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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Transforming Energy Through Computational Excellence: Artificial Intelligence and Machine Learning

The National Renewable Energy Laboratory's (NREL's) expertise accelerates a decarbonized energy economy through the most advanced computational techniques, including artificial intelligence (AI) and Machine Learning (ML). AI is the simulation of human intelligence processes by machines, while ML encompasses the study of computer algorithms to imitate the way humans learn.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators

Next generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. This paper discusses the SODA synthesizer, an automated open source high-level ML framework-to-Verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets based on the LLVM infrastructure. The SODA synthesizers will allow implementing optimal designs by combining templated and fully tunable IPs and macros, and fully custom components generated through high-level synthesis. All these components will be provided through an extendable resource library, characterized with both commercial and open source logic design flows. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimension

Minutoli, Marco↗
DOE OSTI2025

MCPC Friction Stir Welding (FSW) Process Data

Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.

316 Stainless Steel↗
DOE OSTI2022

Constructing A New CHF Look-Up Table Based on the Domain Knowledge Informed Machine Learning Methodology

Accurate prediction of CHF under various fluid flow conditions continues to be required for design, operation and safety analysis of light water reactor rod bundles. Due to the lack of in-depth physical understanding as well as limited high-resolution data in the micro-scale flow and heat transfer, the existing models feature a sub-optimal uncertainty band. In this study, driven by the prior domain knowledge information obtained, an improved CHF look-up table is developed through unified machine learning algorithms for the vertical flow conditions within tube and annulus geometry. The Groeneveld 2006 look-up table is used as the domain knowledge to train machine learning process against tube and annulus CHF data for both DNB and DO type. The new look-up table shows improved accuracy for conditions relevant to PWRs and BWRs. In addition, its domain knowledge informed nature ensures that a rationale prediction can be made, thus accounting for previous valuable information in the machine learning model training process.

Jin, Yue↗
DOE OSTI2021

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

This paper demonstrates how an analytical and experimental method can be used to rapidly define the 45 additive manufacturing settings for a new alloy where the process parameters were previously unknown. A nickel-based superalloy, Haynes 282, was chosen for the analysis. An experimental matrix of focused processing parameters was predicted with a dimensionless number and 100 samples were printed using the Selective Laser Melting technique. High-throughput measurements validated the predicted process conditions needed to achieve desired density and hardness. The whole process was completed in 16 hours. The new technique was confirmed with analytical processing maps adopted by the metal additive manufacturing community. With the predicted set of process parameters, microstructural characterizations and tensile testing were used to confirm the predictions. The resultant as-fabricated microstructures have refined length scales of both microsegregation and secondary phase distributions. As a result, the mechanical properties were comparable within the predicted processing window and exhibited high strength and high ductility.

Laser Material Processing↗
DOE OSTI2020

On the Cutting of Metals: A Mechanics Viewpoint

The mechanics of large-strain deformation in cutting of metals is discussed, primarily from viewpoint of recent developments in in situ analysis of plastic flow and microstructure characterization. It is shown that a broad range of deformation parameters can be accessed in chip formation—strains of 1–10, strain rates of 10–10 5 /s, and temperatures up to 0.7T m —and controlled. This range is far wider than achievable by any other single-stage, severe plastic deformation (SPD) process. The resulting extreme deformation conditions produce a rich variety of microstructures in the chip. Four principal types of chip formation—continuous, shear-localized, segmented, and mushroom-type—as elucidated first by Nakayama (1974, “The Formation of ‘Saw-Toothed Chip’ in Metal Cutting,” Proceedings of International Conference on Production Engineering, Tokyo, pp. 572–577) are utilized to emphasize the diverse plastic flow phenomena, especially unsteady deformation modes that prevail in cutting. These chip types are intimately connected with the underlying flow, each arising from a distinct mode and triggered by an instability phenomenon. The role of plastic flow instabilities such as shear banding, buckling, and fracture in mediating unsteady flow modes is expounded, along with consequences of the flow modes and chip types for the cutting. Sinuous flow is shown to be the reason why gummy (highly strain-hardening) metals, although relatively soft, are so difficult to cut. Synthesizing the various observations, a hypothesis is put forth that it is the stability of flow modes that determines the mechanics of cutting. Furthermore, this leads to a flow-stability phase diagram that could provide a framework for predicting chip types and process attributes.

42 ENGINEERING↗
DOE OSTI2022

Autonomous Wireless Technology Detection in Seamless IoT Applications

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. Finally, the proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.

42 ENGINEERING↗
DOE OSTI2025

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

Machine learning-based multidomain processing for texture-based image segmentation and analysis

Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Finally, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States

This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.

