the strain tensor fields and time series that would be caused by leaks of CO 2 or other fluids from reservoirs. These simulations demonstrated that signals from leaks could be measured with instruments developed for the project, opening a potentially new method for ensuring storage security. We showed that strains in caprock can be used to estimate pressure in a reservoir. This avoids the need to drill monitoring wells into the reservoir, and it expands the capabilities of monitoring in the caprock. The project includes a derivation and application of a novel analytical solution to the strains in the vicinity of a pressurized poroelastic inclusion. This solution explains field data measured during injeciton tests at the North Avant Field, and it will simplify future interpretation of strain tensor data. The project included a broad range of experiments, and of the most significant is the characterization of the strain tensor at an array three strainmeters during six injection tests at the North Avant Field, Oklahoma. This demonstrated repeatability of the strain signal measured by the new instruments developed for the project, and it showed similarities between the strain signal at shallow depths and pressure in the underlying reservoir. We also demonstrated that useful strain data can be measured at reservoir depths. This confirms that strain tensor data can be measured throughout the caprock over a reservoir. The project demonstrated the feasibility of using the strain tensor and distributed strain measured in caprock during a variety of different well tests where the pumping rate was constant, sinusoidal and positive, or a periodic square wave with zero net rate. This further strengthens the validity of using strain data to characterize reservoirs and aquifers. We also demonstrated that strain caused be fluctuations of air pressure and water pressure in the vadose zone can be measured and interpreted, suggesting that high resolution distributed strain measurements hold promise for monitoring the vadose zone. The project partially supported nine graduate students in the Environmental Engineering, Hydrogeology, Electrical Engineering programs at Clemson University. The research was described in nine journal papers, 23 talks and conference abstracts. Additional journal papers are in preparation. A new company called Tensora was started to provide strainmeter technology for commercial applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Mechanically Robust High Magnetic Performance Sm-Co Sintered Magnets (Final Report)

Samarium-cobalt based permanent magnets (SmCo 5 and Sm 2 Co 17 ) have excellent magnetic properties, good corrosion resistance, and long-term thermal stability. Sm-Co sintered magnets have been widely used in electric machines, telecommunication, biomedical devices, and magnetic sensors. They are the most preferred magnets for high-temperature applications (200 - 550 °C). However, Sm-Co sintered magnets are brittle. They cannot be used for applications subjected to high stress, vibration, or mechanical shock. Sm-Co sintered magnets are prone to chipping, and fracture in the course of block magnet manufacturing, part machining, assembly, and operation. The brittleness leads to a magnet production loss of up to 20-30% in volume and imposes limitations on part size and shape. Developing mechanically robust high-performance Sm-Co sintered magnets is of great scientific and technical significance. This project is in response to the current market need for mechanically robust high magnetic performance Sm-Co sintered magnets and their novel manufacturing processes. The project will focus on scaling-up, validation, and technology maturation study of the mechanically tough high magnetic performance Sm-Co magnets developed by Ames Laboratory at a lab-scale with the collaboration of the industry partner Electron Energy Corporation (EEC).

36 MATERIALS SCIENCE↗