AI↗
DOE OSTI2023

Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption

A comprehensive understanding of the interfacial behaviors of biomolecules holds great significance in the development of biomaterials and biosensing technologies. In this work, we used discontinuous molecular dynamics (DMD) simulations and graphic contrastive learning analysis to study the adsorption of ubiquitin protein on a graphene surface. Our high-throughput DMD simulations can explore the whole protein adsorption process including the protein structural evolution with sufficient accuracy. Contrastive learning was employed to train a protein contact map feature extractor aiming at generating contact map feature vectors. Subsequently, these features were grouped using the k-means clustering algorithm to identify the protein structural transition stages throughout the adsorption process. The machine learning analysis can illustrate the dynamics of protein structural changes, including the pathway and the rate-limiting step. Our study indicated that the protein–graphene surface hydrophobic interactions and the π–π stacking were crucial to the seven-stage adsorption process. Upon adsorption, the secondary structure and tertiary structure of ubiquitin disintegrated. The unfolding stages obtained by contrastive learning-based algorithm were not only consistent with the detailed analyses of protein structures but also provided more hidden information about the transition states and pathway of protein adsorption process and structural dynamics. Our combination of efficient DMD simulations and machine learning analysis could be a valuable approach to studying the interfacial behaviors of biomolecules.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High-Silicon Steel Strip by Single-Step Shear Deformation Processing

It is well known that Fe-Si alloys with Si content higher than in conventional electrical sheet steels (>3.2% Si) can make a significant impact in improving the efficiency of electrical motors if they are available in sheet/foil (strip) forms at suitable cost. While the magnetic and electrical attributes (e.g., resistivity, core loss) of these high-Si Fe alloys, of relevance to electrical motor core laminations, are known to be exceptional, the alloys have limited workability, making them difficult to produce consistently in sheet/foil (strip) forms. Current processing techniques such as rolling, while adequate for producing conventional electrical steel sheet, do have important disadvantages - large energy consumption and emissions, limitations in processing of low-workability alloys (e.g., high-Si content steels), large-scale plant infrastructure, and less than adequate capability to engineer sheet metals with specific microstructures (e.g., fine-grained) and crystallographic textures (e.g., shear textures). It is therefore of interest to have an alternative commercial process that can produce sheet/foil (strip) from high-Si Fe alloys and which can also overcome some of the deficiencies of current multistage strip processes. The goal of the present project was design and demonstration of a new energy-efficient pilot process for producing high-Si electrical steel strip of commercial widths and thickness, and with superior electrical and magnetic properties than current electrical steels (Fe-3.2% Si as benchmark). The applications domain for these steels is electrical motor core laminations. We have addressed this goal by accomplishment of the following specific objectives and tasks: a) Development of an Fe-4Si-4Cr alloy with electrical resistivity >80 μΩ-cm, induction flux density >1.48 T at 5000 A/m and core loss 35% lower than the benchmark 3.2% Si alloy. The alloy which meets DOE target specifications for motor core attributes was designed with the Si content controlled for the electrical properties and the Cr content tailored to meet material/process workability requirements. b) A unique machining-based deformation processing system was designed and scaled-up to produce strip of commercial width (25 mm to 150 mm) and thickness (up to 0.5 mm) from the Fe-4Si-4Cr alloy and other alloys of varied workability including copper, Al6061-T6 and naval brass. The key attributes of the machining-based strip production are deformation processing by concentrated simple-shear; single-step production of strip from ingot using compact machine infrastructure; strip surface finish of Ra 0.35 to 1 micrometer that is comparable/superior to that of rolled strip; discrete production of strip that can potentially be done at point of use; and controllability of strip mechanical/formability properties by deformation control. c) The electrical, magnetic, surface quality, mechanical, formability, and metallurgical properties/attributes of the machining-based strip were established by direct ASTM standard or equivalent measurement techniques. d) Punching characteristics of the strip in terms of load, edge quality and macro defects were similar to those of conventional 3.2% Si electrical steels. These punching characteristics are critical from a manufacturability perspective for motor/transformer core applications. e) A modeling framework for energy analysis of multistage rolling and the machining-based deformation processing has been established. Application of this modeling to the two strip-processes showed that the machining-based process requires significantly lower specific energy for processing, ~ 25% of that for rolling. The modeling framework can be adapted for a range of sheet-metal forming, bulk metal forming, and machining processes. It can be used to identify key parameters controlling process specific energy. f) A comparative analysis of advantages and disadvantages of machining-based processing against rolling for strip production. The single stage machining-based processing, with compact infrastructure, represents a new manufacturing paradigm for sheet and foil manufacturing that can potentially also be applied to advanced titanium, aluminum, copper and magnesium alloys. The goals and objectives were accomplished by a cross-disciplinary project team comprising of personnel from Purdue University; M4 Sciences LLC, a small-business focused on advanced manufacturing technology development; the Pacific Northwest National Labs; and tool manufacturers. The team is currently in advanced discussions with multiple entities for future process development for commercialization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robust Decentralized Learning Using ADMM With Unreliable Agents

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. Here, we also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD , a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum.

97 MATHEMATICS AND COMPUTING↗